title: Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns

text: In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even though they may contain information about the image’s global structure. To consider such spatial relations, we measure their co-occurrence among multiple LBPs. The proposed feature is robust against variations in illumination, a feature inherited from the original LBP, and simultaneously retains more detail of image. The significant advantage of the proposed method versus conventional LBP-based features is demonstrated through experimental results of face and texture recognition using public databases.


title: Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites

text: As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.


title: Using Associative Classifiers for Predictive Analysis in Health Care Data Mining

text: Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims to discover a small set of rule in the database that forms an accurate classifier. In this paper, we introduce the combined approach that integrates association rule mining and classification rule mining called Associative Classification (AC). This is new classification approach. The integration is done by focusing on mining a special subset of association rules called classification association rule (CAR). And then classification is being performed using these CAR. Using association rule mining for constructing classification systems is a promising approach. Given the readability of the associative classifiers, they are especially fit to applications were the model may assist domain experts in their decisions. Medical field is a good example was such applications may appear. Consider an example were


title: Modeling and Optimization of Building Mix and Energy Supply Technology for Urban Districts

text: Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system


title: Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

text: Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.


title: Towards the Success of ERP Systems : Case Study in Two Moroccan Companies

text: For over a decade, ERP systems in Morocco have shown a significant increase. Companies make large investments in these scale systems in order to anticipate positive impacts on their organization. Measuring ERP systems success becomes an important criterion for the stakeholders in justifying the associated investment. In this paper, we will examine the success of these systems by using a qualitative research method based on a case study approach. We will provide an insight into how two Moroccan organizations perceive the success of their ERP systems. Findings indicated that Information, system quality and net benefits are the three main dimensions of success.


title: Forward-viewing CMUT arrays for medical imaging

text: This paper reports the design and testing of forward-viewing annular arrays fabricated using capacitive micromachined ultrasonic transducer (CMUT) technology. Recent research studies have shown that CMUTs have broad frequency bandwidth and high-transduction efficiency. One- and two-dimensional CMUT arrays of various sizes already have been fabricated, and their viability for medical imaging applications has been demonstrated. We fabricated 64-element, forward-viewing annular arrays using the standard CMUT fabrication process and carried out experiments to measure the operating frequency, bandwidth, and transmit/receive efficiency of the array elements. The annular array elements, designed for imaging applications in the 20 MHz range, had a resonance frequency of 13.5 MHz in air. The immersion pulse-echo data collected from a plane reflector showed that the devices operate in the 5-26 MHz range with a fractional bandwidth of 135%. The output pressure at the surface of the transducer was measured to be 24 kPa/V. These values translate into a dynamic range of 131.5 dB for 1-V excitation in 1-Hz bandwidth with a commercial low noise receiving circuitry. The designed, forward-viewing annular CMUT array is suitable for mounting on the front surface of a cylindrical catheter probe and can provide Doppler information for measurement of blood flow and guiding information for navigation through blood vessels in intravascular ultrasound imaging.


title: Exploration of Virtual and Augmented Reality for Visual Analytics and 3D Volume Rendering of Functional Magnetic Resonance Imaging (fMRI) Data

text: Statistical analysis of functional magnetic resonance imaging (fMRI), such as independent components analysis, is providing new scientific and clinical insights into the data with capabilities such as characterising traits of schizophrenia. However, with existing approaches to fMRI analysis, there are a number of challenges that prevent it from being fully utilised, including understanding exactly what a 'significant activity' pattern is, which structures are consistent and different between individuals and across the population, and how to deal with imaging artifacts such as noise. Interactive visual analytics has been presented as a step towards solving these challenges by presenting the data to users in a way that illuminates meaning. This includes using circular layouts that represent network connectivity and volume renderings with 'in situ' network diagrams. These visualisations currently rely on traditional 2D 'flat' displays with mouse-and-keyboard input. Due to the constrained screen space and an implied concept of depth, they are limited in presenting a meaningful, uncluttered abstraction of the data without compromising on preserving anatomic context. In this paper, we present our ongoing research on fMRI visualisation and discuss the potential for virtual reality (VR) and augmented reality (AR), coupled with gesture-based inputs to create an immersive environment for visualising fMRI data. We suggest that VR/AR can potentially overcome the identified challenges by allowing for a reduction in visual clutter and by allowing users to navigate the data abstractions in a 'natural' way that lets them keep their focus on the visualisations. We present exploratory research we have performed in creating immersive VR environments for fMRI data.


title: A scrum-based approach to CMMI maturity level 2 in web development environments

text: Scrum has become one of the most popular agile methodologies, either alone or combined with other agile practices. Besides, CMMI (Capability Maturity Model Integration) is accepted as a suitable model to measure the maturity of the organizations when developing or acquiring software. Although these two approaches are often considered antagonist, the use of an agile approach to reach certain CMMI maturity levels may result beneficial to organizations that develop Web systems, since they would take the advantages of both approaches. In Web community, this union may be very interesting, because agile approaches fits with the special needs of Web development, and they could be a useful tool for companies getting a certain grade of maturity. This work analyzes the goals of CMMI maturity level 2 and the feasibility of achieving them using the practices proposed by Scrum, trying to assess whether the use of this methodology is suitable for meeting the CMMI generic and specific goals or not. Finally, and based on this analysis, this paper raises a possible extension of Scrum, based on agile techniques, to accommodate the CMMI maturity level 2.


title: Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types

text: Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. This paper summarises the contribution described in full in [9].


title: The hitchhiker's guide to successful residential sensing deployments

text: Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.


title: Duty-cycling buildings aggressively: The next frontier in HVAC control

text: Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.


title: AMPds: A public dataset for load disaggregation and eco-feedback research

text: A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.


Wefind from four datasets that time expressions are formed by loose structure and the words used to express time information can differentiate time expressions from common text. The findings drive us to design a learning method named TOMN to model time expressions. TOMN defines a constituent-based tagging scheme named TOMN scheme with four tags, namely T, O, M, and N, indicating the constituents of time expression, namely Time token,Modifier,Numeral, and the words Outside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (e.g., BIO scheme and BILOU scheme). Experiments show that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets. Moreover, our analysis can explain many empirical observations in other works about time expression recognition and named entity recognition. ACM Reference Format: Xiaoshi Zhong and Erik Cambria. 2018. Time Expression Recognition Using a Constituent-based Tagging Scheme. In Proceedings of The Web Conference 2018 (WWW’18). ACM, New York, NY, USA, 10 pages. https://doi.org/10. 1145/3178876.3185997


title: Digital Comics Image Indexing Based on Deep Learning

text: The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.


title: Piecewise 3D Euler spirals

text: 3D Euler spirals are visually pleasing, due to their property of having their curvature and their torsion change linearly with arc-length. This paper presents a novel algorithm for fitting piecewise 3D Euler spirals to 3D curves with G2 continuity and torsion continuity. The algorithm can also handle sharp corners. Our piecewise representation is invariant to similarity transformations and it is close to the input curves up to an error tolerance.


Legged Robots That Balance


title: Deep Transfer Learning for Person Re-Identification

text: Person re-identification (Re-ID) poses an inevitable challenge to deep learning: how to learn a robust deep model with millions of parameters on a small training set of few or no labels. In this paper, two deep transfer learning methods are proposed to address the training data sparsity problem, respectively from the supervised and unsupervised settings. First, a two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets. Second, given an unlabelled Re-Iddataset, an unsupervised deep transfer learning model is proposed based on a co-training strategy. Extensive experiments show that the proposed models achieve a good performance of deep Re-ID models.


title: 3 D Indoor Object Recognition by Holistic Scene Understanding

text: In this project, we jointly solve the problem of indoor object recognition and scene classification. The joint problem is modeled by a Conditional Random Field (CRF). Specifically, we implement unary potential on scene appearance, unary potential on object appearance, and binary potential on geometry between objects. The two appearance potentials are obtained by training two neural networks respectively and extracting features from them. Binary potential on object-object geometry relationship is inferred by projecting one object onto the other one’s surface and perceiving the overlap. Other potentials include object cuboid geometry, binary potential on scene-object concurrence, and binary potential on object-object concurrence. Experiments on the challenging NYU-RGBD v2 dataset show that the approach of jointly solving object detection and scene classification problems by integrating several feature representations achieves better performance than vanilla classification. Meanwhile, improving the potentials on scene appearance and object-object geometry relationship achieve fairly satisfactory improvements compared with the performance in [12]. We achieve object recognition precision of 0.3859 and scene classification accuracy of 0.6208 among 21 classes of objects and 13 classes of scenes. Both improve obviously comparing to 0.3829 and 0.6086 in baseline [12]. This result is also comparable to the state-of-art method implemented on the identical dataset [18].


title: CAVE-SOM: Immersive visual data mining using 3D Self-Organizing Maps

text: Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.


title: Experimental Results of High-Efficiency Resonant Coupling Wireless Power Transfer Using a Variable Coupling Method

text: We experimentally demonstrate that the efficiency of wireless power transfer utilizing resonant coupling is improved by applying the “matching condition.” The `matching condition' is extracted from an equivalent circuit model of a magnetically coupled wireless power transfer system. This condition is achieved by varying the coupling factor between the source (load) and the internal resonator. Applying this technique results in efficiency improvements of 46.2% and 29.3% at distances of 60 cm and 1 m, respectively. The maximum efficiency is 92.5% at 15 cm. A circuit model based on the extracted parameters of the resonators produces results similar to the experimental data.


title: Improved Named Entity Translation and Bilingual Named Entity Extraction

text: Translation of named entities (NE), including proper names, temporal and numerical expressions, is very important in multilingual natural language processing, like crosslingual information retrieval and statistical machine translation. In this paper we present an integrated approach to extract a named entity translation dictionary from a bilingual corpus while at the same time improving the named entity annotation quality.Starting from a bilingual corpus where the named entities are extracted independently for each language, a statistical alignment model is used to align the named entities. An iterative process is applied to extract named entity pairs with higher alignment probability. This leads to a smaller but cleaner named entity translation dictionary and also to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the dictionary size is reduced by 51.8% and the annotation quality is improved from70.03 to 78.15 for Chinese and 73.38 to 81.46 in terms of F-score.


title: Zero-Field Readout Electronics for Planar Fluxgate Sensors Without Compensation Coil

text: A simple and sensitive readout electronics for planar fluxgate sensors is presented. The system exploits the sense coils to directly generate the compensation static field avoiding the additional coil/s required in standard closed loop configuration, thus providing clear advantages in terms of size and cost. Moreover, feedback configurations are known to provide better linearity and stability of the system. The sensitivity of the developed demonstration system can be easily set from 13.3 to 104.9 mV/μT with nonlinearity ranging from 0.17% to 0.38% of the measuring interval, whereas the corresponding measuring intervals vary from ±301 to ±38 μT. The measuring uncertainty, the noise field spectral density, and the system bandwidth have been estimated in 12.2 nT, ≈ 10 nT/√{Hz}, and ≈1.5 Hz, respectively. The proposed measuring instrument is extremely easy to use and versatile. Moreover, due to the use of commercially available ferromagnetic material and the simple and via-less design, the proposed fluxgate sensor results in a very low cost and reliable device.


title: Efficient Keyword-Aware Representative Travel Route Recommendation

text: With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.


title: Sudoku as a SAT Problem

text: Sudoku is a very simple and well-known puzzle that has achieved international popularity in the recent past. This paper addresses the problem of encoding Sudoku puzzles into conjunctive normal form (CNF), and subsequently solving them using polynomial-time propositional satisfiability (SAT) inference techniques. We introduce two straightforward SAT encodings for Sudoku: the minimal encoding and the extended encoding. The minimal encoding suffices to characterize Sudoku puzzles, whereas the extended encoding adds redundant clauses to the minimal encoding. Experimental results demonstrate that, for thousands of very hard puzzles, inference techniques struggle to solve these puzzles when using the minimal encoding. However, using the extended encoding, unit propagation is able to solve about half of our set of puzzles. Nonetheless, for some puzzles more sophisticated inference techniques are required.


title: Variational Inference using Implicit Distributions

text: Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data. Implicit distributions are ones we can sample from easily, and take derivatives of samples with respect to model parameters. These models are highly expressive and we argue they can prove just as useful for variational inference (VI) as they are for generative modelling. Several papers have proposed GAN-like algorithms for inference, however, connections to the theory of VI are not always well understood. This paper provides a unifying review of existing algorithms establishing connections between variational autoencoders, adversarially learned inference, operator VI, GAN-based image reconstruction, and more. Secondly, the paper provides a framework for building new algorithms: depending on the way the variational bound is expressed we introduce prior-contrastive and jointcontrastive methods, and show practical inference algorithms based on either density ratio estimation or denoising.


title: Hand3D: Hand Pose Estimation using 3D Neural Network

text: We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-ofthe-art performance. The synthetic hand pose dataset will be available.


title: "If It Matters, I Can Explain It": Social Desirability of Knowledge Increases the Illusion of Explanatory Depth

text: This paper explores whether social desirability affects the illusion of explanatory depth (IEOD) by comparing the magnitude of this illusion in topics with different levels of social desirability within several domains. This question was chosen because prior literature shows that social expectations about how much a person should know about a certain topic affect the magnitude of the IOED. Previous research shows also that social desirability has an effect on a similar illusion related to argumentation, and that the IOED is affected by the way a person thinks knowledge is distributed in his or her social group. In order to do so, 184 participants were assigned randomly to three knowledge domains (history, economics, and devices) and in each domain they rated their understanding of a high-desirability and a low-desirability topic following a standard IOED procedure. Results show that social desirability has an effect on the IOED magnitude and that overestimation of understanding varies among domains. Particularly, participants tend to overestimate their understanding of high desirability topics only. This effect was stronger in the historical domain.


title: Behavioral oscillation in priming: competing perceptual predictions conveyed in alternating theta-band rhythms.

text: The brain constantly creates perceptual predictions about forthcoming stimuli to guide perception efficiently. Abundant studies have demonstrated that perceptual predictions modulate sensory activities depending on whether the actual inputs are consistent with one particular prediction. In real-life contexts, however, multiple and even conflicting predictions might concurrently exist to be tested, requiring a multiprediction coordination process. It remains largely unknown how multiple hypotheses are conveyed and harmonized to guide moment-by-moment perception. Based on recent findings revealing that multiple locations are sampled alternatively in various phase of attentional rhythms, we hypothesize that this oscillation-based temporal organization mechanism may also underlie the multiprediction coordination process. To address the issue, we used well established priming paradigms in combination with a time-resolved behavioral approach to investigate the fine temporal dynamics of the multiprediction harmonization course in human subjects. We first replicate classical priming effects in slowly developing trends of priming time courses. Second, after removing the typical priming patterns, we reveal a new theta-band (∼4 Hz) oscillatory component in the priming behavioral data regardless of whether the prime was masked. Third, we show that these theta-band priming oscillations triggered by congruent and incongruent primes are in an out-of-phase relationship. These findings suggest that perceptual predictions return to low-sensory areas not continuously but recurrently in a theta-band rhythm (every 200-300 ms) and that multiple predictions are dynamically coordinated in time by being conveyed in different phases of the theta-band oscillations to achieve dissociated but temporally organized neural representations.


title: The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?

text: Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial


title: A Comparative Study of Staff Removal Algorithms

text: This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.


Real-Time Object Detection and Recognition System Using OpenCV via SURF Algorithm in Emgu CV for Robotic Handling in Libraries


title: PUSHOVER ANALYSIS OF A MULTISTORIED BUILDING

text: To model the advanced behaviour of reinforced concrete analytically in its non-linear zone is difficult. This has led engineers in the past to believe heavily on empirical formulas that were derived from numerous experiments for the design of reinforced concrete structures. For structural design and assessment of reinforced concrete members, the nonlinear analysis has become a vital tool. The method can be used to study the behaviour of reinforced concrete structures as well as force redistribution. This analysis of the nonlinear response of RC structures to be distributed out in an exceedingly routine fashion. It helps within the investigation of the behaviour of the structure below completely different loading conditions and also the cracks pattern. In the present study, the non-linear response of RCC frame using the loading has been carried out with the intention to investigate the relative importance of many factors in the non-linear analysis of RCC frames. The structure was modelled and analyzed in STAAD Pro V8i, SAP2000 and designed manually. The description of the reinforcement was done in AutoCAD 2010. Keywordsmulti-storied building, STAAD PRO, Floor plan, SAP 2000, Push over analysis.


title: Automated Irrigation System-IoT Based Approach

text: Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.


title: Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread

text: The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.


title: Real-parameter optimization with differential evolution

text: This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization. The test bed includes 25 scalable functions, many of which are both non-separable and highly multi-modal. Results include DE's performance on the 10 and 30-dimensional versions of each function


title: Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy

text: Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm’s past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-theart optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.


title: Lightweight IPS for port scan in OpenFlow SDN networks

text: Security has been one of the major concerns for the computer network community due to resource abuse and malicious flows intrusion. Before a network or a system is attacked, a port scan is typically performed to discover vulnerabilities, like open ports, which may be used to access and control them. Several studies have addressed Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) methods for detecting malicious activities, based on received flows or packet data analysis. However, those methods lead to an increase in switching latency, due to the need to analyze flows or packets before routing them. This may also increase network overhead when flows or packets are duplicated to be parsed by an external IDS. On the one hand, an IDS/IPS may be a bottleneck on the network and may not be useful. On the other hand, the new paradigm called Software Defined Networking (SDN) and the OpenFlow protocol provide some statistical information about the network that may be used for detecting malicious activities. Hence, this work presents a new port scan IPS for SDN based on the OpenFlow switch counters data. A non-intrusive and lightweight method was developed and implemented, with low network overhead, and low memory and processing power consumption. The results showed that our method is effective on detecting and preventing port scan attacks.


title: Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods

text: A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.


title: High quality & low thermal resistance eutectic flip chip LED bonding

text: Die attach process plays a key role in the thermal management of high power LED packages by governing the thermal resistance of the solder interfaces between the LED chips and the substrate materials. In this paper, comparison was made between the GaN based flip chip LED packages fabricated by (1) heated collet and (2) flux reflow eutectic die attach process. Thermal transient characteristics of the samples had been investigated based on the evaluation of the differential structure function and chip temperature measurement by infrared microscopy. It was shown that LEDs with higher void content would result in higher thermal resistance and chip temperature. Under high power operation at 6W, it was estimated that the junction temperature would exceed 80°C with void content 40%, which was at least 10°C higher comparing to that with void content 20%. Based on the result, LEDs samples bonded by heated collet die attach process had an average void content of 8.8% with 0.9% standard deviation, which were smaller comparing to the samples bonded by flux reflow die attach process (40% void content with standard deviation 20.5%). Due to the advantage of small void content control, it is possible to fabricate high power LED package with low thermal resistance, consistent and reliable die attach quality by using heated collet die attach process.


title: Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

text: Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.


title: Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

text: We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.


title: Multiple templates auto exposure control based on luminance histogram for onboard camera

text: In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.


title: What campus-based students think about the quality and benefits of e-learning

text: There is a trend in Irish universities to utilise the benefits of the e-learning as a mechanism to improve learning performance of campus-based students. Whilst traditional methods, such as face-to-face lectures, tutorials, and mentoring, remain dominant in the educational sector, universities are investing heavily in learning technologies, to facilitate improvements with respect to the quality of learning. The technology to support reuse and sharing of educational resources, or learning objects, is becoming more stable, with interoperability standards maturing. However, debate has raged about what constitutes effective use of learning technology. This research expands upon a study carried out in 2003 examining students’ perceptions of e-learning in a large undergraduate accounting class environment. As a result, improvements were made to the instructional design of the course, to enable students to engage interactively with content. The subsequent study, reported in this paper, adopted a broad range of techniques to understand students’ learning experience in depth. The findings of this research provide an insight into how these students really work and learn using technologies, if at all. It is hoped that our findings will improve the experience for both students and lecturers who engage in teaching and learning through this medium. 502 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. Introduction A recent trend in higher education is to create and provide online access to course materials. Over the past two decades academics and institutes of higher education have been diversifying their delivery of instruction through new Internet media such as learning management systems, asynchronous distance learning, and online classrooms amongst a myriad of other burgeoning educational technologies. This combination of traditional face-to-face lectures or tutorials, and web-based course content is better known as “blended learning,” purporting to blend the best aspects of real and virtual environments. Many Irish universities have invested in architectures and platforms to support their teaching staff in delivering material to students in a blended manner. Other institutions have adopted a lecturer-driven approach, whereby teaching staff are left to their own devices to supplement their lectures and tutorials with online material, hosted via their own web servers and typically open source, freeware software, or basic web pages. The reasons behind the drive to incorporate technology into the educational process are threefold. First, pressure to utilise information and communications technology (ICT) at university level comes from changes in the student demography. The rise of “full time part time students” is a phenomenon of recent years, where school leavers take part-time jobs whilst attending university, leaving less time for evening tutorials or weekend study. In addition, there is a drive for what is known as lifelong learning, whereby adults are increasingly returning to institutions of higher education to take supplementary courses whilst in full-time employment, or during short career breaks. In this regard, the Irish government has made a commitment to have 15% of adults in continuing higher education by 2006. The figure currently stands at less than 5% (Department of Education and Science, 2000). This movement to adult continuing education was also reflected in a statement made by Information Commission Society (Ireland): “The issue of integration IT to the teaching process is an important part of future improvements, which it will be crucial to pursue” (Boylan, 2000, p. 31). Second, changes in the market for delivery of education is also shifting. Private for-profit higher education institutions are offering a wide range of certificate and degree courses. The Irish Open University Initiative “Oscail” and its UK equivalent offer distance learning diploma and degree courses. Many of these courses are delivered through ICT, along with weekend face-to-face tutorials. This distance learning market has also seen the arrival of new entrants, such as the Atlantic University Alliance, a collaboration between National University of Ireland, Galway, University College Cork, and the University of Limerick. Similar undertakings are underway in other institutes of higher education, whereby expertise in delivering blended learning within the institution is being extended towards distance learning courses by using a significant amount of ICT to overcome geographic and time barriers. Third, another pressure on traditional institutions of higher education comes from innovations in new technologies. John Seely Brown and Paul Duiguid claim that these technologies “offer new ways to think of producing, distributing and consuming acaWhat students think about e-learning 503 © British Educational Communications and Technology Agency, 2005. demic material. As with so many other institutions, new technologies have caused universities to rethink not simply isolated features but their entire mission and how they go about it” (Seely Brown & Duiguid, 2000). This sentiment is also being echoed by government policy towards higher education where the Irish State is attempting to set certain parameters of performance covering such areas as equity of access, commitment to e-learning and to ICT, commitment to life-long learning, and outreach to other communities. In the subject domain of accountancy, the integration of technology into the classroom is also becoming a central issue for accounting educators for an additional reason. Accountancy professionals in industry require competency in accountancy-related information technology (IT) skills. Higher education institutions, in response, have tried to simulate authentic learning scenarios in the accountancy class and incorporate IT topics into their programmes. However, the efficacy of introducing technology in accounting education remains unclear. Positive learning effects associated with IT in accounting education have been noted (Abdolmohammadi et al , 1998; Fetters, McKenzie & Callaghan, 1986; Friedman, 1981). Equally, evidence has shown that technology has negatively impacted on learning (Dickens & Harper, 1986; Togo & McNamee, 1997). Indeed, some critics harshly criticise its introduction to educational contexts. Selwyn (2002, p. 172) claims in generic terms that “...in the final analysis educational technology remains a diverting pastime and ‘add-on’ to the curriculum for some teaching and learners, whilst for most in education its impact has been slight.” This paper hopes to look at some of the issues raised by individual students with regard to the use of technology in the teaching and learning process. Rather than focusing on macro issues, such as the political decisions behind the integration of technology, this research aims to examine individuals’ acceptance or rejection of using ICT. Exploring the perspective of the individual student when discussing the use of technology is paramount to beginning to understand the nature of ICT in higher educational settings. This is of importance both to policy makers and education technologists in the debate on the role of ICT in education at an organisational level. The module and the medium This research focused on one module of The Principles of Accounting being taught to approximately 600 first-year undergraduate students at the University of Limerick. The module under investigation was part of a wider course, leading to a degree in Business Studies or Law and Accounting. The majority of students were between 17 and 19 years of age, coming from distributed second-level schools in Ireland. A minority were Erasmus students from various European countries, or mature students over the age of 23. For all, except overseas students, this was their first experience in using an e-learning platform, although most had previously informal used the web to gather information, or prepare coursework in second-level education, prior to entering university. This module was delivered by using a blended learning approach, supplementing weekly lectures, tutorials, and laboratory sessions with online course content, interac504 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. tive quizzes, and Excel tasks. A range of different e-learning assessments complemented the traditional weekly meetings. Students were asked to submit a compulsory paperbased accounting case study, and additionally were offered two optional ICT assignments, a stream of online multiple-choice quizzes, and an Excel project. A module web site provided course details, additional readings, and supplementary links. Employing multiple teaching methods simultaneously is a form of blended learning (Saunders & Werner, 2003). Alternative instructional resources can stimulate positive effects on accounting students’ learning experiences, according to (Rebele et al , 1998). Furthermore, instructional innovations are desirable to develop accounting students’ IT competencies (Albrecht & Sack, 2000). Figure 1 illustrates the staged delivery of both the traditional and online elements of the module. The university does not provide institutional support for e-learning on a campus-wide scale, but two other modules in their first year also used a blended learning delivery approach, providing lecture notes online, or sample answers, in the subject area of economics and mathematics. Both of these other modules differ in marking scheme and in the extent to which online supports are provided such as discussion boards, or positive marking schemes for continuous online assessments. As such, blended learning is not yet a norm across modules for first-year students at this current time. Methods The research developed on experience gained in a previous study on a prior


title: A Facial Expression Recognition System Using Convolutional Networks

text: Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.


title: Analysis of user keyword similarity in online social networks

text: How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.


"Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples


title: Effects of energy drinks on the cardiovascular system

text: Throughout the last decade, the use of energy drinks has been increasingly looked upon with caution as potentially dangerous due to their perceived strong concentration of caffeine aside from other substances such as taurine, guarana, and L-carnitine that are largely unknown to the general public. In addition, a large number of energy drink intoxications have been reported all over the world including cases of seizures and arrhythmias. In this paper, we focus on the effect of energy drinks on the cardiovascular system and whether the current ongoing call for the products' sales and regulation of their contents should continue.


title: Boosting item keyword search with spreading activation

text: Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.


title: Advanced Cooling for Power Electronics

text: Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.


title: Evaluating a PID, pure pursuit, and weighted steering controller for an autonomous land vehicle

text: Wright Laboratory, at Tyndall AFB, Florida, has contracted the University ofFlorida to develop autonomous navigation systems for a variety ofrobotic vehicles, capable ofperforming tasks associated with the location and removal of bombs and mines. One ofthe tasks involves surveying closed target ranges for unexploded buried munitions. Accuracy in path following is critical to the task. There are hundreds of acres that currently require surveying. The sites are typically divided into regions, where each mission can take up to 4.5 hours. These sites are usually surveyed along parallel rows. By improving the accuracy ofpath following, the distance betweenthe rows can be increased to nearly the detection width ofthe ground penetrating sensors, resulting in increased acreage surveyed per mission. This paper evaluates a high-level PID and a pure pursuit steering controller. The controllers were combined into a weighted solution so that the desirable features of each controller is preserved. This strategy was demonstrated in simulation and implemented on a Navigation Test Vehicle (NTV). For a test path ofvarying curvature, the average lateral control error was 2 cm at a vehicle speed of 1.34 mIs.


title: A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

text: Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.


title: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

text: Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-ofword conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.


title: Improving TCP Throughput over Two-Way Asymmetric Links: Analysis and Solutions

text: The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.


title: Multiagent Systems Engineering

text: This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.


title: RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing

text: Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.


title: Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset

text: Relation extraction techniques are used to find potential relational facts in textual content. Relation Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.


title: REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING

text: This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners’ language is a product of their history of usage in communicative interaction.


title: OPAQUE: An Asymmetric PAKE Protocol Secure Against Pre-Computation Attacks

text: Password-Authenticated Key Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, current aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks. We initiate the study of Strong aPAKE protocols that are secure as aPAKE’s but are also secure against pre-computation attacks. We formalize this notion in the Universally Composable (UC) settings and present two modular constructions using an Oblivious PRF as a main tool. The first builds a Strong aPAKE from any aPAKE (which in turn can be constructed from any PAKE [23]) while the second builds a Strong aPAKE from any authenticated key-exchange protocol secure against reverse impersonation (a.k.a. KCI). Using the latter transformation, we show a practical instantiation of a UC-secure Strong aPAKE in the Random Oracle model. The protocol (“OPAQUE”) consists of 2 messages (3 with mutual authentication), requires 3 and 4 exponentiations for server and client, respectively (2 to 4 of which can be fixed-base depending on optimizations), provides forward secrecy, is PKI-free, supports user-side hash iterations, has a built-in facility for password-based storage and retrieval of secrets and credentials, and accommodates a user-transparent server-side threshold implementation.


title: Death by complete decapitation of motorcyclist wearing full face helmet: case report.

text: We describe a case of complete decapitation following a motorcycle accident in which the victim was wearing a full face helmet. A young man lost control of his motorcycle and was thrown about 20 m, hitting his head against the barrier separating a tramline from the road. The resulting trauma caused his decapitation, the only fatal wound ascertained by the various forensic investigations. The authors present this rare case and compare it against the other two cases reported in the literature, providing some observations on the ways in which this injury can come about. The absence of abrasions or signs that the wound edges came into contact with a metal structure, the presence of signs of impact on the side of the helmet and the finding of a transversal fracture at the base of the skull point to the violent action of a side-to-side opposite force, due to the resistance provided by the lower edge of the protective helmet.


title: A Regularization Method with Inference of Trust and Distrust in Recommender Systems

text: In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.


title: A New Algorithm of Fully-automatic Red-eye Removal

text: the redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of redeye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.


title: Autonomous Underwater Vehicles : Trends and Transformations

text: Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalities associated with interagency cooperation will play a role in driving this transformation. Recently the Navy joined with the U. S. Environmental Protection Agency, the Narragansett Bay Estuary Program, and the Autonomous Undersea Systems Institute to demonstrate the effectiveness of using a Solar Powered Autonomous Underwater Vehicle (SAUV II) to measure dissolved oxgyen concentrations in Greenwich Bay, Rhode Island. Utilization of an AUV to rapidly move continuously-recording dissolved oxygen sensors over large spatial areas to map the frequency and extent of hypoxia demonstrates cost-effective water quality monitoring (Crimmins, 2005). Real-time monitoring of dissolved oxygen over large spatial scales is important for predicting the onset of hypoxia and mapping its extent (RI Dept Env Mgmt, 2004). The SAUV II (Figure 1) is a solar powered AUV designed for long endurance missions such as monitoring, surveillance, or station keeping where real time bi-directional communications to shore are critical. The SAUV II operates continuously for several months using solar energy to recharge its lithium ion batteries during daylight hours. The SAUV II was fitted with a fast-response galvanic oxygen micro-sensor (AMT Analysenmesstechnik GmbH). This sensor provides rapid in situ profiling of dissolved oxygen at depths of up to 100 m with a response time of a few hundred milliseconds. The SAUV II was also fitted with a NXIC conductivity-temperature-depth (CTD) sensor (Falmouth Scientific Inc.). CTD data are essential for detecting water column stratification, which affects the development and breakdown of hypoxia (Crimmins, 2005). T T R E N D S o move forward wisely with ocean science and technology, the United States needs a unified strategy that engages national and international collaboration. Part VII of the Report by the U.S. Commission on Ocean Policy, “Science-Based Decisions: Understanding of the Oceans,” not only affirms this assertion, but also identifies a framework for federal agencies to proactively develop and implement policies that embrace the spirit of inter-agency cooperation and cost-effective shared technology. The Commission states that a larger, coordinated investment in infrastructure and technology development is necessary to conduct and support ocean science (Commissioners, 2004; Crimmins, 2004a). Future ocean sampling and surveillance systems should be capable of global deployment, sustained presence, three dimensional adaptive aperture, real time control and robust performance. These requirements can be met affordably by a network of Autonomous Underwater Vehicles (AUVs). The first generation of AUVs has transitioned from dream to reality (Griffiths, 2003). Engineering progress is now regularly reported and appli-


title: Ensemble Classifiers for Network Intrusion Detection System

text: Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.


title: Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system

text: The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.


title: Buddha ' s Brain : Neuroplasticity and Meditation

text: In a recent visit to the United States, the Dalai Lama gave a speech at the Society for Neuroscience's annual meeting in Washington, D.C. Over the past several years, he has helped recruit Tibetan Buddhist monks for, and directly encouraged research on the brain and meditation in the Waisman Laboratory for Brain Imaging and Behavior at the University of Wisconsin-Madison. The findings from studies in this unusual sample as well as related research efforts, suggest that, over the course of meditating for tens of thousands of hours, the long-term practitioners had actually altered the structure and function of their brains. In this article we discuss neuroplasticity, which encompasses such alterations, and the findings from these studies. Further, we comment on the associated signal processing (SP) challenges, current status and how SP can contribute to advance these studies.


title: The most effective exercise for strengthening the supraspinatus muscle: evaluation by magnetic resonance imaging.

text:
BACKGROUND
Electromyography has been used to determine the best exercise for strengthening the supraspinatus muscle, but conflicting results have been reported. Magnetic resonance imaging T2 relaxation time appears to be more accurate in determining muscle activation.

PURPOSE
To determine the best exercises for strengthening the supraspinatus muscle.

STUDY DESIGN
Criterion standard.

METHODS
Six male volunteers performed three exercises: the empty can, the full can, and horizontal abduction. Immediately before and after each exercise, magnetic resonance imaging examinations were performed and changes in relaxation time for the subscapularis, supraspinatus, infraspinatus, teres minor, and deltoid muscles were recorded.

RESULTS
The supraspinatus muscle had the greatest change among the studied muscles in relaxation time for the empty can (10.5 ms) and full can (10.5 ms) exercises. After the horizontal abduction exercise the change in relaxation time for the supraspinatus muscle (3.6 ms) was significantly smaller than that for the posterior deltoid muscle (11.5 ms) and not significantly different from that of the other muscles studied.

CONCLUSION
The empty can and full can exercises were most effective in activating the supraspinatus muscle.


title: Neural Network Model for Earthquake Prediction Using DMETER Data and Seismic Belt Information

text: The mechanism of the earthquake remains to be investigated, though some anomalies associated with earthquakes have been found by DEMETER satellite observations. The next step is to use the DEMETER data for earthquake prediction. It is a useful and feasible way to use the self-adaptive artificial neural network to construct relations between various symptom factors and earthquake occurrences. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. In this paper a series of physical quantities measured by the DEMETER satellite including Electron density, Electron temperature, ions temperature and oxygen ion density, together with seismic belt information are used to form sample sets for a back-propagation neural network. The neural network model then can be used to conduct the prediction. In the end, validation tests are performed based on those important seismic events happened in 2008.


title: Collaborative computer-aided design - research and development status

text: In order to facilitate product design and realization processes, presently, research is actively carried out for developing methodologies and technologies of collaborative computer-aided design systems to support design teams geographically dispersed based on the quickly evolving information technologies. In this paper, the developed collaborative systems, methodologies and technologies, which are organized as a horizontal or a hierarchical manner, are reviewed. Meanwhile, a 3D streaming technology, which can effectively transmit visualization information across networks for Web applications, is highlighted and the algorithms behind it are disclosed.


title: Analyzing the performance differences between pattern matching and compressed pattern matching on texts

text: In this study the statistics of pattern matching on text data and the statistics of compressed pattern matching on compressed form of the same text data are compared. A new application has been developed to count the character matching numbers in compressed and uncompressed texts individually. Also a new text compression algorithm that allows compressed pattern matching by using classical pattern matching algorithms without any change is presented in this paper. In this paper while the presented compression algorithm based on digram and trigram substitution has been giving about 30-35% compression factor, the duration of compressed pattern matching on compressed text is calculated less than the duration of pattern matching on uncompressed text. Also it is confirmed that the number of character comparison on compressed texts while doing a compressed pattern matching is less than the number of character comparison on uncompressed texts. Thus the aim of the developed compression algorithm is to point out the difference in text processing between compressed and uncompressed text and to form opinions for another applications.


title: A restricted Boltzmann machine based two-lead electrocardiography classification

text: An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.


title: Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing

text: For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.


title: A* Parsing: Fast Exact Viterbi Parse Selection

text: A PCFGparsingcandramaticallyreducethetime requiredto find theexactViterbi parseby conservatively estimatingoutsideViterbi probabilities.Wediscussvariousestimatesandgive efficientalgorithmsfor computingthem.On Penntreebanksentences, ourmostdetailedestimate reducesthetotalnumberof edgesprocessedto lessthan3%of thatrequiredby exhausti ve parsing,andevenasimplerestimatewhichcanbepre-computedin underaminutestill reduces thework by a factorof 5. ThealgorithmextendstheclassicA graphsearchprocedureto a certainhypergraphassociatedwith parsing.Unlike best-firstandfinite-beammethodsfor achieving thiskind of speed-up, theA parseris guaranteedto returnthemostlikely parse,not justanapproximation.Thealgorithmis alsocorrectfor awide rangeof parsercontrolstrategies andmaintainsaworst-casecubictime bound. A Parsing: FastExact Viterbi ParseSelection


title: Statistical language learning

text: The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface:


title: The PASCAL Recognising Textual Entailment Challenge

text: This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.


title: Adaptive duplicate detection using learnable string similarity measures

text: The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.