title: Non linear hybrid watermarking for High Dynamic Range images

abstract: The present work explores the use of a specific method, called non linear hybrid watermarking to watermark High Dynamic Range (HDR) images. The non linear hybrid technique combines both additive and multiplicative watermark embedding and is based on a square root embedding equation. We evaluate the robustness and objective quality of HDR tone mapped watermarked images on two different HDR databases. The experimentations show that the watermark is imperceptible and successfully survives tone mapping attack.


title: Comparing writing style feature-based classification methods for estimating user reputations in social media

abstract: In recent years, the anonymous nature of the Internet has made it difficult to detect manipulated user reputations in social media, as well as to ensure the qualities of users and their posts. To deal with this, this study designs and examines an automatic approach that adopts writing style features to estimate user reputations in social media. Under varying ways of defining Good and Bad classes of user reputations based on the collected data, it evaluates the classification performance of the state-of-art methods: four writing style features, i.e. lexical, syntactic, structural, and content-specific, and eight classification techniques, i.e. four base learners—C4.5, Neural Network (NN), Support Vector Machine (SVM), and Naïve Bayes (NB)—and four Random Subspace (RS) ensemble methods based on the four base learners. When South Korea’s Web forum, Daum Agora, was selected as a test bed, the experimental results show that the configuration of the full feature set containing content-specific features and RS-SVM combining RS and SVM gives the best accuracy for classification if the test bed poster reputations are segmented strictly into Good and Bad classes by portfolio approach. Pairwise t tests on accuracy confirm two expectations coming from the literature reviews: first, the feature set adding content-specific features outperform the others; second, ensemble learning methods are more viable than base learners. Moreover, among the four ways on defining the classes of user reputations, i.e. like, dislike, sum, and portfolio, the results show that the portfolio approach gives the highest accuracy.


title: An ASIC design for a high speed implementation of the hash function SHA-256 (384, 512)

abstract: An implementation of the hash functions SHA-256, 384 and 512 is presented, obtaining a high clock rate through a reduction of the critical path length, both in the Expander and in the Compressor of the hash scheme. The critical path is shown to be the smallest achievable. Synthesis results show that the new scheme can reach a clock rate well exceeding 1 GHz using a 0.13um technology.


title: A Novel Valley-Fill SEPIC-Derived Power Supply Without Electrolytic Capacitor for LED Lighting Application

abstract: The high-brightness white-light-emitting diode (LED) has attracted a lot of attention for its high efficacy, simple to drive, environmentally friendly, long lifespan, and compact size. The power supply for LED also requires long life, while maintaining high efficiency, high power factor, and low cost. However, a typical power supply design employs an electrolytic capacitor as the storage capacitor, which is not only bulky, but also with a short lifespan, thus hampering performance improvement of the entire LED lighting system. In this paper, a novel power factor correction (PFC) topology is proposed by inserting the valley-fill circuit in the single-ended primary inductance converter (SEPIC)-derived converter, which can reduce the voltage stress of the storage capacitor and output diode under the same power factor condition. This valley-fill SEPIC-derived topology is, then, proposed for LED lighting applications. By allowing a relatively large voltage ripple in the PFC design and operating in the discontinuous conduction mode (DCM), the proposed PFC topology is able to eliminate the electrolytic capacitor, while maintaining high power factor and high efficiency. Under the electrolytic capacitor-less condition, the proposed PFC circuit can reduce the capacitance of the storage capacitor to half for the same power factor and output voltage ripple as comparing to its original circuit. To further increase the efficiency of LED driver proposal, a twin-bus buck converter is introduced and employed as the second-stage current regulator with the PWM dimming function. The basic operating principle and analysis will be described in detail. A 50-W prototype has been built and tested in the laboratory, and the experimental results under universal input-voltage operation are presented to verify the effectiveness and advantages of the proposal.


title: In-vehicle information system as a driver's secondary activity: Case study

abstract: A robust methodology for detecting and evaluating driver distraction induced by in-vehicle information system using artificial neural network and fuzzy logic is introduced in this paper. An artificial neural network is used to predict driver's performance on a specific road segment. The predicted performance-based measures are compared to the driving with secondary task accomplishment. Fuzzy logic is applied to fuse the variables into a single output, which constitutes a level of driver distraction in percentage. The technique was tested on a vehicle simulator by ten drivers that exploited in-vehicle information system as a secondary activity. The driver-in-the-loop experiment outcomes are discussed.


Meaning Representation for Multi-Document Summarization


title: Efficient and Scalable Batch Bayesian Optimization Using K-Means

abstract: We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised learning to efficiently estimate peaks of the model acquisition function. We show in empirical experiments that our method outperforms the current state-of-the-art batch allocation algorithms on a variety of test problems including tuning of algorithm hyper-parameters and a challenging drug discovery problem. In order to accommodate the real-world problem of high dimensional data, we propose a modification to KMBBO by combining it with compressed sensing to project the optimization into a lower dimensional subspace. We demonstrate empirically that this 2-step method outperforms algorithms where no dimensionality reduction has taken place.


title: DIANNE: Distributed Artificial Neural Networks for the Internet of Things

abstract: Nowadays artificial neural networks are widely used to accurately classify and recognize patterns. An interesting application area is the Internet of Things (IoT), where physical things are connected to the Internet, and generate a huge amount of sensor data that can be used for a myriad of new, pervasive applications. Neural networks' ability to comprehend unstructured data make them a useful building block for such IoT applications. As neural networks require a lot of processing power, especially during the training phase, these are most often deployed in a cloud environment, or on specialized servers with dedicated GPU hardware. However, for IoT applications, sending all raw data to a remote back-end might not be feasible, taking into account the high and variable latency to the cloud, or could lead to issues concerning privacy. In this paper the DIANNE middleware framework is presented that is optimized for single sample feed-forward execution and facilitates distributing artificial neural networks across multiple IoT devices. The modular approach enables executing neural network components on a large number of heterogeneous devices, allowing us to exploit the local compute power at hand, and mitigating the need for a large server-side infrastructure at runtime.


title: Predicting Matchups and Preferences in Context

abstract: We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce. Unlike existing models for these tasks, our model not only learns representations of the items in a more expressive latent vector space, but also models how context modifies matchup and preference outcomes. For example, the context "weather" may alter the winning probability in a tennis match, or the fact that the user is on a mobile device may alter his preferences among restaurants. More generally, the model is capable of handling any symmetric game/comparison problem that can be described by vectorized player/item and game/context features. We provide a comprehensive evaluation of its predictive performance with real datasets from both domains to show its ability to predict preference and game outcomes more accurately than existing models. Furthermore, we demonstrate on synthetic datasets the expressiveness of the model when compared against theoretical limits.


Static Analysis for GDPR Compliance


title: © 2004 INFORMS A New Placement Heuristic for the Orthogonal Stock-Cutting Problem

abstract: This paper presents a new best-fit heuristic for the two-dimensional rectangular stock-cutting problem and demonstrates its effectiveness by comparing it against other published approaches. A placement algorithm usually takes a list of shapes, sorted by some property such as increasing height or decreasing area, and then applies a placement rule to each of these shapes in turn. The proposed method is not restricted to the first shape encountered but may dynamically search the list for better candidate shapes for placement. We suggest an efficient implementation of our heuristic and show that it compares favourably to other heuristic and metaheuristic approaches from the literature in terms of both solution quality and execution time. We also present data for new problem instances to encourage further research and greater comparison between this and future methods.


title: Exploring convolutional neural network structures and optimization techniques for speech recognition.

abstract: Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully connected deep neural networks within the hybrid deep neural network / hidden Markov model (DNN/HMM) framework on the phone recognition task. In this paper, we extend the earlier basic form of the CNN and explore it in multiple ways. We first investigate several CNN architectures, including full and limited weight sharing, convolution along frequency and time axes, and stacking of several convolution layers. We then develop a novel weighted softmax pooling layer so that the size in the pooling layer can be automatically learned. Further, we evaluate the effect of CNN pretraining, which is achieved by using a convolutional version of the RBM. We show that all CNN architectures we have investigated outperform the earlier basic form of the DNN on both the phone recognition and large vocabulary speech recognition tasks. The architecture with limited weight sharing provides additional gains over the full weight sharing architecture. The softmax pooling layer performs as well as the best CNN with the manually tuned fixed-pooling size, and has a potential for further improvement. Finally, we show that CNN pretraining produces significantly better results on a large vocabulary speech recognition task.


