title: HMM-based Address Parsing with Massive Synthetic Training Data Generation

abstract: Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches of synthetic training data generation to build robust models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses.


title: Octagonal Ring Antenna for a Compact Dual-Polarized Aperture Array

abstract: A wideband dual-polarized aperture array design is presented based on elements formed by a triangular configuration of octagonal rings. The octagonal ring elements are linked by capacitors and placed a defined distance from a groundplane. A broader frequency bandwidth has been achieved by placing a further layer of conductive rings above the element rings forming a metamaterial superstrate layer. Low dielectric constant material can be used to fill the space between the array rings and the groundplane, and between the elements and the superstrate layer. A dual-polarized finite ORA array has been fabricated and measured. The octagonal ring antenna array inherently exhibits a broad scan range with a stable cross polarization performance, however in practice the cross polarization depends on the feeding arrangements. Two feeding methods are discussed with a stripline feed showing best performance.


title: Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for Verbal Span, Visuospatial Span, Letter and Number Sequencing, Trail Making Test, and Symbol Digit Modalities Test

abstract: As part of the Spanish Multicenter Normative Studies (NEURONORMA project), we provide age- and education-adjusted norms for the following instruments: verbal span (digits), visuospatial span (Corsi's test), letter-number sequencing (WAIS-III), trail making test, and symbol digit modalities test. The sample consists of 354 participants who are cognitively normal, community-dwelling, and age ranging from 50 to 90 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. The current norms should provide clinically useful data for evaluating elderly Spanish people. These data may be of considerable use for comparisons with other normative studies. Limitations of these normative data are mainly related to the techniques of recruitment and stratification employed.


title: "show-me": water consumption at a glance to promote water conservation in the shower

abstract: Water is a scarce resource worldwide. Yet, we have many opportunities to conserve it. One particular opportunity for water conservation is the shower, because depending on the shower head and shower habits, an individual can save many liters of fresh water each day. Feedback proved to be an effective method to promote sustainable behavior. Therefore, in this paper we suggest to promote water conservation by providing feedback in form of an ambient display that can easily be integrated in current shower types. We built a prototype to study the potential of such a feedback device. These shower water meter (show-me) display the amount of water, that is used during one shower in form of LEDs assembled on a stick. Thus, an increasing water level is visualized. The user study revealed two groups. The subjects who considered themselves as ecologically conscious changed their behavior and turned the water down or off while soaping. Also, they are willing to pursue this behavior. Other subjects who did not have the goal to act more sustainable, were surprised about their water consumption and tried to reduce it. However, after the removal of the show-me device they did not maintain their behavior and fell back into their previous habit.


title: A comparative analysis of two distance measures in color image databases

abstract: The Euclidean distance measure has been used in comparing feature vectors of images, while the cosine angle distance measure is used in document retrieval. We theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of a color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, the characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.


title: Sensorless Control for High-Speed Brushless DC Motor Based on the Line-to-Line Back EMF

abstract: A sensorless control method for a high-speed brushless DC motor based on the line-to-line back electromotive force (back EMF) is proposed in this paper. In order to obtain the commutation signals, the line-to-line voltages are obtained by the low-pass filters. However, due to the low-pass filters, wide speed range, and other factors, the actual commutation signals are significantly delayed by more than 90 electrical degrees which limits the acceleration of the motor. A novel sensorless commutation algorithm based on the hysteresis transition between “90-α” and “150-α” is introduced to handle the severe commutation retarding and guarantee the motor works in a large speed range. In order to compensate the remaining existing commutation errors, a novel closed-loop compensation algorithm based on the integration of the virtual neutral voltage is proposed. The integration difference between the adjacent 60 electrical degrees interval before and after the commutation point is utilized as the feedback of the PI regulator to compensate the errors automatically. Several experiment results confirm the feasibility and effectiveness of the proposed method.


title: Effective Discretization and Hybrid feature selection using Naïve Bayesian classifier for Medical datamining

abstract: Feature selection for clustering is a problem rarely addressed in the literature. Although recently there has been some work on the area, there is a lack of extensive empirical evaluation to assess the potential of each method. In this paper, we propose a new implementation of a wrapper and adapt an existing filter method to perform experiments over several data sets and compare both approaches. Results confirm the utility of feature selection for clustering and the theoretical superiority of wrapper methods. However, it raises some problems that arise from using greedy search procedures and also suggest evidence that filters are a reasonably alternative with limited computational cost.


title: Monaural Music Source Separation Using Convolutional Sparse Coding

abstract: We present a comprehensive performance study of a new time-domain approach for estimating the components of an observed monaural audio mixture. Unlike existing time-frequency approaches that use the product of a set of spectral templates and their corresponding activation patterns to approximate the spectrogram of the mixture, the proposed approach uses the sum of a set of convolutions of estimated activations with prelearned dictionary filters to approximate the audio mixture directly in the time domain. The approximation problem can be solved by an efficient convolutional sparse coding algorithm. The effectiveness of this approach for source separation of musical audio has been demonstrated in our prior work, but under rather restricted and controlled conditions, requiring the musical score of the mixture being informed a priori and little mismatch between the dictionary filters and the source signals. In this paper, we report an evaluation that considers wider, and more practical, experimental settings. This includes the use of an audio-based multipitch estimation algorithm to replace the musical score, and an external dataset of audio single notes to construct the dictionary filters. Our result shows that the proposed approach remains effective with a larger dictionary, and compares favorably with the state-of-the-art nonnegative matrix factorization approach. However, in the absence of the score and in the case of a small dictionary, our approach may not be better.


title: A Temporal Attentional Model for Rumor Stance Classification

abstract: Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.


title: Discovering Consumer Insight from Twitter via Sentiment Analysis

abstract: Traditional approaches for studying consumer behavior, such as marketing survey and focus group, require a large amount of time and resources. Moreover, some products, such as smartphones, have a short product life cycle. As an alternative solution, we propose a system, the Micro-blog Sentiment Analysis System (MSAS), based on sentiment analysis to automatically analyze customer opinions from the Twitter micro-blog service. The MSAS consists of five main functions to (1) collect Twitter posts, (2) filter for opinionated posts, (3) detect polarity in each post, (4) categorize product features and (5) summarize and visualize the overall results. We used the product domain of smartphone as our case study. The experiments on 100,000 collected posts related to smartphones showed that the system could help indicating the customers' sentiments towards the product features, such as Application, Screen, and Camera. Further evaluation by experts in smartphone industry confirmed that the system yielded some valid results.


title: Distributed Detection of Single-Stage Multipoint Cyber Attacks in a Water Treatment Plant

abstract: A distributed detection method is proposed to detect single stage multi-point (SSMP) attacks on a Cyber Physical System (CPS). Such attacks aim at compromising two or more sensors or actuators at any one stage of a CPS and could totally compromise a controller and prevent it from detecting the attack. However, as demonstrated in this work, using the flow properties of water from one stage to the other, a neighboring controller was found effective in detecting such attacks. The method is based on physical invariants derived for each stage of the CPS from its design. The attack detection effectiveness of the method was evaluated experimentally against an operational water treatment testbed containing 42 sensors and actuators. Results from the experiments point to high effectiveness of the method in detecting a variety of SSMP attacks but also point to its limitations. Distributing the attack detection code among various controllers adds to the scalability of the proposed method.


title: Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

abstract: Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L’Acadie, Quebec, Agriculture and AgriFood Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r 2 =.81 between estimated


title: Exploiting Data Analytics for Home Automation Services

abstract: The Internet of Thing generates data at an unprecedented pace. This is due to the ever increasing number of connected devices being deployed as well as their volubility. There's a huge potential in exploiting this mass of data. In particular collecting, storing this data over time, and analyzing the resulting deposit offline, enables the extraction of information and discovering of patterns, which can then be transformed into executable structures and new services. This approach can apply to a range of application domains including Smart Home, Smart City, Smart Energy services. In this paper we show how Data Analytics (DA) can leverage such data deposit and produce insights which can then be transformed into enhanced services that will bring the user experience to the next level. Our approach has been assessed in the domain of the Smart Home with real data provided by Orange Homelive solution through the implementation of the MACLEOD system.


title: Full-wave design of H-plane contiguous manifold output multiplexers using the fictitious reactive load concept

abstract: In this paper, a new procedure for the design of contiguous band manifold output multiplexers in H-plane configuration is presented. Such a configuration eliminates the slots between the T-junctions and the filters allowing high power handling, as well as dramatically reducing the risk of multipactor. This paper pursues the division of the design into tasks with low computational effort. The approach comprises: 1) the singly terminated synthesis of the channel filters and their corresponding full-wave responses; 2) the manifold design using fictitious reactive loads simulating the phase response of every channel out of its corresponding band; and 3) the final full-wave optimization of the whole structure using the simulated-annealing method. In order to validate the above procedure, a five-channel Ku-band multiplexer has been designed, manufactured, and measured.


title: Structure from motion for scenes with large duplicate structures

abstract: Most existing structure from motion (SFM) approaches for unordered images cannot handle multiple instances of the same structure in the scene. When image pairs containing different instances are matched based on visual similarity, the pairwise geometric relations as well as the correspondences inferred from such pairs are erroneous, which can lead to catastrophic failures in the reconstruction. In this paper, we investigate the geometric ambiguities caused by the presence of repeated or duplicate structures and show that to disambiguate between multiple hypotheses requires more than pure geometric reasoning. We couple an expectation maximization (EM)-based algorithm that estimates camera poses and identifies the false match-pairs with an efficient sampling method to discover plausible data association hypotheses. The sampling method is informed by geometric and image-based cues. Our algorithm usually recovers the correct data association, even in the presence of large numbers of false pairwise matches.


title: Parallel Computations for Controlling an Arm

abstract: In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a “motion blackboard„ that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-...


title: A review on fundamentals and applications of electrophoretic deposition (EPD)

abstract: This review encompasses the fundamental aspects of electrophoretic deposition technique, factors influencing the deposition process, kinetic aspects, types of EPD, the driving forces, preparation of electrophoretic suspension, stability and control of suspension, mechanisms involved in EPD, multicomponent/composite deposition, drying of deposits obtained by EPD. Numerous applications including coatings, nanoscale assembly, micropatterned thin films, near shape ceramics and glasses, solid oxide fuel cells, laminated or graded materials, hybrid materials, infiltration in porous and woven fibre preforms for preparation of fibre reinforced ceramic matrix composites, etc. have been described. The use of mathematical modeling including kinetic equations for deposit formation and volumetric particle concentration in the suspension, together with brief description of discrete element modeling of EPD process is presented.


title: Applying a Traffic Lights Evolutionary Optimization Technique to a Real Case: “Las Ramblas” Area in Santa Cruz de Tenerife

abstract: In previous research, we have designed and successfully tested a traffic light cycles evolutionary optimization architecture. In this paper, we attempt to validate those results with a real-world test case. For a wide area in Santa Cruz de Tenerife city - Canary islands - we have improved traffic behavior, using our optimized traffic light cycle times in a simulated environment. Throughout this paper, we present some of the experiences, knowledge, and problems encountered.


title: Cascaded Convolutional Neural Networks for Aspect-Based Opinion Summary

abstract: This paper studies aspect-based opinion summary (AOS) of reviews on particular products. In practice, an AOS system needs to address two core subtasks, aspect extraction and sentiment classification. Most existing approaches to aspect extraction, using linguistic analysis or topic modeling, are general across different products but not precise enough or suitable for particular products. Instead we take a less general but more precise scheme, which directly maps each review sentence into pre-defined aspects. To tackle aspect mapping and sentiment classification, we propose a convolutional neural network (CNN) based method, cascaded CNN (C-CNN). C-CNN contains two levels of convolutional networks. Multiple CNNs at level 1 deal with aspect mapping task. If a review sentence belongs to pre-defined aspect categories, a single CNN at level 2 determines its sentiment polarity. Experimental results show that C-CNN with pre-trained word embedding outperform cascaded SVM with feature engineering. We also build a system called OpiSum with C-CNN. The demo of OpiSum can be found at http://114.215.167.42.


Some Fluorine and Chlorine Derivatives of Sulfanilamidobenzenesulfonic Acids


title: 18–40-GHz Beam-Shaping/Steering Phased Antenna Array System Using Fermi Antenna

abstract: This paper concerns 18-40 GHz 1times 16 beam shaping and 1times 8 beam steering phased antenna arrays (PAAs) realized on a single low-cost printed circuit board substrate. The system consists of a wideband power divider with amplitude taper for sidelobe suppression, wideband microstrip-to-slotline transition, a low-cost true time piezoelectric transducer (PET)-controlled phase shifter, and wideband Fermi antennas with corrugations along the sides. A coplanar stripline is used under a PET-controlled phase shifter, which can generate 50% more phase shift compared to the perturbation on microstrip lines previously published. The systems are fabricated using electro-fine-forming microfabrication technology. Measured return loss is better than 10 dB from 18 to 40 GHz for both the beam-shaping and beam-steering PAAs. The beam-shaping PAA has a 12deg 3-dB beamwidth broadening range. The sidelobe ratios (SLRs) are 27, 23, and 20 dB at 20, 30, and 40 GHz, respectively, without perturbation. The SLRs are 20, 16, and 15 dB at 20, 30, and 40 GHz with maximum perturbation. The beam-steering PAA has a 36deg (-17deg to +19deg ) beam-scanning range measured at 30 GHz.


title: Free-form anisotropy: A new method for crack detection on pavement surface images

abstract: This paper presents a new measure which takes into accounts simultaneously brightness and connectivity, in the segmentation step, for crack detection on road pavement images. Features which are calculated along every free-form paths provide detection of cracks with any form and any orientation. The method proposed does not need learning stage of free defect texture to perform default detection. Experimental results were conducted on some samples of different kinds of pavements. Results of the method are also given on other kinds of images and can provide perspectives on other domains as road extraction on satellite images or segment blood vessels in retinal images.


title: Association rules: Normalizing the lift

abstract: Association rules is a popular data mining technique for discovering relations between variables in large amounts of data. Support, confidence and lift are three of the most common measures for evaluating the usefulness of these rules. A concern with the lift measure is that it can only compare items within a transaction set. The main contribution of this paper is to develop a formula for normalizing the lift, as this will allow valid comparisons between distinct transaction sets. Traffic accident data was used to validate the revised formula for lift and the result of this analysis was very strong.


Black Holes and White Rabbits: Metaphor Identification with Visual Features


title: Humanoid robot push-recovery strategy based on CMP criterion and angular momentum regulation

abstract: We propose a push-recovery strategy to stabilize the robot under unmodelled, large external forces. The strategy integrates Center-Of-Gravity (COG) angular momentum regulator, COG state estimator, and stepping control, which online modifies the trajectories of the COG and the swing leg. Using the centroidal-moment-pivot criterion, the COG angular momentum regulator controls the dynamics of the COG as an impedance system through the feedback of COG state estimator based on Kalman filter. The stepping control, on the other hand, selects the appropriate balancing reaction in anticipation of the potential consequences of the external disturbances on the robot. In simulations and experiments, we show the proposed push-recovery strategy can effectively save the robot from falling down and walk more smoothly.


Disclosing False Identity through Hybrid Link Analysis


A Hybrid Approach for Entity Extraction in Code-Mixed Social Media Data


SpeechQoogle: An Open-Domain Question Answering System with Speech Interface


title: Automatic Localization of Bugs to Faulty Components in Large Scale Software Systems Using Bayesian Classification

abstract: We suggest a Bayesian approach to the problem of reducing bug turn-around time in large software development organizations. Our approach is to use classification to predict where bugs are located in components. This classification is a form of automatic fault localization (AFL) at the component level. The approach only relies on historical bug reports and does not require detailed analysis of source code or detailed test runs. Our approach addresses two problems identified in user studies of AFL tools. The first problem concerns the trust in which the user can put in the results of the tool. The second problem concerns understanding how the results were computed. The proposed model quantifies the uncertainty in its predictions and all estimated model parameters. Additionally, the output of the model explains why a result was suggested. We evaluate the approach on more than 50000 bugs.


title: Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model

abstract: Understanding the antecedents and consequences of customer satisfaction in the mobile communications market is important. This study explores the effects of service quality and justice on customer satisfaction, which, in turn, affects continuance intention of mobile services. Service quality, justice and customer satisfaction were measured by multiple dimensions. A research model was developed based on this multidimensional approach and was empirically examined with data collected from about one thousand users of mobile value-added services in China. Results show that all three dimensions of service quality (interaction quality, environment quality and outcome quality) have significant and positive effects on cumulative satisfaction while only one dimension of service quality (interaction quality) has a significant and positive effect on transaction-specific satisfaction. Besides procedural justice, the other two dimensions of justice (distributive justice and interactional justice) significantly influence both transaction-specific satisfaction and cumulative satisfaction. Furthermore, both types of customer satisfaction have significant and positive effects on continuance intention. Implications for research and practice are discussed.


title: Inductor current control of three-phase interleaved DC-DC converter using single DC-link current sensor

abstract: This paper proposes an inductor current control of three-phase interleaved DC-DC converter using single DC-link current sensor. Conventional inductor current control of interleaved DC-DC converter is conducted on the detected inductor current by current sensors in each phase inductor. In this paper, the proposed scheme uses single current sensor for measurement of DC-link current and achieves inductor current control, which eliminates two current sensors from conventional method. The cost, the volume and the weight of the current sensors can be reduced by decreasing number of the current sensors. Inductor current control using single DC-link current sensor utilizes the measurement value which is measured when only one phase switch of the upper arms is on-state at the bottom of each carrier. When only one phase switch of the upper arms is on state, the current value of DC-link equals that of the corresponding inductor. Comparison between the inductor current controls using single current sensor and three current sensors was conducted by the simulation. Additionally experiment using single current sensor was conducted and effectiveness of the proposed control is verified in this paper.


title: Arrays of cricket-inspired sensory hairs with capacitive motion detection

abstract: This paper presents the fabrication of flow-sensors based on the drag-force induced motion of artificial hairs connected to capacitive read-out. Artificial hairs were made either out of moulded silicon-nitride structures or by SU-8. The SU-8 hairs were suspended on membranes containing electrodes to form the variable capacitors. Silicon-rich-nitride hairs were made using a silicon wafer as a dissolvable mould. The longest SU-8 hairs were fabricated using a 470 /spl mu/m thick photoresist layer. Capacitance versus voltage, frequency dependency and directional sensitivity measurements have been carried out on entire arrays and are reported in this paper.


title: Light field image coding with hybrid scan order

abstract: A Light field image contains shear amount of data as it keeps the full spatio-angular information of the real scene. In this paper we propose a light field image coding scheme based on the latest JEM coding technologies. We propose a novel hybrid scan order to rearrange subaperture images into an image sequence and verify its importance to coding performance of light field image format. The experiment on EPFL light field image dataset demonstrates that our scheme achieves 7.06 dB gain compared with directly encoding the image by the JPEG standard. With the QP set to 50, our scheme achieves an average compression ratio of 7107, and still provides larger PSNRs and better viewing experience than JPEG at a compression ratio of 100.


title: Assessment of Homogenization Errors in Transient Problems

abstract: Model-based 1st order homogenization for stationary problems was recently extended to transient problems. Along with the classical averages, a higher order conservation quantity in the macroscale problem is then obtained. This effect depends on the size of the “subscale computational cell” (denoted RVE) that is subjected to different prolongation conditions (Dirichlet, Neumann). The issue addressed in this paper is how to choose the optimal size of the RVE in order to obtain the best possible fit to the single-scale solution. It turns out that there is a trade-off between the RVE-size and the macroscale mesh diameter.


title: A Network Coding Based Information Spreading Approach for Permissioned Blockchain in IoT Settings

abstract: Permissioned Blockchain (PBC) has become a prevalent data structure to ensure that the records are immutable and secure. However, PBC still has significant challenges before it can be realized in different applications. One of such challenges is the overhead of the communication which is required to execute the Byzantine Agreement (BA) protocol that is needed for consensus building. As such, it may not be feasible to implement PBC for resource constrained environments such as Internet-of-Things (IoT). In this paper, we assess the communication overhead of running BA in an IoT environment that consists of wireless nodes (e.g., Raspberry PIs) with meshing capabilities. As the the packet loss ratio is significant and makes BA unfeasible to scale, we propose a network coding based approach that will reduce the packet overhead and minimize the consensus completion time of the BA. Specifically, various network coding approaches are designed as a replacement to TCP protocol which relies on unicasting and acknowledgements. The evaluation on a network of Raspberry PIs demonstrates that our approach can significantly improve scalability making BA feasible for medium size IoT networks.


title: Exploratory survey for biomass estimation

abstract: Bottom trawl surveys are widely used for monitoring demersal stocks when a simple index ::: of abundance is required for scientific and related work. From unfished stocks (or stocks for ::: which no or few data on the fishery are available), preferably the unexploited stocks, biomass ::: and annual yield estimates may also be derived by undertaking bottom trawl surveys. The ::: estimation of total biomass from the catch per unit of effort (or unit area) using a trawl survey, ::: however, involves several crucial assumptions, leaving such estimates rather imprecise. But ::: we can resort to this method when we require an immediate input to be generated and the ::: methodology is less time consuming and easy to carry out.


title: Analysis of the Emitted Wavelet of High-Resolution Bowtie GPR Antennas

abstract: Most Ground Penetrating Radars (GPR) cover a wide frequency range by emitting very short time wavelets. In this work, we study in detail the wavelet emitted by two bowtie GPR antennas with nominal frequencies of 800 MHz and 1 GHz. Knowledge of this emitted wavelet allows us to extract as much information as possible from recorded signals, using advanced processing techniques and computer simulations. Following previously published methodology used by Rial et al. [1], which ensures system stability and reliability in data acquisition, a thorough analysis of the wavelet in both time and frequency domain is performed. Most of tests were carried out with air as propagation medium, allowing a proper analysis of the geometrical attenuation factor. Furthermore, we attempt to determine, for each antenna, a time zero in the records to allow us to correctly assign a position to the reflectors detected by the radar. Obtained results indicate that the time zero is not a constant value for the evaluated antennas, but instead depends on the characteristics of the material in contact with the antenna.


title: R (2011) A Google-like model of road network dynamics and its application to regulation and control

abstract: Inspired by the ability of Markov chains to model complex dynamics and handle large volumes of data in Google's PageRank algorithm, a similar approach is proposed here to model road network dynamics. The central component of the Markov chain is the transition matrix which can be completely constructed by easily collecting traffic data. The proposed model is validated using the popular mobility simulator SUMO. Markov chain theory and spectral analysis of the transition matrix are then shown to reveal non-evident properties of the underlying road network and to correctly predict consequences of road network modifications. Preliminary results from possible applications are shown and simple practical examples are provided throughout this article to clarify and support the theoretical expectations.


title: Parallel memory-efficient processing of BCI data

abstract: Following after magnetic resonance imaging (MRI) and electrocortigraphy (ECoG), electroencephalography (EEG)-based research is entering the world of big data[l]. A research-quality brain-computer interface (BCI) data set can easily number in the hundreds of millions of points, making methodology of processing and classification critical. A selection of broadly applicable optimization methods implemented in R is presented that enables users to take advantage of parallelization, guaranteed call-by-reference to limit memory overhead, and scalable performance with common BCI processing tasks. As proof of concept, classification results for a P300 experiment and performance statistics are presented.


title: Interactions between the Midbrain Superior Colliculus and the Basal Ganglia

abstract: An important component of the architecture of cortico-basal ganglia connections is the parallel, re-entrant looped projections that originate and return to specific regions of the cerebral cortex. However, such loops are unlikely to have been the first evolutionary example of a closed-loop architecture involving the basal ganglia. A phylogenetically older, series of subcortical loops can be shown to link the basal ganglia with many brainstem sensorimotor structures. While the characteristics of individual components of potential subcortical re-entrant loops have been documented, the full extent to which they represent functionally segregated parallel projecting channels remains to be determined. However, for one midbrain structure, the superior colliculus (SC), anatomical evidence for closed-loop connectivity with the basal ganglia is robust, and can serve as an example against which the loop hypothesis can be evaluated for other subcortical structures. Examination of ascending projections from the SC to the thalamus suggests there may be multiple functionally segregated systems. The SC also provides afferent signals to the other principal input nuclei of the basal ganglia, the dopaminergic neurones in substantia nigra and to the subthalamic nucleus. Recent electrophysiological investigations show that the afferent signals originating in the SC carry important information concerning the onset of biologically significant events to each of the basal ganglia input nuclei. Such signals are widely regarded as crucial for the proposed functions of selection and reinforcement learning with which the basal ganglia have so often been associated.


title: Experimental Investigation of Heuristics for Resource-Constrained Project Scheduling: An Update

abstract: This paper considers heuristics for the well‐known resource‐constrained project scheduling problem (RCPSP). It provides an update of our survey which was published in 2000. We summarize and categorize a large number of heuristics that have recently been proposed in the literature. Most of these heuristics are then evaluated in a computational study and compared on the basis of our standardized experimental design. Based on the computational results we discuss features of good heuristics. The paper closes with some remarks on our test design and a summary of the recent developments in research on heuristics for the RCPSP.


title: UWB CMOS Monocycle Pulse Generator

abstract: A low-complexity fully integrated ultrawideband (UWB) monocycle pulse generator realized in 90-nm CMOS technology by ST-Microelectronics is presented. The circuit provides a monocycle pulse when activated by a negative edge of an external trigger signal provided by a microcontroller by exploiting the operating principle of nonlinear waveform shapers. This pulse generator represents a building block of an innovative wearable system-on-chip UWB radar on silicon for cardiopulmonary monitoring. On-chip measurements show that the pulse generator provides monocycle pulses with a duration time equal to 380 ps and a peak-to-peak amplitude of 660 mV (including the losses of the microprobes, cables, and electrostatic-discharge-protected pads), which are in very good agreement with the postlayout simulations. The power consumption is 19.8 mW from a 1.2-V power supply.


title: Caching strategies in on-demand routing protocols for wireless ad hoc networks

abstract: An on-demand routing protocol for wireless and hoc networks is one that searches for and attempts to discover a route to some destination node only when a sending node originates a data packet addressed to that node. In order to avoid the need for such a route discovery to be performed before each data packet is sent, such routing protocols must cache routes previously discovered. This paper presents an analysis of the effects of different design choices for this caching in on-demand routing protocols in wireless ad hoc networks, dividing the problem into choices of cache structure , cache capacity , and cache timeout . Our analysis is based on the Dynamic Source Routing protocol (DSR), which operates entirely on-demand. Using detailed simulations of wireless ad hoc networks of 50 mobile nodes, we studied a large number of different caching algorithms that utilize a range of design choices, and simulated each cache primarily over a set of 50 different movement scenarios drawn from 5 different types of mobility models. We also define a set of new mobility metrics that allow accurate characterization of the relative difficulty that a given movement scenario presents to an ad hoc network routing protocol, and we analyze each mobility metric's ability to predict the actual difficulty in terms of routing overhead experienced by the routing protocol across the scenarios in our study.


title: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

abstract: Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women . In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.


title: A Survey on Anomalies Detection Techniques and Measurement Methods

abstract: Dynamic research area has been applied and researched on anomaly detection in various domains. And various techniques have been proposed to identify unexpected items or events in datasets which differ from the norm. This review tries to provide a basic and structured overview of the anomaly detection techniques. Also, this review discusses major anomaly detection techniques using statistical based and machine learning based techniques. The outcome of this review may facilitate a better understanding of the different techniques in which research has been done on this topic by comparing the pros and cons of the identified techniques. In addition, this review also discusses on the measurement methods used by other researchers in validating their anomalies detection techniques.


title: Phelgmasia Cerulea Dolens Diagnosed by Point-of-Care Ultrasound

abstract: Phlegmasia cerulea dolens (PCD) is a rare entity that is associated with significant morbidity and mortality, including limb ischemia and pulmonary embolism. Point-of-care ultrasound (POCUS) can expedite the diagnosis, leading to earlier life- and limb-saving treatment. Although primarily used for assessing for the presence of deep venous thrombosis, in the appropriate clinical setting POCUS can also be used to diagnosis PCD as well as to distinguish between venous and arterial occlusion, which can lead to a difference in management. We present a case of phlegmasia cerulea dolens after mild trauma in a patient with an underlying hypercoagulability disorder diagnosed by an emergency physician using POCUS, which expedited treatment with catheter-directed thrombolytic therapy.


title: Working Memory From the Psychological and Neurosciences Perspectives: A Review

abstract: Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.


title: Modeling and characteristic curves of electric power steering system

abstract: Electric Power Steering (EPS) has important significance in improving the auto's dynamic and static performances. This paper deals with the mathematical model and characteristic curves of EPS System. First, the principle and structure of EPS are discussed. Then the characteristics of EPS system are analyzed, including the typical power curves which deal the relationship of the assist torque and the steering wheel torque. At last the EPS system's dynamic model and the computerized simulation model have been established in MATLAB/Simulink in teams of three aspects which are the mechanical steering without power system, EPS system and EPS system with PID control. The simulation result that the actual current of the motor follows precisely the target current validates the designed assist characteristic.


title: A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers

abstract: [1] We present here a new InSAR persistent scatterer (PS) method for analyzing episodic crustal deformation in non-urban environments, with application to volcanic settings. Our method for identifying PS pixels in a series of interferograms is based primarily on phase characteristics and finds low-amplitude pixels with phase stability that are not identified by the existing amplitude-based algorithm. Our method also uses the spatial correlation of the phases rather than a well-defined phase history so that we can observe temporally-variable processes, e.g., volcanic deformation. The algorithm involves removing the residual topographic component of flattened interferogram phase for each PS, then unwrapping the PS phases both spatially and temporally. Our method finds scatterers with stable phase characteristics independent of amplitudes associated with man-made objects, and is applicable to areas where conventional InSAR fails due to complete decorrelation of the majority of scatterers, yet a few stable scatterers are present.


title: Knowledge-based fast evolutionary MCTS for general video game playing

abstract: General Video Game Playing is a game AI domain in which the usage of game-dependent domain knowledge is very limited or even non existent. This imposes obvious difficulties when seeking to create agents able to play sets of different games. Taken more broadly, this issue can be used as an introduction to the field of General Artificial Intelligence. This paper explores the performance of a vanilla Monte Carlo Tree Search algorithm, and analyzes the main difficulties encountered when tackling this kind of scenarios. Modifications are proposed to overcome these issues, strengthening the algorithm's ability to gather and discover knowledge, and taking advantage of past experiences. Results show that the performance of the algorithm is significantly improved, although there remain unresolved problems that require further research. The framework employed in this research is publicly available and will be used in the General Video Game Playing competition at the IEEE Conference on Computational Intelligence and Games in 2014.


title: Multiring Circular Transmission Line Model for Ultralow Contact Resistivity Extraction

abstract: Accurate determination of contact resistivities ( ${\rho_{c}}$ ) below $1 \times 10^{-8}\Omega \cdot \text {cm}^{2}$ is challenging. Among the frequently applied transmission line models (TLMs), circular TLM (CTLM) has a simple process flow, while refined TLM (RTLM) has a high $\rho _{c}$ accuracy at the expense of a more complex fabrication. In this letter, we will present a novel model—multiring CTLM (MR-CTLM) , which combines the advantages of a simple process and a high $\rho _{c}$ extraction resolution. We fabricated ultralow- $\rho _{c}$ Ti/n-Si contacts and demonstrated the capability of MR-CTLM to extract the $\rho _{c}$ as low as $6.2 \times 10^{-9}~\Omega \cdot {\rm cm}^{2}$ with high precision.


title: Current Trends in Industrial Electronics Education

abstract: Technology development creates many challenges in the education of industrial-electronics (IE)-related subjects. At the same time, it allows new educational paradigms to be implemented. The main contribution of this paper is to initiate a discussion for the needs and challenges of IE education both at university level and in lifelong learning, in order to meet the requirements of the emerging technologies of the 21st century. Educational challenges and opportunities are first identified and analyzed. Afterward, an overview of state-of-the-art learning methodologies and tools is presented. New educational paradigms and future directions are also identified.


title: Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

abstract: I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.


title: Journalists and the Stock Market ∗

abstract: We use exogenous scheduling of Wall Street Journal columnists to identify a causal relation between financial reporting and stock market performance. To measure the media's unconditional effect, we add columnist fixed effects to a daily regression of excess Dow Jones Industrial Average returns. Relative to standard control variables, these fixed effects increase the R-super-2 by about 35%, indicating each columnist's average persistent "bullishness" or "bearishness." To measure the media's conditional effect, we interact columnist fixed effects with lagged returns. This increases explanatory power by yet another one-third, and identifies amplification or attenuation of prevailing sentiment as a tool used by financial journalists. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.


title: Anti-Plaque Efficacy of Herbal and 0.2% Chlorhexidine Gluconate Mouthwash: A Comparative Study

abstract: BACKGROUND ::: Mouthwashes are an adjunct to, not a substitute for, regular brushing and flossing. Chlorohexidine is cationic bis-biguanide broad spectrum antiseptic with both anti-plaque and antibacterial properties. It has side-effects like brownish discoloration of teeth and dorsum of the tongue, taste perturbation, oral mucosal ulceration, etc. To compare the antiplaque efficacy of herbal and chlorohexidine gluconate mouthwash. ::: ::: ::: MATERIALS AND METHODS ::: A double-blinded parallel, randomized controlled clinical trial was conducted in the Department of Periodontics, MNR Dental College. Totally 100 preclinical dental students were randomized into three groups (0.2% chlorohexidine, Saline and herbal mouthwash). All the groups were made to refrain from their regular mechanical oral hygiene measures and were asked to rinse with given respective mouthwashes for 4 days. The gingival and plaque scores are evaluated on 1(st) day, and 5(th) day, and differences were compared statistically. ::: ::: ::: RESULTS ::: There was no significant difference in the gingival index (GI) and plaque index (PI) scores of the pre-rinsing scores of three groups and mean age of subjects in the three age groups, suggesting selected population for the three groups was homogenous. Mean GI and PI scores at the post rinsing stage were least for the Group A, followed by B and C. The difference of post rinsing PI and GI scores between Group A and Group B were statistically non-significant, which means anti-gingivitis and plaque inhibiting properties are similar for both. ::: ::: ::: CONCLUSION ::: Within the limitations of this study chlorhexidine gluconate and herbal mouthwash (Hiora) showed similar anti plaque activity with latter showing no side effects.


title: Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis

abstract: This paper presents a generalization of self-organizing maps with 1-D neighborhoods (neuron chains) that can be effectively applied to complex cluster analysis problems. The essence of the generalization consists in introducing mechanisms that allow the neuron chain—during learning—to disconnect into subchains, to reconnect some of the subchains again, and to dynamically regulate the overall number of neurons in the system. These features enable the network—working in a fully unsupervised way (i.e., using unlabeled data without a predefined number of clusters)—to automatically generate collections of multiprototypes that are able to represent a broad range of clusters in data sets. First, the operation of the proposed approach is illustrated on some synthetic data sets. Then, this technique is tested using several real-life, complex, and multidimensional benchmark data sets available from the University of California at Irvine (UCI) Machine Learning repository and the Knowledge Extraction based on Evolutionary Learning data set repository. A sensitivity analysis of our approach to changes in control parameters and a comparative analysis with an alternative approach are also performed.


title: Many-Core Token-Based Adaptive Power Gating

abstract: Among power dissipation components, leakage power has become more dominant with each successive technology node. Leakage energy waste can be reduced by power gating. In this paper, we extend token-based adaptive power gating (TAP), a technique to power gate an actively executing core during memory accesses, to many-core Chip Multi-Processors (CMPs). TAP works by tracking every system memory request and its estimated time of arrival so that a core may power gate itself without performance or energy loss. Previous work on TAP [11] shows several benefits compared to earlier state-of-the-art techniques [10], including zero performance hit and 2.58 times average energy savings for out-of-order cores. We show that TAP can adapt to increasing memory contention by increasing power-gated time by 3.69 times compared to a low memory-pressure case. We also scale TAP to many-core architectures with a distributed wake-up controller that is capable of supporting staggered wake-ups and able to power gate each core for 99.07% of the time, achieved by a non-scalable centralized scheme.


title: Optimal Packet Scheduling in an Energy Harvesting Communication System

abstract: We consider the optimal packet scheduling problem in a single-user energy harvesting wireless communication system. In this system, both the data packets and the harvested energy are modeled to arrive at the source node randomly. Our goal is to adaptively change the transmission rate according to the traffic load and available energy, such that the time by which all packets are delivered is minimized. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the data traffic arrivals, we consider two different scenarios. In the first scenario, we assume that all bits have arrived and are ready at the transmitter before the transmission starts. In the second scenario, we consider the case where packets arrive during the transmissions, with known arrival times and sizes. We develop optimal off-line scheduling policies which minimize the time by which all packets are delivered to the destination, under causality constraints on both data and energy arrivals.


title: Empathy between human and robot?

