title: Organizational learning culture, innovative culture and innovations in South Korean firms

abstract: The aim of this paper is to present and test a model of innovativeness improvement based on the impact of organizational learning culture. The concept of organizational learning culture (OLC) is presented and defined as a set of norms and values about the functioning of an organization. They should support systematic, in-depth approaches aimed at achieving higher-level organizational learning. The elements of an organizational learning process that we use are information acquisition, information interpretation, and behavioral and cognitive changes. Within the competing values framework OLC covers some aspects of all four different types of cultures: group, developmental, hierarchical, and rational. Constructs comprising innovativeness are innovative culture and innovations, which are made of technical (product and service) and administrative (process) innovations. We use data from 201 Korean companies employing more than 50 people. The impact of OLC on innovations empirically tested via structural equation modeling (SEM). The results show that OLC has a very strong positive direct effect on innovations as well as moderate positive indirect impact via innovative culture.


An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning


title: Propulsion Electric Grid Simulator (PEGS) for Future Turboelectric Distributed Propulsion Aircraft

abstract: NASA Glenn Research Center, in collaboration with the aerospace industry and academia, has begun the development of technology for a future hybrid-wing body electric airplane with a turboelectric distributed propulsion (TeDP) system. It is essential to design a subscale system to emulate the TeDP power grid, which would enable rapid analysis and demonstration of the proof-of-concept of the TeDP electrical system. This paper describes how small electrical machines with their controllers can emulate all the components in a TeDP power train. The whole system model in Matlab/Simulink was first developed and tested in simulation, and the simulation results showed that system dynamic characteristics could be implemented by using the closed-loop control of the electric motor drive systems. Then we designed a subscale experimental system to emulate the entire power system from the turbine engine to the propulsive fans. Firstly, we built a system to emulate a gas turbine engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft. We programmed the first motor and its drive to mimic the speed-torque characteristic of the gas turbine engine, while the second motor and drive act as a generator and produce a torque load on the first motor. Secondly, we built another system of two PM motors and drives to emulate a motor driving a propulsive fan. We programmed the first motor and drive to emulate a wound-rotor synchronous motor. The propulsive fan was emulated by implementing fan maps and flight conditions into the fourth motor and drive, which produce a torque load on the driving motor. The stator of each PM motor is designed to travel axially to change the coupling between rotor and stator. This feature allows the PM motor to more closely emulate a wound-rotor synchronous machine. These techniques can convert the plain motor system into a unique TeDP power grid emulator that enables real-time simulation performance using hardware-in-the-loop (HIL).


RNN Simulations of Grammaticality Judgments on Long-distance Dependencies


A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages.


title: Quantum Process Capability

abstract: Physical processes in the quantum regime possess non-classical properties of quantum mechanics. However, methods for quantitatively identifying such processes are still lacking. Accordingly, in this study, we develop a framework for characterizing and quantifying the ability of processes to cause quantum-mechanical effects on physical systems. We start by introducing a new concept, referred to as quantum process capability, to evaluate the effects of an experimental process upon a prescribed quantum specification. Various methods are then introduced for measuring such a capability. It is shown that the methods are adapted to quantum process tomography for implementation of process capability measure and applicable to all physical processes that can be described using the general theory of quantum operations. The utility of the proposed framework is demonstrated through several examples, including processes of entanglement, coherence, and superposition. The formalism proposed in this study provides a generic approach for the identification of dynamical processes in quantum mechanics and facilitates the general classification of quantum-information processing.


title: The design and implementation of INGRES

abstract: {PG,1 A sport of the cultivar Dolly characterized by its unique medium bronze color and somewhat larger flower size, its other characteristics being similar to those of the parent variety.


title: A faceted approach to conceptualizing tasks in information seeking

abstract: The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals' information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User's perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users' work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design.


Automated Essay Scoring 1 Automated Essay Scoring: Writing Assessment and Instruction


title: Computing large matchings fast

abstract: In this article we present algorithms for computing large matchings in 3-regular graphs, graphs with maximum degree 3, and 3-connected planar graphs. The algorithms give a guarantee on the size of the computed matching and take linear or slightly superlinear time. Thus they are faster than the best-known algorithm for computing maximum matchings in general graphs, which runs in O(snm) time, where n denotes the number of vertices and m the number of edges of the given graph. For the classes of 3-regular graphs and graphs with maximum degree 3, the bounds we achieve are known to be best possible. We also investigate graphs with block trees of bounded degree, where the d-block tree is the adjacency graph of the d-connected components of the given graph. In 3-regular graphs and 3-connected planar graphs with bounded-degree 2- and 4-block trees, respectively, we show how to compute maximum matchings in slightly superlinear time.


title: Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing

abstract: 2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014, Toronto, ON, 27 April-2 May 2014


title: Selection of requirement elicitation techniques using laddering

abstract: Requirement elicitation is critical to the success of a project, but it is not known which techniques can identify the maximum requirement of the customer. Therefore, many researchers have proposed various requirement elicitation techniques and studied the pros and cons of these techniques. According to the results of these studies, there is no requirement elicitation technique that is optimal regardless of its environments, such as attributes of the project, elicitors, and stakeholders. In this paper, we analyze the cognitive structure of IT professionals based on the factors that actually affect the use of the requirement elicitation techniques using laddering. We also use various statistical techniques to examine the relationship between the attributes of the actual project and the requirement elicitation techniques.


title: Requirements, Traceability and DSLs in Eclipse with the Requirements Interchange Format (ReqIF)

abstract: Requirements engineering (RE) is a crucial aspect in systems development and is the area of ongoing research and process improvement.However, unlike in modeling, there has been no established standard that activities could converge on. In recent years, the emerging Requirements Interchange Format (RIF/ReqIF) gained more and more visibility in the industry, and research projects started to investigate these standards. To avoid redundant efforts in implementing the standard, the VERDE and Deploy projects cooperate to provide a stable common basis for ReqIF implementation that could be leveraged by other research projects as well. In this paper, we present an Eclipse-based extensible implementation of a RIF/ReqIFbased requirements editing platform. In addition, we also investigate two related aspects of RE that take advantage of the common platform. Firstly, how can the quality of requirements be improved by replacing or complementing natural language requirements with formal approaches such as domain specific languages or models. Secondly, how can we establish a robust traceability mechanism that links different artifacts of a development process like requirements, design etc.


title: Similarity-Based Fuzzy Clustering for User Profiling

abstract: User profiling is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user profiles by capturing similar user interests from Web usage data available in log files. Often, fuzzy clustering is based on the assumption that data lay on an Euclidean space; however, clustering based on Euclidean distance can lead the clustering process to find user representations that do not capture the semantic information incorporated in the original Web usage data. In this paper, we propose a different approach to express similarity between Web users. The measure is based on the evaluation of similarity between fuzzy sets. The proposed measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive profiles modeling the real user preferences. An application example on usage data extracted from log files of a sample Web site is reported and a comparison with the results obtained using the cosine measure is shown to demonstrate the effectiveness of the proposed similarity measure.


title: A perception-based color space for illumination-invariant image processing

abstract: Motivated by perceptual principles, we derive a new color space in which the associated metric approximates perceived distances and color displacements capture relationships that are robust to spectral changes in illumination. The resulting color space can be used with existing image processing algorithms with little or no change to the methods.


title: Fail-Stop Failure Algorithm-Based Fault Tolerance for Cholesky Decomposition

abstract: Cholesky decomposition is a widely used algorithm to solve linear equations with symmetric and positive definite coefficient matrix. With large matrices, this often will be performed on high performance supercomputers with a large number of processors. Assuming a constant failure rate per processor, the probability of a failure occurring during the execution increases linearly with additional processors. Fault tolerant methods attempt to reduce the expected execution time by allowing recovery from failure. This paper presents an analysis and implementation of a fault tolerant Cholesky factorization algorithm that does not require checkpointing for recovery from fail-stop failures. Rather, this algorithm uses redundant data added in an additional set of processes. This differs from previous works with algorithmic methods as it addresses fail-stop failures rather than fail-continue cases. The proposed fault tolerance scheme is incorporated into ScaLAPACK and validated on the supercomputer Kraken. Experimental results demonstrate that this method has decreasing overhead in relation to overall runtime as the matrix size increases, and thus shows promise to reduce the expected runtime for Cholesky factorizations on very large matrices.


