title: Ship Resistance and Propulsion: Practical Estimation of Ship Propulsive Power

abstract: Ship Resistance and Propulsion is dedicated to providing a comprehensive and modern scientific approach to evaluating ship resistance and propulsion. This book, written by experts in the field, includes the latest developments from applied research, including those in experimental and CFD techniques, and provides guidance for the practical estimation of ship propulsive power for a range of ship types. A large number of fully worked examples are included to illustrate applications of the data and powering methodologies; these include cargo and container ships, tankers and bulk carriers, ferries, warships, patrol craft, work boats, planing craft and yachts. The book is aimed at a broad readership including practising naval architects and marine engineers, seagoing officers, small craft designers, undergraduate and postgraduate degree students. It should also appeal to others involved in transportation, transport efficiency and eco-logistics who need to carry out reliable estimates of ship power requirements. Contents 1. Introduction; 2. Propulsive power; 3. Components of ship resistance; 4. Model-ship extrapolation; 5. Model-ship correlation; 6. Restricted water depth and breadth; 7. Measurements of resistance components; 8. Wake and thrust deduction; 9. Numerical estimation of ship resistance; 10. Resistance design data; 11. Propulsor types; 12. Propeller characteristics; 13. Powering process; 14. Hull from design; 15. Numerical methods for propeller analysis; 16. Propulsor design data; 17. Applications.


title: ABSTRACT Purpose of this paper Using the Web to Look for Work: Implications for Online Job Seeking and Recruiting

abstract: Purpose – The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the ultimate effectiveness of this process. The specific research questions guiding this study are: how do people search for job‐related information on the web? How effective are these searches? And how likely are job seekers to find an appropriate job posting or application?Design/methodology/approach – The data used to examine these questions come from job seekers submitting job‐related queries to a major web search engine at three points in time over a five‐year period.Findings – Results indicate that individuals seeking job information generally submit only one query with several terms and over 45 percent of job‐seeking queries contain a specific location reference. Of the documents retrieved, findings suggest that only 52 percent are relevant and only 40 percent of job‐specific searches retrieve job postings.Research limitations...


title: Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies

abstract: Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future.


title: Building Generalizable Agents with a Realistic and Rich 3D Environment

abstract: Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely \emph{generalization}. In this work, we build \emph{House3D}, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multi-storeyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, \emph{RoomNav}, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g., segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.


title: Permanent magnet brushless machines with unequal tooth widths and similar slot and pole numbers

abstract: This paper presents a comparative study of three-phase permanent-magnet brushless machines in which the slot and pole numbers are similar, with reference to conventional brushless dc machines in which the ratio of the slot number to pole number is usually 3 : 2. Three different motor designs are considered. Two have equal tooth widths, with one having a coil wound on every tooth and the other only having a coil wound on alternate teeth, while the third machine also has coils wound on alternate teeth but these are wider than the unwound teeth while the width of their tooth tips is almost equal to the rotor pole pitch in order to maximize the flux linkage and torque. Analytical and finite-element methods are employed to predict the flux-linkage and back-electromotive-force waveforms, and the self- and mutual-inductances, and these are shown to be in good agreement with measured results. It is also shown that the third machine is eminently appropriate for brushless dc operation.


title: An Electro Optical Honeycomb Networks-on-Chip based on a New Nonblocking Switch

abstract: Network-on-Chip (NoC) is an emerging paradigm for communications within large VLSI systems implemented on a single silicon chip. However, the electronic network on chip design faces many problems like energy consumption, bandwidth and QoS. Compared with traditional electronic NoC, optical Network on Chip exhibits advantages of high-bandwidth, low latency and power dissipation. In this paper, a new 4×4 optical non-blocking switch is introduced on the basis of the latest development of nanophotonic technologies and of the prompt progress of optical integration technologies. The new approach can solve the problem of internal blocking with a fine symmetrical feature, while it needs the minimal number of resonators. Optical-loss budget under this architecture is also evaluated, calculated and compared with currently existing optical switches in this paper. An electro optical honeycomb network-on-chip is constructed based on the new switch architecture. It functions with low power dissipation and high bandwidth for the future network on chip.


title: Amplifying scientific paper's abstract by leveraging data-weighted reconstruction

abstract: This paper explores the impact of heterogeneous bibliographic network for generating scientific paper's amplified abstract.The amplified abstract is generated by leveraging target scientific paper's abstract and citation sentence's content and structure, which is addressed through document summarization manner.Sentence's weight is learned by exploiting regularization for ranking on heterogeneous bibliographic network.Data-weighted reconstruction is proposed to assign different priority to sentences when reconstructing the original document.Various evaluation metrics are designed to validate the effectiveness of our approach. In this paper, we focus on the problem of automatically generating amplified scientific paper's abstract which represents the most influential aspects of scientific paper. The influential aspects can be illustrated by the target scientific paper's abstract and citation sentences discussing the target paper, which are provided in papers citing the target paper. In this paper, we extract representative sentences through data-weighted reconstruction approach(DWR) by jointly leveraging target scientific paper's abstract and citation sentences' content and structure. In our study, we make two-folded contributions.Firstly, sentence's weight was learned by exploiting regularization for ranking on heterogeneous bibliographic network. Specially, Sentences-similar-Sentences relationship was identified by language modeling-based approach and added to the bibliographic network. Secondly, a data-weighted reconstruction objective function is optimized to select the most representative sentences which reconstructs the original sentence set with minimum error. In this process, sentences' weight plays a critical role. Experimental evaluation over real dataset confirms the effectiveness of our approach.


title: Mobile Edge Fog Computing in 5G Era: Architecture and Implementation

abstract: In the recent past there has been much research and development on cloud based computing. While cloud computing provides huge opportunities, it also imposes several challenges. One of the challenges that current data network operators and future 5G network are foreseeing is huge increase in data traffic. To fulfill such exponential data traffic growth, along with other user expectation, requires multiple innovative approaches and re-consideration of the current network design principals. Mobile Edge Fog computing, as proposed in this paper offers huge opportunities to future data network operators, as well as to equipment vendors. Due to its relatively newer concept, still much research is ongoing and detail architecture is still evolving. This paper proposes and discusses a novel Mobile Edge Computing design and architecture, along with real-time implementation details of the proposed solutions. It exploits many benefits of D2D by incorporating D2D functionalities into the proposed relay-gateways. The application running on top of the proposed network shows significant benefits e.g. in terms of delay as well as core-network signalling and data offloading.


WIDEBAND MINIATURIZED L-PROBE FED FRACTAL CLOVER LEAF MICROSTRIP PATCH ANTENNA


title: Power management for mobile games on asymmetric multi-cores

abstract: Gaming on mobile platforms is highly power hungry and rapidly drains the limited-capacity battery. In multi-threaded gaming, each thread has different processing requirements and even a single slow thread may lead to Quality of Service (QoS) violations. Further, modern mobile platforms are equipped with asymmetric multi-core processors, so that different cores exhibit diverse power and performance properties. These asymmetric cores along with different Dynamic Power Management (DPM) techniques enable a high degree of power efficiency in mobile gaming. The default Linux power manager (i.e. “Governor”) of asymmetric multi-cores performs power-wise inefficient for mobile games as it over allocates resources for processing threads by being oblivious to the QoS. The state-of-the-art Governor for mobile gaming does not account for multi-threaded gaming workloads, which are mainstream in mobile gaming. In this work, we present a power-performance characterization of multi-threaded mobile games by executing them on a real-world mobile platform with an asymmetric multi-core. This analysis is leveraged to propose a QoS-aware Governor running a lightweight online heuristic that holistically accounts for thread-to-core mapping and DPM. This solution, when integrated into the platform's Operating System (OS), provides 12% improved power efficiency on average.


title: THE IMPACT OF PETROLEUM PRICES ON VEGETABLE OILS PRICES: EVIDENCE FROM COINTEGRATION TESTS

abstract: This paper investigate the long-term relationship between the petroleum and vegetable oils prices represented by palm, soybean, sunflower and rapeseed oils prices. To that end, the bivariate cointegration approach using Engle-Granger two-stage estimation procedure is applied. The study utilises monthly data over the period of January 1983 through March 2008. The results provide a strong evidence of long-run equilibrium relation between the two products prices. The estimates of the error correction models reveal a unidirectional long-run causality flowing from petroleum to each of the vegetable oils prices under study.


title: Grooved Floor System for Cattle Housing: Ammonia Emission Reduction and Good Slip Resistance

abstract: To improve the slip resistance of solid floors in dairy cow houses and to achieve the ammonia emission reduction prescribed by the Dutch government, precast concrete floors with grooves and a dung scraper were investigated. The grooves parallel to the alley had 160 mm center-to-center spacing and were 35 mm wide and 30 mm deep. The urine could drain along the grooves. Perforations in the grooves were spaced 1.1 m apart and could be open or closed. When the perforations were open, urine could be drained directly into a slurry pit below. In case of closed perforations, draining of urine was only possible at one alley end. The feces were dragged to one end of the alley using a scraper, provided with facilities, that also cleaned the grooves. The floor system was constructed in a compartment of a mechanically ventilated experimental cow house. In another compartment a traditional slotted floor served as a reference. Ammonia emissions from both compartments were recorded continuously. The effects on ammonia emission and the performance of the perforations in the floor were determined. The floor system was also implemented into various practical farms and some practical experiences were gathered. Ammonia emission from the compartment with the grooved solid floor operating with open perforations was reduced by 46% compared with the reference compartment. Closing of the perforations resulted in an ammonia emission reduction of 35% compared to the reference compartment.


title: A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection

abstract: A real-time wireless electroencephalogram (EEG)-based brain-computer interface (BCI) system for drowsiness detection has been proposed. Drowsy driving has been implicated as a causal factor in many accidents. Therefore, real-time drowsiness monitoring can prevent traffic accidents effectively. However, current BCI systems are usually large and have to transmit an EEG signal to a back-end personal computer to process the EEG signal. In this study, a novel BCI system was developed to monitor the human cognitive state and provide biofeedback to the driver when drowsy state occurs. The proposed system consists of a wireless physiological signal-acquisition module and an embedded signal-processing module. Here, the physiological signal-acquisition module and embedded signal-processing module were designed for long-term EEG monitoring and real-time drowsiness detection, respectively. The advantages of low owner consumption and small volume of the proposed system are suitable for car applications. Moreover, a real-time drowsiness detection algorithm was also developed and implemented in this system. The experiment results demonstrated the feasibility of our proposed BCI system in a practical driving application.


title: A Review of Non-Isolated High Step-Up DC/DC Converters in Renewable Energy Applications

abstract: With the shortage of the energy and ever increasing of the oil price, research on the renewable and green energy sources, especially the solar arrays and the fuel cells, becomes more and more important. How to achieve high step-up and high efficiency DC/DC converters is the major consideration in the renewable grid-connected power applications due to the low voltage of PV arrays and fuel cells. The topology study with high step-up conversion is concentrated and most topologies recently proposed in these applications are covered and classified. The advantages and disadvantages of these converters are discussed and the major challenges of high step-up converters in renewable energy applications are summarized. This paper would like to make a clear picture on the general law and framework for the next generation non-isolated high step-up DC/DC converters.


title: SuperMIC: Analyzing Large Biological Datasets in Bioinformatics with Maximal Information Coefficient

abstract: The maximal information coefficient (MIC) has been proposed to discover relationships and associations between pairs of variables. It poses significant challenges for bioinformatics scientists to accelerate the MIC calculation, especially in genome sequencing and biological annotations. In this paper, we explore a parallel approach which uses MapReduce framework to improve the computing efficiency and throughput of the MIC computation. The acceleration system includes biological data storage on HDFS, preprocessing algorithms, distributed memory cache mechanism, and the partition of MapReduce jobs. Based on the acceleration approach, we extend the traditional two-variable algorithm to multiple variables algorithm. The experimental results show that our parallel solution provides a linear speedup comparing with original algorithm without affecting the correctness and sensitivity.


title: Flexible HALS algorithms for sparse non-negative matrix/tensor factorization

abstract: In this paper we propose a family of new algorithms for non-negative matrix/tensor factorization (NMF/NTF) and sparse nonnegative coding and representation that has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. We have developed a class of local algorithms which are extensions of hierarchical alternating least squares (HALS) algorithms proposed by us in . For these purposes, we have performed simultaneous constrained minimization of a set of robust cost functions called alpha and beta divergences. Our algorithms are locally stable and work well for the NMF blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, new algorithms can be potentially accommodated to different noise statistics by just adjusting a single parameter. Extensive experimental results confirm the validity and high performance of the developed algorithms, especially, with usage of the multi-layer hierarchical approach .


An Embedded System for Artificial Intelligence Applications


title: Creating people-aware IoT applications by combining design thinking and user-centered design methods

abstract: This article presents a methodology based on design thinking and user experience design methods for creating what we call ‘people-aware’ IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.


title: Fingerprint Retrieval for Identification

abstract: This paper presents a front-end filtering algorithm for fingerprint identification, which uses orientation field and dominant ridge distance as retrieval features. We propose a new distance measure that better quantifies the similarity evaluation between two orientation fields than the conventional Euclidean and Manhattan distance measures. Furthermore, fingerprints in the data base are clustered to facilitate a fast retrieval process that avoids exhaustive comparisons of an input fingerprint with all fingerprints in the data base. This makes the proposed approach applicable to large databases. Experimental results on the National Institute of Standards and Technology data base-4 show consistent better retrieval performance of the proposed approach compared to other continuous and exclusive fingerprint classification methods as well as minutia-based indexing schemes


title: Sentiment Knowledge Discovery in Twitter Streaming Data

abstract: Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing on classification problems, and then consider these streams for opinion mining and sentiment analysis. To deal with streaming unbalanced classes, we propose a sliding window Kappa statistic for evaluation in time-changing data streams. Using this statistic we perform a study on Twitter data using learning algorithms for data streams.


title: A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

abstract: We present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: 1 it is purely conceptual without the need to be implemented, and 2 it applies to all datasets that can be represented in two dimensions plus time e.g. geo-spatial, videos, networks, multivariate data. The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension ofi?źBach eti?źal.'s 2014 work.


title: Machine learning in cell biology – teaching computers to recognize phenotypes

abstract: Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.


title: How Transferable are Neural Networks in NLP Applications?

abstract: Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.


title: From face images and attributes to attributes

abstract: The face is an important part of the identity of a person. Numerous applications benefit from the recent advances in prediction of face attributes, including biometrics (like age, gender, ethnicity) and accessories (eyeglasses, hat). We study the attributes’ relations to other attributes and to face images and propose prediction models for them. We show that handcrafted features can be as good as deep features, that the attributes themselves are powerful enough to predict other attributes and that clustering the samples according to their attributes can mitigate the training complexity for deep learning. We set new state-of-the-art results on two of the largest datasets to date, CelebA and Facebook BIG5, by predicting attributes either from face images, from other attributes, or from both face and other attributes. Particularly, on Facebook dataset, we show that we can accurately predict personality traits (BIG5) from tens of ‘likes’ or from only a profile picture and a couple of ‘likes’ comparing positively to human reference.


title: Open versus Percutaneous Stabilization of Thoracolumbar Spine Fractures: A Short-Term Functional and Radiological Follow-up.

abstract: PURPOSE OF THE STUDY ::: A prospective cohort study evaluates the functional and radiological outcome of thoracolumbar spine fractures treated either with open or percutaneous dorsal instrumentation. In recent years, several studies advocate percutaneous stabilization of spinal fractures in patients without neurological deficits. However, it is still debated whether percutaneous stabilization is superior to open dorsal instrumentation in spinal trauma. ::: ::: ::: MATERIAL AND METHODS ::: This study was performed between 2010 and 2012 at a Level 1 trauma center. Patients treated either with an open or a percutaneous dorsal instrumentation for traumatic fractures of the thoracolumbar spine (T11 to L2) were included. Fracture morphology, screw positioning and clinical parameters were analyzed. Standardized questionnaires (VAS-spine-score; Oswestry-disability-score; SF-36) and follow up radiographs were performed. ::: ::: ::: RESULTS ::: Overall 72 patients (29 percutaneous; 43 open) could be included. The surgical and the early postsurgical course were similar between both groups. Furthermore the operative approach had no influence on the functional and radiological outcome one year after surgery, but the questionnaires showed moderate impairments within both groups. Also both groups showed a significant loss of reduction after the first postoperative month (p < 0.01). Within the open group a significantly higher amount of fracture reduction (p < 0.01) and a significantly reduced intraoperative radiation exposure was seen (open 105.9 sec.; percutaneous 143.1 sec; p < 0.05); whereas the percutaneous approach was associated with significantly reduced intraoperative blood loss (open 2.2 g/dl; percutaneous 1.2 g/dl; p < 0.001). ::: ::: ::: CONCLUSION ::: The functional and the radiological outcome of both groups was comparable one year after trauma. Minor advantages of the percutaneous system was less blood loss, whereas the open approach was associated with a significantly higher amount of initial reduction and significantly less intraoperative radiation exposure. Independent from the type of posterior fixation loss of reduction was already significant in the early postoperative course.


title: Anaerobic digestion of the Barcelona central food market organic wastes: Experimental study

abstract: Experiments carried out to obtain data for a preliminary design of an anaerobic digestion plant treating the organic wastes coming from a large food market are described. Four hydraulic retention times (8, 12, 14 and 20 days) were tested in 3-litre laboratory digesters. The methane yields were high (around 0·478 m3 CH4/kg VS added). Kinetic analysis, using a first-order model, resulted in a kinetic constant of 3·1 day−1 and an ultimate methane yield of 0·489 CH4/kg VS added. The biogeradability of the soluble and non-soluble fractions of the Volatile Solids (VS) is discussed.


title: Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists.

abstract: This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.


title: Wideband double layer Substrate Integrated Waveguide directional coupler

abstract: Substrate Integrated Waveguide (SIW) is a new type of guided wave structure which could be implemented in both microwave and millimeter wave integrated circuits. In this paper a novel waveguide type directional coupler based on substrate integrated waveguide consisting a double layer dielectric substrate is proposed. The design process is carried out using coupling theory and Chebyshev method. The proposed structure is fully based on SIW structure except at the input-output ports which are microstrip lines. The SIW bends are optimized for the lowest insertion loss using HFSS software. The center frequency of designed 20dB coupler is at 10GHz with 6GHz bandwidth. Based on the simulations the coupling remains almost constant over the coupler bandwidth with less than 2 dB variations. Larger bandwidth can be achieved by increasing the number of apertures.


title: Adapting Graph Application Performance via Alternate Data Structure Representation

abstract: Graph processing is used extensively in areas from social networking mining to web indexing. We demonstrate that the performance and dependability of such applications critically hinges on the graph data structure used, because a fixed, compile-time choice of data structure can lead to poor performance or applications unable to complete. To address this problem, we introduce an approach that helps programmers transform regular, off-the-shelf graph applications into adaptive, more dependable applications where adaptations are performed via runtime selection from alternate data structure representations. Using our approach, applications dynamically adapt to the input graph's characteristics and changes in available memory so they continue to run when faced with adverse conditions such as low memory. Experiments with graph algorithms on real-world (e.g., Wikipedia metadata, Gnutella topology) and synthetic graph datasets show that our adaptive applications run to completion with lower execution time and/or memory utilization in comparison to their non-adaptive versions.


title: Cybersecurity as an Application Domain for Multiagent Systems

abstract: The science of cybersecurity has recently been garnering much attention among researchers and practitioners dissatisfied with the ad hoc nature of much of the existing work on cybersecurity. Cybersecurity offers a great opportunity for multiagent systems research. We motivate cybersecurity as an application area for multiagent systems with an emphasis on normative multiagent systems. First, we describe ways in which multiagent systems could help advance our understanding of cybersecurity and provide a set of principles that could serve as a foundation for a new science of cybersecurity. Second, we argue how paying close attention to the challenges of cybersecurity could expose the limitations of current research in multiagent systems, especially with respect to dealing with considerations of autonomy and interdependence.


title: Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height

abstract: The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating plot-level tree height by measuring individual trees identifiable on the three-dimensional lidar surface. Lidar processing techniques included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The lidar system used for this study produced an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous and coniferous stands with settings typical of the southeastern United States. The lidar-derived tree measurements were used with regression models and cross-validation to estimate tree height on 0.017-ha plots. For the pine plots, lidar measurements explained 97 percent of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79 percent of the mean height variance for dominant trees. Filtering for local maximum with circular windows gave better fitting models for pines, while for deciduous trees, filtering with square windows provided a slightly better model fit. Using lidar and optical data fusion to differentiate between forest types provided better results for estimating average plot height for pines. Estimating tree height for deciduous plots gave superior results without calibrating the search window size based on forest type.


title: Constant time O(1) bilateral filtering

abstract: This paper presents three novel methods that enable bilateral filtering in constant time O(1) without sampling. Constant time means that the computation time of the filtering remains same even if the filter size becomes very large. Our first method takes advantage of the integral histograms to avoid the redundant operations for bilateral filters with box spatial and arbitrary range kernels. For bilateral filters constructed by polynomial range and arbitrary spatial filters, our second method provides a direct formulation by using linear filters of image powers without any approximation. Lastly, we show that Gaussian range and arbitrary spatial bilateral filters can be expressed by Taylor series as linear filter decompositions without any noticeable degradation of filter response. All these methods drastically decrease the computation time by cutting it down constant times (e.g. to 0.06 seconds per 1MB image) while achieving very high PSNRpsilas over 45 dB. In addition to the computational advantages, our methods are straightforward to implement.


title: Rethinking the Inception Architecture for Computer Vision

abstract: Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set.


title: Assessing Self-maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living

abstract: The aging of the population of the United States and a concern for the well-being of older people have hastened the emergence of measures of functional health. Among these, measures of basic activities of daily living, mobility, and instrumental activities of daily living have been particularly useful and are now widely available. Many are defined in similar terms and are built into available comprehensive instruments. Although studies of reliability and validity continue to be needed, especially of predictive validity, there is documented evidence that these measures of self-maintaining function can be reliably used in clinical evaluations as well as in program evaluations and in planning. Current scientific evidence indicates that evaluation by these measures helps to identify problems that require treatment or care. Such evaluation also produces useful information about prognosis and is important in monitoring the health and illness of elderly people.


title: Slip-aware Model Predictive optimal control for Path following

abstract: Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.


title: A 200 MHz CMOS pipelined multiplier-accumulator using a quasi-domino dynamic full-adder cell design

abstract: A bit-level pipelined 12 b*12 b two's complement multiplier with a 27 b accumulator has been designed and fabricated in 1.0 mu m p-well CMOS technology. A new quasi N-P domino logic structure has been adopted to increase the throughput rate, and special pipeline structures were used in the accumulator to reduce the total latency. The chip complexity is approximately 10000 transistors and the die area is 2.5 mm*3.7 mm. The measured maximum clock rate is 200 MHz (i.e. 200 million multiply-accumulate operations per second), and the power-speed ratio is 6.5 mW/MHz. A unique output buffer design was also adopted to achieve 200 MHz off-chip communication while maintaining full CMOS logic levels. >


title: An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

abstract: The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary step. The key challenge for automatically classifying the modality of a medical image is due to the visual characteristics of different modalities: some are visually distinct while others may have only subtle differences. This challenge is compounded by variations in the appearance of images based on the diseases depicted and a lack of sufficient training data for some modalities. In this paper, we introduce a new method for classifying medical images that uses an ensemble of different convolutional neural network (CNN) architectures. CNNs are a state-of-the-art image classification technique that learns the optimal image features for a given classification task. We hypothesise that different CNN architectures learn different levels of semantic image representation and thus an ensemble of CNNs will enable higher quality features to be extracted. Our method develops a new feature extractor by fine-tuning CNNs that have been initialized on a large dataset of natural images. The fine-tuning process leverages the generic image features from natural images that are fundamental for all images and optimizes them for the variety of medical imaging modalities. These features are used to train numerous multiclass classifiers whose posterior probabilities are fused to predict the modalities of unseen images. Our experiments on the ImageCLEF 2016 medical image public dataset (30 modalities; 6776 training images, and 4166 test images) show that our ensemble of fine-tuned CNNs achieves a higher accuracy than established CNNs. Our ensemble also achieves a higher accuracy than methods in the literature evaluated on the same benchmark dataset and is only overtaken by those methods that source additional training data.


title: Money Laundering Risk Evaluation of Financial Institution with AHP Model

abstract: Money laundering risk assessment is a primary tool to combat the perils of money laundering. This study is based on a model of risk assessment, which assists the management of financial institution to evaluate the range and level of money laundering risk (MLR). In this model, MLR is primarily divided into two risk levels, i.e. Inherent Risk & Control Risk with their auxiliary subdivisions. Analytic Hierarchy Process (AHP) software assists in its computations and provides for pairwise weights and comparisons. By using this model, money laundering risk of a financial institution could be assessed.


title: On the Complexity and Approximation of Binary Evidence in Lifted Inference

abstract: Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to non-lifted inference when computing conditional probabilities, because the evidence breaks many of the model's symmetries. Recent theoretical results paint a grim picture, showing that conditioning on binary relations is #P-hard, and in the worst case, no lifting can be expected. In this paper, we identify Boolean rank of the evidence as a key parameter in the complexity of conditioning. We contrast the hardness result by showing that conditioning on binary evidence with bounded Boolean rank is efficient. This opens up the possibility of approximating evidence by a low-rank Boolean matrix factorization that maintains the model's symmetries and admits efficient lifted inference.


Interactive Visualization and Manipulation of Attention-based Neural Machine Translation.


title: A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

abstract: Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2.


title: Millimeter Wave Wireless Communications: New Results for Rural Connectivity

abstract: This paper shows the remarkable distances that can be achieved using millimeter wave communications, and presents a new rural macrocell (RMa) path loss model for millimeter wave frequencies, based on measurements at 73 GHz in rural Virginia. Path loss models are needed to estimate signal coverage and interference for wireless network design, yet little is known about rural propagation at millimeter waves. This work identifies problems with the RMa model used by the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14, and offers a close-in (CI) reference distance model that has improved accuracy, fewer parameters, and better stability as compared with the existing 3GPP RMa path loss model. The measurements and models presented here are the first to validate rural millimeter wave path loss models.


title: Generalized Loss-Sensitive Adversarial Learning with Manifold Margins

abstract: The classic Generative Adversarial Net and its variants can be roughly categorized into two large families: the unregularized versus regularized GANs. By relaxing the non-parametric assumption on the discriminator in the classic GAN, the regularized GANs have better generalization ability to produce new samples drawn from the real distribution. It is well known that the real data like natural images are not uniformly distributed over the whole data space. Instead, they are often restricted to a low-dimensional manifold of the ambient space. Such a manifold assumption suggests the distance over the manifold should be a better measure to characterize the distinct between real and fake samples. Thus, we define a pullback operator to map samples back to their data manifold, and a manifold margin is defined as the distance between the pullback representations to distinguish between real and fake samples and learn the optimal generators. We justify the effectiveness of the proposed model both theoretically and empirically.


title: Large Scale Indexing of Geofences

abstract: The modern smart phone and car concepts provide a fertile ground for new location-aware applications, ranging from traffic management to social services. While the functionality is partly implemented at the mobile terminal, there is a rising need for efficient backend processing of high-volume, high update rate location streams. It is in this environment that geofencing, the detection of objects traversing virtual fences, is becoming a universal primitive required by an ever-growing number of applications. To satisfy the functionality and performance requirements of large-scale geofencing applications, we present in this work a backend system for indexing massive quantities of mobile objects and geofences. Our system runs on a cluster of servers, achieving a throughput of location updates that scales linearly with number of machines. The key ingredients to achieve a high performance are a specialized spatial index, a dynamic caching mechanism, and a load-sharing principle that reduces communication overhead to a minimum and enables a shared-nothing architecture. The throughput of the spatial index as well as the performance of the overall system are demonstrated by experiments using simulations of large-scale geofencing applications.


Shadow Mapping Oh , divide by W , you so crazy


title: Performance comparison and analysis of ad hoc routing algorithms

abstract: A mobile ad-hoc network (Manet) is a system of wireless mobile nodes dynamically self-organizing in arbitrary and temporary network topologies. People and vehicles can thus be internetworked in areas without a pre-existing communication infrastructure, or when the use of such infrastructure requires wireless extension. Therefore, such networks are designed to operate in widely varying environments, from military networks to low-power sensor networks and other embedded systems. Frequent topology changes caused by node mobility make routing in ad hoc wireless networks a challenging problem. We focus upon routing protocols for ad hoc networks. We study and compare the performance of several routing protocols. A variety of workload and scenarios using a variety of mobility, load and size of the ad hoc network were simulated.


title: Efficient logo retrieval through hashing shape context descriptors

abstract: In this paper we present a method for organizing and indexing logo digital libraries like the ones of the patent and trademark offices. We propose an efficient queried-by-example retrieval system which is able to retrieve logos by similarity from large databases of logo images. Logos are compactly described by a variant of the shape context descriptor. These descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The experiments demonstrate the effectiveness and efficiency of this system on realistic datasets as the Tobacco-800 logo database.


title: Play Ms. Pac-Man Using an Advanced Reinforcement Learning Agent

abstract: Reinforcement Learning (RL) algorithms have been promising methods for designing intelligent agents in games. Although their capability of learning in real time has been already proved, the high dimensionality of state spaces in most game domains can be seen as a significant barrier. This paper studies the popular arcade video game Ms. Pac-Man and outlines an approach to deal with its large dynamical environment. Our motivation is to demonstrate that an abstract but informative state space description plays a key role in the design of efficient RL agents. Thus, we can speed up the learning process without the necessity of Q-function approximation. Several experiments were made using the multiagent MASON platform where we measured the ability of the approach to reach optimum generic policies which enhances its generalization abilities.


title: Online nod detection in human-robot interaction

abstract: Nodding is an important factor in human communication, providing a physical cue for socially communicative acts such as turn taking, backchanneling, and confirmation. In this article, we describe a vision-based online head nodding detector that works with monocular camera images. Using SVM regression, our system estimates the head pose based on facial landmarks. Subsequence dynamic time-warping is then used to compare head pose features against nod templates. In contrast to many other previous implementations, our system was evaluated with study participants who were not instructed to reply by nodding, and shows good results while maintaining a low false positive rate.


title: Low-Cost, Direct-Fed Slot Antenna Built in Metal Cover of Notebook Computer for 2.4-/5.2-/5.8-GHz WLAN Operation

abstract: The slot antenna has been utilized for some recent notebook computers on the market. The FR4 substrates are usually required to print the metal patterns for coupled-feeding the 2.4 GHz (2400–2484 MHz) slot and also for contributing the 5.2 GHz (5150–5350 MHz) and 5.8 GHz (5725–5825 MHz) bands. This communication presents a simple, direct-fed slot antenna with a low profile of 7 mm to the top edge of the metal cover for 2.4-/5.2-/5.8-GHz WLAN operation. The antenna design did not require any substrate and can be implemented directly in the metal cover of a notebook computer at low cost. The design comprised two parallel-connected, 0.5-wavelength-slot portions, formed in a linear structure of length 79 mm and constant width 2 mm, and fed by a common antenna feed port using a mini-coaxial cable. The larger slot generated a 0.5-wavelength resonant mode in the 2.4-GHz band and a 1-wavelength mode for 5.2-GHz operation. The smaller slot operated in a 0.5-wavelength resonant mode, which covered the 5.8-GHz band. With a matching inductor properly set in the slot, good input impedance over the 2.4-, 5.2-, and 5.8-GHz bands can be attained.


title: Data Workflow - A Workflow Model for Continuous Data Processing

abstract: Online data or streaming data are getting more and more important for enterprise information systems, e.g. by integrating sensor data and workflows. The continuous flow of data provided e.g. by sensors requires new workflow models addressing the data perspective of these applications, since continuous data is potentially infinite while business process instances are always finite. ::: In this paper a formal workflow model is proposed with data driven coordination and explicating properties of the continuous data processing. These properties can be used to optimize data workflows, i.e., reducing the computational power for processing the workflows in an engine by reusing intermediate processing results in several workflows.


title: Will it stick?: exploring the sustainability of computational thinking education through game design

abstract: A strategy exposing middle school students to computer science through game design appears to be a promising means to mitigate the computer science pipeline challenge. Particularly when short game design activities are integrated into already existing middle school courses, research suggests that game design is effective in broadening participation and motivating large numbers of students, along with large percentages of women and minorities. A study with over 10,000 students is exploring the sustainability of this approach and finding positive responses to inquiries such as these: Do teachers continue to use game design? Can they advance beyond extrinsic rewards such as research stipends? After building one game, do students advance, building more games or even STEM simulations?


title: Taking a Multi-Faceted View of the Uni-Dimensional Measurement from Rasch Analysis in Language Tests.

abstract: The study reported in this paper originated from a need to design a short English for Academic Purposes (EAP) proficiency test for incoming undergraduate students. The approach to solving the problem was to focus on establishing consistent person comparison between the students at Hong Kong Baptist College who did not meet the minimum entry requirement in the English language and a reference group who met the required grade level. The comparison was made on the basis of a short English-as-a-Second-Language (ESL) proficiency test taken by both students groups. The establishment of equivalence between the reference and studeo.t groups was achieved through the employment of FACETS (Linacre and Wright, 1990). The logit level of the reference group (-0.93) can be taken as the equivalence of the minimal required ESL level for entry into university. Findings suggest the possible extensions of the Rasch model in terms of both item calibration and person measurement through the employment of FACETS. The EAP Test is appended. (Contains 19 references.) (JP) ******************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** Taking a multi-faceted view of the uni-dimensional measurement from Reach analysis in language tests


title: Inducing Interpretable Representations with Variational Autoencoders

abstract: We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints of a given graphical model, and use deep generative models to deal with messy, high-dimensional domains where it is often difficult to model all the variation. Learning in this framework is carried out end-to-end with a variational objective, applying to both unsupervised and semi-supervised schemes.


title: Real-time curvature estimation of printable soft gripper using electro-conductive yarn

abstract: Automatic handling of many types of food materials are required to realize the automation of production of commercially prepared box lunches. A printable soft gripper was developed for food handling which is simple to produce with a 3D printer. However, the sensing ability of the printable soft gripper was not discussed in previous research. In this paper, a novel method for estimating the curvature of a printable soft gripper using electro-conductive yarn is presented. Electro-conductive yarn is a conductive material, and the resistance of strings is changed by stretching. It is less expensive than other sensors that can be used for curvature measurement. Additionally, it is easy to assemble and disassemble by hand. Electro-conductive yarn is applied to a prototype printable soft gripper, and the proposed estimation method is verified experimentally. From the experimental results, the estimated curvature from the resistance of the electro-conductive yarn coincide with the actual curvature of the gripper. Our proposed method of using electro-conductive yarn was successful for estimating the curvature of a printable soft gripper.


title: A conformal dual-polarized all-metal vivaldi array for feeding broadband luneburg lens

abstract: This paper demonstrates a design of dual-polarized Vivaldi array to feed high-gain broadband Luneburg lens. The proposed conformal Vivaldi array, placed around the lens surface, is composed of 8 × 4 horizontal polarized elements and 7 × 3 vertical polarized elements. The performance of the entire 3-D graded index Luneburg lens antenna (30 cm diameter) has been evaluated based on simulated radiation patterns using commercial full-wave electromagnetic software.


title: Security and Privacy in Cyber-Physical Systems: A Survey of Surveys

abstract: The following is a survey on surveys and may help the interested reader to find a way through the jungle of literature on the security and CPS topics out there already. In order to ease the search, the authors have provided a classification in CPS Domains, Attacks, Defenses, Research-trends, Network-security, Security level implementation, and Computational Strategies which makes this survey a unique and I believe very helpful article. —Jorg Henkel, Karlsruhe Institute of Technology


title: Safe Policy Improvement by Minimizing Robust Baseline Regret

abstract: An important problem in sequential decision-making under uncertainty is to use limited data to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as a given baseline strategy. In this paper, we develop and analyze a new model-based approach to compute a safe policy when we have access to an inaccurate dynamics model of the system with known accuracy guarantees. Our proposed robust method uses this (inaccurate) model to directly minimize the (negative) regret w.r.t. the baseline policy. Contrary to the existing approaches, minimizing the regret allows one to improve the baseline policy in states with accurate dynamics and seamlessly fall back to the baseline policy, otherwise. We show that our formulation is NP-hard and propose an approximate algorithm. Our empirical results on several domains show that even this relatively simple approximate algorithm can significantly outperform standard approaches.


title: The implicit sequence learning deficit in patients with Parkinson's disease: A matter of impaired sequence integration?

abstract: Abstract Despite the wealth of research investigating the serial reaction time (SRT) learning abilities of people with Parkinson's disease (PD), the role of the basal ganglia in implicit sequence learning remains largely unclear. The present research sought to examine the ability of people with PD to implicitly learn simultaneously operating sequences and integrate patterned information from each sequence dimension. Using a version of the SRT which reduced motor demands, the present experiment investigated the implicit learning of a spatial sequence, a stimulus-response sequence, and an integrated spatial/stimulus-response sequence, all of which are usually confounded in the standard SRT task. Whereas both PD and control groups demonstrated robust learning for the individual spatial and response sequences, only control participants evidenced learning for the integrated sequence. Further, unlike implicit learning for the spatial and object sequences, impaired integrated sequence acquisition was specifically related to the severity of patients’ PD symptomatology. The implicit learning deficits of PD patients are discussed with regard to the role played by the basal ganglia in integrative sequence learning in the SRT.


title: Soil Respiration Studies in Tallgrass Prairie in Missouri

abstract: Rates of carbon dioxide evolution from soil in mid—Missouri tallgrass prairie were highly correlated with temperature of the surface soil. Values increased exponentially with rising temperatures through the seasonal range under observation. Twenty—four—hour measurements employing infrared analysis were conducted periodically during all seasons from August 1968 to June 1970. Weekly averages calculated from regression for the entire year ranged from zero during the coldest period to 450 mg CO_2m(—2)hr(—1) during summer. The annual total was 1,675 g CO_2m(—2). The organic matter equivalent was 1,005 g, approximately 2.2 times the indicated biomass turnover for the root system for the same period. The difference was attributed to root respiration by determining a correlation between CO2 evolution and root biomass with KOH absorption and titration and by converting these data to infrared equivalents. It was estimated that 60% of the total respiration (surface litter excluded) was due to microbial processes in organic turnover and the balance to root metabolism.


title: Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

abstract: The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.


title: Similarity-based surface modelling using geodesic fans

abstract: We present several powerful new techniques for similarity-based modelling of surfaces using geodesic fans, a new framework for local surface comparison. Similarity-based surface modelling provides intelligent surface manipulation by simultaneously applying a modification to all similar areas of the surface. We demonstrate similarity-based painting, deformation, and filtering of surfaces, and show how to vary our similarity measure to encompass geometry, textures, or other arbitrary signals. Geodesic fans are neighbourhoods uniformly sampled in the geodesic polar coordinates of a point on a surface. We show how geodesic fans offer fast approximate alignment and comparison of surface neighbourhoods using simple spoke reordering. As geodesic fans offer a a structurally equivalent definition of neighbourhoods everywhere on a surface, they are amenable to standard acceleration techniques and are well-suited to extending image domain methods for modelling by example to surfaces.


title: Highly adaptable MEMS-based display with wide projection angle

abstract: We demonstrate a MEMS-based display system with a very wide projection angle of up to 120deg. The system utilizes a gimbal-less two-axis micromirror scanner for high-speed laser beam-steering in both axes. The optical scan angle of the micromirrors is up to 16deg on each axis. A custom-designed fisheye lens is utilized to magnify scan angles. The system can display a variety of vector graphics as well as multiframe animations at arbitrary refresh rates, up to the overall bandwidth limit of the MEMS device. It is also possible to operate the scanners in point-to-point scanning, resonant and/or rastering modes. The system is highly adaptable for projection on a variety of surfaces including projection on specially coated transparent surfaces (Fig. 3.) The size of the displayed area, refresh rate, display mode (vector graphic or image raster,) and many other parameters are all adjustable by the user. The small size of the MEMS devices and lens as well as the ultra-low power consumption of the MEMS devices, in the milliwatt range, makes the overall system highly portable and miniaturizable.


title: A 4.2 GHz PLL Frequency Synthesizer with an Adaptively Tuned Coarse Loop

abstract: A 4.2 GHz integer-N PLL frequency synthesizer for WLANs is described. An analog split tuned LC-VCO is controlled by coarse and fine loops to achieve both a large frequency tuning range and a small VCO gain. An averaging varactor is employed to reduce the amplitude sensitivity of the varactor, thereby reducing the AM-to-FM noise conversion. A new adaptively tuned switched capacitor integrator is used in the coarse loop for a fast lock time. The prototype test chip in a 0.13-mum CMOS process has a measured phase noise of -110 dBc/Hz at 1 MHz offset, and a settling time of 50 mus.


title: Titanium allergy in dental implant patients: a clinical study on 1500 consecutive patients.

abstract: BACKGROUND ::: In dentistry, allergic reactions to Ti implants have not been studied, nor considered by professionals. Placing permanent metal dental implants in allergic patients can provoke type IV or I reactions. Several symptoms have been described, from skin rashes and implant failure, to non-specific immune suppression. ::: ::: ::: OBJECTIVE ::: Our objective was to evaluate the presence of titanium allergy by the anamnesis and examination of patients, together with the selective use of cutaneous and epicutaneous testing, in patients treated with or intending to receive dental implants of such material. ::: ::: ::: MATERIAL AND METHODS ::: Thirty-five subjects out of 1500 implant patients treated and/or examined (2002-2004) were selected for Ti allergy analysis. Sixteen presented allergic symptoms after implant placement or unexplained implant failures [allergy compatible response group (ACRG)], while 19 had a history of other allergies, or were heavily Ti exposed during implant surgeries or had explained implant failures [predisposing factors group (PFG)]. Thirty-five controls were randomly selected (CG) in the Allergy Centre. Cutaneous and epicutaneous tests were carried out. ::: ::: ::: RESULTS ::: Nine out of the 1500 patients displayed positive (+) reactions to Ti allergy tests (0.6%): eight in the ACRG (50%), one in the PFG (5.3%)(P=0.009) and zero in the control group. Five positives were unexplained implant failures (five out of eight). ::: ::: ::: CONCLUSIONS ::: Ti allergy can be detected in dental implant patients, even though its estimated prevalence is low (0.6%). A significantly higher risk of positive allergic reaction was found in patients showing post-op allergy compatible response (ACRG), in which cases allergy tests could be recommended.


title: TensorLy: Tensor Learning in Python

abstract: Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of traditional machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed TensorLy, a Python library that provides a high-level API for tensor methods and deep tensorized neural networks. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and to seamlessly integrate with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with several libraries such as NumPy or PyTorch to name but a few. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows to easily design and train deep tensorized neural networks. TensorLy is available at https://github.com/tensorly/tensorly


1 A Personalized Company Recommender System for Job Seekers


title: The perceived autonomy support scale for exercise settings (PASSES): Development, validity, and cross-cultural invariance in young people

abstract: Abstract Objective : The study aimed to develop a perceived autonomy support scale for exercise settings (PASSES) in young people. Design : Cross-sectional questionnaire survey. Methods : In Study 1,432 school pupils responded to an initial pool of perceived autonomy support items with physical education (PE) teachers as the source of support. The validity of the initial factor structure of the PASSES was evaluated using confirmatory factor analysis. In Study 2, three versions of the PASSES were developed measuring perceived autonomy support from three sources: PE teachers, parents, and peers. British ( N =210), Estonian ( N =268), and Hungarian ( N =235) school pupils completed each version. The proposed model of perceived autonomy support established in Study 1 was tested for structural invariance and mean differences across the three cultures. Results : In Study 1, the deletion of items contributing to model misspecification produced a final 12-item PASSES which exhibited acceptable fit with the data. The perceived autonomy support factor also demonstrated discriminant and convergent validity with regulation styles from the perceived locus of causality. In Study 2, the hypothesized model exhibited acceptable goodness-of-fit statistics in all samples and for all sources. The structure of the model was found to be invariant across the cultural groups for each source. Contrary to hypotheses, mean levels of perceived autonomy support from parents and peers were found to be higher in Estonian participants relative to their British and Hungarian counterparts. Conclusion : Results support the use of the PASSES as a valid measure of perceived autonomy support in exercise settings for three different sources in young people.


title: Annoki: a MediaWiki-based collaboration platform

abstract: Communication plays a vital role throughout all the activities of software engineering processes. As Web 2.0 paradigms concentrate on communication, collaboration, and information sharing, it is only natural that these applications should become part of the software engineering toolkit. In this paper, we describe Annoki, our collaboration platform built on top of the popular wiki software MediaWiki. Annoki supports collaboration by improving the organization of, managing access to, assisting in the creation of, and graphically displaying information about content stored on the wiki. We follow our description of Annoki with a discussion of the current users of Annoki, the largest of whom is the Software Engineering Research Lab at the University of Alberta, where it is used to manage research and development software engineering activities on a daily basis.


title: Quantum simulation study of gate-all-around (GAA) silicon nanowire transistor and double gate metal oxide semiconductor field effect transistor (DG MOSFET)

abstract: In this paper, electrical characteristics of the double gate metal oxide semiconductor field effect transistor (DG MOSFET) and that of gate all around silicon nanowire transistor (GAA SNWT) have been investigated. We have evaluated the variations of the threshold voltage, the subthreshold slope, drain-induced barrier lowering, ON and OFF state currents when channel length decreases. Quantum mechanical transport approach based on non-equilibri


title: A global-local optimization framework for simultaneous multi-mode multi-corner clock skew variation reduction

abstract: As combinations of signoff corners grow in modern SoCs, minimization of clock skew variation across corners is important. Large skew variation can cause difficulties in multi-corner timing closure because fixing violations at one corner can lead to violations at other corners. Such "ping-pong" effects lead to significant power and area overheads and time to signoff. We propose a novel framework encompassing both global and local clock network optimizations to minimize the sum of skew variations across different PVT corners between all sequentially adjacent sink pairs. The global optimization uses linear programming to guide buffer insertion, buffer removal and routing detours. The local optimization is based on machine learning-based predictors of latency change; these are used for iterative optimization with tree surgery, buffer sizing and buffer displacement operators. Our optimization achieves up to 22% total skew variation reduction across multiple testcases implemented in foundry 28nm technology, as compared to a best-practices CTS solution using a leading commercial tool.


title: Monitoring of Vital Signs with Flexible and Wearable Medical Devices

abstract: Advances in wireless technologies, low-power electronics, the internet of things, and in the domain of connected health are driving innovations in wearable medical devices at a tremendous pace. Wearable sensor systems composed of flexible and stretchable materials have the potential to better interface to the human skin, whereas silicon-based electronics are extremely efficient in sensor data processing and transmission. Therefore, flexible and stretchable sensors combined with low-power silicon-based electronics are a viable and efficient approach for medical monitoring. Flexible medical devices designed for monitoring human vital signs, such as body temperature, heart rate, respiration rate, blood pressure, pulse oxygenation, and blood glucose have applications in both fitness monitoring and medical diagnostics. As a review of the latest development in flexible and wearable human vitals sensors, the essential components required for vitals sensors are outlined and discussed here, including the reported sensor systems, sensing mechanisms, sensor fabrication, power, and data processing requirements.


title: Prognostics-enhanced Automated Contingency Management for advanced autonomous systems

abstract: This paper introduces a novel prognostics-enhanced automated contingency management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity unmanned aerial vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircrafts.


Recommendations on a Knowledge Graph


title: Guidelines for the Process of Cross-Cultural Adaptation of Self-Report Measures

abstract: With the increase in the number of multinational and multicultural research projects, the need to adapt health status measures for use in other than the source language has also grown rapidly. 1,4,27 Most questionnaires were developed in English-speaking countries, 11 but even within these countries, researchers must consider immigrant populations in studies of health, especially when their exclusion could lead to a systematic bias in studies of health care utilization or quality of life. 9,11 The cross-cultural adaptation of a health status selfadministered questionnaire for use in a new country, culture, and/or language necessitates use of a unique method, to reach equivalence between the original source and target versions of the questionnaire. It is now recognized that if measures are to be used across cultures, the items must not only be translated well linguistically, but also must be adapted culturally to maintain the content validity of the instrument at a conceptual level across different cultures. 6,11‐13,15,24 Attention to this level of detail allows increased confidence that the impact of a disease or its treatment is described in a similar manner in multinational trials or outcome evaluations. The term “cross-cultural adaptation” is used to encompass a process that looks at both language (translation) and cultural adaptation issues in the process of preparing a questionnaire for use in another setting. Cross-cultural adaptations should be considered for several different scenarios. In some cases, this is more obvious than in others. Guillemin et al 11 suggest five different examples of when attention should be paid to this adaptation by comparing the target (where it is going to be used) and source (where it was developed) language and culture. The first scenario is that it is to be used in the same language and culture in which it was developed. No adaptation is necessary. The last scenario is the opposite extreme, the application of a questionnaire in a different culture, language and country—moving the Short Form 36-item questionnaire from the United States (source) to Japan (target) 7 which would necessitate translation and cultural adaptation. The other scenarios are summarized in Table 1 and reflect situations when some translation and/or adaptation is needed. The guidelines described in this document are based on a review of cross-cultural adaptation in the medical, sociological, and psychological literature. This review led to the description of a thorough adaptation process designed to maximize the attainment of semantic, idiomatic, experiential, and conceptual equivalence between the source and target questionnaires. 13 . Further experience in cross-cultural adaptation of generic and diseasespecific instruments and alternative strategies driven by different research groups 18 have led to some refinements


title: Asymmetrical voltage-cancellation control for a series-series fixed-frequency inductive power transfer system

abstract: The main objective of this paper is to study and analyze the power supply stage of a series-series (SS) fixed-frequency inductive power transfer (IPT) system, in order to suggest effective soft switching techniques to improve its performance. Because of the characteristics of the system, zero voltage switching (ZVS) operation was used and three control strategies were compared: phase-shift (PS) control, asymmetrical duty cycle (ADC) control and optimal asymmetrical voltage cancellation (o AVC) control. The o AVC control is found to be more efficient for this application. Additionally, this paper presents an inductive power transfer transformer (IPTT) prototype that was built in the laboratory, which helped validate the theoretical analysis and simulation results with practical tests. The plots and circuit simulations were obtained from MATLAB and SIMULINK respectively.


title: Delivering CRISPR: a review of the challenges and approaches

abstract: AbstractGene therapy has long held promise to correct a variety of human diseases and defects. Discovery of the Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR), the mechanism of the CRISPR-based prokaryotic adaptive immune system (CRISPR-associated system, Cas), and its repurposing into a potent gene editing tool has revolutionized the field of molecular biology and generated excitement for new and improved gene therapies. Additionally, the simplicity and flexibility of the CRISPR/Cas9 site-specific nuclease system has led to its widespread use in many biological research areas including development of model cell lines, discovering mechanisms of disease, identifying disease targets, development of transgene animals and plants, and transcriptional modulation. In this review, we present the brief history and basic mechanisms of the CRISPR/Cas9 system and its predecessors (ZFNs and TALENs), lessons learned from past human gene therapy efforts, and recent modifications of CRISPR/Cas9 to pro...


title: Sampling strategies for bag-of-features image classification

abstract: Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance. They evolved from texton methods in texture analysis. The basic idea is to treat images as loose collections of independent patches, sampling a representative set of patches from the image, evaluating a visual descriptor vector for each patch independently, and using the resulting distribution of samples in descriptor space as a characterization of the image. The four main implementation choices are thus how to sample patches, how to describe them, how to characterize the resulting distributions and how to classify images based on the result. We concentrate on the first issue, showing experimentally that for a representative selection of commonly used test databases and for moderate to large numbers of samples, random sampling gives equal or better classifiers than the sophisticated multiscale interest operators that are in common use. Although interest operators work well for small numbers of samples, the single most important factor governing performance is the number of patches sampled from the test image and ultimately interest operators can not, provide enough patches to compete. We also study the influence of other factors including codebook size and creation method, histogram normalization method and minimum scale for feature extraction.


title: Using recurrent neural network models for early detection of heart failure onset

abstract: Objective: We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Materials and Methods: Data were from a health system’s EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Results: Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Conclusion: Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12–18 months.


title: The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

abstract: Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence.


title: Application of Hidden Markov Model in SQL Injection Detection

abstract: Due to the increasing complexity of web and client application's structure, security problem has become more and more critical. Among all the threats reported, SQL Injection Attacks (SQLIAs) have always been top-ranked in recent years, and network logs, which are very important for the detection of SQLIA, are often utilized to analyze the user's attacking behaviors. However, the collection of network logs is often compromised due to the growing complexity of network structure, leading to a great challenge to the log-based SQLIA detection. In view of this, this paper proposes a novel approach to the detection of SQLIA based on log analyzing with Hidden Markov Model (HMM), combined with statistical characteristic and feature matching. At first, we build browsing behavior models of attackers and legal users. Furthermore, we use HMM to restore user's browsing procedure from the customised user logs. Finally, the method detects SQLIAs by analyzing the behavior of users in reality, without requiring sensitive information submitted by users. Our experiments show that the proposed method can detect possible SQLIAs and identify malicious users effectively, and has higher accuracy in comparison with the Kmeans method.


title: Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors

abstract: A transistor-free metal-oxide memristor crossbar with low device variability is realised and trained to perform a simple classification task, opening the way to integrated neuromorphic networks of a complexity comparable to that of the human brain, with high operational speed and manageable power dissipation.


title: Statistical physics of vaccination

abstract: Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.


title: The mutational landscape of lethal castration-resistant prostate cancer

abstract: Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However, less is known about the role of mutations. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.


title: A Mixed Trigrams Approach for Context Sensitive Spell Checking

abstract: This paper addresses the problem of real-word spell checking, i.e., the detection and correction of typos that result in real words of the target language. This paper proposes a methodology based on a mixed trigrams language model. The model has been implemented, trained, and tested with data from the Penn Treebank. The approach has been evaluated in terms of hit rate, false positive rate, and coverage. The experiments show promising results with respect to the hit rates of both detection and correction, even though the false positive rate is still high.


title: Deep Recurrent Neural Networks for Human Activity Recognition

abstract: Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.


title: Implementation of emotional features on satire detection

abstract: Recognition of satirical language in social multimedia outlets turn out to be a trending research area in computational linguistics. Many researchers have analyzed satirical language from various point of views: lexically, syntactically, and semantically. However, due to the ironic dimension of emotion embedded in satirical language, emotional study of satirical language has ever left behind. In this study, we propose the new emotion-based satire detection model using supervised and unsupervised weighting approaches (TFRF and TFIDF). We implement the model with Ensemble Bagging classifier compared with benchmark classifier: SVM. The model not only outperform the word-based baseline: BoW but also handle both short text and long text configurations. Our work in recognition of satirical language can aid in lessening the impact of implicit language in public opinion mining, sentiment analysis, fake news detection and cyberbullying.