abstract: This paper aims at finding the answer to the essential question: Can people perceive a robot's presence as having a social existence? We attempt to apply a sociological and psychological approach to understand the influence of robot beings, by observing human emotion and perception changes while subjects watched a funny video clip in the presence of a robot or a human companion, each of which made their own typical laughing sounds. From this experiment, we found that the robot did not affect the human's positive emotions as much as a human companion did, but the robot did discourage negative emotions. However, the subjects were, in general, amused when they were watching the video with the robot. This amusement is similar to the contagious effect of sharing humor with another human being. Our findings suggest that the subjects accepted the robot's presence as a kind of existence empathically.


title: Ontologies and Semantic Web for the Internet of Things - a survey

abstract: The reality of Internet of Things (IoT), with its growing number of devices and their diversity is challenging current approaches and technologies for a smarter integration of their data, applications and services. While the Web is seen as a convenient platform for integrating things, the Semantic Web can further improve its capacity to understand things' data and facilitate their interoperability. In this paper we present an overview of some of the Semantic Web technologies used in IoT systems, as well as some of the well accepted ontologies used to develop applications and services for the IoT. We finally present the Semantic Web Stack for the Internet of Things pointing out some of its shortcomings in the development of an IoT application or service.


title: MOON: MapReduce On Opportunistic eNvironments

abstract: MapReduce offers an ease-of-use programming paradigm for processing large data sets, making it an attractive model for distributed volunteer computing systems. However, unlike on dedicated resources, where MapReduce has mostly been deployed, such volunteer computing systems have significantly higher rates of node unavailability. Furthermore, nodes are not fully controlled by the MapReduce framework. Consequently, we found the data and task replication scheme adopted by existing MapReduce implementations woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. Our tests on an emulated volunteer computing system, which uses a 60-node cluster where each node possesses a similar hardware configuration to a typical computer in a student lab, demonstrate that MOON can deliver a three-fold performance improvement to Hadoop in volatile, volunteer computing environments.


title: A Low Loss High Isolation DC-60 GHz SPDT Traveling-Wave Switch With a Body Bias Technique in 90 nm CMOS Process

abstract: In this letter, a low loss high isolation broadband single-port double-throw (SPDT) traveling-wave switch using 90 nm CMOS technology is presented. A body bias technique is utilized to enhance the circuit performance of the switch, especially for the operation frequency above 30 GHz. The parasitic capacitance between the drain and source of the NMOS transistor can be further reduced using the negative body bias technique. Moreover, the insertion loss, the input 1 dB compression point (P1 dB)> and the third-order intermodulation (IMD3) of the switch are all improved. With the technique, the switch demonstrates an insertion loss of 3 dB and an isolation of better than 48 dB from dc to 60 GHz. The chip size of the proposed switch is 0.68 × 0.87 mm2 with a core area of only 0.32 × 0.21 mm2.


title: Human-computer interaction for hybrid carving

abstract: In this paper we explore human-computer interaction for carving, building upon our previous work with the FreeD digital sculpting device. We contribute a new tool design (FreeD V2), with a novel set of interaction techniques for the fabrication of static models: personalized tool paths, manual overriding, and physical merging of virtual models. We also present techniques for fabricating dynamic models, which may be altered directly or parametrically during fabrication. We demonstrate a semi-autonomous operation and evaluate the performance of the tool. We end by discussing synergistic cooperation between human and machine to ensure accuracy while preserving the expressiveness of manual practice.


title: Simulated annealing for complex portfolio selection problems

abstract: Abstract This paper describes the application of a simulated annealing approach to the solution of a complex portfolio selection model. The model is a mixed integer quadratic programming problem which arises when Markowitz’ classical mean–variance model is enriched with additional realistic constraints. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of heuristic techniques. Computational experiments indicate that the approach is promising for this class of problems.


title: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease

abstract: In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter γ in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV.


title: Thermocouple sensor technology applied in detecting thermal flaw inner cylinder head

abstract: Under Analyzing temperature field distribution of cylinder head, the flaw inner cylinder head was machined on electric discharge machine. According to engine practical working condition, a special experimental platform was set up to detect flaw inner cylinder head by using thermocouple sensor. The results showed thermocouple sensors fixed in cylinder head can directly detect flaw, especially on point 2 and point 8, which were near the flaw position, and can provide effective method for further monitoring thermal condition of cylinder head.


Zooplankton communities of some tanzanian lake victoria basin water bodies


title: Performance Evaluation of Integrated Multi-Access Edge Computing and Fiber-Wireless Access Networks

abstract: With the widespread use of smart mobile devices, the exponential growth of mobile Internet traffic and newly emerging services, such as Internet of Things, virtual reality/augmented reality, and serious games, the network performance requirements for delay and bandwidth are increasing. The inherent long-distance propagation and possible network congestion of mobile cloud computing may lead to excessive latency, which cannot satisfy the new delay-sensitive mobile applications. The proximity of edge computing provides the possibility of low-latency access and raises increasing interest from non-mobile operators; therefore, edge computing faces a variety of access network technologies, including wired (fixed) and wireless (mobile) access. In this paper, we propose an integrated heterogeneous networking scheme for multi-access edge computing and fiber-wireless access networks that uses network virtualization to achieve the dynamic orchestration of the network, storage, and computing resources to meet diverse application demands. The global view and centralized control of the entire network and the unified scheduling of the resources in the scheme anticipate the convergence of various types of access networks and the edge cloud. The multipath transmission of the service flows is further combined as an instance of integrated edge cloud networking. An experimental testbed is established in the laboratory, and the performance of the multi-access edge computing and networking is evaluated to verify the feasibility and effectiveness of the scheme. The results demonstrate that the scheme can effectively improve the network performance.


title: Handling Massive N-Gram Datasets Efficiently

abstract: Two fundamental problems concern the handling of large n-gram language models: indexing, that is, compressing the n-grams and associated satellite values without compromising their retrieval speed, and estimation, that is, computing the probability distribution of the n-grams extracted from a large textual source. ::: Performing these two tasks efficiently is vital for several applications in the fields of Information Retrieval, Natural Language Processing, and Machine Learning, such as auto-completion in search engines and machine translation. ::: Regarding the problem of indexing, we describe compressed, exact, and lossless data structures that simultaneously achieve high space reductions and no time degradation with respect to the state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word of an n-gram following a context of fixed length k, that is, its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before, allowing the indexing of billions of strings. Despite the significant savings in space, our technique introduces a negligible penalty at query time. ::: Specifically, the most space-efficient competitors in the literature, which are both quantized and lossy, do not take less than our trie data structure and are up to 5 times slower. Conversely, our trie is as fast as the fastest competitor but also retains an advantage of up to 65% in absolute space. ::: Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models that have emerged as the de-facto choice for language modeling in both academia and industry thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. ::: The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step by exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5 times on the total runtime of the previous approach.


title: Programming Intelligent Environments in Natural Language: An Extensible Interactive Approach

abstract: The proliferation of Ambient Intelligence (AmI) devices and services and their integration in smart environments creates the need for a simple yet effective way of controlling and communicating with them. Towards that direction, the application of the Trigger -- Action model has attracted a lot of research with many systems and applications having been developed following that approach. This work introduces ParlAmI, a multimodal conversational interface aiming to give its users the ability to determine the behavior of AmI environments, by creating rules using natural language as well as a GUI. The paper describes ParlAmI, its requirements and functionality, and presents the findings of a user-based evaluation which was conducted.


title: Text Segmentation of Consumer Magazines in PDF Format

abstract: Text segmentation is usually the first step taken towards the reuse and repurposing of PDF documents. Through experimental evaluation, we found that the leading text segmentation algorithms have limitations for contemporary consumer magazines. We propose a new local homogeneity measure based on line space, and incorporate this new feature into a region growing algorithm. Using a fixed set of parameters, our algorithm achieved robust performance on PDF magazines with wide-ranging layouts and styles.


title: Understanding Design Fundamentals: How Synthesis and Analysis Drive Creativity, Resulting in Emergence

abstract: Abstract This paper presents results of an ongoing interdisciplinary study to develop a computational theory of creativity for engineering design. Human design activities are surveyed, and popular computer-aided design methodologies are examined. It is argued that semiotics has the potential to merge and unite various design approaches into one fundamental theory that is naturally interpretable and so comprehensible in terms of computer use. Reviewing related work in philosophy, psychology, and cognitive science provides a general and encompassing vision of the creativity phenomenon. Basic notions of algebraic semiotics are given and explained in terms of design. This is to define a model of the design creative process, which is seen as a process of semiosis, where concepts and their attributes represented as signs organized into systems are evolved, blended, and analyzed, resulting in the development of new concepts. The model allows us to formally describe and investigate essential properties of the design process, namely its dynamics and non-determinism inherent in creative thinking. A stable pattern of creative thought — analogical and metaphorical reasoning — is specified to demonstrate the expressive power of the modeling approach; illustrative examples are given. The developed theory is applied to clarify the nature of emergence in design: it is shown that while emergent properties of a product may influence its creative value, emergence can simply be seen as a by-product of the creative process. Concluding remarks summarize the research, point to some unresolved issues, and outline directions for future work.


Combined Tree Kernel-based classifiers for Assessing Quality of Scientific Text


title: Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering

abstract: Abstract In-wheel motors have tremendous potential to create an advanced all-wheel drive system. In this paper, a novel power assisted steering technology and its torque distribution control system were proposed, due to the independent driving characteristics of four-wheel-independent-drive electric vehicle. The first part of this study deals with the full description of the basic theory of differential drive assisted steering system. After that, 4-wheel-drive (4WD) electric vehicle dynamics model as well as driver model were built. Furthermore, the differential drive assisted steering control system, as well as the drive torque distribution and compensation control system, was also presented. Therein, the proportional–integral (PI) feedback control loop was employed to track the reference steering effort by controlling the drive torque distribution between the two sides wheels of the front axle. After that, the direct yaw moment control subsystem and the traction control subsystem were introduced, which were both employed to make the differential drive assisted steering work as well as wished. Finally, the open-loop and closed-loop simulation for validation were performed. The results verified that, the proposed differential drive torque assisted steering system cannot only reduce the steering efforts significantly, as well as ensure a stiffer steering feel at high vehicle speed and improve the returnability of the vehicle, but also keep the lateral stability of the vehicle.


title: COMBINING CLASSIFIERS FOR FACE RECOGNITION

abstract: Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.


Measuring the physical in physical attractiveness : Quasiexperiments on the sociology of female facial beauty


title: A Genealogical Interpretation of Principal Components Analysis

abstract: Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference.


title: Extraction of palmprint texture features using combined DWT-DCT and local binary pattern

abstract: The palm-print verification system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the palm-print features are acquired from the sensor and stored in a database along with the person's identity for the recognition of his/her identity. In the recognition mode, the palm-print features are re-acquired from the sensor and compared against the stored data to determine the user identity. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of palm. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the palm is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed. Twenty hand-scanned images and ten samples of CASIA palmprint database were used in this experiment. The performance of the proposed system is found to be satisfactory.


title: Dynamic BLE-based fingerprinting for location-aware smart homes

abstract: We have seen an ever-expanding set of location-aware services and devices become broadly available over the past years. Despite these promising applications and thorough research, indoor localization remains a very challenging topic and (near) perfect accuracy continues to be an open research challenge. In this paper, we acknowledge the growth and potential of smart home environments and focus on the usage of Bluetooth Low Energy (BLE) for wireless indoor positioning in such cases. This is in contrast with existing work around BLE that more focuses on use-cases in large public areas or buildings. In particular, we compare the applicability of different distance functions for the fingerprinting localization method and propose a dynamic approach of selecting the most suited distance function. Furthermore, we investigate the impact on the quality of the fingerprints by looking at directional fingerprints and search space restrictions. Finally, we discuss the design and usage of a real-life location-aware smart home application, where a user controls the lights in a room through gestures.


title: See No Evil, Say No Evil: Description Generation from Densely Labeled Images

abstract: This paper studies generation of descriptive sentences from densely annotated images. Previous work studied generation from automatically detected visual information but produced a limited class of sentences, hindered by currently unreliable recognition of activities and attributes. Instead, we collect human annotations of objects, parts, attributes and activities in images. These annotations allow us to build a significantly more comprehensive model of language generation and allow us to study what visual information is required to generate human-like descriptions. Experiments demonstrate high quality output and that activity annotations and relative spatial location of objects contribute most to producing high quality sentences.


title: A Taxonomy of Workflow Management Systems for Grid Computing

abstract: With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.


N I ] 2 O ct 2 01 7 Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing


title: Health benefits observed after probiotic Lactobacillus fermentum CCM 7421 application in dogs

abstract: The importance of the intestinal microbiota has attracted much interest in recent years particularly with respect to ways in which the microbiota can be manipulated in order to improve health. Improving gut health through the use of probiotic microorganisms has become an area of research activity in both human and animal nutrition. However, the amount of research using companion animals is insufficient. The present review evaluates and compares the effects achieved after application of canine-derived probiotic strain Lactobacillus fermentum CCM 7421 to healthy dogs as well as to dogs suffering from gastrointestinal disorders. The experiments involved varying duration of application (4 days-14 days), dosage (107-109 CFU), form of application (fresh culture or lyophilized from) or combination with natural substances. Results from nine independent studies show the ability of probiotic strains to establish themselves in the canine gastrointestinal tract, alter the composition of intestinal microbiota and metabolites (organic acids), and modulate the physiology (serum biochemical parameters) and immunity parameters in dogs. Almost all observed effects were irrespective of dose or duration of probiotic administration.


title: Associations between flame retardant applications in furniture foam, house dust levels, and residents' serum levels.

abstract: Polyurethane foam (PUF) in upholstered furniture frequently is treated with flame retardant chemicals (FRs) to reduce its flammability and adhere to rigorous flammability standards. For decades, a commercial mixture of polybrominated diphenyl ethers (PBDEs) called PentaBDE was commonly applied to foam to fulfill these regulations; however, concerns over toxicity, bioaccumulation, and persistence led to a global phase-out in the mid-2000s. Although PentaBDE is still detected in older furniture, other FR compounds such as tris(1,3-dichloroisopropyl) phosphate (TDCIPP) and Firemaster® 550 (FM550) have been increasingly used as replacements. While biomonitoring studies suggest exposure is widespread, the primary sources of exposure are not clearly known. Here, we investigated the relationships between specific FR applications in furniture foam and human exposure. Paired samples of furniture foam, house dust and serum samples were collected from a cohort in North Carolina, USA and analyzed for FRs typically used in PUF. In general, the presence of a specific FR in the sofa of a home was associated with an increase in the concentration of that FR in house dust. For example, the presence of PentaBDE in sofas was associated with significantly higher levels of BDE-47, a major component of PentaBDE, in house dust (10β=6.4, p<0.001). A similar association was observed with a component of FM550, 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB), with levels that were approximately 3 times higher in house dust when FM550 was identified in the sofa foam (p<0.01). These relationships were modified by dust loading rates in the living room and the ratio of sofa size to room size. Interestingly, levels of TDCIPP and tris(1-chloro-2-isopropyl) phosphate (TCIPP) were also higher in dust with detections in sofa foam; however, these associations were not statistically significant and may suggest there are other prominent sources of these compounds in the home. In addition, the presence of PentaBDE in sofa foam was associated with significantly higher levels of BDE-47 in serum (p<0.01). These results suggest that FR applications in sofas are likely major sources of exposure to these compounds in the home.


title: Recurrent reciprocal 1q21.1 deletions and duplications associated with microcephaly or macrocephaly and developmental and behavioral abnormalities

abstract: Chromosome region 1q21.1 contains extensive and complex low-copy repeats, and copy number variants (CNVs) in this region have recently been reported in association with congenital heart defects, developmental delay, schizophrenia and related psychoses. We describe 21 probands with the 1q21.1 microdeletion and 15 probands with the 1q21.1 microduplication. These CNVs were inherited in most of the cases in which parental studies were available. Consistent and statistically significant features of microcephaly and macrocephaly were found in individuals with microdeletion and microduplication, respectively. Notably, a paralog of the HYDIN gene located on 16q22.2 and implicated in autosomal recessive hydrocephalus was inserted into the 1q21.1 region during the evolution of Homo sapiens; we found this locus to be deleted or duplicated in the individuals we studied, making it a probable candidate for the head size abnormalities observed. We propose that recurrent reciprocal microdeletions and microduplications within 1q21.1 represent previously unknown genomic disorders characterized by abnormal head size along with a spectrum of developmental delay, neuropsychiatric abnormalities, dysmorphic features and congenital anomalies. These phenotypes are subject to incomplete penetrance and variable expressivity.


title: CPW two-stage balanced monolithic 15 GHz amplifier using reduced-size branchline couplers

abstract: A two-stage balanced coplanar waveguide (CPW) 15 GHz amplifier design is presented which uses reduced-size branchline couplers. Impedance transformation is introduced in to the coupler to reduce the complexity of the devices matching networks. The amplifier has a chip size of only 2mm × 1.7mm and exhibits a measured gain of 16 dB with less than ±0.1dB of ripple over the intended operating range of 14.75-15.25 GHz.


title: MauveDB: supporting model-based user views in database systems

abstract: Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.


title: Scale-dependent and example-based grayscale stippling

abstract: We present an example-based approach to synthesizing stipple illustrations for static 2D images that produces scale-dependent results appropriate for an intended spatial output size and resolution. We show how treating stippling as a grayscale process allows us to both produce on-screen output and to achieve stipple merging at medium tonal ranges. At the same time we can also produce images with high spatial and low color resolution for print reproduction. In addition, we discuss how to incorporate high-level illustration considerations into the stippling process based on discussions with and observations of a stipple artist. Also, certain features such as edges can be extracted and used to control the placement of dots to improve the result. The implementation of the technique is based on a fast method for distributing dots using halftoning and can be used to create stipple images interactively. We describe both a GPU implementation of the basic algorithm that creates stipple images in real-time for large images and an extended CPU method that allows a finer control of the output at interactive rates.


Surveillance for HIV infection in MSM selected through respondent driven sampling in Beijing, 2005-2012


title: A Synchronous Chopping Demodulator and Implementation for High-Frequency Inductive Position Sensors

abstract: We describe a new method for high-frequency precision sensing. The method combines synchronous detection with chopping in a fully differential architecture that includes an instrumentation amplifier. An integrated circuit implementation of the proposed synchronous chopping demodulator front end was designed and fabricated in a 0.35-mum CMOS process technology and tested with high-frequency inductive position sensors. The measured results show that the new technique offers considerable advantages in terms of offset reduction compared to traditional techniques for these sensors, which rely on a microcontroller to measure the offset before each position measurement is taken. The measured average input-referred offset for the 20 fabricated chip samples is 87 muV at a chopping frequency of 500 kHz when the resonant target is off and synchronous demodulation and transmitter excitation are both applied at 1 MHz. The technique, in addition to improving system resolution and immunity to common-mode interference, allows these high-frequency position sensors to work with multiple targets, thus increasing speed and functionality.


title: Nonlinear air drag damping of torsional microscanners

abstract: An empirical model is described for the air drag damping of resonant torsional microscanners. Experimentally determined drag coefficients are presented for four scanning mirror designs, and comparisons between the designs suggest that a simple empirical model for the damping of such devices can be predictive.


title: Design of a Fingertip-Mounted Tactile Display with Tangential Skin Displacement Feedback

abstract: Application of tangential skin displacement at the fingertip has been shown to be effective in communicating direction and has potential for several applications. We have developed a portable, fingertip-mounted tactile display capable of displacing and stretching the skin of the fingerpad, using a 7 mm hemispherical tactor. In vivo tests of fingerpad skin stiffness were performed to determine the forces required to effectively render stimuli. Other design parameters such as stimulus speed and displacement were derived from our earlier work. The tactile display is capable of rendering \pm 1 mm of displacement at arbitrary orientations within a plane and with rates of approximately 5 mm/s. Compliance and backlash in the device's drive train were characterized using external measurements, and were compensated for in software to reduce the impact on device hysteresis.


title: Credit Risk Modeling for Commercial Banks

abstract: The aim of this paper is to examine the efficiency of two credit risk modeling (CRM) to predict the credit risk of commercial Iranian banks: (1) Logistic regression model (LRM); (2) Artificial neural networks (ANNs). The calculations have been done by using SPSS and MATLAB software. Number of samples was 316 and 5 dependent variables. The results showed that, artificial neural network is more proper to identify bad customers in commercial bank. The major contribution of this paper is specifying the most important determinants for rating of customers in Iran’s banking sector.


title: A three-chambed soft actuator module with omnidirectional bending motion

abstract: Unlike traditional rigid robots, soft robots can deform in a wide range to suit environment and contact compliantly with operating objects. Therefore, soft robots have wide potential application prospects in terms of physical rehabilitation, minimally invasive surgery and other fields. This paper presents the concept design and fabrication of a three-chambed soft actuator module with omnidirectional bending capability. The effect of geometrical parameters on the module bending properties was investigated by using finite element methods (FEM). Preliminary tests as proof of concept and assessment of performances were implemented, indicating that the module has good deformation capacity and can be used to assemble soft multi fingers hand, soft manipulators and other soft robotic systems.


title: Code Smells in iOS Apps: How Do They Compare to Android?

abstract: With billions of app downloads, the Apple App Store and Google Play Store succeeded to conquer mobile devices. However, this success also challenges app developers to publish high-quality apps to keep attracting and satisfying end-users. In particular, taming the ever-growing complexity of mobile apps to cope with maintenance and evolution tasks under such a pressure may lead to bad development choices. While these bad choices, a.k.a. code smells, are widely studied in object-oriented software, their study in the context of mobile apps, and in particular iOS apps, remains in its infancy. Therefore, in this paper, we consider the presence of object-oriented and iOS-specific code smells by analyzing 279 open-source iOS apps. As part of this empirical study, we extended the Paprika toolkit, which was previously designed to analyze Android apps, in order to support the analysis of iOS apps developed in Objective-C or Swift. We report on the results of this analysis as well as a comparison between iOS and Android apps. We comment our findings related to the quality of apps in these two ecosystems. Interestingly, we observed that iOS apps tend to contain the same proportions of code smells regardless of the development language, but they seem to be less prone to code smells compared to Android apps.


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