title: Software-defined batteries

abstract: Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single chemistry that best meets their diverse needs, thereby compromising other desirable features. In this paper, we present a new hardware--software system, called Software Defined Battery (SDB), which allows system designers to integrate batteries of different chemistries. SDB exposes application programming interfaces (APIs) to the operating system, which controls the amount of charge flowing in and out of each battery, enabling it to dynamically trade one battery property for another depending on application and/or user needs. Using micro-benchmarks from our prototype SDB implementation, and through detailed simulations, we demonstrate that it is possible to combine batteries which individually excel along different axes to deliver an enhanced collective performance when compared to traditional battery packs.


title: Biological Inspiration: From Carangiform Fish to Multi-Joint Robotic Fish

abstract: This paper presents a novel approach to modelling carangiform fish-like swimming motion for multi-joint robotic fish so that they can obtain fish-like behaviours and mimic the body motion of carangiform fish. A given body motion function of fish swimming is firstly converted to a tail motion function which describes the tail motion relative to the head. Then, the tail motion function is discretized into a series of tail postures over time. Thirdly, a digital approximation method calculates the turning angles of joints in the tail to approximate each tail posture; and finally, these angles are grouped into a look-up table, or regressed to a time-dependent function, for practically controlling the tail motors in a multi-joint robotic fish. The paper made three contributions: tail motion relative to the head, an error function for digital approximation and regressing a look-up table for online optimization. To prove the feasibility of the proposed methodology, two basic swimming motion patterns, cruise straight and C-shape sharp turning, are modelled and implemented in our robotic fish. The experimental results show that the relative tail motion and the approximation error function are good choices and the proposed method is feasible.


title: Neural Responding Machine for Short-Text Conversation

abstract: We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoderdecoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming stateof-the-arts in the same setting, including retrieval-based and SMT-based models.


title: Transfer learning for Latin and Chinese characters with Deep Neural Networks

abstract: We analyze transfer learning with Deep Neural Networks (DNN) on various character recognition tasks. DNN trained on digits are perfectly capable of recognizing uppercase letters with minimal retraining. They are on par with DNN fully trained on uppercase letters, but train much faster. DNN trained on Chinese characters easily recognize uppercase Latin letters. Learning Chinese characters is accelerated by first pretraining a DNN on a small subset of all classes and then continuing to train on all classes. Furthermore, pretrained nets consistently outperform randomly initialized nets on new tasks with few labeled data.


License Plate Localization: A Review


title: WAEM: A Web Accessibility Evaluation Metric Based on Partial User Experience Order

abstract: Quantitative accessibility metrics are widely used in accessibility evaluation, which synthesize a summative value to represent the accessibility level of a website. Many of these metrics are the results of a two-step process. The first step is the inspection with regard to potential barriers while different properties are reported, and the second step aggregates these fine-grained reports with varying weights for checkpoints. Existing studies indicate that finding appropriate weights for different checkpoint types is a challenging issue. Although some metrics derive the checkpoint weights from the WCAG priority levels, previous investigations reveal that the correlation between the WCAG priority levels and the user experience is not significant. Moreover, our website accessibility evaluation results also confirm the mismatches between the ranking of websites using existing metrics and the ranking based on user experience. To overcome this limitation, we propose a novel metric called the Web Accessibility Experience Metric (WAEM) that can better match the accessibility evaluation results with the user experience of people with disabilities by aligning the evaluation metric with the partial user experience order (PUEXO), i.e. pairwise comparisons between different websites. A machine learning model is developed to derive the optimal checkpoint weights from the PUEXO. Experiments on real-world web accessibility evaluation data sets validate the effectiveness of WAEM.


title: Improve Lexicon-based Word Embeddings By Word Sense Disambiguation

abstract: There have been some works that learn a lexicon together with the corpus to improve the word embeddings. However, they either model the lexicon separately but update the neural networks for both the corpus and the lexicon by the same likelihood, or minimize the distance between all of the synonym pairs in the lexicon. Such methods do not consider the relatedness and difference of the corpus and the lexicon, and may not be the best optimized. In this paper, we propose a novel method that considers the relatedness and difference of the corpus and the lexicon. It trains word embeddings by learning the corpus to predicate a word and its corresponding synonym under the context at the same time. For polysemous words, we use a word sense disambiguation filter to eliminate the synonyms that have different meanings for the context. To evaluate the proposed method, we compare the performance of the word embeddings trained by our proposed model, the control groups without the filter or the lexicon, and the prior works in the word similarity tasks and text classification task. The experimental results show that the proposed model provides better embeddings for polysemous words and improves the performance for text classification.


title: The application of Fuzzy controller for fuel cell generating studies

abstract: Abstract The number of energy-consuming products is increasing. The use of renewable energy has already become the global goal to solve the global energy shortage. The development of the fuel cell is an important subject. This study plots the V–I characteristic curve of fuel cells using LabView. The experimental result demonstrates that the efficiency with which a fuel cell generates electricity when controlled by a Fuzzy controller is 37% – which significantly exceeds that obtained using a PID controller, 14.67%. Hence, much less bio-hydrogen is wasted, so the cost of generating electricity using the fuel cell is lower. Moreover, the Fuzzy controller need not be used for a long time as a PID controller to estimate and test variables. This research will contribute to the development of the bio-hydrogen fuel cell generators.


Current Correction Methods After Temperature Data Assimilation in the Tropical Atlantic with MOM 3


title: Simplifying Firewall Setups by Using Offline Validation

abstract: Firewalls are crucial for the security of most networks and implemented by packet filters. Those packet filters can be considered the direct opposite of a simple-to-use system. Configured with complex commands in plain text files, only experts are able to understand or even modify such a setup. Recently, this process has been improved by using a combination of external tools, a graphical modeling environment, and a model checker, enabling more users to participate in the process and clearing the way to automatic testing. This paper revists this concept from a simplicity perspective and shows how the whole process can be simplified while simultaneously the level of abstraction is increased and new ways of verifying the result are possible.


title: Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

abstract: One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef- ficient than a large kernel, given the same computational complexity. However, in the field of semantic segmenta- tion, where we need to perform dense per-pixel prediction, we find that the large kernel (and effective receptive field) plays an important role when we have to perform the clas- sification and localization tasks simultaneously. Following our design principle, we propose a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation. We also suggest a residual-based boundary refinement to further refine the ob- ject boundaries. Our approach achieves state-of-art perfor- mance on two public benchmarks and significantly outper- forms previous results, 82.2% (vs 80.2%) on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset.


title: Optimizing your digital business model

abstract: This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


title: Land-mark placement for reliable localization of automatic guided vehicle in warehouse environment

abstract: The reliable and effective localization system is the basis of Automatic Guided Vehicle (AGV) to complete given tasks automatically in warehouse environment. However, there are no obvious features that can be used for localization of AGV to be extracted in warehouse environment and it dose make it difficult to realize the localization of AGV. So in this paper, we concentrate on the problem of optimal landmarks placement in warehouse so as to improve the reliability of localization. Firstly, we take the practical warehouse environment into consideration and transform the problem of landmarks placement into an optimization problem which aims at maximizing the difference degree between each basic unit of localization. Then Genetic Algorithm (GA) is used to solve the optimization problem. Then we match the observed landmarks with the already known ones stored in the map and the Triangulation method is used to estimate the position of AGV after the matching has been done. Finally, experiments in a real warehouse environment validate the effectiveness and reliability of our method.


title: The effects of chordwise wing optimization of single-winged samara in autorotation

abstract: Single-winged rotating aerial vehicles such as monocopters have recently gained popularity, due to the development of small and powerful mechanical actuators and lightweight electronic sensors. Even though monocopters have to rely on their single wings for the total lift generation, few have looked into an efficient wing design for monocopters. In this paper, a chordwise optimization of wing design for single-winged samara in autorotative flight is presented. The resulting wing design has been tested in a numerical simulation and verified in a vertical wind tunnel, as well as with drop tests in a high-speed camera-based motion capture system. The wing is further equipped with an actuator to change angle of attack during flight, which has an effect of softening the landing at the end of the autorotation.


title: A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization

abstract: Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoder-decoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available WEATHERGOV dataset show around 18 BLEU (~ 30%) improvement over the current state-of-the-art.


title: Iterative and incremental developments. a brief history

abstract: Although many view iterative and incremental development as a modern practice, its application dates as far back as the mid-1950s. Prominent software-engineering thought leaders from each succeeding decade supported IID practices, and many large projects used them successfully. These practices may have differed in their details, but all had a common theme-to avoid a single-pass sequential, document-driven, gated-step approach.


title: A programmable CMOS voltage controlled ring oscillator for radio-frequency diathermy on-chip circuit

abstract: In this work we present the design of a digitally controlled ring type oscillator in 0.5 μm CMOS technology for a low-cost and portable radio-frequency diathermy (RFD) device. The oscillator circuit is composed by a low frequency ring oscillator (LFRO), a voltage controlled ring oscillator (VCRO), and a logic control. The digital circuit generates an input signal for the LFO, which generates a voltage ramp that controls the oscillating output signal of the VCRO in the range of 500 KHz to 1 MHz. Simulation results show that the proposed circuit exhibits controllable output characteristics in the range of 500 KHz–1 MHz, with low power consumption and low phase noise, making it suitable for a portable RFD device.


title: A Compact EADFB Laser Array Module for a Future 100-Gb/s Ethernet Transceiver

abstract: A compact electroabsorption modulators integrated distributed feedback (EADFB) laser array module has been developed for 100-Gb/s Ethernet. Four 25-Gb/s EADFB lasers and an optical multiplexer are monolithically integrated on one chip in an area of only 2 mm × 2.6 mm. As a result, a compact 12 mm × 20 mm EADFB laser array module is achieved. A bridge-type RF circuit board is employed to transmit high-frequency, 4 × 25-Gb/s, electrical signals effectively in a narrow package and improve the electrical/optical response of the module. The module has a 3-dB frequency bandwidth of 20 GHz for the four lanes assigned in 100-Gb/s Ethernet. Furthermore, the possibility of realizing low-driving voltage operation is investigated to reduce the power consumption of the module. An error-free transmission through 10 km of single-mode fiber at 100 Gb/s with a modulation voltage of only 1 V is demonstrated.


title: Optimal sizing of a hybrid grid-connected photovoltaic and wind power system

abstract: Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic–wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be economically profitable in the studied rural township in the Mediterranean climate region of central Catalonia (Spain), being the system paid off after 18years of operation out of 25years of system lifetime. Although the annual costs of the system are notably lower compared with the cost of electricity purchase, which is the current alternative, a significant upfront investment of over $10M – roughly two thirds of total system lifetime cost – would be required to install such system.


title: A Multibeam Antenna Based on Substrate Integrated Waveguide Technology for MIMO Wireless Communications

abstract: A 24-beams slot array antenna, which achieves 360-degrees coverage over azimuth direction, based on substrate integrated waveguide (SIW) technology is designed, fabricated and measured for supporting the concept of triangle cell, beam diversity and multiple input multiple output (MIMO) wireless communications. Constructed by six 4-beams single layer SIW antennas which integrate the 4times8 modified Butler matrix with a low side lobe level (SLL) amplitude distribution, measured SLLs are reduced as low as -14.5 dB for all beams in E-plane. The size of the SIW single layer multibeam antenna is 385 mm times 110 mm including feeding network. The usable bandwidth of the proposed antenna is 3.75% at 16 GHz.


title: Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

abstract: [1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.


title: High performance discrete Fourier transforms on graphics processors

abstract: We present novel algorithms for computing discrete Fourier transforms with high performance on GPUs. We present hierarchical, mixed radix FFT algorithms for both power-of-two and non-power-of-two sizes. Our hierarchical FFT algorithms efficiently exploit shared memory on GPUs using a Stockham formulation. We reduce the memory transpose overheads in hierarchical algorithms by combining the transposes into a block-based multi-FFT algorithm. For non-power-of-two sizes, we use a combination of mixed radix FFTs of small primes and Bluestein's algorithm. We use modular arithmetic in Bluestein's algorithm to improve the accuracy. We implemented our algorithms using the NVIDIA CUDA API and compared their performance with NVIDIA's CUFFT library and an optimized CPU-implementation (Intel's MKL) on a high-end quad-core CPU. On an NVIDIA GPU, we obtained performance of up to 300 GFlops, with typical performance improvements of 2-4times over CUFFT and 8-40times improvement over MKL for large sizes.


title: Alar setback technique: a controlled method of nasal tip deprojection.

abstract: OBJECTIVES ::: To describe an alar cartilage-modifying technique aimed at decreasing nasal tip projection in cases with overdeveloped alar cartilages and to compare it with other deprojection techniques used to correct such deformity. ::: ::: ::: DESIGN ::: Selected case series. ::: ::: ::: SETTINGS ::: University and private practice settings in Alexandria, Egypt. ::: ::: ::: PATIENTS ::: Twenty patients presenting for rhinoplasty who had overprojected nasal tips primarily due to overdeveloped alar cartilages. All cases were primary cases except for one patient, who had undergone 2 previous rhinoplasties. ::: ::: ::: INTERVENTION ::: An external rhinoplasty approach was used to set back the alar cartilages by shortening their medial and lateral crura. The choice of performing a high or low setback depended on the preexisting lobule-to-columella ratio. Following the setback, the alar cartilages were reconstructed in a fashion that increased the strength and stability of the tip complex. ::: ::: ::: MAIN OUTCOME MEASURES ::: Subjective evaluation included clinical examination, analysis of preoperative and postoperative photographs, and patient satisfaction. Objective evaluation of nasal tip projection, using the Goode ratio and the nasofacial angle, was performed preoperatively and repeated at least 6 months postoperatively. ::: ::: ::: RESULTS ::: A low setback was performed in 16 cases (80%) and a high setback in 4 (20%). The mean follow-up period was 18 months (range, 6-36 months). The technique effectively deprojected the nasal tip as evidenced by the considerable postoperative decrease in values of the Goode ratio and the nasofacial angle. No complications were encountered and no revision surgical procedures were required. ::: ::: ::: CONCLUSIONS ::: The alar setback technique has many advantages; it results in precise predictable amounts of deprojection, controls the degree of tip rotation, preserves the natural contour of the nasal tip, respects the tip support mechanisms, increases the strength and stability of nasal tip complex, preserves or restores the normal lobule-to-columella proportion, and does not lead to alar flaring. However, the technique requires an external rhinoplasty approach and fine technical precision.


title: A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm

abstract: K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods.


title: The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack

abstract: Postcapture refocusing effect in smartphone cameras is achievable using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map, which has been an open issue for decades. To tackle this issue, a framework is proposed based on a preconditioned alternating direction method of multipliers for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy, the optimization function of the proposed framework can, in fact, converge faster and better than state-of-the-art methods. The qualitative evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against five other methods. Later, 10 light field image sets have been transformed into focal stacks for quantitative evaluation purposes. Preliminary results indicate that the proposed framework has a better performance in terms of structural accuracy and optimization in comparison to the current state-of-the-art methods.


title: Compact magnetic wheeled robot for inspecting complex shaped structures in generator housings and similar environments

abstract: In this paper, we present the mechanical design of a compact magnetic wheeled robot - with the goal to do inspection and vibration measurements in the housings of large generators and similar environments in power plants. After a detailed analysis of the specifications in this application, we present a new vehicle structure that allows for passing sharp concave corners - even with low friction coefficient between wheels and surface - and without using an additional active DOF. The advantages of this structure and the core parameters for its optimization are described in a quasi-static 2D calculation model. A prototype was implemented and successfully tested both in laboratory and real environments. The paper concludes pointing out the future improvements for a final industrial version.


title: Hemingway: Modeling Distributed Optimization Algorithms

abstract: Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult for users as the performance and convergence rate of optimization algorithms vary with the size of the cluster. In this paper we make the case for an ML-optimizer that can select the appropriate algorithm and cluster size to use for a given problem. To do this we propose building two models: one that captures the system level characteristics of how computation, communication change as we increase cluster sizes and another that captures how convergence rates change with cluster sizes. We present preliminary results from our prototype implementation called Hemingway and discuss some of the challenges involved in developing such a system.


title: Detecting Patterns of Anomalies

abstract: An anomaly is an observation that does not conform to the expected normal behavior. With the ever increasing amount of data being collected universally, automatic surveillance systems are becoming more popular and are increasingly using data mining methods to detect patterns of anomalies. Detecting anomalies can provide useful and actionable information in a variety of real-world scenarios. For example, in disease monitoring, a timely detection of an epidemic can potentially save many lives. ::: The diverse nature of real-world datasets, and the difficulty of obtaining labeled training data make it challenging to develop a universal framework for anomaly detection. We focus on a key feature of most real world scenarios, that multiple anomalous records are usually generated by a common anomalous process. In this thesis we develop methods that utilize the similarity between records in these groups or patterns of anomalies to perform better detection. We also investigate new methods for detection of individual record anomalies, which we then incorporate into the group detection methods. A recurring feature of our methods is combinatorial search over some space (e.g. over all subsets of attributes, or over all subsets of records). We use a variety of computational speedup tricks and approximation techniques to make these methods scalable to large datasets. Since most of our motivating problems involve datasets having categorical or symbolic values, we focus on categorical valued datasets. Apart from this, we make few assumptions about the data, and our methods are very general and applicable to a wide variety of domains. ::: Additionally, we investigate anomaly pattern detection in data structured by space and time. Our method generalizes the popular method of spatio-temporal scan statistics to learn and detect specific, time-varying spatial patterns in the data. Finally, we show an efficient and easily interpretable technique for anomaly detection in multivariate time series data. We evaluate our methods on a variety of real world data sets including both real and synthetic anomalies.


title: Servant Leadership, Employee Satisfaction, and Organizational Performance in Rural Community Hospitals

abstract: Servant leadership in today’s healthcare settings provides a unique avenue through which to assess leadershipbehaviors and the relationship to employee satisfaction and healthcare patient satisfaction measures. This studysought to determine the degree that leaders in community hospitals were perceived as servant leaders and thelevel of employee satisfaction at these rural community hospitals. Two hundred nineteen surveys werecompleted from 10 community hospitals. This research revealed that servant leadership and employeesatisfaction are strongly correlated. In addition, servant leadership has a significant correlation between intrinsicsatisfaction and HCAHPS scores. Further research can be extended to additional categories and geographic areasof the United States to determine how servant leadership, employee satisfaction, and HCAHPS are related.Hospital administrators should examine the findings of this study for possible implications to their leadershipstyle and practice in determining how it may impact the organization they lead.


title: ActionFlowNet: Learning Motion Representation for Action Recognition

abstract: We present a data-efficient representation learning approach to learn video representation with small amount of labeled data. We propose a multitask learning model ActionFlowNet to train a single stream network directly from raw pixels to jointly estimate optical flow while recognizing actions with convolutional neural networks, capturing both appearance and motion in a single model. Our model effectively learns video representation from motion information on unlabeled videos. Our model significantly improves action recognition accuracy by a large margin (23.6%) compared to state-of-the-art CNN-based unsupervised representation learning methods trained without external large scale data and additional optical flow input. Without pretraining on large external labeled datasets, our model, by well exploiting the motion information, achieves competitive recognition accuracy to the models trained with large labeled datasets such as ImageNet and Sport-1M.


title: Future Directions of Conceptual Modeling

abstract: This paper discusses several important future research directions in conceptual modeling. Since this field is very broad, the list of topics discussed here is not an exhaustive list but rather the "partial" composite views of the coauthors on the important issues from their own perspectives. Some of the important issues are omitted because they were covered extensively in [1], and the readers should also read the papers in that volume to get a more comprehensive view of where the field is going. Also, the selection of research topics is independent of their difficulties. Some of the research problems discussed here could be solved in the next decades while the others may take much longer to develop a reasonable solution.


title: Fixed-length lossy compression in the finite blocklength regime

abstract: This paper studies the minimum achievable source coding rate as a function of blocklength $n$ and probability $\epsilon$ that the distortion exceeds a given level $d$. Tight general achievability and converse bounds are derived that hold at arbitrary fixed blocklength. For stationary memoryless sources with separable distortion, the minimum rate achievable is shown to be closely approximated by $R(d) + \sqrt{\frac{V(d)}{n}} Q^{-1}(\epsilon)$, where $R(d)$ is the rate-distortion function, $V(d)$ is the rate dispersion, a characteristic of the source which measures its stochastic variability, and $Q^{-1}(\epsilon)$ is the inverse of the standard Gaussian complementary cdf.


title: Airbag-Induced Thumb Avulsion: Two Case Reports

abstract: Although airbags are designed to save lives and protect victims from serious injuries, airbag deployment can cause unwanted lesions. In this case report, two cases are presented of young women who sustained an important fracture dislocation of the first carpometacarpal joint (CMC I joint) caused by airbag deployment during a car collision.


title: Security and Efficiency Analysis of One Time Password Techniques

abstract: As a result of the rapid growth of available services provided via Internet, as well as multiple accounts a person owns, reliable user authentication schemes are mandatory for security purposes. OTP systems have prevailed as the best viable solution for security over sensitive information and pose an interesting field for research. Although, OTP schemes enhance authentication's security through various algorithmic customizations and extensions, certain compromises should be made; especially since excessively tolerable to vulnerability systems tend to have high computational and storage needs. In order to minimize the risk of a non-authenticated user having access to sensitive data, depending on the use, OTP system's architecture differs; as its tolerance towards already known attack methods. In this paper, the most widely accepted and promising OTP schemes are described and evaluated in terms of resistance against security attacks and in terms of computational intensity (performance efficiency). The results showed that there is a correlation between the security level, the computational efficiency and the storage needs of an OTP system.


title: Survey on pLSA based scene classification techniques

abstract: A comprehensive survey of scene classification based on pLSA formulation literature is presented. Due to the growth in robotics there is an increase in the concern towards visual technology adaption and the interest in the concern has been growing over past years. Vision creates the premises for brain-processing. Our brain receives and keeps unconscious processing over the stupendous amount of visual data every second. Scene Classification is the pioneer as well as a challenging discipline of the functional scope of Computer Vision. In this paper we have introduced the concept of different approaches for scene classification for enabling the viewing and classifying objects or scene around, the way human eyes do. The focus is based on the general overview of Scene Classification based on pLSA (probabilistic latent semantic analysis) integrated with different classifiers and algorithms. Each method is discussed to provide a complete reference for the description and comparison. Our aim is to highlight the rewarding direction for future research and real life applications of Scene Classification.


title: Distance estimation with 2.5D anchors and its application to robot navigation

abstract: Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide the candidate of distances based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of the regression with our proposed anchors, by comparing with ordinary regression methods and state-of-the-art 3D object detection methods, through Pascal 3D+ TV monitor and KITTI car experiments. In addition, we also show an example of practical uses of our proposed method in a real-time system, robot navigation, by integrating with ROS-based simultaneous localization and mapping.


title: Huang et al. Link Analysis for Recommendation under Sparse Data A Link Analysis Approach to Recommendation under Sparse Data Zan Huang

abstract: Collaborative filtering is one most successful approach to recommendation reported in the literature. It automates the “Word of Mouth” recommendation by suggesting products liked by other consumers who showed similar preference patterns as the target consumer. A serious limitation of the collaborative filtering approach is the sparsity problem, referring to the situation where insufficient historical transactions are available for inferring reliable consumer similarities. In this paper, we represent the consumer transactional data as a graph consisting nodes representing consumers and products and links representing transactions. Under this consumer-product graph, we propose to explore the global graph structure to facilitate collaborative filtering under sparse data. We developed a link analysis recommendation algorithm based on the similar ideas implemented in Web graph analysis algorithms. We tested the proposed algorithm using an online bookstore dataset against standard collaborative filtering algorithms that do not consider transitive associations. The experimental results showed that our proposed algorithm outperformed the benchmark algorithms, especially when insufficient amount of transactional data is available.


title: Bandits with unobserved confounders: a causal approach

abstract: The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine. One of the hallmarks of a bandit setting is the agent's capacity to explore its environment through active intervention, which contrasts with the ability to collect passive data by estimating associational relationships between actions and payouts. The existence of unobserved confounders, namely unmeasured variables affecting both the action and the outcome variables, implies that these two data-collection modes will in general not coincide. In this paper, we show that formalizing this distinction has conceptual and algorithmic implications to the bandit setting. The current generation of bandit algorithms implicitly try to maximize rewards based on estimation of the experimental distribution, which we show is not always the best strategy to pursue. Indeed, to achieve low regret in certain realistic classes of bandit problems (namely, in the face of unobserved confounders), both experimental and observational quantities are required by the rational agent. After this realization, we propose an optimization metric (employing both experimental and observational distributions) that bandit agents should pursue, and illustrate its benefits over traditional algorithms.


When Tests Collide : Evaluating and Coping with the Impact of Test Dependence


title: Potential for human exposure to mercury and mercury compounds from hat collections

abstract: Mercury salts have been detected in a significant number of felted hats within the collection of the Victoria and Albert (V&A) museum. The most likely source of this mercury is as the sulfate product of the salts used in the preparation of the animal hair to enhance their felting properties. Two potential groups of user—the conservator and the curator/public— have been identified as being at risk. Qualitative analysis by energy dispersive X-ray fluorescence spectroscopy (EDXRF) has identified a wide date of manufacture of felted hats containing these salts and new quantitative analysis has shown that one hat contained 1% w/w mercury. At this level, it is necessary to label the hats with appropriate hazardous chemical warning stickers and to minimise handling. Care should be exercised when transporting or disposing of such hats.


title: Human Activity Recognition with Wearable Sensors

abstract: This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information -- others include the user's location or the state of his environment -- which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors -- mainly accelerometers -- to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.


Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)


A Practically Efficient Approach for Solving Adaptive Distributionally Robust Linear Optimization Problems


title: The impact of knowledge sharing, organizational capability and partnership quality on IS outsourcing success

abstract: In recent studies, there has been much interest in knowledge sharing between the service receiver and provider through an outsourcing partnership and its effect on Information Systems (IS) outsourcing success. This study examines the relationship between knowledge sharing and outsourcing success. The effect of the ability of the service receiver to absorb the needed knowledge and of companies to build a partnership on these relationships are modeled and hypotheses defined. These were tested using a sample of 195 public sector organizations in Korea. Findings indicate that all hypothesized paths in the model are significant.


title: Tuning Range Optimization of a Planar Inverted F Antenna for the LTE Low Frequency Bands

abstract: This paper presents a Planar Inverted F Antenna (PIFA) tuned with a fixed capacitor to the low frequency bands supported by the Long Term Evolution (LTE) technology. The tuning range is investigated and optimized with respect to the bandwidth and the efficiency of the resulting antenna. Simulations and mock-ups are presented.


title: The K-admissibility of SL(2, 5)

abstract: Let K be a field and let G be a finite group. G is K-admissible if there exists a Galois extension L of K with G=Gal(L/K) such that L is a maximal subfield of a central K-division algebra. We characterize those number fields K such that H is K-admissible where H is any subgroup of SL(2, 5) which contains a S2-group. The method also yields refinements and alternate proofs of some known results including the fact that A5 is K-admissible for every number field K.


title: Concepts, methods, and tools to support science-base decision-making in Integrated Coastal and Ocean Management: examples from the Balearic Islands

abstract: Capitulo publicado en: Farinos, Joaquin (ed.). La gestion integrada de zonas costeras. ?Algo mas que una ordenacion del litoral revisada?. Valencia: PUV, Publicacions de la Universitat de Valencia, 2011


title: Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia

abstract: Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. AML is a heterogeneous malignancy characterized by distinct genetic and epigenetic abnormalities. Recent genome-wide DNA methylation studies have highlighted an important role of dysregulated methylation signature in AML from biological and clinical standpoint. In this review, we will outline the recent advances in the methylome study of AML and overview the impacts of DNA methylation on AML diagnosis, treatment, and prognosis.


title: Development of medium voltage solid-state fault isolation devices for ultra-fast protection of distribution systems

abstract: Three generations of medium voltage fault isolation devices (FID) for a power electronics based distribution system, the FREEDM System, are reported in this paper. In the Gen-I FID, three 6.5 kV silicon IGBTs are series connected to achieve 15 kV class distribution voltage blocking capability. Whereas in the Gen-II and the Gen-III FIDs, 15 kV Silicon Carbide (SiC) ETO devices are used. Since one single such device can meet the voltage withstand requirement, the operation losses as well as dimensions are significantly reduced. Future improvements in the Gen-III FID include the use of hybrid schemes and the development of symmetrical blocking high voltage SiC devices to further minimize the operation losses.


title: Memory-based Parameter Adaptation

abstract: Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling.


title: Document Modeling with Hierarchical Deep Learning Approach for Sentiment Classification

abstract: Sentiment analysis has recently been considered as most active research field in NLP domain. Deep learning is a growing trend of machine learning due to its automatic learning capability with impressive results across different NLP task. In this paper a model is proposed to analyze the deep sentiment representation based on CNN and LSTM (modified version of RNN) network. We aim to improve the performance of traditional machine learning method by merging them with deep learning techniques to tackle the challenge of sentiment prediction of massive amount of unsupervised product review dataset. We make our model first learn to sentence representation with CNN. Next, the semantics of sentences are encoded with LSTM network for document representation. We conduct experiments on two review datasets based on movie review with evaluation metric 'accuracy'. The result shows that proposed model outperformed traditional machine learning as well as baseline neural network model


title: A Service-Oriented Architecture for Virtualizing Robots in Robot-as-a-Service Clouds

abstract: Exposing software and hardware computing resources as services through a cloud is increasingly emerging in the recent years. This comes as a result of extending the service-oriented architecture (SOA) paradigm to virtualize computing resources. In this paper, we extend the paradigm of the SOA approach to virtualize robotic hardware and software resources to expose them as services through the Web. This allows non-technical users to access, interact and manipulate robots simply through a Web browser. The proposed RoboWeb system is based on a SOAP-based Web service middleware that binds robots computing resources as services and publish them to the end-users. We consider robots that operates with the Robotic Operating System (ROS), as it provides hardware abstraction that makes easier applications development. We describe the implementation of RoboWeb and demonstrate how researchers can use it to interact remotely with the robots. We believe that this work consistently contributes to enabling remote robotic labs using the cloud paradigm.


title: Torque characteristics of IPMSM with spoke and axial type magnets

abstract: This paper describes torque characteristics of the interior permanent magnet synchronous motor (IPMSM) using the inexpensive ferrite magnets. IPMSM model used in this study has the spoke and the axial type magnets in the rotor, and torque characteristics are analyzed by the three-dimensional finite element method (3D-FEM). As a result, torque characteristics can be improved by using both the spoke type magnets and the axial type magnets in the rotor.


title: Calculating ROI for software product lines

abstract: Product line engineering has become an important and widely used approach for efficiently developing portfolios of software products. The idea is to develop a set of products as a single, coherent development task from a core asset base (sometimes called a platform), a collection of artifacts specifically designed for use across a portfolio. This approach produces order-of-magnitude economic improvements compared to one-at-a-time software system development. Because the product line approach isn't limited to specific technical properties of the planned software but rather focuses on economic characteristics, high return on investment has become the anthem of the approach's protagonists. Our software product line cost model can calculate the costs and benefits (and hence the ROI) that we can expect to accrue from various product line development situations. It's also straightforward and intuitive.


title: Adaptive IEEE 802.15.4 MAC for Throughput and Energy Optimization

abstract: IEEE 802.15.4 defines a popular MAC standard for wireless sensor and actuator networks. With the default parameters, under medium to high load, 802.15.4 generates excessive collisions and packet losses. Low duty cycles even exacerbate the problem, because more nodes become active after long periods of sleep and contend for channel access. In this paper, we have applied the models that led to the Idle Sense access method for 802.11 to the 802.15.4 slotted CSMA/CA, taking into account the central role of the coordinator and also the bursty nature of the traffic. Surprisingly, the approach perfectly applies to 802.15.4 even if the principles of the two access methods fundamentally differ. Based on the model, we propose ABE, an adaptation method that adjusts the contention window to optimal values so that the network obtains high throughput along with low duty cycles leading to low energy consumption. The method converges to near-optimal backoff values even under bursty traffic and for any number of contending nodes.


title: Post-Mortem Memory Analysis of Cold-Booted Android Devices

abstract: As recently shown in 2013, Android-driven smartphones and tablet PCs are vulnerable to so-called cold boot attacks. With physical access to an Android device, forensic memory dumps can be acquired with tools like FROST that exploit the remanence effect of DRAM to read out what is left in memory after a short reboot. While FROST can in some configurations be deployed to break full disk encryption, encrypted user partitions are usually wiped during a cold boot attack, such that a post-mortem analysis of main memory remains the only source of digital evidence. Therefore, we provide an in-depth analysis of Android's memory structures for system and application level memory. To leverage FROST in the digital investigation process of Android cases, we provide open-source Volatility plugins to support an automated analysis and extraction of selected Dalvik VM memory structures.


title: On upper-confidence bound policies for switching bandit problems

abstract: Many problems, such as cognitive radio, parameter control of a scanning tunnelling microscope or internet advertisement, can be modelled as non-stationary bandit problems where the distributions of rewards changes abruptly at unknown time instants. In this paper, we analyze two algorithms designed for solving this issue: discounted UCB (D-UCB) and sliding-window UCB (SW-UCB). We establish an upperbound for the expected regret by upper-bounding the expectation of the number of times suboptimal arms are played. The proof relies on an interesting Hoeffding type inequality for self normalized deviations with a random number of summands. We establish a lower-bound for the regret in presence of abrupt changes in the arms reward distributions. We show that the discounted UCB and the sliding-window UCB both match the lower-bound up to a logarithmic factor. Numerical simulations show that D-UCB and SW-UCB perform significantly better than existing soft-max methods like EXP3.S.


title: Pedestrian Detection for Transformer Substation Based on Gaussian Mixture Model and YOLO

abstract: Safety is a core requirement of the transformer substation where is dangerous due to high voltage. It requires to detect pedestrians efficiently based on the surveillance video near the substation to ensure the safety of the pedestrian and the device. This paper presents a new method of pedestrian detection for transformer substation based on Gaussian Mixture Model (GMM) and YOLO. We use GMM to model the background and detect the pedestrians preliminarily, at the same time, YOLO, a kind of detection method based on convolution neural network (CNN), is also applied for pedestrian detection. Through combining the two results with different weights, the network outputs a better detection result. Our extensive experiments show that the work reaches 20% higher than the single method.


title: Load angle estimation in dynamic stepping motor applications based on Phase Locked Loop

abstract: Stepping motors are well suited for open-loop positioning tasks at low-power. The rotor position of the machine can simply be controlled by the user. Every time a next pulse is sent by the user, the stepping motor driver excites the correct stator phases to rotate the rotor over a pre-defined discrete angular position. In this way, counting the step command pulses enables open-loop positioning. However, when the motor is overloaded or stuck, the relation between the expected rotor position based on the number of step command pulses and the actual rotor position is lost. This is called step loss. Open-loop stepping motor methodologies do not detect this step loss or stall. While the absolute rotor position is lost, the open-loop control continues to send unnecessary and unwanted step command pulses. This can lead to excessive vibrations, wear and noise. Especially for dynamically demanding applications, step loss should be avoided or at least be detected. Using a mechanical position sensor to achieve closed loop control would increase the cost and complexity of the system and omits the straightforward open loop control. Therefore in this paper, a sensorless estimator based on a Phase Locked Loop is presented which provides feedback of the load angle, even during transients. The load angle contains information about the torque generation and the margin to step loss. To estimate the load angle, the back-EMF is considered. This algorithm can be used with the typical full-, half- and micro-stepping algorithm and only needs one current and one voltage measurement and electrical parameters such as resistance and inductance to estimate the back-EMF and subsequently estimate the load angle. The proposed algorithm is validated through measurements on a hybrid stepper motor.


title: The grand challenges of Science Robotics

abstract: One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further.


title: ATME: Accurate Traffic Matrix Estimation in Both Public and Private Datacenter Networks

abstract: Understanding the pattern of end-to-end traffic flows in datacenter networks (DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers often assume the availability of traffic tracing tools (e.g., OpenFlow) when their proposals require traffic information as input, but these tools may have high monitoring overhead and consume significant switch resources even if they are available in a DCN. Although estimating the traffic matrix (TM) between origin-destination pairs using only basic switch SNMP counters is a mature practice in IP networks, traffic flows in DCNs show totally different characteristics, while the large number of redundant routes in a DCN further complicates the situation. To this end, we propose to utilize resource provisioning information in public cloud datacenters and the service placement information in private datacenters for deducing the correlations among top-of-rack switches, and to leverage the uneven traffic distribution in DCNs for reducing the number of routes potentially used by a flow. These allow us to develop ATME as an efficient TM estimation scheme that achieves high accuracy for both public and private DCNs. We compare our two algorithms with two existing representative methods through both experiments and simulations; the results strongly confirm the promising performance of our algorithms.


title: A Grouping-Based Cooperative Driving Strategy for CAVs Merging Problems

abstract: In general, there are two kinds of cooperative driving strategies, planning-based strategy, and ad hoc negotiation-based strategy, for connected and automated vehicles merging problems. The planning-based strategy aims to find the globally optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation-based strategy runs fast, but it always finds a locally optimal solution. In this paper, we propose a grouping-based cooperative driving strategy to make a good tradeoff between computation time and coordination performance. The key idea is to fix the passing orders for some vehicles whose inter-vehicle headways are small enough (e.g., smaller than the pre-selected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. Then, two analyses are given to explain why this kind of strategy is good and how to determine suitable values for the strategy parameters. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less computation time and is promising to be used in practice.


title: Freeform surfaces from single curved panels

abstract: Motivated by applications in architecture and manufacturing, we discuss the problem of covering a freeform surface by single curved panels. This leads to the new concept of semi-discrete surface representation, which constitutes a link between smooth and discrete surfaces. The basic entity we are working with is the developable strip model. It is the semi-discrete equivalent of a quad mesh with planar faces, or a conjugate parametrization of a smooth surface. We present a B-spline based optimization framework for efficient computing with D-strip models. In particular we study conical and circular models, which semi-discretize the network of principal curvature lines, and which enjoy elegant geometric properties. Together with geodesic models and cylindrical models they offer a rich source of solutions for surface panelization problems.


title: Using DCT Features for Printing Technique and Copy Detection

abstract: The ability to discriminate between original documents and their photocopies poses a problem when conducting automated forensic examinations of large numbers of confiscated documents. This paper describes a novel frequency domain approach for printing technique and copy detection of scanned document images. Tests using a dataset consisting of 49 laser-printed, 14 inkjet-printed and 46 photocopied documents demonstrate that the approach outperforms existing spatial domain methods for image resolutions exceeding 200 dpi. An increase in classification accuracy of approximately 5% is achieved for low scan resolutions of 300 dpi and 400 dpi. In addition, the approach has the advantage of increased processing speed.


title: Intrusion Detection Using an Ensemble of Intelligent Paradigms

abstract: Soft computing techniques are increasingly being used for problem solving. This paper addresses using an ensemble approach of different soft computing and hard computing techniques for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. We studied the performance of Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Multivariate Adaptive Regression Splines (MARS). We show that an ensemble of ANNs, SVMs and MARS is superior to individual approaches for intrusion detection in terms of classification accuracy.


title: A systematic review and meta-analysis of dressings used for wound healing: the efficiency of honey compared to silver on burns.

abstract: Background: Honey has the antibacterial effect of silver without the toxic effect of silver on the skin. Even so, silver is the dominant antibacterial dressing used in wound healing. Objectives: To ...


title: Robust Sensing of Distal Pulse Waveforms on a Modified Weighing Scale for Ubiquitous Pulse Transit Time Measurement

abstract: The measurement of aortic pulse transit time (PTT), the time for the arterial pulse wave to travel from the carotid to the femoral artery, can provide valuable insight into cardiovascular health, specifically regarding arterial stiffness and blood pressure (BP). To measure aortic PTT, both proximal and distal arterial pulse timings are required. Recently, our group has demonstrated that the ballistocardiogram signal measured on a modified weighing scale can provide an unobtrusive, yet accurate, means of obtaining a proximal timing reference; however, there are no convenient, reliable methods to extract the distal timing from a subject standing on the modified weighing scale. It is common to use a photoplethysmograph (PPG) attached to a toe to measure this distal pulse, but we discovered that this signal is greatly deteriorated as the subject stands on the scale. In this paper, we propose a novel method to measure the distal pulse using a custom reflective PPG array attached to the dorsum side of the foot (D-PPG). A total of 12 subjects of varying skin tones were recruited to assess the preliminary validation of this approach. Pulse measurements using the D-PPG were taken from seated and standing subjects, and the commercially available PPG were measured for facilitating comparison of timing measurements. We show that the D-PPG was the only sensor to retain the high detection rate of feasible timing values. To further test and optimize the system, various factors such as applied pressure, measurement location, and LED/photodiode configuration were tested.


title: Automated border control: Problem formalization

abstract: This paper introduces a formalization of the Automated Border Control (ABC) machines deployed worldwide as part of the eBorder infrastructure for automated traveller clearance. Proposed formalization includes classification of the eBorder technologies, definition of the basic components of the ABC machines, identification of their key properties, establishment of metrics for their evaluation and comparison, as well as development of a dedicated architecture based on the assistant-based concept. Specifically, three generations of the ABC machines are identified: Gen-1 ABC machines which are biometric enabled kiosks, such as Canada's NEXUS or UK IRIS, to process low-risk pre-enrolled travellers; Gen-2 ABC machines which are eGate systems to serve travellers with biometric eID / ePassports; and Gen-3 ABC machines that will be working to support the eBorder process of the future. These ABC machines are compared in this paper based on certain criteria, such as availability of the dedicated architectural components, and in terms of the life-cycle performance metrics. This paper addresses the related problems of deployment and evaluation of the ABC technologies and machines, including the vulnerability analysis and strategic planning of the eBorder infrastructure.


title: Evidence of activation and suppression during the early immune response to foot-and-mouth disease virus.

abstract: Summary ::: Foot-and-mouth disease virus causes a serious disease of livestock species, threatening free global trade and food security. The disease spreads rapidly between animals, and to ensure a window of opportunity for such spread, the virus has evolved multiple mechanisms to subvert the early immune response. The cycle of infection in the individual animal is very short, infection is initiated, disseminated throughout the body and infectious virus produced in <7 days. Foot-and-mouth disease virus has been shown to disrupt the innate response in vitro and also interacts directly with antigen-presenting cells and their precursors. This interaction results in suboptimal immune function, favouring viral replication and the delayed onset of specific adaptive T-cell responses. Detailed understanding of this cycle is crucial to effectively control disease in livestock populations. Knowledge-based vaccine design would specifically target and induce the immunological mechanisms of early protection and of robust memory induction. Specifically, information on the contribution of cytokines and interferon, innate immune cells as well as humoral and cellular immunity can be employed to design vaccines promoting such responses. Furthermore, understanding of viral escape mechanisms of immunity can be used to create attenuated viruses that could be used to develop novel vaccines and to study viral pathogenesis.


title: Automatic discovery of attributes in relational databases

abstract: In this work we design algorithms for clustering relational columns into attributes, i.e., for identifying strong relationships between columns based on the common properties and characteristics of the values they contain. For example, identifying whether a certain set of columns refers to telephone numbers versus social security numbers, or names of customers versus names of nations. Traditional relational database schema languages use very limited primitive data types and simple foreign key constraints to express relationships between columns. Object oriented schema languages allow the definition of custom data types; still, certain relationships between columns might be unknown at design time or they might appear only in a particular database instance. Nevertheless, these relationships are an invaluable tool for schema matching, and generally for better understanding and working with the data. Here, we introduce data oriented solutions (we do not consider solutions that assume the existence of any external knowledge) that use statistical measures to identify strong relationships between the values of a set of columns. Interpreting the database as a graph where nodes correspond to database columns and edges correspond to column relationships, we decompose the graph into connected components and cluster sets of columns into attributes. To test the quality of our solution, we also provide a comprehensive experimental evaluation using real and synthetic datasets.


title: BackHand: Sensing Hand Gestures via Back of the Hand

abstract: In this paper, we explore using the back of hands for sensing hand gestures, which interferes less than glove-based approaches and provides better recognition than sensing at wrists and forearms. Our prototype, BackHand, uses an array of strain gauge sensors affixed to the back of hands, and applies machine learning techniques to recognize a variety of hand gestures. We conducted a user study with 10 participants to better understand gesture recognition accuracy and the effects of sensing locations. Results showed that sensor reading patterns differ significantly across users, but are consistent for the same user. The leave-one-user-out accuracy is low at an average of 27.4%, but reaches 95.8% average accuracy for 16 popular hand gestures when personalized for each participant. The most promising location spans the 1/8~1/4 area between the metacarpophalangeal joints (MCP, the knuckles between the hand and fingers) and the head of ulna (tip of the wrist).


title: Unpaired Image Captioning by Language Pivoting

abstract: Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description. In general, the mapping function is learned from a training set of image-caption pairs. However, for some language, large scale image-caption paired corpus might not be available. We present an approach to this unpaired image captioning problem by language pivoting. Our method can effectively capture the characteristics of an image captioner from the pivot language (Chinese) and align it to the target language (English) using another pivot-target (Chinese-English) sentence parallel corpus. We evaluate our method on two image-to-English benchmark datasets: MSCOCO and Flickr30K. Quantitative comparisons against several baseline approaches demonstrate the effectiveness of our method.


title: Knowledge and practices of pregnant women in relation to the intake of drugs during pregnancy.

abstract: In Egypt 400 randomly selected pregnant women attending prenatal clinics affiliated with El-Shatby Maternity Hospital Dar El-Welada Hospital Gamal Abdel Nasser Hospital Boharram Bay Maternal and Child Health Center and Bacous Maternal and Child Health Center were interviewed to determine their knowledge of and practices related to drug intake during pregnancy. 47.7% had adequate knowledge (i.e. > 75% correct answers) of drug use during pregnancy. Only 14% did not use any drugs during pregnancy. The remaining 86% used drugs without a prescription. The most common drugs used were vitamins and tonics (78.8%) antacids (66.5%) analgesics (41.8%) and antiemetics (35.5%). The leading reasons for drug use included general weakness (78.8%) heart burn and indigestion (66.5%) headaches (41.8%) vomiting (35.5%) and cough and insomnia (27.5%). Factors associated with poor knowledge of drug use during pregnancy were: younger than 30 illiteracy being a housewife primigravidity and history of abortion (p < 0.05 for all factors). These findings indicate a need to inform pregnant women about the dangers of drug use during pregnancy especially during the first 12 weeks of pregnancy. Nurses should play a key role in communicating these risks.


Chatbot Technology with Synthetic Voices in the Acquisition of an Endangered Language: Motivation, Development and Evaluation of a Platform for Irish.


title: Energy Harvesting From Human and Machine Motion for Wireless Electronic Devices

abstract: Energy harvesting generators are attractive as inexhaustible replacements for batteries in low-power wireless electronic devices and have received increasing research interest in recent years. Ambient motion is one of the main sources of energy for harvesting, and a wide range of motion- powered energy harvesters have been proposed or demon- strated, particularly at the microscale. This paper reviews the principles and state-of-art in motion-driven miniature energy harvesters and discusses trends, suitable applications, and possible future developments.


title: 8T SRAM Cell as a Multibit Dot-Product Engine for Beyond Von Neumann Computing

abstract: Large-scale digital computing almost exclusively relies on the von Neumann architecture, which comprises separate units for storage and computations. The energy-expensive transfer of data from the memory units to the computing cores results in the well-known von Neumann bottleneck. Various approaches aimed toward bypassing the von Neumann bottleneck are being extensively explored in the literature. These include in-memory computing based on CMOS and beyond CMOS technologies, wherein by making modifications to the memory array, vector computations can be carried out as close to the memory units as possible. Interestingly, in-memory techniques based on CMOS technology are of special importance due to the ubiquitous presence of field-effect transistors and the resultant ease of large-scale manufacturing and commercialization. On the other hand, perhaps the most important computation required for applications such as machine learning, etc., comprises the dot-product operation. Emerging nonvolatile memristive technologies have been shown to be very efficient in computing analog dot products in an in situ fashion. The memristive analog computation of the dot product results in much faster operation as opposed to digital vector in-memory bitwise Boolean computations. However, challenges with respect to large-scale manufacturing coupled with the limited endurance of memristors have hindered rapid commercialization of memristive-based computing solutions. In this paper, we show that the standard 8 transistor (8T) digital SRAM array can be configured as an analoglike in-memory multibit dot-product engine (DPE). By applying appropriate analog voltages to the read ports of the 8T SRAM array and sensing the output current, an approximate analog–digital DPE can be implemented. We present two different configurations for enabling multibit dot-product computations in the 8T SRAM cell array, without modifying the standard bit-cell structure. We also demonstrate the robustness of the present proposal in presence of nonidealities such as the effect of line resistances and transistor threshold voltage variations. Since our proposal preserves the standard 8T-SRAM array structure, it can be used as a storage element with standard read–write instructions and also as an on-demand analoglike dot-product accelerator.


title: Measures of aggregate credit conditions and their potential use by central banks

abstract: Understanding the nature of credit risk has important implications for financial stability. Since authorities—notably, central banks—focus on risks that have systemic implications, it is crucial to develop ways to measure these risks. The difficulty lies in finding reliable measures of aggregate credit risk in the economy, as opposed to firmlevel credit risk. In this paper, the authors examine two models recently developed for this purpose: a reduced-form model applied to credit default swap index tranches, and a structural model applied to the spread on U.S. corporate bond indexes. The authors find that these models provide information on the nature of credit events—that is, whether the event is systemic or not—and on the type of risk priced in corporate bonds (i.e., credit or liquidity risk). However, although the two models provide potentially useful information for policy-makers, at this stage it is difficult to corroborate the accuracy of the information obtained from them. Further work is needed before authorities can include conclusions drawn from the two models into their policy decisions.


title: A new calibration technique for multi-camera systems of limited overlapping field-of-views

abstract: State-of-the-art calibration methods typically choose to use a checkerboard as the calibration target for its simplicity and robustness. They however require the complete checkerboard be captured to break symmetry. More recent multi-camera systems such as Google Jump, Jaunt, and camera arrays have limited overlapping field-of-view (FoV) and having all cameras viewing the complete checkerboard is extremely difficult in reality. Tailored patterns such as CALTag [1] introduce new image features within the checker blocks for breaking symmetry but they also break the grid topology. We present a new technique using such patterned calibration targets for a broad range of multi-camera systems. Our key observation is that applying directional gradient filters yields to heterogeneous responses on grid vs. non-grid features: the former are isolated and the latter are highly inter-connected. We therefore apply a simple but highly efficient technique to eliminate non-grid outliers based on connected component analysis and gradient histograms. Finally, we recover the complete grid by approximating each local checkerboard as a parallelogram and imposing the topology constraint. We conduct comprehensive experiments on a number of recent multi-camera systems and our technique significantly outperforms the state-of-the-art in accuracy and robustness.


title: Argument-Based Critics and Recommenders: A Qualitative Perspective on User Support Systems

abstract: In recent years we have witnessed the wide-spread evolution of support tools that operate in association with the user to accomplish a range of computer-mediated tasks. Two examples of these tools are critics and recommenders. Critics are cooperative tools that observe the user interacting with a computer system and present reasoned opinions about a product under development. Recommender systems are tools that assist users by facilitating access to relevant items. At the same time, defeasible argumentation has evolved as a successful approach in AI to model commonsense qualitative reasoning, with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents a novel approach towards the integration of user support systems, such as critics and recommender systems, with a defeasine argumentation framework. The final goal is to enhance practical reasoning capabilities of current user support tools by incorporating argument-based qualitative inference.


title: Adaptive Malicious URL Detection: Learning in the Presence of Concept Drifts

abstract: Hackers can implant malwares (e.g. Trojans, Worms, etc.) in the web pages to steal user information and acquire money illegally. In fact, it is noted that close to one-third of all websites are potentially malicious in nature. Therefore, It makes sense to quickly detect malicious URLs on the Internet. Different from most of previous methods, in this paper, we propose a method for online malicious URL detection based on adaptive learning. By collecting the network traffic from backbone networks, we train machine learning models to detect malicious URLs. But there is a serious problem in dynamically changing environments where the statistical properties of target variable change over time, which is known as concept drift. To address this problem, we apply a nonparametric test to correctly detect concept drifts in adaptive learning. Extensive experiments with different types of concept drifts are performed to demonstrate the feasibility of our proposed method on both artificial and real datasets. Our empirical study shows that this approach has good performance in detecting malicious URLs and concept drifts.


title: Designing a business model for smart water management system with the smart metering system as a core technology: Case study: Indonesian drinking water utilities

abstract: There are four main pillars of water management principles in Indonesia: quality, quantity, continuity, and connectivity. ICT is an enabler for smart water management used to optimize, monitor and control. Many existing technologies need to conform with its business aspect. A business model can illustrate the utilities to create, deliver, and capture the value of a technology. The utilities need to design a business model for drinking water supply system that can resolve the problem of quantity, quality, continuity and connectivity on water distribution network in Indonesian Drinking Water Utilities (PDAM). PDAM business model should combine social and commercial aspect and there are not many studies related to them. There are four business model methods for designing business models such as Canvas Business Model, Lean Business Model, Non-Profit Business Model, and Zenn Business Model. The authors develop a new method to build a business model for confirming technology and business aspect, and also integrating commercial and social purposes by combining the four models. This research also found a new method to capture how public enterprise creates, delivers and captures the value of smart metering system on smart water management. Furthermore, the business model for smart water management with the smart metering system as the core technology will confirm the condition, standard, and regulation in Indonesia.


title: Medium voltage power converter design and demonstration using 15 kV SiC N-IGBTs

abstract: This paper summarizes the different steps that have been undertaken to design medium voltage power converters using the state-of-the-art 15 kV SiC N-IGBTs. The 11 kV switching characterization results, 11 kV high dv/dt gate driver validation, and the heat-run test results of the SiC IGBT at 10 kV, 550 W/cm2 (active area) have been recently reported as individual topics. In this paper, it is attempted to link all these individual topics and present them as a complete subject from the double pulse tests to the converter design, for evaluating these novel high voltage power semiconductor devices. In addition, the demonstration results of two-level H-Bridge and three-level NPC converters, both at 10 kV dc input, are being presented for the first-time. Lastly, the performance of two-chip IGBT modules for increased current capability and demonstration of three-level poles, built using these modules, at 10 kV dc input with sine-PWM and square-PWM modulation for rectifier and dc-dc stages of a three-phase solid state transformer are presented.