title: Multi-armed Bandits with Application to 5G Small Cells

abstract: Due to the pervasive demand for mobile services, next generation wireless networks are expected to be able to deliver high data rates while wireless resources become more and more scarce. This requires the next generation wireless networks to move toward new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services. Examples of such paradigms foreseen for the emerging 5G cellular networks include very densely deployed small cells and device-to-device communications. For 5G networks, it will be imperative to search for spectrum and energy-efficient solutions to the resource allocation problems that i) are amenable to distributed implementation, ii) are capable of dealing with uncertainty and lack of information, and iii) can cope with users’ selfishness. The core objective of this article is to investigate and establish the potential of the MAB framework to address this challenge. In particular, we provide a brief tutorial on bandit problems, including different variations and solution approaches. Furthermore, we discuss recent applications as well as future research directions. In addition, we provide a detailed example of using an MAB model for energy-efficient small cell activation in 5G networks.


title: Theoretical analysis and optimal design of LLC resonant converter

abstract: A LLC resonant topology is analyzed to derive efficiency and cost optimal design for wide input ranges and load variations. In the LLC converter, a wide range of output power is controlled with only a narrow variation in operating frequency since this converter is capable of both step-up and step-down. In addition, ZVS turn-on and ZCS turn-off of MOSFETs and diode rectifiers can be achieved over the entire operating range. Finally, the inductance of a resonant tank in the primary side can be merged in the main power transformer by resonant inductance and the absence of the secondary filter inductor makes low voltage stress on secondary rectifier and cost-effective property. DC characteristics and input-output response in frequency domain are obtained with the equivalent circuit derived by first harmonic approximation (FHA) method. In addition, operational principles are explained to show the ZVS and ZCS conditions of primary switches and output diode rectifiers, respectively. Efficiency and cost optimal design rules of the LLC resonant converter are derived by a primary resonant network, operating frequency, and dead time duration. Proposed analysis and designation are proved by experimental results with a 400 W LLC resonant converter.


title: Traction control for a rocker-bogie robot with wheel-ground contact angle estimation

abstract: A method for kinematics modeling of a six-wheel Rocker-Bogie mobile robot is described in detail. The forward kinematics is derived by using wheel Jacobian matrices in conjunction with wheel-ground contact angle estimation. The inverse kinematics is to obtain the wheel velocities and steering angles from the desired forward velocity and turning rate of the robot. Traction Control is also developed to improve traction by comparing information from onboard sensors and wheel velocities to minimize wheel slip. Finally, a simulation of a small robot using rocker-bogie suspension has been performed and simulate in two conditions of surfaces including climbing slope and travel over a ditch.


title: A Brief Overview of the Developments of the Cognitive Radio Technology

abstract: Cognitive radio’s rising popularity among various engineers, scientists and researchers can be credited to the increasing number of users of wireless technology and the radio spectrum which is limited. In this paper, various advances which have been made so far to improvise cognitive radio technology have been discussed in brief to give the reader an overview of this emerging technology. The basics of cognitive radio networks are introduced first and then various developments on different aspects of this technology are discussed.


title: A Comparison of Features for Automatic Readability Assessment

abstract: Several sets of explanatory variables - including shallow, language modeling, POS, syntactic, and discourse features - are compared and evaluated in terms of their impact on predicting the grade level of reading material for primary school students. We find that features based on in-domain language models have the highest predictive power. Entity-density (a discourse feature) and POS-features, in particular nouns, are individually very useful but highly correlated. Average sentence length (a shallow feature) is more useful - and less expensive to compute - than individual syntactic features. A judicious combination of features examined here results in a significant improvement over the state of the art.


Cyberspace 2000: dealing with information overload


Cost Analysis of On-Premise versus Cloud-hosted Implementation in Sub-Saharan Countries Research Paper


title: Emotional memories are not all created equal: Evidence for selective memory enhancement

abstract: Human brain imaging studies have shown that greater amygdala activation to emotional relative to neutral events leads to enhanced episodic memory. Other studies have shown that fearful faces also elicit greater amygdala activation relative to neutral faces. To the extent that amygdala recruitment is sufficient to enhance recollection, these separate lines of evidence predict that recognition memory should be greater for fearful relative to neutral faces. Experiment 1 demonstrated enhanced memory for emotionally negative relative to neutral scenes; however, fearful faces were not subject to enhanced recognition across a variety of delays (15 min to 2 wk). Experiment 2 demonstrated that enhanced delayed recognition for emotional scenes was associated with increased sympathetic autonomic arousal, indexed by the galvanic skin response, relative to fearful faces. These results suggest that while amygdala activation may be necessary, it alone is insufficient to enhance episodic memory formation. It is proposed that a sufficient level of systemic arousal is required to alter memory consolidation resulting in enhanced recollection of emotional events.


title: 3D face model reconstruction from single 2D frontal image

abstract: Modeling a 3D face model that resembles a human face has been an important issue in computer graphics. A number of researchers have proposed various modeling methodologies to model the human face realistically. However, despite the fact that these methods give high-quality results, the time, cost, and labor required to generate such models by these methods are often high. In this paper, we propose a fully automatic face modeling method that can rapidly create a realistic 3D face model on the basis of a single 2D image. We studied 200 frontal facial images and captured their statistical shape variations. Using these properties, facial features were detected from the images. The depth information of the facial features was then estimated using an anthropometrical measurement database of human faces. Finally, by aligning the feature points three-dimensionally and interpolating them, a facial surface is generated. Finally, upon these 3-dimensional alignment of the facial features, a surface interpolates these feature points are created. As a result, we could obtain realistic 3D faces models of several individuals without any efforts. Further, the computational cost of our method was much lower than that of other methods. Thus, our method showed remarkably fast performance.


title: A Machine Learning Approach to Building Domain-Specific Search Engines

abstract: Domain-specific search engines are becoming increasingly popular because they offer increased accuracy and extra features not possible with general, Web-wide search engines. Unfortunately, they are also difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, text classification and information extraction that enables efficient spidering, populates topic hierarchies, and identifies informative text segments. Using these techniques, we have built a demonstration system: a search engine for computer science research papers available at www.cora.justrcsettrch.com.


title: Microphone array processing for distant speech recognition: Towards real-world deployment

abstract: Distant speech recognition (DSR) holds out the promise of providing a natural human computer interface in that it enables verbal interactions with computers without the necessity of donning intrusive body- or head-mounted devices. Recognizing distant speech robustly, however, remains a challenge. This paper provides a overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beamforming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a 64-channel linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Furthermore, we report the results of speech recognition experiments on data captured with a popular device, the Kinect [1]. Even for speakers at a distance of four meters from the Kinect, our DSR system achieved acceptable recognition performance on a large vocabulary task, a WER of 24.1%, beginning from a WER of 42.5% with a single array channel.


title: Blade-type crawler vehicle with gyro wheel for stably traversing uneven terrain at high speed

abstract: Unmanned rescue, observation, and/or research vehicles with high terrain adaptability, high speed, and high reliability are needed in difficult-to-reach locations. However, for most vehicles, high performance over rough terrain reduces the travel speed and/or requires complex mechanisms. We have developed a blade-type crawler robot with a very simple and reliable mechanism, which traverses uneven terrain at high speed. Moreover, the gyro wheel design stabilizes the success of this approach in improving the motion, ensuring robust traversal. The improvement in traveling speed and robustness over uneven terrain by our approach was confirmed by experiment.


title: A proposed index of usability: a method for comparing the relative usability of different software systems

abstract: Abstract Usability is becoming a more and more important software criterion, but the present usability measurement methods are either difficult to apply, or overly dependent upon evaluators' expertise. Based on human information processing theory, this study identified eight human factors considerations which are relevant to software usability. These considerations as well as the three stages of human information processing theory formed the framework from which our Purdue Usability Testing Questionnaire (PUTQ) is derived. An experiment was conducted to test the validity of PUTQ. The experiment result showed high correlation between PUTQ and the Questionnaire for User Interaction Satisfaction (QUIS version 5.5). In addition, PUTQ detected the differences in user performance between two experimental interface systems, but QUIS failed to do so.


Extending AIDA framework by incorporating coreference resolution on detected mentions and pruning based on popularity of an entity


title: A wideband ultra-low-current on-chip ammeter

abstract: A high-bandwidth ultra-low-current measurement circuit is presented in this paper. The circuit is capable of measuring an on-chip 75 fA current at a bandwidth up to 1 kHz with a noise floor of 0.235 fA rms /√Hz. A low leakage reset scheme is utilized to improve the precision. Nested auto-zeroing combined with modified correlated double sampling is employed to mitigate the error due to various circuit imperfections. Noise analysis is carried out and “capacitive noise matching” is proposed to minimize the noise floor. The circuit is also capable of digitizing the measured current and streaming the data through a serial interface. The measurement circuit occupies 0.065 mm2 of active silicon area in a 90 nm CMOS process and consumes 147 μW from 2.5 V and 1V supplies.


title: Perceived determinants to alcohol consumption and misuse: a survey of university students:

abstract: Aim:Before an attempt is made to develop any population-specific behavioural change programme, it is important to know what the factors that influence behaviours are. The aim of this study was to i...


title: Software development lifecycle models

abstract: This history column article provides a tour of the main software development life cycle (SDLC) models. (A lifecycle covers all the stages of software from its inception with requirements definition through to fielding and maintenance.) System development lifecycle models have drawn heavily on software and so the two terms can be used interchangeably in terms of SDLC, especially since software development in this respect encompasses software systems development. Because the merits of selecting and using an SDLC vary according to the environment in which software is developed as well as its application, I discuss three broad categories for consideration when analyzing the relative merits of SDLC models. I consider the waterfall model before the other models because it has had a profound effect on software development, and has additionally influenced many SDLC models prevalent today. Thereafter, I consider some of the mainstream models and finish with a discussion of what the future could hold for SDLC models.


title: Hybrid recommendation models for binary user preference prediction problem

abstract: This paper presents detailed information of our solutions to the task 2 of KDD Cup 2011. The task 2 is called binary user preference prediction problem in the paper because it aims at separating tracks rated highly by specific users from tracks not rated by them, and the solutions of this task can be easily applied to binary user behavior data. In the contest, we firstly implemented many different models, including neighborhood-based models, latent factor models, content-based models, etc. Then, linear combination is used to combine different models together. Finally, we used robust post-processing to further refine the special user-item pairs. The final error rate is 2.4808% which placed number 2 in the Leaderboard.


Circuit Analysis Method for Determination of A-C Impedances of Machine Conductors


Performance Monitoring in Virtual Organization Using Domain Driven Data Mining and Opinion Mining


title: The REA Ontology to Supplement Teaching Data Flow Diagrams

abstract: Systems Analysis and Design is a core component of most information systems curricula, and generally includes coverage of process modeling with d ata f low diagrams. I n o rder to relate process modeling to something with w hich all students are familiar, the content of a traditional Systems Analysis and Design course can be supplemented with an ontological framework. This manuscript proposes that an ontology from accounting literature (REA) be used to supplement traditional modeling approaches. The REA Ontology incorporates elementary accounting concepts familiar to many business students, and as a consequence makes contextual learning possible. This manuscript points out several synergies between the REA Ontology and data flow diagrams, and notes that the contextual learning factor enhances student learning. As a result, students are able to develop an efficient and enriched mental concept of data flow modeling and the business processes the models seek to capture.


SLIC Superpixels ⋆


title: Recurrent Memory Addressing for Describing Videos

abstract: Deep Neural Network architectures with external memory components allow the model to perform inference and capture long term dependencies, by storing information explicitly. In this paper, we generalize Key-Value Memory Networks to a multimodal setting, introducing a novel key-addressing mechanism to deal with sequence-to-sequence models. The advantages of the framework are demonstrated on the task of video captioning, i.e generating natural language descriptions for videos. Conditioning on the previous time-step attention distributions for the key-value memory slots, we introduce a temporal structure in the memory addressing schema. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. This allows us to exploit the temporal dependencies at multiple hierarchies (in the recurrent key-addressing; and in the language decoder). Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Extensive experiments on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.


title: Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA.

abstract: Abstract This paper proposed a new screening method for the simultaneous detection of five common adulterants in raw cow milk by using attenuated total reflectance (ATR) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrimination analysis – PLSDA). The method was able to detect the presence of the adulterants water, starch, sodium citrate, formaldehyde and sucrose in milk samples containing from one up to five of these analytes, in the range of 0.5–10% w/v. A multivariate qualitative validation was performed, estimating specific figures of merit, such as false positive and false negative rates, selectivity, specificity and efficiency rates, accordance and concordance. The proposed method does not need any sample pretreatment, requires a small amount of sample (30 μL), is fast and simple, being suitable for the control of raw milk in a dairy industry or for the quality inspection of commercialized milk.


title: Double-Boost DC to DC Converter

abstract: In this paper a new boost topology is proposed. The circuit is similar with two parallel boost DC-to-DC converters, but the two inductors are charged in parallel and release energy in series, thus enhancing the voltage boost ratio. After a short analysis of the circuit, a comparative study with other classic boost converter (single boost and two-cascade) is presented. The simulation results show a net improvement of the boost ratio for the new proposed topology.


title: Organizing Ontology Design Patterns as Ontology Pattern Languages

abstract: Ontology design patterns have been pointed out as a promising ap- proach for ontology engineering. The goal of this paper is twofold. Firstly, based on well-established works in Software Engineering, we revisit the notion of ontology patterns in Ontology Engineering to introduce the notion of ontolo- gy pattern language as a way to organize related ontology patterns. Secondly, we present an overview of a software process ontology pattern language.


title: Towards Automated Cost-Efficient Data Management for Federated Cloud Services

abstract: Cloud computing has transformed the accessibility and usage of information technology resources, by offering them as services via the Internet. Cloud service provisioning spans across infrastructure, platform and software levels. The management of the services at these levels is based on monitoring. The analysis of monitoring data provides insight into Cloud operations in order to make informed decisions. Due to the emergence of numerous heterogenous Cloud platforms with proprietary APIs, service and monitoring data are being formatted using diverse and mostly incompatible data interchange formats. This results to interoperability issues and makes the analysis of monitoring data from multi-Cloud service deployments difficult to handle. The existing research efforts on data interchange formats have been mainly focused on general performance analyses. Little or no effort has been channelled towards a combination of multiple data interchange formats based on data type to achieve efficient serialisation that can facilitate interoperability in federated Clouds, and also reduce the size of data and bandwidth utilisation cost. This paper addresses these issues by presenting automated framework that is capable of automatically selecting the most suitable data interchange formats for achieving an efficient formatting and serialisation outcome. The goal of the framework is to enable robust and transparent communication within and between multiple Cloud deployments. Based on three use case scenarios, we evaluate the proposed framework to demonstrate its efficacy in formatting and serialising data.


title: Automated Discourse Generation Using Discourse Structure Relations

abstract: Abstract This paper summarizes work over the past five years on the automated planning and generation of multisentence texts using discourse structure relations, placing it in context of ongoing efforts by computational linguists and linguists to understand the structure of discourse. Based on a series of studies by the author and others, the paper describes how the orientation of generation toward communicative intentions illuminates the central structural role played by intersegment discourse relations. It outlines several facets of discourse structure relations as they are required by and used in text planners—their nature, number, and extension to associated tasks such as sentence planning and text formatting.


Modelica-Based Modeling and Application Framework on the Hybrid Electric Vehicles


title: You Had Me at Hello: How Phrasing Affects Memorability

abstract: Understanding the ways in which information achieves widespread public awareness is a research question of significant interest. We consider whether, and how, the way in which the information is phrased --- the choice of words and sentence structure --- can affect this process. To this end, we develop an analysis framework and build a corpus of movie quotes, annotated with memorability information, in which we are able to control for both the speaker and the setting of the quotes. We find that there are significant differences between memorable and non-memorable quotes in several key dimensions, even after controlling for situational and contextual factors. One is lexical distinctiveness: in aggregate, memorable quotes use less common word choices, but at the same time are built upon a scaf-folding of common syntactic patterns. Another is that memorable quotes tend to be more general in ways that make them easy to apply in new contexts --- that is, more portable. We also show how the concept of "memorable language" can be extended across domains.


Designed Using CPLD/FPGAs for Biomedical Devices Applications


ADAPTIVE MISMATCHED FILTER DESIGN FOR RADAR PULSE COMPRESSION


title: Steady-State Power Flow Model of Energy Router Embedded AC Network and Its Application in Optimizing Power System Operation

abstract: The energy router is an emerging device concept that is based on an advanced power electronic technique. It is able to realize flexible and dynamic electric power distribution in power systems analogous to the function of information routers in the Internet. It is of great interest to investigate how the energy router can be used to optimize power system operation. This paper formulates the steady-state power flow model of the energy router embedded system network and the related optimal power flow formulation. The role of the energy router in providing extra flexibility to optimize the system operation is studied. Case studies are carried out on a modified IEEE RTS-79 system and a modified IEEE 118 bus system with the energy router. The results show that the energy router is able to optimize the operation of the power system through controlling the power injections and voltage of ports of the energy router. Operating objective such as adjusting branch power flow, improving bus voltage, and reducing active power losses of the grid can be reached under different objective functions.


title: An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter

abstract: This paper presents a robust but simple object pose tracking algorithm based on Kalman filtering. Compared to markerless pose tracking, a fiducial marker called ArUco provides a fast and accurate solution to the problem. With these advantages, this marker-based technique is ready to be used in virtual reality and operate in low-cost wearable devices. However, it still suffers from the problem of occlusion and noise. If a large part of the marker is occluded, no pose information can be acquired for the moment. Noises due to hand shaking also affect the quality of the resulting pose. This is not desirable in a real-time environment. We tackle the problems by employing a linear Kalman filter. The pose information can be estimated even if the camera view is blocked temporarily. We have performed real experiments to demonstrate the effects of the application of Kalman filter. The results are satisfactory.


title: The Mechanics of n-Player Differentiable Games

abstract: The cornerstone underpinning deep learning is the guarantee that gradient descent on an objective converges to local minima. Unfortunately, this guarantee fails in settings, such as generative adversarial nets, where there are multiple interacting losses. The behavior of gradient-based methods in games is not well understood -- and is becoming increasingly important as adversarial and multi-objective architectures proliferate. In this paper, we develop new techniques to understand and control the dynamics in general games. The key result is to decompose the second-order dynamics into two components. The first is related to potential games, which reduce to gradient descent on an implicit function; the second relates to Hamiltonian games, a new class of games that obey a conservation law, akin to conservation laws in classical mechanical systems. The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in general games. Basic experiments show SGA is competitive with recently proposed algorithms for finding stable fixed points in GANs -- whilst at the same time being applicable to -- and having guarantees in -- much more general games.


title: Application of robot arm using fiber knitted type pneumatic artificial rubber muscles

abstract: In the restoration work from disasters, the remote control of construction machine is required to ensure the worker's safety. However, conventional remote-controlled construction machine is larger than ordinary ones and limited in types and numbers, so there is a problem that the transportation to the destructed sites takes time and is troublesome. We have been developing the pneumatic humanoid type robot arm, which can be installed in any models of construction machine. In consideration of portability, the lightweight fiber knitted type pneumatic artificial rubber muscle (FARM) was adopted as the actuator. In this paper, we developed the static and dynamic characteristic models of the FARM taking the effect of elasticity of rubber and frictional force into consideration. Then we realized the remote control of the construction machine using the pneumatic robot that has 6 degree of freedom using the FARM. Moreover, we did some experiments on the remote control of the construction machine. Experimental results show that the developed system is available in remote control of a construction machine.


title: reacTIVision and TUIO: a tangible tabletop toolkit

abstract: This article presents the recent updates and an evaluation of reacTIVision, a computer vision toolkit for fiducial marker tracking and multi-touch interaction. It also discusses the current and future development of the TUIO protocol and framework, which has been primarily designed as an abstraction layer for the description and transmission of pointers and tangible object states in the context of interactive tabletop surfaces. The initial protocol definition proved to be rather robust due to the simple and straightforward implementation approach, which also supported its widespread adoption within the open source community. This article also discusses the current limitations of this simplistic approach and provides an outlook towards a next generation protocol definition, which will address the need for additional descriptors and the protocol's general extensibility.


title: On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques

abstract: This paper discusses the construction principles and performance of a pulsed time-of-flight (TOF) laser radar based on high-speed (FWHM $\sim$ 100 ps) and high-energy ( $\sim$ 1 nJ) optical transmitter pulses produced with a specific laser diode working in an “enhanced gain-switching” regime and based on single-photon detection in the receiver. It is shown by analysis and experiments that single-shot precision at the level of 2…3 cm is achievable. The effective measurement rate can exceed 10 kHz to a noncooperative target (20% reflectivity) at a distance of $>\ 50\ \hbox{m}$ , with an effective receiver aperture size of $2.5\ \hbox{cm}^{2} $ . The effect of background illumination is analyzed. It is shown that the gating of the SPAD detector is an effective means to avoid the blocking of the receiver in a high-level background illumination case. A brief comparison with pulsed TOF laser radars employing linear detection techniques is also made.


title: Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection

abstract: This paper addresses a spatiotemporal pattern recognition problem. The main purpose of this study is to find a right representation and matching of action video volumes for categorization. A novel method is proposed to measure video-to-video volume similarity by extending Canonical Correlation Analysis (CCA), a principled tool to inspect linear relations between two sets of vectors, to that of two multiway data arrays (or tensors). The proposed method analyzes video volumes as inputs avoiding the difficult problem of explicit motion estimation required in traditional methods and provides a way of spatiotemporal pattern matching that is robust to intraclass variations of actions. The proposed matching is demonstrated for action classification by a simple Nearest Neighbor classifier. We, moreover, propose an automatic action detection method, which performs 3D window search over an input video with action exemplars. The search is speeded up by dynamic learning of subspaces in the proposed CCA. Experiments on a public action data set (KTH) and a self-recorded hand gesture data showed that the proposed method is significantly better than various state-of-the-art methods with respect to accuracy. Our method has low time complexity and does not require any major tuning parameters.


title: In-Database Learning with Sparse Tensors

abstract: In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them. The algorithms and models proposed in the paper have already been implemented and deployed in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.


title: An Embedded System-on-Chip Architecture for Real-time Visual Detection and Matching

abstract: Detecting and matching image features is a fundamental task in video analytics and computer vision systems. It establishes the correspondences between two images taken at different time instants or from different viewpoints. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a new FPGA-based embedded system architecture for feature detection and matching. It consists of scale-invariant feature transform (SIFT) feature detection, as well as binary robust independent elementary features (BRIEF) feature description and matching. It is able to establish accurate correspondences between consecutive frames for 720-p (1280x720) video. It optimizes the FPGA architecture for the SIFT feature detection to reduce the utilization of FPGA resources. Moreover, it implements the BRIEF feature description and matching on FPGA. Due to these contributions, the proposed system achieves feature detection and matching at 60 frame/s for 720-p video. Its processing speed can meet and even exceed the demand of most real-life real-time video analytics applications. Extensive experiments have demonstrated its efficiency and effectiveness.


title: The Rijksmuseum Challenge: Museum-Centered Visual Recognition

abstract: This paper offers a challenge for visual classification and content-based retrieval of artistic content. The challenge is posed from a museum-centric point of view offering a wide range of object types including paintings, photographs, ceramics, furniture, etc. The freely available dataset consists of 112,039 photographic reproductions of the artworks exhibited in the Rijksmuseum in Amsterdam, the Netherlands. We offer four automatic visual recognition challenges consisting of predicting the artist, type, material and creation year. We include a set of baseline results, and make available state-of-the-art image features encoded with the Fisher vector. Progress on this challenge improves the tools of a museum curator while improving content-based exploration by online visitors of the museum collection.


title: Viewing Real-World Faces in 3D

abstract: We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.


title: Performance of a Precoding MIMO System for Decentralized Multiuser Indoor Visible Light Communications

abstract: This paper investigates the performance of our recently proposed precoding multiuser (MU) MIMO system in indoor visible light communications (VLC). The transmitted data of decentralized users are transmitted by light-emitting-diode (LED) arrays after precoding in a transmitter, by which the MU interference is eliminated. Thus, the complexity of user terminals could be reduced, which results in the reduction of power consumption. The limitation of block diagonalization precoding algorithm in VLC systems is investigated. The corresponding solution by utilizing optical detectors with different fields of view (FOV) is derived, and the impact of FOV to the proposed system is also analyzed. In this paper, we focus on BER and signal-to-noise-ratio performances of the proposed system with the consideration of the mobility of user terminals. Simulation results show that the majority of the indoor region can achieve 100 Mb/s at a BER of 10-6 when single LED chip's power is larger than 10 mW.


title: Heart rate analysis in normal subjects of various age groups

abstract: BackgroundAnalysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. Linear parameters, Power spectral indice (LF/HF) is calculated with nonlinear indices Poincare plot geometry(SD1,SD2), Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE) and Detrended Fluctuation Analysis(DFA). The results show that, with aging the heart rate variability decreases. In this work, the ranges of all linear and nonlinear parameters for four age group normal subjects are presented with an accuracy of more than 89%.As a pre-analysis step, the HRV data is tested for nonlinearity using surrogate data analysis and the results exhibited a significant difference in the ApEn, LLE, SD1/SD2 and DFA parameters of the actual data and the surrogate data.MethodsThe heart rate is analyzed using the various time domain parameters, frequency domain parameter and nonlinear parameters like Poincare geometry, ApEn, LLE and DFA.ResultsIn this work, the different linear and nonlinear parameters evaluated show a particular range for various cardiac abnormalities. And the results of these were subjected to 't' test with more than 89% confidence interval giving excellent 'p' values in all cases.ConclusionsHeart rate variability (HRV) signal can be used as a reliable indicator of state of the heart. It becomes less random with the aging(less chaotic). This is evaluated by using various time domain, frequency domain and nonlinear parameters like SD1/SD2, ApEn, LLE αsand αl. Different ranges of non-linear parameters for various age groups are presented with 'p' value ≤ 0.12.


title: Cerebral bases of subliminal speech priming

abstract: article i nfo Article history: While the neural correlates of unconscious perception and subliminal priming have been largely studied for visual stimuli, little is known about their counterparts in the auditory modality. Here we used a subliminal speech priming method in combination with fMRI to investigate which regions of the cerebral network for language can respond in the absence of awareness. Participants performed a lexical decision task on target items preceded by subliminal primes, which were either phonetically identical or different from the target. Moreover, the prime and target could be spoken by the same speaker or by two different speakers. Word repetition reduced the activity in the insula and in the left superior temporal gyrus. Although the priming effect on reaction times was independent of voice manipulation, neural repetition suppression was modulated by speaker change in the superior temporal gyrus while the insula showed voice-independent priming. These results provide neuroimaging evidence of subliminal priming for spoken words and inform us on the first, unconscious stages of speech perception.


title: Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks

abstract: Sprout is an end-to-end transport protocol for interactive applications that desire high throughput and low delay. Sprout works well over cellular wireless networks, where link speeds change dramatically with time, and current protocols build up multi-second queues in network gateways. Sprout does not use TCP-style reactive congestion control; instead the receiver observes the packet arrival times to infer the uncertain dynamics of the network path. This inference is used to forecast how many bytes may be sent by the sender, while bounding the risk that packets will be delayed inside the network for too long. ::: ::: In evaluations on traces from four commercial LTE and 3G networks, Sprout, compared with Skype, reduced self-inflicted end-to-end delay by a factor of 7.9 and achieved 2.2× the transmitted bit rate on average. Compared with Google's Hangout, Sprout reduced delay by a factor of 7.2 while achieving 4.4× the bit rate, and compared with Apple's Facetime, Sprout reduced delay by a factor of 8.7 with 1.9× the bit rate. ::: ::: Although it is end-to-end, Sprout matched or outperformed TCP Cubic running over the CoDel active queue management algorithm, which requires changes to cellular carrier equipment to deploy. We also tested Sprout as a tunnel to carry competing interactive and bulk traffic (Skype and TCP Cubic), and found that Sprout was able to isolate client application flows from one another.


title: Complex spectrogram enhancement by convolutional neural network with multi-metrics learning

abstract: This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider multiple metrics simultaneously. To solve the first problem, we propose a novel convolutional neural network (CNN) model for complex spectrogram enhancement, namely estimating clean real and imaginary (RI) spectrograms from noisy ones. The reconstructed RI spectrograms are directly used to synthesize enhanced speech waveforms. In addition, since log-power spectrogram (LPS) can be represented as a function of RI spectrograms, its reconstruction is also considered as another target. Thus a unified objective function, which combines these two targets (reconstruction of RI spectrograms and LPS), is equivalent to simultaneously optimizing two commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and log-spectral distortion (LSD). Therefore, the learning process is called multi-metrics learning (MML). Experimental results confirm the effectiveness of the proposed CNN with RI spectrograms and MML in terms of improved standardized evaluation metrics on a speech enhancement task.


title: Recognition of Expertise in Decision-Making Groups: Effects of Group Size and Participation Patterns

abstract: Two studies examined the effects of group size and participation requirements on group performance, expertise, and recognition of group expertise. In Study 1, 324 college students were assigned to work units of 1, 2, 5, or 10 persons. They answered a 20-item multiplechoice quiz covering a variety of topics and assigned point values to items to reflect confidence in the answer. Dependent variables were an overall performance score that reflected point values assigned to correct items, number of correct items (expertise), and percentage of possible points given the number of correct items (recognition of group expertise). Results indicate that all three dependent variables were increased by group size. In Study 2, 216 subjects were assigned to 2-or 10-person groups that used procedures to equalize participation. Results suggest that the procedures affected participation patterns in 10-person groups but did notaffectperformance measures. Skewedparticipation patterns that are typical of moderately sized or la...


title: Music Emotion Identification from Lyrics

abstract: Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text.


title: A MEMS-Based Flow Rate and Flow Direction Sensing Platform with Integrated Temperature Compensation Scheme

abstract: This study develops a MEMS-based low-cost sensing platform for sensing gas flow rate and flow direction comprising four silicon nitride cantilever beams arranged in a cross-form configuration, a circular hot-wire flow meter suspended on a silicon nitride membrane, and an integrated resistive temperature detector (RTD). In the proposed device, the flow rate is inversely derived from the change in the resistance signal of the flow meter when exposed to the sensed air stream. To compensate for the effects of the ambient temperature on the accuracy of the flow rate measurements, the output signal from the flow meter is compensated using the resistance signal generated by the RTD. As air travels over the surface of the cross-form cantilever structure, the upstream cantilevers are deflected in the downward direction, while the downstream cantilevers are deflected in the upward direction. The deflection of the cantilever beams causes a corresponding change in the resistive signals of the piezoresistors patterned on their upper surfaces. The amount by which each beam deflects depends on both the flow rate and the orientation of the beam relative to the direction of the gas flow. Thus, following an appropriate compensation by the temperature-corrected flow rate, the gas flow direction can be determined through a suitable manipulation of the output signals of the four piezoresistors. The experimental results have confirmed that the resulting variation in the output signals of the integrated sensors can be used to determine not only the ambient temperature and the velocity of the air flow, but also its direction relative to the sensor with an accuracy of ± 7.5° error.


title: Color difference thresholds for computer‐simulated human Gingiva

abstract: OBJECTIVE ::: To determine 50:50% perceptibility threshold (PT) and 50:50% acceptability threshold (AT) for computer-simulated samples of human gingiva using CIEDE2000 and CIELAB color difference formulas. ::: ::: ::: MATERIALS AND METHODS ::: Each of the 60 pairs of simulated human gingiva was displayed on a calibrated monitor, together with three pairs of upper central incisors of different lightness. The color of gingiva left and right from the midline was compared. A total of 30 observers (15 dentists, 15 laypersons) participated in the study. CIEDE2000 and CIELAB formulas were used to calculate the thresholds and a Takagi-Sugeno-Kang Fuzzy Approximation model was used as fitting procedure. Paired t-test (α = 0.05) was used in evaluation of statistical significance of differences. ::: ::: ::: RESULTS ::: The PT and AT for CIEDE2000 and 95% confidence intervals were 1.1 [0.4, 1.7] and 2.8 [1.8, 4.0], respectively. Corresponding CIELAB values were 1.7 [0.2, 2.6] and 3.7 [2.1, 5.7]. Significant differences (P < .01) were recorded between PT and AT, between the corresponding threshold values in CIEDE2000 and CIELAB formulas as well as between dentists and laypersons. ::: ::: ::: CONCLUSIONS ::: The difference between the perceptibility and acceptability threshold for gingiva was statistically significant in both CIEDE2000 and CIELAB. The same was true for differences between the corresponding thresholds using two color difference formulas, and between dentists and laypersons. Visual thresholds of human gingiva were not dependent upon lightness of adjacent teeth. Overall, CIEDE2000 color difference formula provided better fit than CIELAB formula in the evaluation of color difference thresholds of human gingiva. ::: ::: ::: CLINICAL SIGNIFICANCE ::: The data on visual thresholds for healthy human gingiva can be used as quality control tool/guide for selection and evaluation of dental materials, interpretation of color-related findings in clinical dentistry and research, and for standardization in dentistry. It is of particular value that this study was designed based on in-vivo color evaluation of healthy keratinized gingiva of subjects of different ethnicities, age groups, and gender.


title: Emotion Detection and Analysis on Social Media

abstract: In this paper, we address the problem of detection, classification and quantification of emotions of text in any form. We consider English text collected from social media like Twitter, which can provide information having utility in a variety of ways, especially opinion mining. Social media like Twitter and Facebook is full of emotions, feelings and opinions of people all over the world. However, analyzing and classifying text on the basis of emotions is a big challenge and can be considered as an advanced form of Sentiment Analysis. This paper proposes a method to classify text into six different Emotion-Categories: Happiness, Sadness, Fear, Anger, Surprise and Disgust. In our model, we use two different approaches and combine them to effectively extract these emotions from text. The first approach is based on Natural Language Processing, and uses several textual features like emoticons, degree words and negations, Parts Of Speech and other grammatical analysis. The second approach is based on Machine Learning classification algorithms. We have also successfully devised a method to automate the creation of the training-set itself, so as to eliminate the need of manual annotation of large datasets. Moreover, we have managed to create a large bag of emotional words, along with their emotion-intensities. On testing, it is shown that our model provides significant accuracy in classifying tweets taken from Twitter.


title: Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey

abstract: 1. We studied the responses of 103 neurons in visual area V4 of anesthetized macaque monkeys to two novel classes of visual stimuli, polar and hyperbolic sinusoidal gratings. We suspected on both theoretical and experimental grounds that these stimuli would be useful for characterizing cells involved in intermediate stages of form analysis. Responses were compared with those obtained with conventional Cartesian sinusoidal gratings. Five independent, quantitative analyses of neural responses were carried out on the entire population of cells. 2. For each cell, responses to the most effective Cartesian, polar, and hyperbolic grating were compared directly. In 18 of 103 cells, the peak response evoked by one stimulus class was significantly different from the peak response evoked by the remaining two classes. Of the remaining 85 cells, 74 had response peaks for the three stimulus classes that were all within a factor of 2 of one another. 3. An information-theoretic analysis of the trial-by-trial responses to each stimulus showed that all but two cells transmitted significant information about the stimulus set as a whole. Comparison of the information transmitted about each stimulus class showed that 23 of 103 cells transmitted a significantly different amount of information about one class than about the remaining two classes. Of the remaining 80 cells, 55 had information transmission rates for the three stimulus classes that were all within a factor of 2 of one another. 4. To identify cells that had orderly tuning profiles in the various stimulus spaces, responses to each stimulus class were fit with a simple Gaussian model. Tuning curves were successfully fit to the data from at least one stimulus class in 98 of 103 cells, and such fits were obtained for at least two classes in 87 cells. Individual neurons showed a wide range of tuning profiles, with response peaks scattered throughout the various stimulus spaces; there were no major differences in the distributions of the widths or positions of tuning curves obtained for the different stimulus classes. 5. Neurons were classified according to their response profiles across the stimulus set with two objective methods, hierarchical cluster analysis and multidimensional scaling. These two analyses produced qualitatively similar results. The most distinct group of cells was highly selective for hyperbolic gratings. The majority of cells fell into one of two groups that were selective for polar gratings: one selective for radial gratings and one selective for concentric or spiral gratings. There was no group whose primary selectivity was for Cartesian gratings. 6. To determine whether cells belonging to identified classes were anatomically clustered, we compared the distribution of classified cells across electrode penetrations with the distribution that would be expected if the cells were distributed randomly. Cells with similar response profiles were often anatomically clustered. 7. A position test was used to determine whether response profiles were sensitive to precise stimulus placement. A subset of Cartesian and non-Cartesian gratings was presented at several positions in and near the receptive field. The test was run on 13 cells from the present study and 28 cells from an earlier study. All cells showed a significant degree of invariance in their selectivity across changes in stimulus position of up to 0.5 classical receptive field diameters. 8. A length and width test was used to determine whether cells preferring non-Cartesian gratings were selective for Cartesian grating length or width. Responses to Cartesian gratings shorter or narrower than the classical receptive field were compared with those obtained with full-field Cartesian and non-Cartesian gratings in 29 cells. Of the four cells that had shown significant preferences for non-Cartesian gratings in the main test, none showed tuning for Cartesian grating length or width that would account for their non-Cartesian res


title: ADS-B via Iridium NEXT satellites

abstract: Iridium NEXT will replace Iridium's current satellite constellation, ensuring continuity of the first generation of global mobile satellite services. Iridium NEXT constellation will consist of 66 cross-linked, LEO satellites in six orbital planes intersecting over the North and South Poles. Each Iridium Next satellite will integrate a secondary payload. This paper is based on the feasibility study to embark a dedicated ADS-B secondary payload, on board half or all satellites.


title: A time-varying subjective quality model for mobile streaming videos with stalling events

abstract: Over-the-top mobile video streaming is invariably influenced by volatile network conditions which cause playback interruptions (stalling events), thereby impairing users' quality of experience (QoE). Developing models that can accurately predict users' QoE could enable the more efficient design of quality-control protocols for video streaming networks that reduce network operational costs while still delivering high-quality video content to the customers. Existing objective models that predict QoE are based on global video features, such as the number of stall events and their lengths, and are trained and validated on a small pool of ad hoc video datasets, most of which are not publicly available. The model we propose in this work goes beyond previous models as it also accounts for the fundamental effect that a viewer's recent level of satisfaction or dissatisfaction has on their overall viewing experience. In other words, the proposed model accounts for and adapts to the recency, or hysteresis effect caused by a stall event in addition to accounting for the lengths, frequency of occurrence, and the positions of stall events - factors that interact in a complex way to affect a user's QoE. On the recently introduced LIVE-Avvasi Mobile Video Database, which consists of 180 distorted videos of varied content that are afflicted solely with over 25 unique realistic stalling events, we trained and validated our model to accurately predict the QoE, attaining standout QoE prediction performance.


title: Image Segmentation by Improved Watershed Transformation in Programming Environment MATLAB

abstract: Image segmentation is the foundation of object recognition and computer vision. Watershed transform is usually adopted for image segmentation in the area of image processing and image analysis because it always generates closed contours for each region in the original image. The concept of watershed transform is based on a processing simulating the immersion of a landscape in a lake that is dams have to be built to prevent the merging of different catchment basins. But the watershed transformation leads to over segmentation. In this paper we will discuss the image segmentation by improved watershed transformation in MATLAB programming environment.


title: Table Recognition in Heterogeneous Documents Using Machine Learning

abstract: Tables are an easy way to represent information in a structural form. Table recognition is important for the extraction of such information from document images. Usually, modern OCR systems provide textual information coming from tables without recognizing actual table structure. However, recognition of table structure is important to get the contextual meaning of the contents. Table structure recognition in heterogeneous documents is challenging due to a variety of table layouts. It becomes harder where no physical rulings are present in a table. This work proposes a novel learning based methodology for the recognition of table contents in heterogeneous document images. Textual contents of documents are classified as table or non-table elements using a pre-trained neural network model. The output of the neural network is further enhanced by applying a contextual post processing on each element to correct the classifications errors if any. The system is trained using a subset of UNLV and UW3 document images and depicted more than 97% accuracy on a test set in detection of table and non-table elements.


title: Scheduling Real-Time Communication in IEEE 802.1Qbv Time Sensitive Networks

abstract: The enhancements being developed by the Time-Sensitive Networking Task Group as part of IEEE 802.1 emerge as the future of real-time communication over Ethernet networks for automotive and industrial application domains. In particular IEEE 802.1Qbv is key to enabling timeliness guarantees via so-called time-aware shapers. In this paper, we address the computation of fully deterministic schedules for 802.1Qbv-compliant multi-hop switched networks. We identify and analyze key functional parameters affecting the deterministic behaviour of real-time communication under 802.1Qbv and, based on a generalized configuration of these parameters, derive the required constraints for computing offline schedules guaranteeing low and bounded jitter and deterministic end-to-end latency for critical communication flows. Furthermore, we discuss several optimization directions and concrete configurations exposing trade-offs against the required computation time. We also show the performance of our approach via synthetic network workloads on top of different network configurations.


title: Face Attributes Recognition via Deep Multi-Task Cascade

abstract: Predicting face attributes in images is challenging due to complex face variations. We observe that the signals of some attributes are strong in roughly fixed facial area. In this paper, we propose a deep multi-task cascaded network for face attributes recognition, which is trained in an end-to-end way by the combination of attribute-associated region discovery and attribute prediction together. Associated region is mapped by the first sub-network, which is a fully convolutional network (FCN). Then the second sub-network takes the arbitrary-sized associated regions as input. We use RoI pooling to transform them into a uniform-sized region, and train the whole model by stochastic gradient descent (SGD). We also introduce several useful training strategies, including the batch sampling and the early stopping. We adopt sample augmentation for the balance of positive and negative training data in batch sampling. We involve the early stopping to terminate the iterations flexibly for each attribute in case of the model deviation caused by the overfitting of easy attributes. We evaluate our model on two widely used attributes recognition databases, and the experimental results demonstrate that the performance of our approach is better than other state-of-the-art methods on the most of face attributes.


title: Learning from Paper, Learning from Screens: Impact of Screen Reading and Multitasking Conditions on Reading and Writing among College Students

abstract: Electronic screens on laptop and tablet computers are being used for reading text, often while multitasking. Two experimental studies with college students explored the effect of medium and opportunities to multitask on reading Study 1 and report writing Study 2. In Study 1, participants N = 120 read an easy and difficult passage on paper, a laptop, or tablet, while either multitasking or not multitasking. Neither multitasking nor medium impacted reading comprehension, but those who multitasked took longer to read both passages, indicating loss of efficiency with multitasking. In Study 2, participants N = 67 were asked to synthesize source material in multiple texts to write a one-page evidence-based report. Participants read the source texts either on 1 paper, 2 computer screen without Internet or printer access, or 3 computer screen with Internet and printer access called the "real-world" condition. There were no differences in report quality or efficiency between those whose source materials were paper or computer. However, global report quality was significantly better when participants read source texts on a computer screen without Internet or printer access, compared with when they had Internet and printer access. Active use of paper for note-taking greatly reduced the negative impact of Internet and printer access in the real-world condition. Although participants expressed a preference for accessing information on paper, reading the texts on paper did not make a significant difference in report quality, compared with either of the two computer conditions. Implications for formal and informal learning are discussed.


title: A Hybrid Technique for Medical Image Segmentation

abstract: Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.


title: Defining immunological dysfunction in sepsis: A requisite tool for precision medicine.

abstract: OBJECTIVES ::: Immunological dysregulation is now recognised as a major pathogenic event in sepsis. Stimulation of immune response and immuno-modulation are emerging approaches for the treatment of this disease. Defining the underlying immunological alterations in sepsis is important for the design of future therapies with immuno-modulatory drugs. ::: ::: ::: METHODS ::: Clinical studies evaluating the immunological response in adult patients with Sepsis and published in PubMed were reviewed to identify features of immunological dysfunction. For this study we used key words related with innate and adaptive immunity. ::: ::: ::: RESULTS ::: Ten major features of immunological dysfunction (FID) were identified involving quantitative and qualitative alterations of [antigen presentation](FID1), [T and B lymphocytes] (FID2), [natural killer cells] (FID3), [relative increase in T regulatory cells] (FID4), [increased expression of PD-1 and PD-ligand1](FID5), [low levels of immunoglobulins](FID6), [low circulating counts of neutrophils and/or increased immature forms in non survivors](FID7), [hyper-cytokinemia] (FID8), [complement consumption] (FID9), [defective bacterial killing by neutrophil extracellular traps](FID10). ::: ::: ::: CONCLUSIONS ::: This review article identified ten major features associated with immunosuppression and immunological dysregulation in sepsis. Assessment of these features could help in utilizing precision medicine for the treatment of sepsis with immuno-modulatory drugs.


Antecedents of software-as-a-service (SaaS) adoption: a structural equation model


title: RECON - A Controlled English for Business Rules

abstract: Capturing business rules in a formal logic representation supports the enterprise in two important ways: it enables the evaluation of logs and audit records for conformance to, or violation of, the rules; and it enables the conforming automation of some enterprise activities. The problem is that formal logic representations of the rules are very difficult for an industry expert to read and even more difficult to write, and translating the natural language of the enterprise to formal logic is an unsolved problem. RECON – Restricted English for Constructing Ontologies – is a subset of English that can be easily read by an industry expert, while having a formal grammar and an unambiguous translation to formal logic. This paper describes the principal features of the RECON language, with examples, and shows the corresponding formal logic constructs that are produced by the RECON tool.


title: Learning Gaussian processes from multiple tasks

abstract: We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (GPs). The resulting models can be learned easily via an EM-algorithm. Empirical studies on multi-label text categorization suggest that the presented models allow accurate solutions of these multi-task problems.


title: Matching under Preferences

abstract: Matching theory studies how agents and/or objects from different sets can be matched with each other while taking agents' preferences into account. The theory originated in 1962 with a celebrated paper by David Gale and Lloyd Shapley (1962), in which they proposed the Stable Marriage Algorithm as a solution to the problem of two-sided matching. Since then, this theory has been successfully applied to many real-world problems such as matching students to universities, doctors to hospitals, kidney transplant patients to donors, and tenants to houses. This survey will focus on algorithmic as well as strategic issues of matching theory.


title: A full stack for quick prototyping of IoT solutions

abstract: The paper presents a novel approach to the prototyping of interconnected products belonging to the Internet of Things field. The chosen solution tries to merge the benefits of monolithic vertical approaches (where everything is chosen from the board to the cloud) with those of horizontal solutions (where every time all the elements can be chosen and the code for integrating them written). The proposed solution allows to speed up the prototyping process and to let the developers focus on coding the product behaviours instead of solving customization issues. The advantages of the proposed solution goes beyond the prototyping, as the prototyped solution can be easily converted in an industrial grade one. The paper ends with a real case application that shows how an IoT industrial refrigerator smar-tification unit has been developed thanks to the horizontal stack here proposed.


title: Fast event-based Harris corner detection exploiting the advantages of event-driven cameras

abstract: The detection of consistent feature points in an image is fundamental for various kinds of computer vision techniques, such as stereo matching, object recognition, target tracking and optical flow computation. This paper presents an event-based approach to the detection of corner points, which benefits from the high temporal resolution, compressed visual information and low latency provided by an asynchronous neuromorphic event-based camera. The proposed method adapts the commonly used Harris corner detector to the event-based data, in which frames are replaced by a stream of asynchronous events produced in response to local light changes at μs temporal resolution. Responding only to changes in its field of view, an event-based camera naturally enhances edges in the scene, simplifying the detection of corner features. We characterised and tested the method on both a controlled pattern and a real scenario, using the dynamic vision sensor (DVS) on the neuromorphic iCub robot. The method detects corners with a typical error distribution within 2 pixels. The error is constant for different motion velocities and directions, indicating a consistent detection across the scene and over time. We achieve a detection rate proportional to speed, higher than frame-based technique for a significant amount of motion in the scene, while also reducing the computational cost.


title: Orchestrated Reduction of Quantum Coherence in Brain Microtubules: A Model for Consciousness

abstract: Orch OR (“Orchestrated Objective Reduction”) is a theory of consciousness put forth in the mid-1990s by British physicist Sir Roger Penrose and American anesthesiologist Stuart Hameroff. Whereas most theories assume consciousness emerges from complex computation at the level of synapses among brain neurons, Orch OR involves a specific form of quantum computation which underlies these neuronal synaptic activities. The proposed quantum computations occur in structures inside the brain’s neurons called microtubules.


title: An in-pipe robot with underactuated parallelogram crawler modules

abstract: In this paper, we present a new in-pipe robot with independent underactuated parallelogram crawler modules, which can automatically overcome inner obstacles in the pipes. The parallelogram crawler modules are adopted to maintain the anterior-posterior symmetry of forward and backward movements, and a simple differential mechanism based on a pair of spur gears is installed to provide underactuated mechanisms. A central base unit connects each crawler module through foldable pantograph mechanisms. To verify the basic behavior of this robot, primary experiments in pipes with different diameters and at partial steps were conducted.


title: TextBoxes: A Fast Text Detector with a Single Deep Neural Network

abstract: This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.


title: Barriers to e-commerce and competitive business models in developing countries: A case study

abstract: This paper integrates and extends research on e-commerce in the developing world. We use three categories of feedback systems-economic, sociopolitical and cognitive-to offer a simple model of e-commerce barriers in the developing world. We also examine characteristics of e-business models that can be successfully employed in developing countries. Then, we provide the case of an e-business model followed by a Nepal-based multiple international award winning online provider. This paper's theoretical contribution is to explain the 'hows' and 'whys' of e-commerce in developing countries and to identify clear contexts and attendant mechanism.


title: A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression

abstract: Several clinical studies on major depressive disorder (MDD) have shown that blood brain-derived neurotrophic factor (BDNF) – a factor used to index neuroplasticity – is associated with depression response; however, the results are mixed. The purpose of our study was to evaluate whether BDNF levels are correlated with improvement of depression. We performed a systematic review and meta-analysis of the literature, searching Medline, Cochrane Central, SciELO databases and reference lists from retrieved articles for clinical studies comparing mean BDNF blood levels in depressed patients pre- and post-antidepressant treatments or comparing depressed patients with healthy controls. Two reviewers independently searched for eligible studies and extracted outcome data using a structured form previously elaborated. Twenty articles, including 1504 subjects, met our inclusion criteria. The results showed that BDNF levels increased significantly after antidepressant treatment (effect size 0.62, 95% CI 0.36–0.88, random effects model). In addition, there was a significant correlation between changes in BDNF level and depression scores changes ( p =0.02). Moreover, the results were robust according to the sensitivity analysis and Begg's funnel plot results did not suggest publication bias. Finally, there was a difference between pre-treatment patients and healthy controls (effect size 0.91, 95% CI 0.70–1.11) and a small but significant difference between treated patients and healthy controls (effect size 0.34, 95% CI 0.02–0.66). Our results show that BDNF levels are associated with clinical changes in depression; supporting the notion that depression improvement is associated with neuroplastic changes.


title: SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS

abstract: In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. ::: ::: The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.


title: Automatic Fingerprint Verification Using Neural Networks

abstract: This paper presents an application of Learning Vector Quantization (LVQ) neural network (NN) to Automatic Fingerprint Verification (AFV). The new approach is based on both local (minutiae) and global image features (shape signatures). The matched minutiae are used as reference axis for generating shape signatures which are then digitized to form a feature vector describing the fingerprint. A LVQ NN is trained to match the fingerprints using the difference of a pair of feature vectors. The results show that the integrated system significantly outperforms the minutiae-based system alone in terms of classification accuracy. It also confirms the ability of the trained NN to have consistent performance on unseen databases.


title: Source separated MSW composting in the U.S

abstract: From Michigan and Minnesota to Washington and California, household organics are being separated and sent to specially-designed composting sites. Part II.


title: An 8-bit QVGA AMOLED driver IC with a polynomial interpolation DAC

abstract: The paper proposes an 8-bit AMOLED driver IC with a polynomial interpolation DAC. This architecture maintains high-accuracy AMOLED panels with 8-bit compensated gamma correction and supporting low-complex configuration which results in additional occupied die area. The proposed driver consists of a 6-bit gamma correction resistor-string DAC and a 2-bit polynomial interpolation current-modulation sub-DAC. The two-stage DAC leads to a compact die size compared with conventional 8-bit resister-string DAC, and the polynomial interpolation method provides high accurate grey level voltages than linear one. The AMOLED driver was realized in 0.35-μm CMOS process with DNL and INL of 0.43 LSB and 0.43 LSB.


title: Machine Self-Confidence in Autonomous Systems via Meta-Analysis of Decision Processes

abstract: Algorithmic assurances assist human users in trusting advanced autonomous systems appropriately. This work explores one approach to creating assurances in which systems self-assess their decision-making capabilities, resulting in a ‘self-confidence’ measure. We present a framework for self-confidence assessment and reporting using meta-analysis factors, and then develop a new factor pertaining to ‘solver quality’ in the context of solving Markov decision processes (MDPs), which are widely used in autonomous systems. A novel method for computing solver quality self-confidence is derived, drawing inspiration from empirical hardness models. Numerical examples show our approach has desirable properties for enabling an MDP-based agent to self-assess its performance for a given task under different conditions. Experimental results for a simulated autonomous vehicle navigation problem show significantly improved delegated task performance outcomes in conditions where self-confidence reports are provided to users.


title: Online Target Recognition for Time-Sensitive Space Information Networks

abstract: The key difficulties of online target recognition task for space information networks lie in the contradiction between time-sensitive response requirement and resource constraints(e.g., computation resource, communication resource, and training samples). To deal with the above problems, an effective online target recognizing approach is proposed, which seamlessly integrates fast online information processing task and efficient target-specific high-rate compression task. The proposed approach begins with enhancing the target-background separability by introducing intraclass and interclass couples, the new model adapted for the hospt spot image is then obtained by capturing the relation between the online target data and the massive historical data. The light-weight target-specific information is efficiently transmitted into the ground system, and the whole scene is capable of being recovered while the details of targets are being preserved. Compared with the traditional target recognition methods, the proposed approach is more promising for time-sensitive space information networks. The experiments demonstrate the effectiveness of the proposed approach.


title: Building Better Detection with Privileged Information

abstract: Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While this approach to detection has been proven to be effective in many environments, it is limited to training on only features that can be reliably collected at test-time. Hence, they fail to leverage the often vast amount of ancillary information available from past forensic analysis and post-mortem data. In short, detection systems don't train (and thus don't learn from) features that are unavailable or too costly to collect at run-time. In this paper, we leverage recent advances in machine learning to integrate privileged information --features available at training time, but not at run-time-- into detection algorithms. We apply three different approaches to model training with privileged information; knowledge transfer, model influence, and distillation, and empirically validate their performance in a range of detection domains. Our evaluation shows that privileged information can increase detector precision and recall: we observe an average of 4.8% decrease in detection error for malware traffic detection over a system with no privileged information, 3.53% for fast-flux domain bot detection, 3.33% for malware classification, 11.2% for facial user authentication. We conclude by exploring the limitations and applications of different privileged information techniques in detection systems.


title: The Cajun French Debate in Louisiana

abstract: Ethnic nationalism is often accompanied by movements to restore all or some of the former importance of what has become a secondary language. When a minority language has evolved into a number of regional vernaculars, all of which are stigmatized and shorn of their public and literary importance in the wider society (and this was the outcome for the French language in post-Bellum Louisiana), attempts to reinstate standardized speech can by themselves become the symbolic objectives of ethnic revival. This discussion is centered on the revival of the French language in Louisiana and the resulting debate between those who would place their priorities in teaching an international or standard French, and those who prefer to perpetuate the vernacular that is still used in many regions of South Louisiana. The Cajun French debate, as it is referred to on occasion, is here presented not as a consideration of viable alternatives for minority language maintenance, but as a debate between social groups in South Louisiana for whom the French language and a ‘Cajun’ ethnic identity do not necessarily have the same meaning.1


title: Equilateral Measurement of Rotational Positions With Magnetic Encoders

abstract: A magnetic encoder with a novel arrangement of Hall-effect sensors is proposed for measurement of rotational angles. Three or six Hall-effect sensors are installed around a ring magnet to form an equilateral triangle. In contrast to a quadrature measurement, the equilateral arrangement is capable of eliminating harmonics of the third order and its multiples. Like quadrature measurements, even-order harmonics can also be canceled with a complementary set of Hall-effect sensors. As a result, measurement accuracy is greatly improved without resorting to lookup tables or sophisticated signal conditioning algorithms. The method is applied to a ring magnet revolving about an eccentric axis. Both numerical simulations and experimental results are conducted to verify its performance. It is shown that when a complementary set of Hall-effect sensors are used, the equilateral method yields a higher accuracy over the quadrature one by about 30 times in the simulations with ideal Hall-effect sensors. In the experiments with actual Hall-effect sensors, the proposed method still improves accuracy by about four to five times.


title: Unifying software engineering and systems engineering

abstract: The author describes CMMI (Capability Maturity Model Integration) and the emerging project methods which demonstrate the opportunities for process improvement gains open to organizations. The organization that changes from separated software and system engineering processes to a more unified approach will find itself far more suited to developing dynamically changing, software-intensive systems. Culture change is never easy, but the alternative is even less palatable.


title: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation

abstract: Abstract Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented.