title: The Digital Life of Walkable Streets

abstract: Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.


title: Stemming of Amharic Words for Information Retrieval

abstract: This paper presents a stemmer for processing document and query words to facilitate searching databases of Amharic text. An iterative stemmer has been developed that involves the removal of both prefixes and suffixes and that also takes account of letter inconsistency and reiterative verb forms. Application of the stemmer to a test file of 1221 words suggested that appropriate stems were generated for ca. 95 per cent of them, with only limited overstemming and understemming.


title: Abstract: Introduction to Methods for Music Classification Based on Audio Data

abstract: The subject of music information retrieval (MIR) is to analyze and categorize music pieces. Over the last years many approaches have been designed to automatically extract music data from the digitized audio signal. This article presents a survey of the state-of-the-art algorithms on the basis of a broad literature study and a tool analysis. It should help to navigate through different MIR techniques and tools. An overview of different music features to characterize timbre, harmony, melody and rhythmic information is given. The various time scales of feature extraction to form meta-features from basic features are discussed. The task-specific pruning of features is presented to reduce the computational complexity. The article continues with a discussion of different classification techniques and how the results are evaluated. Finally the properties of four state-of-the-art MIR tools are outlined.


A Review on Individualized Educational Program in Some Countries


title: An Analytics-Based Approach to Managing Cognitive Load by Using Log Data of Learning Management Systems and Footprints of Social Media

abstract: Introduction The cognitive processing capacity of human memory is limited, as discussed in the literature on limited capacity assumptions (Mayer, 2005). Students participating in online learning experience changes in their cognitive load over time. Instructional activities as well as the topics discussed on forums and the interaction during synchronous video conferences contribute to the cognitive load of online students. Cognitive load varies dynamically with the learning process. Paas and van Merrienboer (1994) proposed an instructional design model for providing instructional strategies for controlling cognitive load during training in complex cognitive tasks. Mental-effort-based measures were recommended as suitable tools for investigating and determining the cognitive load of instructional manipulations. In our research, an analytics-based approach was proposed for measuring and managing cognitive load. Measurements of cognitive load were associated with learning behaviors that were captured by learning management system (LMS) log data and social media footprints. LMSs enable teachers to monitor online student learning through system-generated reports. Teachers may query the system to determine the amount of time that their students have spent browsing course content or the number of discussion forum posts to which they have contributed. The size and value of the log data from LMSs can be called a type of big data, as discussed by Snijders, Matzat, and Reips (2012). Theories and technologies have been developed to mine actionable information from big data in various application areas. In this research, we explored the possibility of using educational platform log data to manage the cognitive load of learners. Managing cognitive load by using knowledge of learning behaviors and analytics computing Cognitive Load Theory (CLT) is a psychological learning theory that has provided a basis for exploring instructional design and learning processes with human cognitive architecture (Sweller, van Merrienboer, & Paas, 1998; Sweller, Ayres, & Kalyuga, 2011). Three sources of cognitive load were identified in CLT: intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) (Sweller et al., 1998). ICL is determined by the inherent nature of the learning material and learners' prior knowledge. ICL cannot be changed by instructional interventions. ECL is determined by instructional design, including how instructional content is designed and the activities required of learners. Sweller et al. (1998) described GCL as the effort involved in relating prior knowledge to current instructional content in order to construct schemas stored in the long-term memory. The three types of cognitive load are additive and contribute to a learner's global cognitive load that cannot exceed his or her cognitive resources. Otherwise the learning process will fail. On the other hand, learning is ineffective with no or low cognitive load. Although high ICL and ECL is detrimental to the learning process, GCL can help learners to construct schemas that are stored in long-term memory and can be retrieved later in learning or problem-solving. According to CLT, the aim of the instructional design is to reduce ICL and ECL, and to induce GCL while keeping the global cognitive load under the limit of learners' cognitive resources. The level of ICL for a learning task is determined by the level of element interactivity and learner's prior knowledge (Sweller, 2010). An element can be a concept or a procedure that needs to be learned. Low element interactivity materials allow individual elements to be learned with minimal reference to other elements and impose a low working memory load. The concept of element interactivity can be used to explain why some material is difficult to learn or understand (Sweller, 1994). Figure 1 illustrates the relationships among learning behaviors, log data, and students in a technology-based learning environment. …


FINGERPRINT CLASSIFICATION BASED ON NOVEL DIRECTIONAL FEATURES USING NEURAL NETWORK


title: Image preprocessing using quick color averaging approach for color machine vision (CMV) systems

abstract: Image preprocessing is a preceding technique that an image undergoes before being subject to a main process. This is necessary since most unprocessed images are hard and sometimes impractical to manipulate. A color machine vision (CMV) system is an application where colors of a certain image is used with vision techniques. Some CMV applications in the industry include inspection, sorting, indication, detection, and measuring. However, CMV has limitations like proper lighting, camera calibration, etc. Due to its limitations, preprocessing techniques are essential to its success. With that, this paper proposed the application of quick color averaging as an image preprocessing technique for CMV applications. Color averaging is the process of changing the color values of the pixels of an image contour to the mean color value of all the pixels in the contour. From the results, this study concluded that color averaging using vision techniques is viable for image preprocessing in machine vision. Recommendations for future studies that may improve the color averaging process were also suggested.


title: Image quality measures for fingerprint image enhancement

abstract: Fingerprint image quality is an important factor in the performance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerprint image quality measurement. We propose limited ring-wedge spectral measure to estimate the global fingerprint image features, and inhomogeneity with directional contrast to estimate local fingerprint image features. Experimental results demonstrate the effectiveness of our proposal.


Hand function--a practical method of assessment.


title: Don’t Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML

abstract: A basic step for each data-mining or machine learning task is to determine which model to choose based on the problem and the data at hand. In this paper we investigate when non-linear classifiers outperform linear classifiers by means of a large scale experiment. We benchmark linear and non-linear versions of three types of classifiers (support vector machines; neural networks; and decision trees), and analyze the results to determine on what type of datasets the non-linear version performs better. To the best of our knowledge, this work is the first principled and large scale attempt to support the common assumption that non-linear classifiers excel only when large amounts of data are available.


title: Prediction of subscriber churn using social network analysis

abstract: In today's world, mobile phone penetration has reached a saturation point. As a result, subscriber churn has become an important issue for mobile operators as subscribers switch operators for a variety of reasons. Mobile operators typically employ churn prediction algorithms based on service usage metrics, network performance indicators, and traditional demographic information. A newly emerging technique is the use of social network analysis (SNA) to identify potential churners. Intuitively, a subscriber who is churning will have an impact on the churn propensity of his social circle. Call detail records are useful to understand the social connectivity of subscribers through call graphs but do not directly provide the strength of their relationship or have enough information to determine the diffusion of churn influence. In this paper, we present a way to address these challenges by developing a new churn prediction algorithm based on a social network analysis of the call graph. We provide a formulation that quantifies the strength of social ties between users based on multiple attributes and then apply an influence diffusion model over the call graph to determine the net accumulated influence from churners. We combine this influence and other social factors with more traditional metrics and apply machine-learning methods to compute the propensity to churn for individual users. We evaluate the performance of our algorithm over a real data set and quantify the benefit of using SNA in churn prediction.


title: Optimization of nested SQL queries revisited

abstract: Current methods of evaluating nested queries in the SQL language can be inefficient in a variety of query and data base contexts. Previous research in the area of nested query optimization which sought methods of reducing evaluation costs is summarized, including a classification scheme for nested queries, algorithms designed to transform each type of query to a logically equivalent form which may then be evaluated more efficiently, and a description of a major bug in one of these algorithms. Further examination reveals another bug in the same algorithm. Solutions to these bugs are proposed and incorporated into a new transformation algorithm, and extensions are proposed which will allow the transformation algorithms to handle a larger class of predicates. A recursive algorithm for processing a general nested query is presented and the action of this algorithm is demonstrated. This algorithm can be used to transform any nested query.


Leveraging Preposition Ambiguity to Assess Representation of Semantic Interaction in CDSM


title: EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

abstract: Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.


title: Implementing an untrusted operating system on trusted hardware

abstract: Recently, there has been considerable interest in providing "trusted computing platforms" using hardware~---~TCPA and Palladium being the most publicly visible examples. In this paper we discuss our experience with building such a platform using a traditional time-sharing operating system executing on XOM~---~a processor architecture that provides copy protection and tamper-resistance functions. In XOM, only the processor is trusted; main memory and the operating system are not trusted.Our operating system (XOMOS) manages hardware resources for applications that don't trust it. This requires a division of responsibilities between the operating system and hardware that is unlike previous systems. We describe techniques for providing traditional operating systems services in this context.Since an implementation of a XOM processor does not exist, we use SimOS to simulate the hardware. We modify IRIX 6.5, a commercially available operating system to create xomos. We are then able to analyze the performance and implementation overheads of running an untrusted operating system on trusted hardware.


title: A Theoretical Analysis of 2D Sensor Arrays for TDOA Based Localization

abstract: In source localization from time difference of arrival, the impact of the sensor array geometry to the localization accuracy is not well understood yet. A first rigorous analysis can be found in B. Yang and J. Scheuing (2005). It derived sufficient and necessary conditions for optimum array geometry in terms of minimum Cramer-Rao bound. This paper continues the above work and studies theoretically the localization accuracy of two-dimensional sensor arrays. It addresses different issues: a) optimum vs. uniform angular array b) near-field vs. far-field array c) using all sensor pairs vs. those with a common reference sensor as required from spherical position estimators. The paper ends up with some new insights into the sensor placement problem


abstract: A Near-Field Direct Antenna Modulation (NFDAM) transmitter at 2.4GHz is demonstrated on a microstrip setting. The transmitter is capable of transmitting information in a direction-dependent fashion by sending the correct signal constellation only in the desired direction while leaving the signal constellation points scrambled in undesired directions. This direction-dependent constellation scrambling nature of the NFDAM systems prevents undesired receivers to correctly demodulate the signal transmitted to a desired receiver at a specific direction.


title: When fingers do the talking: a study of text messaging

abstract: SMS or text messaging is an area of growth in the communications field. The studies described below consisted of a questionnaire and a diary study. The questionnaire was designed to examine texting activities in 565 users of the mobile phone. The diary study was carried out by 24 subjects over a period of 2 weeks. The findings suggest that text messaging is being used by a wide range of people for all kinds of activities and that for some people it is the preferred means of communication. These studies should prove interesting for those examining the use and impact of SMS.


title: Maximizing text-mining performance

abstract: The authors' adaptive resampling approach surpasses previous decision-tree performance and validates the effectiveness of small, pooled local dictionaries. They demonstrate their approach using the Reuters-21578 benchmark data and a real-world customer E-mail routing system.


title: An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

abstract: Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.


title: A 0.7V Fully-on-Chip Pseudo-Digital LDO Regulator with 6.3μA Quiescent Current and 100mV Dropout Voltage in 0.18-μm CMOS

abstract: This paper presents an NMOS pseudo-digital low-dropout (PD-LDO) regulator that supports low-voltage operation by eliminating the amplifier of an analog LDO. The proposed pseudo-digital control loop consists of a latched comparator, a 2X charge pump and a RC network. It detects the output voltage and provides a continuous gate control signal for the power transistor by charging and discharging the RC network. Fast transient response is achieved due to the source follower structure of the power NMOS, with a small output capacitor and small occupied chip area and without consuming large quiescent current. The proof-of-concept design of the proposed PD-LDO is implemented in a 0.18-J.1m CMOS process. The minimum supply voltage is 0.7 V, with a dropout voltage of 100 mV and a maximum load current of 100 mA. Using only 20 pF of on-chip output capacitor and 10 MHz comparator clock frequency, the undershoot is 106 mV with 90 mA load current step and 150 ns edge time. The quiescent current is only 6.3 μA and the active chip area is 0.08 mm2.


title: Latency-driven design for FPGA-based convolutional neural networks

abstract: In recent years, Convolutional Neural Networks (ConvNets) have become the quintessential component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of applications, the performance needs vary significantly, from high-throughput image recognition to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be optimally configured based on different performance requirements. However, with the increasing complexity of ConvNet models, the architectural design space becomes overwhelmingly large, asking for principled design flows that address the application-level needs. This paper presents a latency-driven design methodology for mapping ConvNets on FPGAs. The proposed design flow employs novel transformations over a Synchronous Dataflow-based modelling framework together with a latency-centric optimisation procedure in order to efficiently explore the design space targeting low-latency designs. Quantitative evaluation shows large improvements in latency when latency-driven optimisation is in place yielding designs that improve the latency of AlexNet by 73.54× and VGG16 by 5.61× over throughput-optimised designs.


title: Optimal Design Strategy for Improved Operation of IPM BLDC Motors With Low-Resolution Hall Sensors

abstract: This study proposes an interior permanent magnet (IPM) brushless dc (BLDC) motor design strategy that utilizes BLDC control based on Hall sensor signals. The magnetic flux of IPM motors varies according to the rotor position and abnormal Hall sensor problems are related to magnetic flux. To find the cause of the abnormality in the Hall sensors, an analysis of the magnetic flux density at the Hall sensor position by finite element analysis is conducted. In addition, an IPM model with a notch structure is proposed to solve abnormal Hall sensor problems and its magnetic equivalent circuit (MEC) model is derived. Based on the MEC model, an optimal rotor design method is proposed and the final model is derived. However, the Hall sensor signal achieved from the optimal rotor is not perfect. To improve the accuracy of the BLDC motor control, a rotor position estimation method is proposed. Finally, experiments are performed to evaluate the performance of the proposed IPM-type BLDC motor and the Hall sensor compensation method.


title: A MODEL OF USER ADOPTION OF MOBILE PORTALS

abstract: The purpose of the study is to present a conceptual model of user adoption of mobile portals. This model identifies factors that may potentially influence an individual’s decision whether to start or continue utilizing wireless portals. The major distinction of the proposed model from those of prior MIS technology adoption projects is that it includes not only widely employed MIS constructs but also the perceived value construct of a mobile portal. This construct is adapted from the marketing literature. It reflects the perceived level of a wireless service quality relative to the airtime cost. The rationale for the introduction of perceived value in terms of an individual’s direct financial expenses lies in the unique nature of mobile communication device usage. The proposed model also identifies two individual-specific antecedents and five portal-specific antecedents of those key constructs because they may potentially explicate the variance of users’ perceptions of portal experiences. In addition, the paper presents a survey of real-life users of mobile portals, designs a questionnaire, and selects appropriate data-analysis techniques. CHAPTER 4


Skull Stripping Magnetic Resonance Images Brain Images : Region Growing versus Mathematical Morphology


title: Modified S-Band Satellite Antenna With Isoflux Pattern and Circularly Polarized Wide Beamwidth

abstract: A modified isoflux pattern antenna suitable for S-band satellite communication is introduced and investigated with respect to its beam pattern and axial ratios. For uniform coverage over the Earth's surface, an isoflux pattern antenna is required because of its uniform power density over a wide coverage area. A final optimization for the parameter values is carried out from the parametric studies of the antenna, and a simple analysis of the combination of horizontal dipoles and vertically attached top-hat elements is also carried out. The electrical performances of the proposed antenna are verified in terms of the reflection coefficients, radiation pattern, and axial ratios according to a wide beamwidth rather than at a fixed observation angle such as a 0° main beam direction.


METABOLIC RATE AND TEMPERATURE CONTROL IN THE HUMAN BABY


Performance Evaluation of Convolutional Coding for the Reduction of AWGN in a Communication Channel for Digital Video Broadcasting


title: Decoding Actions at Different Levels of Abstraction

abstract: Brain regions that mediate action understanding must contain representations that are action specific and at the same time tolerate a wide range of perceptual variance. Whereas progress has been made in understanding such generalization mechanisms in the object domain, the neural mechanisms to conceptualize actions remain unknown. In particular, there is ongoing dissent between motor-centric and cognitive accounts whether premotor cortex or brain regions in closer relation to perceptual systems, i.e., lateral occipitotemporal cortex, contain neural populations with such mapping properties. To date, it is unclear to which degree action-specific representations in these brain regions generalize from concrete action instantiations to abstract action concepts. However, such information would be crucial to differentiate between motor and cognitive theories. Using ROI-based and searchlight-based fMRI multivoxel pattern decoding, we sought brain regions in human cortex that manage the balancing act between specificity and generality. We investigated a concrete level that distinguishes actions based on perceptual features (e.g., opening vs closing a specific bottle), an intermediate level that generalizes across movement kinematics and specific objects involved in the action (e.g., opening different bottles with cork or screw cap), and an abstract level that additionally generalizes across object category (e.g., opening bottles or boxes). We demonstrate that the inferior parietal and occipitotemporal cortex code actions at abstract levels whereas the premotor cortex codes actions at the concrete level only. Hence, occipitotemporal, but not premotor, regions fulfill the necessary criteria for action understanding. This result is compatible with cognitive theories but strongly undermines motor theories of action understanding.


title: Vertebral Fractures Predict Subsequent Fractures

abstract: This population-based study documents an increase in most types of fractures following the occurrence of a clinically recognized vertebral fracture among 820 Rochester, Minnesota, residents. During 4349 person-years of follow-up, 896 new fractures were observed. Relative to incidence rates in the community, there was a 2.8-fold increase in the risk of any fracture, which was greater in men (standardized incidence ratio (SIR), 4.2; 95% CI, 3.2–5.3) than women (SIR, 2.7; 95% CI, 2.4–3.0). The estimated cumulative incidence of any fracture after 10 years was 70%. The greatest increase in risk was for subsequent fractures of the axial skeleton, in particular a 12.6-fold increase (95% CI, 11–14) in additional vertebral fractures. There was a lesser increase in most limb fractures, including a 2.3-fold increase (95% CI, 1.8–2.9) in hip fractures and a 1.6-fold increase (95% CI, 1.01–2.4) in distal forearm fractures. There was a slightly greater association with distal forearm fractures among those whose first vertebral fracture occurred before age 70 years but a similar relationship with hip fractures, including cervical and intertrochanteric hip fractures separately, regardless of age at the initial vertebral fracture. There was also an equivalent increase in subsequent fracture risk whether the initial vertebral fracture was attributed to severe or moderate trauma. These data show that vertebral fractures represent an important risk factor for fractures in general, not just those of the spine and hip.


title: Frequency-Modulated Lorentz Force Magnetometer With Enhanced Sensitivity via Mechanical Amplification

abstract: This letter presents a micromachined silicon Lorentz force magnetometer, which consists of a flexural beam resonator coupled to current-carrying silicon beams via a microleverage mechanism. The flexural beam resonator is a force sensor, which measures the magnetic field through resonant frequency shift induced by the Lorentz force, which acts as an axial load. Previous frequency-modulated Lorentz force magnetometers suffer from low sensitivity, limited by both fabrication restrictions and lack of a force amplification mechanism. In this letter, the microleverage mechanism amplifies the Lorentz force, thereby enhancing the sensitivity of the magnetometer by a factor of 42. The device has a measured sensitivity of 6687 ppm/(mA $\cdot $ T), which is two orders of magnitude larger than the prior state-of-the-art. The measured results agree with an analytical model and finite-element analysis. The frequency stability of the sensor is limited by the quality factor ( ${Q}$ ) of 540, which can be increased through improved vacuum packaging.


title: Fully Inkjet Printed RF Inductors and Capacitors Using Polymer Dielectric and Silver Conductive Ink With Through Vias

abstract: In this paper, fully inkjet printed multilayer capacitors and inductors are fabricated and characterized using poly 4-vinylphenol (PVP) ink as the dielectric layer and silver nanoparticle ink as the conductor. Inkjet printed through vias, created with a novel dissolving method are used to make RF structures in a multilayer inkjet printing process. The vias have been realized in a 350-nm PVP film and exhibit resistance better than 0.1 $\Omega $ . Spiral inductors from 10 to 75 nH have been realized with maximum quality factors around five. The 10-nH inductor exhibits a self-resonant frequency slightly below 1 GHz. Metal–insulator–metal capacitors are realized with densities of 50 pF/mm $^{2}$ . These capacitors demonstrate values ranging from 16 to 50 pF. The 16-pF capacitor shows a self-resonant frequency over 1.5 GHz. The successful implementation of inductors and capacitors in an all inkjet printed multilayer process with vias is an important step toward fully inkjet-printed large area and flexible RF systems.


Access Pattern disclosure on Searchable Encryption: Ramification, Attack and Mitigation.


title: Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

abstract: The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.


Game-Based Learning: Latest Evidence and Future Directions


title: Harmonic analysis of AC-DC hybrid microgrid based on ETAP

abstract: Under the background of the energy crisis and environmental pollution, the AC-DC hybrid microgrid, which have great ability to accept distributed renewable energy, less converter loss as well as better economy and reliability, become the development trend of microgrid gradually. Due to the nonlinear devices, the growing harmonic pollution in the microgrid is the problem we must face and solve. On the basis of knowledge of the AC-DC hybrid microgrid power quality, this paper build a simulation model of AC-DC hybrid microgrid to analyzing the characteristics of harmonic with the help of Electrical Transient Analyzer Program(ETAP). The above work attempt to explore the generation and distribution of harmonics, in order to providing guidance on construction of microgrid and elimination of harmonics.


R EASONING OVER RDF K NOWLEDGE B ASES USING D EEP L EARNING


title: TR 02-014 Comparison of Agglomerative and Partitional Document Clustering Algorithms

abstract: Abstract : Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters, and in greatly improving the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of document clustering and the expanded range of its applications led to the development of a number of novel algorithms and new clustering criterion functions, especially in the context of partitional clustering. The focus of this paper is to experimentally evaluate the performance of seven different global criterion functions in the context of agglomerative clustering algorithms and compare the clustering results of agglomerative algorithms and partitional algorithms for each one of the criterion functions. Our experimental evaluation shows that for every criterion function, partitional algorithms always lead to better clustering results than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance.


title: A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours

abstract: Selfies have become increasingly fashionable in the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention, especially psychologists. In computer vision and machine learning areas, little attention has been paid to this phenomenon as a valuable data source. In this paper, we focus on exploring the deeper personal patterns behind people's different kinds of selfie-posting behaviours. We develop this work based on a dataset of WeChat, one of the most extensively used instant messaging platform in China. In particular, we first propose an unsupervised approach to classify the images posted by users. Based on the classification result, we construct three types of user-level features that reflect user preference, activity and posting habit. Based on these features, for a series of selfie related tasks, we build classifiers that can accurately predict two sets of users with opposite selfie-posting behaviours. We have found that people's interest, activity and posting habit have a great influence on their selfie-posting behaviours. Taking selfie frequency as an example, the classification accuracy between selfie-posting addict and nonaddict can reach 89.36%. We also prove that using user's image information to predict these behaviours achieve better performance than using text information. More importantly, for each set of users with a specific selfie-posting behaviour, we extract and visualize significant personal patterns about them. In addition, to concisely construct the relation between personal pattern and selfie-posting behaviour, we cluster users and extract their high-level attributes, revealing the correlation between these attributes and users' selfie-posting behaviours. In the end, we demonstrate that users' selfie-posting behaviour, as a good predictor, could predict their different preferences toward these high-level attributes accurately.


title: DeepFM: An End-to-End Wide&Deep Learning Framework for CTR Prediction

abstract: Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias towards low- or high-order interactions, or rely on expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed framework, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared raw feature input to both its "wide" and "deep" components, with no need of feature engineering besides raw features. DeepFM, as a general learning framework, can incorporate various network architectures in its deep component. In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data. We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D leads to more than 10% improvement of click-through rate in the production environment, compared to a well-engineered LR model. We also covered related practice in deploying our framework in Huawei App Market.


title: Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization

abstract: Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm relies on solving only a small number of instances of a discrete combinatorial optimization problem subject to randomly generated parity constraints used as a hash function. As an application, we demonstrate that with a small number of MAP queries we can efficiently approximate the partition function of discrete graphical models, which can in turn be used, for instance, for marginal computation or model selection.


title: 3D Deformable Face Tracking with a Commodity Depth Camera

abstract: Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor's accuracy. Furthermore, an l1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information.


title: Light management for photovoltaics using high-index nanostructures

abstract: Strategies to increase light-trapping in solar cells can significantly improve the power-conversion efficiency of these devices. This Review discusses the use of nanostructured high-index layers to manipulate photons in thin-film solar cells, as well as the recent efforts aimed at integrating such layers in large-area devices.


Breaking Down Barriers to Sustainable Eating at the University of Dayton


title: Detecting wormhole attacks in delay-tolerant networks [Security and Privacy in Emerging Wireless Networks]

abstract: Delay-tolerant networks are especially useful in providing mission-critical services including emergency scenarios and battlefield applications. However, DTNs are vulnerable to wormhole attacks, in which a malicious node records the packets at one location and tunnels them to another colluding node, which relays them locally into the network. Wormhole attacks are a severe threat to normal network operation in DTNs. In this article we describe various methods that have been developed to detect wormhole attacks. However, most of them cannot work efficiently in DTNs. To detect the presence of a wormhole attack, we propose a detection mechanism that exploits the existence of a forbidden topology in the network. We evaluated our approach through extensive simulations using both Random Way Point and Zebranet mobility models. Our results show that the proposed method can detect wormhole attacks efficiently and effectively in DTNs.


title: A Theoretical Analysis of Feature Pooling in Visual Recognition

abstract: Many modern visual recognition algorithms incorporate a step of spatial 'pooling', where the outputs of several nearby feature detectors are combined into a local or global 'bag of features', in a way that preserves task-related information while removing irrelevant details. Pooling is used to achieve invariance to image transformations, more compact representations, and better robustness to noise and clutter. Several papers have shown that the details of the pooling operation can greatly influence the performance, but studies have so far been purely empirical. In this paper, we show that the reasons underlying the performance of various pooling methods are obscured by several confounding factors, such as the link between the sample cardinality in a spatial pool and the resolution at which low-level features have been extracted. We provide a detailed theoretical analysis of max pooling and average pooling, and give extensive empirical comparisons for object recognition tasks.


title: The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception

abstract: Using functional magnetic resonance imaging (fMRI), we found an area in the fusiform gyrus in 12 of the 15 subjects tested that was significantly more active when the subjects viewed faces than when they viewed assorted common objects. This face activation was used to define a specific region of interest individually for each subject, within which several new tests of face specificity were run. In each of five subjects tested, the predefined candidate “face area” also responded significantly more strongly to passive viewing of (1) intact than scrambled two-tone faces, (2) full front-view face photos than front-view photos of houses, and (in a different set of five subjects) (3) three-quarter-view face photos (with hair concealed) than photos of human hands; it also responded more strongly during (4) a consecutive matching task performed on three-quarter-view faces versus hands. Our technique of running multiple tests applied to the same region defined functionally within individual subjects provides a solution to two common problems in functional imaging: (1) the requirement to correct for multiple statistical comparisons and (2) the inevitable ambiguity in the interpretation of any study in which only two or three conditions are compared. Our data allow us to reject alternative accounts of the function of the fusiform face area (area “FF”) that appeal to visual attention, subordinate-level classification, or general processing of any animate or human forms, demonstrating that this region is selectively involved in the perception of faces.


title: Similarity Measure Selection for Clustering Time Series Databases

abstract: In the past few years, clustering has become a popular task associated with time series. The choice of a suitable distance measure is crucial to the clustering process and, given the vast number of distance measures for time series available in the literature and their diverse characteristics, this selection is not straightforward. With the objective of simplifying this task, we propose a multi-label classification framework that provides the means to automatically select the most suitable distance measures for clustering a time series database. This classifier is based on a novel collection of characteristics that describe the main features of the time series databases and provide the predictive information necessary to discriminate between a set of distance measures. In order to test the validity of this classifier, we conduct a complete set of experiments using both synthetic and real time series databases and a set of five common distance measures. The positive results obtained by the designed classification framework for various performance measures indicate that the proposed methodology is useful to simplify the process of distance selection in time series clustering tasks.


title: Bilingual experience and executive functioning in young children

abstract: Advanced inhibitory control skills have been found in bilingual speakers as compared to monolingual controls (Bialystok, 1999). We examined whether this effect is generalized to an unstudied language group (Spanish-English bilingual) and multiple measures of executive function by administering a battery of tasks to 50 kindergarten children drawn from three language groups: native bilinguals, monolinguals (English), and English speakers enrolled in second-language immersion kindergarten. Despite having significantly lower verbal scores and parent education/income level, Spanish-English bilingual children’s raw scores did not differ from their peers. After statistically controlling for these factors and age, native bilingual children performed significantly better on the executive function battery than both other groups. Importantly, the relative advantage was significant for tasks that appear to call for managing conflicting attentional demands (Conflict tasks); there was no advantage on impulse-control (Delay tasks). These results advance our understanding of both the generalizability and specificity of the compensatory effects of bilingual experience for children’s cognitive development.


title: Perisylvian language networks of the human brain

abstract: Early anatomically based models of language consisted of an arcuate tract connecting Broca's speech and Wernicke's comprehension centers; a lesion of the tract resulted in conduction aphasia. However, the heterogeneous clinical presentations of conduction aphasia suggest a greater complexity of perisylvian anatomical connections than allowed for in the classical anatomical model. This article re-explores perisylvian language connectivity using in vivo diffusion tensor magnetic resonance imaging tractography. Diffusion tensor magnetic resonance imaging data from 11 right-handed healthy male subjects were averaged, and the arcuate fasciculus of the left hemisphere reconstructed from this data using an interactive dissection technique. Beyond the classical arcuate pathway connecting Broca's and Wernicke's areas directly, we show a previously undescribed, indirect pathway passing through inferior parietal cortex. The indirect pathway runs parallel and lateral to the classical arcuate fasciculus and is composed of an anterior segment connecting Broca's territory with the inferior parietal lobe and a posterior segment connecting the inferior parietal lobe to Wernicke's territory. This model of two parallel pathways helps explain the diverse clinical presentations of conduction aphasia. The anatomical findings are also relevant to the evolution of language, provide a framework for Lichtheim's symptom-based neurological model of aphasia, and constrain, anatomically, contemporary connectionist accounts of language. Ann Neurol 2005


Transformation invariance in pattern recognition: Tangent distance and propagation


title: Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models

abstract: The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A con-volutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances. Then it can flexibly generate new intent embeddings without the need of training samples and model-retraining, which bridges the semantic relation between seen and unseen intents and further performs more robust results. Experiments show that CDSSM is capable of performing zero-shot learning effectively, e.g. generating embeddings of previously unseen intents, and therefore expand to new intents without re-training, and outperforms other semantic embeddings. The discussion and analysis of experiments provide a future direction for reducing human effort about annotating data and removing the domain constraint in spoken dialogue systems.


title: Open BMS - IoT driven architecture for the internet of buildings

abstract: This paper describes the creation of an IoT driven architecture to support the realization of an OpenBMS approach to managing blocks of buildings. The objective is to overcome the complexities of integration, operation and management of heterogeneous building systems by leveraging existing IoT approaches. The goal is to eliminate vertical data silos and enable the holistic management of energy across existing and new building blocks.


title: Semi-Autonomous Data Enrichment and Optimisation for Intelligent Speech Analysis

abstract: To address the challenges surrounding data quantity and quality in Intelligent Speech Analysis, this thesis proposes and analyses semi-autonomous data enrichment and optimisation approaches. Particularly, both labelled and unlabelled data from heterogeneous resources are exploited; Split Vector Quantisation is employed for feature compression; and Long Short-Term Memory recurrent neural networks is evaluated to mitigate reverberation. With these approaches, better performance is delivered.


title: Structural Health Monitoring of Railroad Wheels Using Wheel Impact Load Detectors

abstract: This paper proposes two quantitative criteria for removing railroad wheels from service, based on real-time structural health monitoring trends that are developed using data collected from trains while in service. The data is collected using wheel impact load detectors (WILDs). These impact load trends are able to distinguish wheels with a high probability of failure from high-impact wheels with a low probability of failure. The trends indicate the critical wheels that actually need to be removed, while at the same time allowing wheels that aren’t critical to remain in service. As a result, the safety of the railroad will be much improved by being able to identify and remove wheels that have high likelihood of causing catastrophic failures.


title: Neuromorphic architectures for spiking deep neural networks

abstract: We present a full custom hardware implementation of a deep neural network, built using multiple neuromorphic VLSI devices that integrate analog neuron and synapse circuits together with digital asynchronous logic circuits. The deep network comprises an event-based convolutional stage for feature extraction connected to a spike-based learning stage for feature classification. We describe the properties of the chips used to implement the network and present preliminary experimental results that validate the approach proposed.


title: Successful enterprise resource planning implementation: taxonomy of critical factors

abstract: Purpose – The purpose of this paper is to investigate the current literature base of critical success factors (CSFs) of enterprise resource planning (ERP) implementations, provide a systematic compilation of CSFs, and present a new comprehensive taxonomy of CSFs for ERP system implementation.Design/methodology/approach – This paper compiles literature that highlighted possible references to CSFs for ERP implementation projects. Given that the purpose of this paper is to achieve a depth of understanding of the various CSFs already identified by other researchers, “content analysis” is used. Four stages of content analysis are adopted to collect and analyse the literature, i.e. data collection, open coding, axial coding, and selective coding.Findings – By analyzing all CFSs mentioned in literature during the last ten years (1999‐2008), taxonomy of ERP CSFs implementation was formulated. In total 17 CSFs were identified, which is then categorized into five main categories.Research limitations/implications – ...


title: Introduction to Persistent Homology

abstract: This video presents an introduction to persistent homology, aimed at a viewer who has mathematical aptitude but not necessarily knowledge of algebraic topology. Persistent homology is an algebraic method of discerning the topological features of complex data, which in recent years has found applications in fields such as image processing and biological systems. Using smooth animations, the video conveys the intuition behind persistent homology, while giving a taste of its key properties, applications, and mathematical underpinnings.


title: A microeconomic approach to optimal resource allocation in distributed computer systems

abstract: Decentralized algorithms are examined for optimally distributing a divisible resource in a distributed computer system. To study this problem in a specific context, the problem of optimal file allocation is considered. In this case, the optimization criteria include both the communication cost and average processing delay associated with a file access. The algorithms examined have their origins in the field of mathematical economics. They are shown to have several attractive properties, including their simplicity and distributed nature, the computation of feasible and increasingly better resource allocations as the result of each iteration, and, in the case of file allocation, rapid convergence. Conditions are formally derived under which the algorithms are guaranteed to coverage, and their convergence behavior is additionally examined through simulation. >


title: JSNOSE: Detecting JavaScript Code Smells

abstract: JavaScript is a powerful and flexible prototype-based scripting language that is increasingly used by developers to create interactive web applications. The language is interpreted, dynamic, weakly-typed, and has first-class functions. In addition, it interacts with other web languages such as CSS and HTML at runtime. All these characteristics make JavaScript code particularly error-prone and challenging to write and maintain. Code smells are patterns in the source code that can adversely influence program comprehension and maintainability of the program in the long term. We propose a set of 13 JavaScript code smells, collected from various developer resources. We present a JavaScript code smell detection technique called JSNOSE. Our metric-based approach combines static and dynamic analysis to detect smells in client-side code. This automated technique can help developers to spot code that could benefit from refactoring. We evaluate the smell finding capabilities of our technique through an empirical study. By analyzing 11 web applications, we investigate which smells detected by JSNOSE are more prevalent.


title: Productivity effects of environmental performance: evidence from TFP analysis on marketing cooperatives

abstract: The object of the present article is to offer a decomposition of productivity indices incorporating environmental changes. The model followed is an input oriented approach related to undesirable output (environmental performance attribute) in horticultural marketing cooperatives. This study is motivated by both the lack of analysis of environmental performance's effects on agribusiness productivity and the relevance of cooperative firms in the European agricultural model for attaining sustainability. For this purpose, bootstrapped envelopment analysis is applied and the best practice frontier obtained corresponds to decision making units showing the best environmental behaviour. The Malmquist total factor productivity (TFP) is decomposed into technological change, efficiency and environmental change. In the second stage, the correlations of these changes with other management variables of cooperatives are analysed. The indicators obtained, on one hand, advice about the nonseparable distance function estimations when the environmental factor is introduced. On the other hand, the results display a relevant increase in efficiency and environmental components for the period under study showing a high influence from labour qualification, environmental effort and spillover effect in the sector.


title: Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles

abstract: The performance of a vehicle control strategy, in terms of fuel economy improvement and emission reduction, is strongly influenced by driving conditions and drivers' driving styles. The term of ‘driving conditions’ here means the traffic conditions and road type, which is usually indicated by standard driving cycles, say FTP 75 and NEDC; the term of ‘driving styles’ here relates to the drivers' behavior, especially how drivers apply pressure on acceleration and brake pedal. To realize optimal fuel economy, it is ideal to obtain the information of future driving conditions and drivers' driving styles. This paper summarizes the methods and parameters that have been utilized to attain this end as well as the results. Based on this study, methods and parameters can be better selected for further improvement of driving conditions prediction and driving style recognition based hybrid electric vehicle control strategy.


Loss Distribution Evaluation for Synthetic CDOs ∗


Ultrasensitive detection of TCR hypervariable-region sequences in solid-tissue RNA–seq data


title: An Asymmetric 2.4 GHz Directional Coupler Using Electrical Balance

abstract: The concept of electrical balance is used to achieve wideband isolation in an asymmetric 2.4 GHz directional coupler. An intuitive approach using impedance transformation property of transmission lines is used to develop the theory for designing arbitrary-coupling-ratio directional couplers. A coupler based on this theory is designed and fabricated on a two-layered RO4003C PCB. The coupler achieves a return loss of better than 10 dB over a 1.7-2.7 GHz range. The transmission coefficients of the strongly and weakly coupled ports are around −3.5 dB and −5 dB respectively, at 2.4 GHz. The isolation is better than 25 dB over a 1.4-3 GHz range.


title: A MINIATURIZED PRINTED DIPOLE ANTENNA WITH V-SHAPED GROUND FOR 2.45 GHZ RFID READERS

abstract: In this paper, a miniaturized printed dipole antenna with the V-shaped ground is proposed for radio frequency identification (RFID) readers operating at the frequency of 2.45 GHz. The principles of the microstrip balun and the printed dipole are analyzed and design considerations are formulated. Through extending and shaping the ground to reduce the coupling between the balun and the dipole, the antenna’s impedance bandwidth is broadened and the antenna’s radiation pattern is improved. The 3D finite difference time domain (FDTD) Electromagnetic simulations are carried out to evaluate the antenna’s performance. The effects of the extending angle and the position of the ground are investigated to obtain the optimized parameters. The antenna was fabricated and measured in a microwave anechoic chamber. The results show that the proposed antenna achieves a broader impedance bandwidth, a higher forward radiation gain and a stronger suppression to backward radiation compared with the one without such a ground.


title: Adaptive shadow maps

abstract: Shadow maps provide a fast and convenient method of identifying shadows in scenes but can introduce aliasing. This paper introduces the Adaptive Shadow Map (ASM) as a solution to this problem. An ASM removes aliasing by resolving pixel size mismatches between the eye view and the light source view. It achieves this goal by storing the light source view (i.e., the shadow map for the light source) as a hierarchical grid structure as opposed to the conventional flat structure. As pixels are transformed from the eye view to the light source view, the ASM is refined to create higher-resolution pieces of the shadow map when needed. This is done by evaluating the contributions of shadow map pixels to the overall image quality. The improvement process is view-driven, progressive, and confined to a user-specifiable memory footprint. We show that ASMs enable dramatic improvements in shadow quality while maintaining interactive rates.


title: A Universal UHF RFID Reader IC in 0.18-µm CMOS Technology

abstract: A highly integrated UHF RFID reader IC in 0.18-m CMOS process covering the entire 860 MHz to 960 MHz RFID band supporting the EPCglobaltrade Class-1 Generation-2 and ISO-18000-6A/B/C standards is presented. The IC features a transmitter with an output of 10 dBm and a receiver with sensitivity of 96 dBm in listen-before-talk mode (LBT) and 85 dBm in talk-mode. Direct-conversion architecture is used for the receiver for a high level of integration and low power consumption. On-chip dual-loop synthesizer generates high-purity LO signal with frequency resolution of 50 kHz and phase noise of 101 dBc/Hz at 100 kHz offset over the entire 860 to 960 MHz band. The IC integrates 10-bit DACs, pulse-shaping filters, an IQ modulator and a power amplifier in the transmit chain and a low-noise amplifier (LNA), an IQ downconverter, channel-select filters, variable-gain amplifiers and 10-bit ADCs in the receive chain. On-chip ASK demodulator provides demodulated I and Q raw data outputs. The chip has a die area of 6 mm times 6 mm. It operates over a wide range of voltage and temperature, from 1.6 V to 2.0 V and from C to C and consumes 540 mW from a 1.8 V supply at C.


title: Newborn Respiratory Distress.

abstract: Newborn respiratory distress presents a diagnostic and management challenge. Newborns with respiratory distress commonly exhibit tachypnea with a respiratory rate of more than 60 respirations per minute. They may present with grunting, retractions, nasal flaring, and cyanosis. Common causes include transient tachypnea of the newborn, respiratory distress syndrome, meconium aspiration syndrome, pneumonia, sepsis, pneumothorax, persistent pulmonary hypertension of the newborn, and delayed transition. Congenital heart defects, airway malformations, and inborn errors of metabolism are less common etiologies. Clinicians should be familiar with updated neonatal resuscitation guidelines. Initial evaluation includes a detailed history and physical examination. The clinician should monitor vital signs and measure oxygen saturation with pulse oximetry, and blood gas measurement may be considered. Chest radiography is helpful in the diagnosis. Blood cultures, serial complete blood counts, and C-reactive protein measurement are useful for the evaluation of sepsis. Most neonates with respiratory distress can be treated with respiratory support and noninvasive methods. Oxygen can be provided via bag/mask, nasal cannula, oxygen hood, and nasal continuous positive airway pressure. Ventilator support may be used in more severe cases. Surfactant is increasingly used for respiratory distress syndrome. Using the INSURE technique, the newborn is intubated, given surfactant, and quickly extubated to nasal continuous positive airway pressure. Newborns should be screened for critical congenital heart defects via pulse oximetry after 24 hours but before hospital discharge. Neonatology consultation is recommended if the illness exceeds the clinician's expertise and comfort level or when the diagnosis is unclear in a critically ill newborn.


title: Seed-Based Event Trigger Labeling: How far can event descriptions get us?

abstract: The task of event trigger labeling is typically addressed in the standard supervised setting: triggers for each target event type are annotated as training data, based on annotation guidelines. We propose an alternative approach, which takes the example trigger terms mentioned in the guidelines as seeds, and then applies an eventindependent similarity-based classifier for trigger labeling. This way we can skip manual annotation for new event types, while requiring only minimal annotated training data for few example events at system setup. Our method is evaluated on the ACE-2005 dataset, achieving 5.7% F1 improvement over a state-of-the-art supervised system which uses the full training data.


title: A hybrid license plate extraction method based on edge statistics and morphology

abstract: This work presents a hybrid license plate extraction algorithm based on the edge statistics and morphology for monitoring the highway ticketing systems. The method can improve the location rate only by the edge statistics. The proposed approach can be divided into four sections, which are, vertical edge detection, edge statistical analysis, hierarchical-based license plate location, and morphology-based license plate extraction. The algorithm can quickly and correctly detect the region of vehicle license plates. Under the experiment databases, which were taken from real scene, 9786 from 9825 images are successfully detected. The average accuracy of locating vehicle license plate is 99.6%.


title: Babinet Principle Applied to the Design of Metasurfaces and Metamaterials

abstract: The electromagnetic theory of diffraction and the Babinet principle are applied to the design of artificial metasurfaces and metamaterials. A new particle, the complementary split rings resonator, is proposed for the design of metasurfaces with high frequency selectivity and planar metamaterials with a negative dielectric permittivity. Applications in the fields of frequency selective surfaces and polarizers, as well as in microwave antennas and filter design, can be envisaged. The tunability of all these devices by an applied dc voltage is also achievable if these particles are etched on the appropriate substrate.


title: Kernel Implicit Variational Inference.

abstract: Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when applied to models with high-dimensional latent variables. In this paper, we present a new approach named Kernel Implicit Variational Inference that addresses these challenges. As far as we know, for the first time implicit variational inference is successfully applied to Bayesian neural networks, which shows promising results on both regression and classification tasks.


title: A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)

abstract: The alignment of heterogeneous sequential data (video to text) is an important and challenging problem. Standard techniques for this task, including Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from inherent drawbacks. Mainly, the Markov assumption implies that, given the immediate past, future alignment decisions are independent of further history. The separation between similarity computation and alignment decision also prevents end-to-end training. In this paper, we propose an end-to-end neural architecture where alignment actions are implemented as moving data between stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture supports a large variety of alignment tasks, including one-to-one, one-to-many, skipping unmatched elements, and (with extensions) non-monotonic alignment. Extensive experiments on semi-synthetic and real datasets show that our algorithm outperforms state-of-the-art baselines.


title: Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain

abstract: During rest, multiple cortical brain regions are functionally linked forming resting-state net- works. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffu- sion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, pri- mary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 30:3127-3141, 2009. V C 2009 Wiley-Liss, Inc.


title: Shakespeare's Queer Children: Sexual Politics and Contemporary Culture.

abstract: Introduction - "This island is mine" successors to his name - Shakespearean family romances wise children and foolish fathers - carnival in the family rewriting the narratives of shame - women's transformations in "The Tempest" "Strange worship" - Oscar Wilde and the key to Shekespearean sonnets "The past is our mirror" - Marlowe, Shakespeare, Jarman.


title: Collaborative transportation using MAVs via passive force control

abstract: This paper shows a strategy based on passive force control for collaborative object transportation using Micro Aerial Vehicles (MAVs), focusing on the transportation of a bulky object by two hexacopters. The goal is to develop a robust approach which does not rely on: (a) communication links between the MAVs, (b) the knowledge of the payload shape and (c) the position of grasping point. The proposed approach is based on the master-slave paradigm, in which the slave agent guarantees compliance to the external force applied by the master to the payload via an admittance controller. The external force acting on the slave is estimated using a non-linear estimator based on the Unscented Kalman Filter (UKF) from the information provided by a Visual-Inertial (VI) navigation system. Experimental results (online video [1]) demonstrate the performance of the force estimator and show the collaborative transportation of a 1.2 m long object.


Region Filling and Object Removal by Exemplar- Based Image Inpainting


title: Language Modeling by Clustering with Word Embeddings for Text Readability Assessment

abstract: We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that the features formed on our clustering-based language model significantly improve the previously known results for the same corpus in readability prediction. We also evaluate the task of sentence matching based on semantic relatedness using the Wiki-SimpleWiki corpus and find that our features lead to superior matching performance.


title: Cyanide detoxification by microbial consortia of natural-industrial complexes of gold heap leaching

abstract: Microorganisms that have adapted not only to high concentrations of pollutants but also to environmental conditions develop in autochthonous microbial communities of natural-industrial complexes of gold heap leaching. The biotechnological potential and diversity of autochthonous microbial communities involved in cyanide detoxification was evaluated by the example of a deposit situated in the Sakha (Yakutia) Republic. Under the zoning conditions of the ore heap, the biological component had a greater impact on cyanide destruction than chemical transformation. Metabolically active representatives of a microbial consortium are capable of surviving developed under these conditions. Phylotypes of the genus Serratia and family Alcaligenaceae that are capable of cyanide destruction and are potentially promising for the detoxification of wastes of gold heap leaching were revealed.


title: Scheduling I/O in virtual machine monitors

abstract: This paper explores the relationship between domain scheduling in avirtual machine monitor (VMM) and I/O performance. Traditionally, VMM schedulers have focused on fairly sharing the processor resources among domains while leaving the scheduling of I/O resources as asecondary concern. However, this can resultin poor and/or unpredictable application performance, making virtualization less desirable for applications that require efficient and consistent I/O behavior. This paper is the first to study the impact of the VMM scheduler on performance using multiple guest domains concurrently running different types of applications. In particular, different combinations of processor-intensive, bandwidth-intensive, andlatency-sensitive applications are run concurrently to quantify the impacts of different scheduler configurations on processor and I/O performance. These applications are evaluated on 11 different scheduler configurations within the Xen VMM. These configurations include a variety of scheduler extensions aimed at improving I/O performance. This cross product of scheduler configurations and application types offers insight into the key problems in VMM scheduling for I/O and motivates future innovation in this area.


title: Binary Optimized Hashing

abstract: This paper studies the problem of learning to hash, which is essentially a mixed integer optimization problem, containing both the binary hash code output and the (continuous) parameters forming the hash functions. Different from existing relaxation methods in hashing, which have no theoretical guarantees for the error bound of the relaxations, we propose binary optimized hashing (BOH), in which we prove that if the loss function is Lipschitz continuous, the binary optimization problem can be relaxed to a bound-constrained continuous optimization problem. Then we introduce a surrogate objective function, which only depends on unbinarized hash functions and does not need the slack variables transforming unbinarized hash functions to discrete functions, to approximate the relaxed objective function. We show that the approximation error is bounded and the bound is small when the problem is optimized. We apply the proposed approach to learn hash codes from either handcraft feature inputs or raw image inputs. Extensive experiments are carried out on three benchmarks, demonstrating that our approach outperforms state-of-the-arts with a significant margin on search accuracies.


SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation


title: Effects of Bonding Parameters on the Reliability of Fine-Pitch Cu/Ni/SnAg Micro-Bump Chip-to-Chip Interconnection for Three-Dimensional Chip Stacking

abstract: As the demands for portable electronic products increase, through-silicon-via (TSV)-based three-dimensional integrated-circuit (3-D IC) integration is becoming increasingly important. Micro-bump-bonded interconnection is one approach that has great potential to meet this requirement. In this paper, a 30-μm pitch chip-to-chip (C2C) interconnection with Cu/Ni/SnAg micro bumps was assembled using the gap-controllable thermal bonding method. The bonding parameters were evaluated by considering the variation in the contact resistance after bonding. The effects of the bonding time and temperature on the IMC thickness of the fabricated C2C interconnects are also investigated to determine the correlation between its thickness and reliability performance. The reliability of the C2C interconnects with the selected underfill was studied by performing a -55°C- 125°C temperature cycling test (TCT) for 2000 cycles and a 150°C high-temperature storage (HTS) test for 2000 h. The interfaces of the failed samples in the TCT and HTS tests are then inspected by scanning electron microscopy (SEM), which is utilized to obtain cross-sectional images. To validate the experimental results, finite-element (FE) analysis is also conducted to elucidate the interconnect reliability of the C2C interconnection. Results show that consistent bonding quality and stable contact resistance of the fine-pitch C2C interconnection with the micro bumps were achieved by giving the appropriate choice of the bonding parameters, and those bonded joints can thus serve as reliable interconnects for use in 3-D chip stacking.


title: Effect of heavy strength training on thigh muscle cross-sectional area, performance determinants, and performance in well-trained cyclists

abstract: The purpose of this study was to investigate the effect of heavy strength training on thigh muscle cross-sectional area (CSA), determinants of cycling performance, and cycling performance in well-trained cyclists. Twenty well-trained cyclists were assigned to either usual endurance training combined with heavy strength training [E + S; n = 11 (male symbol = 11)] or to usual endurance training only [E; n = 9 (male symbol = 7, female symbol = 2)]. The strength training performed by E + S consisted of four lower body exercises [3 x 4-10 repetition maximum (RM)], which were performed twice a week for 12 weeks. Thigh muscle CSA, maximal force in isometric half squat, power output in 30 s Wingate test, maximal oxygen consumption (VO(2max)), power output at 2 mmol l(-1) blood lactate concentration ([la(-)]), and performance, as mean power production, in a 40-min all-out trial were measured before and after the intervention. E + S increased thigh muscle CSA, maximal isometric force, and peak power in the Wingate test more than E. Power output at 2 mmol l(-1) [la(-)] and mean power output in the 40-min all-out trial were improved in E + S (P < 0.05). For E, only performance in the 40-min all-out trial tended to improve (P = 0.057). The two groups showed similar increases in VO(2max) (P < 0.05). In conclusion, adding strength training to usual endurance training improved determinants of cycling performance as well as performance in well-trained cyclists. Of particular note is that the added strength training increased thigh muscle CSA without causing an increase in body mass.


title: Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex

abstract: Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.


title: Heuristic regularization methods for numerical differentiation

abstract: In this paper, we use smoothing splines to deal with numerical differentiation. Some heuristic methods for choosing regularization parameters are proposed, including the L-curve method and the de Boor method. Numerical experiments are performed to illustrate the efficiency of these methods in comparison with other procedures.


title: On understanding the energy consumption of ARM-based multicore servers

abstract: There is growing interest to replace traditional servers with low-power multicore systems such as ARM Cortex-A9. However, such systems are typically provisioned for mobile applications that have lower memory and I/O requirements than server application. Thus, the impact and extent of the imbalance between application and system resources in exploiting energy efficient execution of server workloads is unclear. This paper proposes a trace-driven analytical model for understanding the energy performance of server workloads on ARM Cortex-A9 multicore systems. Key to our approach is the modeling of the degrees of CPU core, memory and I/O resource overlap, and in estimating the number of cores and clock frequency that optimizes energy performance without compromising execution time. Since energy usage is the product of utilized power and execution time, the model first estimates the execution time of a program. CPU time, which accounts for both cores and memory response time, is modeled as an M/G/1 queuing system. Workload characterization of high performance computing, web hosting and financial computing applications shows that bursty memory traffic fits a Pareto distribution, and non-bursty memory traffic is exponentially distributed. Our analysis using these server workloads reveals that not all server workloads might benefit from higher number of cores or clock frequencies. Applying our model, we predict the configurations that increase energy efficiency by 10% without turning off cores, and up to one third with shutting down unutilized cores. For memory-bounded programs, we show that the limited memory bandwidth might increase both execution time and energy usage, to the point where energy cost might be higher than on a typical x64 multicore system. Lastly, we show that increasing memory and I/O bandwidth can improve both the execution time and the energy usage of server workloads on ARM Cortex-A9 systems.


title: Probing the independence of formant control using altered auditory feedback

abstract: Two auditory feedback perturbation experiments were conducted to examine the nature of control of the first two formants in vowels. In the first experiment, talkers heard their auditory feedback with either F1 or F2 shifted in frequency. Talkers altered production of the perturbed formant by changing its frequency in the opposite direction to the perturbation but did not produce a correlated alteration of the unperturbed formant. Thus, the motor control system is capable of fine-grained independent control of F1 and F2. In the second experiment, a large meta-analysis was conducted on data from talkers who received feedback where both F1 and F2 had been perturbed. A moderate correlation was found between individual compensations in F1 and F2 suggesting that the control of F1 and F2 is processed in a common manner at some level. While a wide range of individual compensation magnitudes were observed, no significant correlations were found between individuals' compensations and vowel space differences. Similarly, no significant correlations were found between individuals' compensations and variability in normal vowel production. Further, when receiving normal auditory feedback, most of the population exhibited no significant correlation between the natural variation in production of F1 and F2.


title: Pre-service Secondary School Teachers’ Knowledge in Mathematical Modelling – A Case Study

abstract: This paper reports an exploratory study which examined how a group of pre-service secondary school mathematics teachers’ knowledge in mathematical modelling was shaped by their independent modelling experiences. It also determined the pre-service teachers’ views on teaching mathematical modelling. The findings show that independent modelling experiences with reflection activities had enabled the pre-service teachers to acquire knowledge on the important elements of modelling activities in the various stages of the modelling process. The pre-service teachers’ views on teaching mathematical modelling were mainly limited to the scientific-humanistic perspective. Implications for these findings on mathematical modelling instructional practice were discussed.


Chapter 3 EFFICIENT SECOND-ORDER LEARNING ALGORITHMS FOR DISCRETE-TIME RECURRENT NEURAL NETWORKS


Automatic Generation of Review Matrices as Multi-document Summarization of Scientific Papers


title: Pedestrian detection with an improved Adaboost

abstract: This paper focuses on improving the performance of Adaboost (Adaptive Boosting) by using weak classifiers that make classification with a confidence score. Single thresholds and nearest neighbor classifiers are used as base classifiers. The proposed method is applied to the problem of pedestrian detection in still images. Haar-like basic features are used to construct weak classifiers.


title: Exploring Domain Knowledge for Affective Video Content Analyses

abstract: The well-established film grammar is often used to change visual and audio elements of videos to invoke audiences' emotional experience. Such film grammar, referred to as domain knowledge, is crucial for affective video content analyses, but has not been thoroughly explored yet. In this paper, we propose a novel method to analyze video affective content through exploring domain knowledge. Specifically, take visual elements as an example, we first infer probabilistic dependencies between visual elements and emotions from the summarized film grammar. Then, we transfer the domain knowledge as constraints, and formulate affective video content analyses as a constrained optimization problem. Experiments on the LIRIS-ACCEDE database and the DEAP database demonstrate that the proposed affective content analyses method can successfully leverage well-established film grammar for better emotion classification from video content.


title: Real-time compression of point cloud streams

abstract: We present a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data. Our proposed compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the point clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive point clouds. By encoding their structural differences, we can successively extend the point clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the point cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.


title: Purpose and Polarity of Citation: Towards NLP-based Bibliometrics

abstract: Bibliometric measures are commonly used to estimate the popularity and the impact of published research. Existing bibliometric measures provide “quantitative” indicators of how good a published paper is. This does not necessarily reflect the “quality” of the work presented in the paper. For example, when hindex is computed for a researcher, all incoming citations are treated equally, ignoring the fact that some of these citations might be negative. In this paper, we propose using NLP to add a “qualitative” aspect to biblometrics. We analyze the text that accompanies citations in scientific articles (which we term citation context). We propose supervised methods for identifying citation text and analyzing it to determine the purpose (i.e. author intention) and the polarity (i.e. author sentiment) of citation.


title: Follow me cloud: interworking federated clouds and distributed mobile networks

abstract: This article introduces the Follow-Me Cloud concept and proposes its framework. The proposed framework is aimed at smooth migration of all or only a required portion of an ongoing IP service between a data center and user equipment of a 3GPP mobile network to another optimal DC with no service disruption. The service migration and continuity is supported by replacing IP addressing with service identification. Indeed, an FMC service/application is identified, upon establishment, by a session/service ID, dynamically changing along with the service being delivered over the session; it consists of a unique identifier of UE within the 3GPP mobile network, an identifier of the cloud service, and dynamically changing characteristics of the cloud service. Service migration in FMC is triggered by change in the IP address of the UE due to a change of data anchor gateway in the mobile network, in turn due to UE mobility and/or for load balancing. An optimal DC is then selected based on the features of the new data anchor gateway. Smooth service migration and continuity are supported thanks to logic installed at UE and DCs that maps features of IP flows to the session/service ID.


Entity Based Sentiment Analysis on Twitter


title: Integrative review of the development of a multi-object tracker from a dual camera system in an unmanned aerial vehicle (UAV)

abstract: In the modern age, unmanned aerial vehicles are used in industries for a variety of reasons ranging from surveillance uses to product deliveries. They are also used by people for recreational purposes such as aerial photography or drone racing. However, the capabilities of these UAVs are limited to merely recording and storing the videos. This paper introduces the development of a quadcopter capable of multi-object detection. It also explores the possibility of using a dual camera system for extended range.


title: Graphical Models for Game Theory

abstract: We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs.


title: The dining cryptographers problem: Unconditional sender and recipient untraceability

abstract: Keeping confidential who sends which messages, in a world where any physical transmission can be traced to its origin, seems impossible. The solution presented here is unconditionally or cryptographically secure, depending on whether it is based on one-time-use keys or on public keys, respectively. It can be adapted to address efficiently a wide variety of practical considerations.


title: Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces

abstract: This article introduces a model-based reinforcement learning RL approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization PSO is applied to search for optimal solutions. This Particle Swarm Optimization Policy PSO-P is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on the two standard benchmarks: mountain car and cart pole.


title: Cluster based rank query over multidimensional data streams

abstract: Many data stream monitoring applications involve rank queries and hence a number of efficient evaluation algorithms are proposed recently. Most of these techniques assume that rank queries are executed directly over the whole data space. However, we observe that many applications often require to perform clustering over the data streams before rank queries are run on each cluster. To address the problem, we propose a novel algorithm for integral clustering and ranking processing and we refer to such integrated queries as cluster-based rank queries. The algorithm includes two phases, namely the online phase which maintains the required data structures and statistics, and the query phase which uses these data structures to process queries. Extensive experiments indicate that the proposed algorithm is efficient in both space consumption and query processing.


Experiences Using the RISC-V Ecosystem to Design an Accelerator-Centric SoC in TSMC 16 nm


title: One tool to rule them all: a modular troubleshooting framework for SDN (and other) networks

abstract: Due to the heterogeneous and distributed nature of computer networks, the detection of misconfigurations and software/hardware failures is frequently reported to be notoriously non-trivial. The advent of SDN complicates the situation even more, since besides troubleshooting, the problem of finding software bugs in controller/switch/VNF implementations also has to be solved. Today a wealth of general and SDN-specific troubleshooting tools are available which are usually tailored to identify network-related errors and bugs of a particular nature. In this paper we define a troubleshooting framework which can assemble many of these tools in a single platform and makes possible to flexibly combine them. As we see network operators and SDN developers execute similar tasks anyway, e.g. combine ping, traceroute and tcpdump (or more complex tools) manually to see what is going on in the network. Our framework can ease their work by consolidating the available troubleshooting tools in a flexible and automated manner.


What is Soft Computing? Revisiting Possible Answers


title: The challenge of interoperability: Model-based integration for automotive control software

abstract: Model-Based Engineering (MBE) is a promising approach to cope with the challenges of designing the next-generation automotive systems. The increasing complexity of automotive electronics, the platform, distributed real-time embedded software, and the need for continuous evolution from one generation to the next has necessitated highly productive design approaches. However, heterogeneity, interoperability, and the lack of formal semantic underpinning in modeling, integration, validation and optimization make design automation a big challenge, which becomes a hindrance to the wider application of MBE in the industry. This paper briefly presents the interoperability challenges in the context of MBE and summarizes our current contribution to address these challenges with regard to automotive control software systems. A novel model-based formal integration framework is being developed to enable architecture modeling, timing specification, formal semantics, design by contract and optimization in the system-level design. The main advantages of the proposed approach include its pervasive use of formal methods, architecture analysis and design language (AADL) and associated tools, a novel timing annex for AADL with an expressive timing relationship language, a formal contract language to express component-level requirements and validation of component integration, and the resulting high assurance system delivery.


title: Symbolic Models and Emergent Models: A Review

abstract: There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent models is abstraction, which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain's outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain.