title: Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

abstract: Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.


title: FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition

abstract: Machine-learning algorithms have shown outstanding image recognition/classification performance for computer vision applications. However, the compute and energy requirement for implementing such classifier models for large-scale problems is quite high. In this paper, we propose feature driven selective classification (FALCON) inspired by the biological visual attention mechanism in the brain to optimize the energy-efficiency of machine-learning classifiers. We use the consensus in the characteristic features (color/texture) across images in a dataset to decompose the original classification problem and construct a tree of classifiers (nodes) with a generic-to-specific transition in the classification hierarchy. The initial nodes of the tree separate the instances based on feature information and selectively enable the latter nodes to perform object specific classification. The proposed methodology allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. Additionally, we propose a programmable and scalable neuromorphic engine (NeuE) that utilizes arrays of specialized neural computational elements to execute the FALCON-based classifier models for diverse datasets. The structure of FALCON facilitates the reuse of nodes while scaling up from small classification problems to larger ones thus allowing us to construct classifier implementations that are significantly more efficient. We evaluate our approach for a 12-object classification task on the Caltech101 dataset and ten-object task on CIFAR-10 dataset by constructing FALCON models on the NeuE platform in 45-nm technology. Our results demonstrate up to $3.66\boldsymbol \times $ improvement in energy-efficiency for no loss in output quality, and even higher improvements of up to $5.91\boldsymbol \times $ with 3.9% accuracy loss compared to an optimized baseline network. In addition, FALCON shows an improvement in training time of up to $1.96\boldsymbol \times $ as compared to the traditional classification approach.


title: Behavior change support systems: the next frontier for web science

abstract: new generation of the web has opened up opportunities for developing new kinds of information systems for influencing users. For instance, one of the most prominent areas for future healthcare improvement is the role of the web in fostering improved health and healthier lifestyles. The success or failure of web information systems seems to rely on their social features. For this reason, a better understanding of the techno-social aspects of web information systems and the way they influence people is needed. This article is conceptual and theory-creating by its nature. It introduces the concept of a behavior change support system and suggests it as a key construct for web science research. The behavior change support systems are characterized by their persuasive purpose.


title: A Simple Weight Decay Can Improve Generalization

abstract: It has been observed in numerical simulations that a weight decay can improve generalization in a feed-forward neural network. This paper explains why. It is proven that a weight decay has two effects in a linear network. First, it suppresses any irrelevant components of the weight vector by choosing the smallest vector that solves the learning problem. Second, if the size is chosen right, a weight decay can suppress some of the effects of static noise on the targets, which improves generalization quite a lot. It is then shown how to extend these results to networks with hidden layers and non-linear units. Finally the theory is confirmed by some numerical simulations using the data from NetTalk.


title: A generic neural acoustic beamforming architecture for robust multi-channel speech processing

abstract: Generic robust beamforming approach supported by a neural network.Microphone array agnostic.No assumptions on acoustic transfer function.Robust against noise as well as reverberation.Achieves state-of-the-art ASR results for CHiME 3 and REVERB. Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition(ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multi-channel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3challenge, which features challenging real-world noise distortions, and the Reverbchallenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.


title: Spatio-spectral filters for improving the classification of single trial EEG

abstract: Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, nonstationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.


title: Discovering Activities to Recognize and Track in a Smart Environment

abstract: The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.


title: G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

abstract: G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of thet, F, and χ2 test families. In addition, it includes power analyses forz tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.


title: A flexible modified dipole antenna printed on PET film

abstract: A flexible transparent modify dipole antenna printed on PET film is presented in this paper. The proposed antenna was designed to operate at 2.4GHz for ISM applications. The impedance characteristic and the radiation characteristic were simulated and measured. The proposed antenna has good performance. It can be easily mounted on conformal shape, because it is fabricated on PET film having the flexible characteristic.


title: Open Knowledge Extraction Challenge 2017

abstract: The Open Knowledge Extraction Challenge invites researchers and practitioners from academia as well as industry to compete to the aim of pushing further the state of the art of knowledge extraction from text for the Semantic Web. The challenge has the ambition to provide a reference framework for research in this field by redefining a number of tasks typically from information and knowledge extraction by taking into account Semantic Web requirements and has the goal to test the performance of knowledge extraction systems. This year, the challenge goes in the third round and consists of three tasks which include named entity identification, typing and disambiguation by linking to a knowledge base depending on the task. The challenge makes use of small gold standard datasets that consist of manually curated documents and large silver standard datasets that consist of automatically generated synthetic documents. The performance measure of a participating system is twofold base on (1) Precision, Recall, F1-measure and on (2) Precision, Recall, F1-measure with respect to the runtime of the system.


title: Adaptive Feature Extraction and Image matching Based on Haar Wavelet Transform and SIFT

abstract: Recently, Scale Invariant Feature Transform (SIFT) algorithm is widely used in feature extraction and image matching. However, it has some defects, such as large volume of computational data and low efficiency of image matching. To address these defects, adaptive feature extraction and image matching based on Haar Wavelet Transform and SIFT (AHWT-SIFT) is proposed in this paper. In view of the characteristics of Haar wavelet, the low-frequency components of image can be decomposed adaptively by DWT, which represents the main features of the image and avoids the high-frequency of instability redundant information. Then SIFT is applied in these low-frequency components to extract the feature points. Furthermore, nearest neighbor algorithm is utilized for image matching. The experimental results have shown that the proposed scheme not only retains the general characteristics of SIFT, but the speed and accuracy of feature points matching have been greatly improved.


title: RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks

abstract: Linguistic steganography based on text carrier auto-generation technology is a current topic with great promise and challenges. Limited by the text automatic generation technology or the corresponding text coding methods, the quality of the steganographic text generated by previous methods is inferior, which makes its imperceptibility unsatisfactory. In this paper, we propose a linguistic steganography based on recurrent neural networks, which can automatically generate high-quality text covers on the basis of a secret bitstream that needs to be hidden. We trained our model with a large number of artificially generated samples and obtained a good estimate of the statistical language model. In the text generation process, we propose fixed-length coding and variable-length coding to encode words based on their conditional probability distribution. We designed several experiments to test the proposed model from the perspectives of information hiding efficiency, information imperceptibility, and information hidden capacity. The experimental results show that the proposed model outperforms all the previous related methods and achieves the state-of-the-art performance.


title: AWZone: Ad hoc WiFi Zonification in Post-Disaster Settings

abstract: Indoor localization or zonification in disaster affected settings is a challenging research problem. Existing studies encompass localization and tracking of first-responders or fire fighters using wireless sensor networks. In addition to that, fast evacuation, routing, and planning have also been proposed. However, the problem of locating survivors or victims is yet to be explored to the full potential. State-of-the-art literature often employ infrastructure dependent solutions, for example, WiFi localization using WiFi access points exploiting fingerprinting techniques, Pedestrian Dead Reckoning (PDR) starting from known locations, etc. Owing to unpredictable and dynamic nature of disaster affected environments, infrastructure dependent solutions are seldom useful. Therefore, in this study, we propose an ad hoc WiFi zonification technique (named as AWZone) that is independent of any infrastructural settings. AWZone attempts to perform localization through exploiting commodity smartphones as a beaconing device and successively searching and narrowing down the search space. We perform two testbed experiments. The results reveal that, for a single survivor or victim, AWZone can identify the search space and estimate a location with an approximate 1.5m localization error through eliminating incorrect zones from a set of possible results.


Boronic acid-based lectin mimics (boronolectins) that can recognize cancer biomarker, the Thomsen-Friedenrich antigen.


title: A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: Integrating BIMs and SNS

abstract: The problem of data integration throughout the lifecycle of a construction project among multiple collaborative enterprises remains unsolved due to the dynamics and fragmented nature of the construction industry. This study presents a novel cloud approach that, focusing on China's special construction requirements, proposes a series of as-built BIM (building information modeling) tools and a self-organised application model that correlates project engineering data and project management data through a seamless BIM and BSNS (business social networking services) federation. To achieve a logically centralised single-source data structure, a unified data model is constructed that integrates two categories of heterogeneous databases through the adoption of handlers. Based on these models, key technical mechanisms that are critical to the successful management of large amounts of data are proposed and implemented, including permission, data manipulation and file version control. Specifically, a dynamic Generalised List series is proposed to address the sophisticated construction file versioning issue. The proposed cloud has been successfully used in real applications in China. This research work can enable data sharing not only by individuals and project teams but also by enterprises in a consistent and sustainable way throughout the life of a construction project. This system will reduce costs for construction firms by providing effective and efficient means and guides to complex project management, and by facilitating the conversion of project data into enterprise-owned properties.


title: W4: Real-time surveillance of people and their activities

abstract: W/sup 4/ is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W/sup 4/ employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W/sup 4/ can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W/sup 4/ can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320/spl times/240 resolution images on a 400 MHz dual-Pentium II PC.


NB-IoT for D 2 D-Enhanced Content Uploading with Social Trustworthiness in 5 G Systems †


title: Optimal Resource Allocation in Full-Duplex Wireless-Powered Communication Network

abstract: This paper studies optimal resource allocation in the wireless-powered communication network (WPCN), where one hybrid access point (H-AP) operating in full duplex (FD) broadcasts wireless energy to a set of distributed users in the downlink (DL) and, at the same time, receives independent information from the users via time-division multiple access in the uplink (UL). We design an efficient protocol to support simultaneous wireless energy transfer (WET) in the DL and wireless information transmission (WIT) in the UL for the proposed FD-WPCN. We jointly optimize the time allocations to the H-AP for DL WET and different users for UL WIT and the transmit power allocations over time at the H-AP to maximize the users' weighted sum rate of UL information transmission with harvested energy. We consider both the cases with perfect and imperfect self-interference cancellation (SIC) at the H-AP, for which we obtain optimal and suboptimal time and power allocation solutions, respectively. Furthermore, we consider the half-duplex (HD) WPCN as a baseline scheme and derive its optimal resource allocation solution. Simulation results show that the FD-WPCN outperforms the HD-WPCN when effective SIC can be implemented and more stringent peak power constraint is applied at the H-AP.


title: Bat algorithm for constrained optimization tasks

abstract: In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.


Twitter as a Corpus for Sentiment Analysis and Opinion Mining


Light Weight VANET using Multi-Hop Cluster Based WAVE and LTE


title: Comparison of Active Battery Balancing Systems

abstract: A quantitative model-based comparison of 10 active balancing circuits is presented for equalizing imbalanced cell energies of lithium-ion batteries. A mean current model approach is introduced for active balancing to describe energy transfers between battery cells for long charge/discharge operation. A Th233;venin model for 96 battery cells is used as a subsystem of a mid-size electric vehicle. In quasi-static simulations, FTP75 driving cycles are continuously repeated until the first cell is discharged from initial full charge. Simulation results are compared for cases of new and aged cells with different capacity distributions concerning equalization speeds and final differences of cell energy.


title: An Active Learning Method for Speaker Identity Annotation in Audio Recordings

abstract: Given that manual annotation of speech is an expensive and long process, we attempt in this paper to assist an anno-tator to perform a speaker diarization. This assistance takes place in an annotation background for a large amount of archives. We propose a method which decreases the intervention number of a human. This method corrects a diarization by taking into account the human interventions. The experiment is done using French broadcast TV shows drawn from ANR-REPERE evaluation campaign. Our method is mainly evaluated in terms of KSR (Keystroke Saving Rate), and we reduce the number of actions needed to correct a speaker diarization output by 6.8% in absolute value.


title: Neuroimaging in Alzheimer's disease: current role in clinical practice and potential future applications

abstract: 'Alzheimer's disease is the most common cause of dementia and its prevalence is expected to increase in the coming years. Therefore, accurate diagnosis is crucial for patients, clinicians and researchers. Neuroimaging techniques have provided invaluable information about Alzheimer's disease and, owing to recent advances, these methods will have an increasingly important role in research and clinical practice. The purpose of this article is to review recent neuroimaging studies of Alzheimer's disease that provide relevant information to clinical practice, including a new modality: in vivo amyloid imaging. Magnetic resonance imaging, single photon emission computed tomography and 18F-fluorodeoxyglucose-positron emission tomography are currently available for clinical use. Patients with suspected Alzheimer's disease are commonly investigated with magnetic resonance imaging because it provides detailed images of brain structure and allows the identification of supportive features for the diagnosis. Neurofunctional techniques such as single photon emission computed tomography and 18F-fluorodeoxyglucose-positron emission tomography can also be used to complement the diagnostic investigation in cases of uncertainty. Amyloid imaging is a non-invasive technique that uses positron emission tomography technology to investigate the accumulation of the β-amyloid peptide in the brain, which is a hallmark of Alzheimer's disease. This is a promising test but currently its use is restricted to very few specialized research centers in the world. Technological innovations will probably increase its availability and reliability, which are the necessary steps to achieve robust clinical applicability. Thus, in the future it is likely that amyloid imaging techniques will be used in the clinical evaluation of patients with Alzheimer's disease.


DR AF T Probabilistic Model Checking : Advances and Applications


title: A 13.56 MHz RFID transponder front-end with merged load modulation and voltage doubler-clamping rectifier circuits

abstract: A 13.56 MHz RF transponder front-end circuit was designed for RFID or wireless sensor applications using 0.35-/spl mu/m CMOS technologies. It converts RF power to DC and extracts clock and data; a smartly positioned NMOS device implements load modulation for efficient data sending back. The new design merges the load modulation and voltage clamping into the voltage doubler rectifier circuits to achieve a longer operating distance. Simulation results show that the transponder operates over a wide range of electromagnetic field strength with induced voltage from 0.1 V to 10 V. The chip also generates a power-on reset (POR) signal for digital circuits. The low power analog blocks consume less than 1 /spl mu/A current for the 0.1 V induced voltage.


title: Dynamic Modeling and Performance Analysis of a Grid-Connected Current-Source Inverter-Based Photovoltaic System

abstract: Voltage-source inverter (VSI) topology is widely used for grid interfacing of distributed generation (DG) systems. However, when employed as the power conditioning unit in photovoltaic (PV) systems, VSI normally requires another power electronic converter stage to step up the voltage, thus adding to the cost and complexity of the system. To make the proliferation of grid-connected PV systems a successful business option, the cost, performance, and life expectancy of the power electronic interface need to be improved. The current-source inverter (CSI) offers advantages over VSI in terms of inherent boosting and short-circuit protection capabilities, direct output current controllability, and ac-side simpler filter structure. Research on CSI-based DG is still in its infancy. This paper focuses on modeling, control, and steady-state and transient performances of a PV system based on CSI. It also performs a comparative performance evaluation of VSI-based and CSI-based PV systems under transient and fault conditions. Analytical expectations are verified using simulations in the Power System Computer Aided Design/Electromagnetic Transient Including DC (PSCAD/EMTDC) environment, based on a detailed system model.


title: An Algebraic Fault Attack on the LED Block Cipher

abstract: In this paper we propose an attack on block ciphers where we combine techniques derived from algebraic and fault based cryptanalysis. The recently introduced block cipher LED serves us as a target for our attack. We show how to construct an algebraic representation of the encryption map and how to cast the side channel information gained from a fault injection into polynomial form. The resulting polynomial system is converted into a logical formula in conjunctive normal form and handed over to a SAT solver for reconstruction of the secret key. Following this approach we were able to mount a new, successful attack on the version of LED that uses a 64-bit secret key, requiring only a single fault injection.


title: Computer vision based object grasping 6DoF robotic arm using picamera

abstract: This article presents a project to design a robust robotic arm which can perform multifunctional tasks. The controller of the manipulator is based on Arduino mega 2560 Microcontroller. The aim of the project is to focus all axes of manipulator to lift, carry and unload the objects at a desired location. This requires a precise drive motion control that incorporate electric motors as a drive system. Further experiments are done to implement a camera based 3D vision system integrated with a computer vision algorithm to recognize object deformation and spatial coordination to control the deviation from the original training. The 3D visualization systems are able to detect the objects as well as their distance from the End-effecter and transmit the signals to the drive system. The vision system requires a separate computing hardware capable of processing complex vision algorithms. We utilize Raspberry pi microcontroller for processing the vision data, separately making the vision system capable of recognizing the specified object as per program commands.


title: Capsule Routing for Sound Event Detection

abstract: The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as well as estimating their onset and offset times. We approach this problem with a neural network architecture that uses the recently-proposed capsule routing mechanism. A capsule is a group of activation units representing a set of properties for an entity of interest, and the purpose of routing is to identify part-whole relationships between capsules. That is, a capsule in one layer is assumed to belong to a capsule in the layer above in terms of the entity being represented. Using capsule routing, we wish to train a network that can learn global coherence implicitly, thereby improving generalization performance. Our proposed method is evaluated on Task 4 of the DCASE 2017 challenge. Results show that classification performance is state-of-the-art, achieving an F-score of 58.6%. In addition, overfitting is reduced considerably compared to other architectures.


title: IoT: Designing for Human Values

abstract: This one-day, embedded workshop will explore the design intersections of human values and internet of things (IoT) applications. In a day-long session we will configure and build small IoT devices (using the Particle platform), then deploy them to collect, share and publish the data they harvest throughout the conference. During the conference program we will reconvene to debate how a world of connected devices intersects with human values (such as privacy and transparency) and to articulate the specific challenges for designing a value-conscious IoT.


title: Studying Invariances of Trained Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) define an exceptionally powerful class of models for image classification, but the theoretical background and the understanding of how invariances to certain transformations are learned is limited. In a large scale screening with images modified by different affine and nonaffine transformations of varying magnitude, we analyzed the behavior of the CNN architectures AlexNet and ResNet. If the magnitude of different transformations does not exceed a class- and transformation dependent threshold, both architectures show invariant behavior. In this work we furthermore introduce a new learnable module, the Invariant Transformer Net, which enables us to learn differentiable parameters for a set of affine transformations. This allows us to extract the space of transformations to which the CNN is invariant and its class prediction robust.


title: Escaping Saddles with Stochastic Gradients

abstract: We analyze the variance of stochastic gradients along negative curvature directions in certain non-convex machine learning models and show that stochastic gradients exhibit a strong component along these directions. Furthermore, we show that - contrary to the case of isotropic noise - this variance is proportional to the magnitude of the corresponding eigenvalues and not decreasing in the dimensionality. Based upon this observation we propose a new assumption under which we show that the injection of explicit, isotropic noise usually applied to make gradient descent escape saddle points can successfully be replaced by a simple SGD step. Additionally - and under the same condition - we derive the first convergence rate for plain SGD to a second-order stationary point in a number of iterations that is independent of the problem dimension.


title: Stacked What-Where Auto-encoders

abstract: We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervised, semi-supervised and unsupervised learning without relying on sampling during training. An instantiation of SWWAE uses a convolutional net (Convnet) (LeCun et al. (1998)) to encode the input, and employs a deconvolutional net (Deconvnet) (Zeiler et al. (2010)) to produce the reconstruction. The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet. Each pooling layer produces two sets of variables: the "what" which are fed to the next layer, and its complementary variable "where" that are fed to the corresponding layer in the generative decoder.


title: Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning

abstract: We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of obtaining large numbers of facial images with nationality labels. Then we train state-of-the-art neural networks with these labeled images. We are able to achieve an accuracy of 75.03% in the classification task, with chances being 33.33% and human accuracy 38.89% . Further, we train multiple facial attribute classifiers to identify the most distinctive features for each group. We find that Chinese, Japanese and Koreans do exhibit substantial differences in certain attributes, such as bangs, smiling, and bushy eyebrows. Along the way, we uncover several gender-related cross-country patterns as well. Our work, which complements existing APIs such as Microsoft Cognitive Services and Face++, could find potential applications in tourism, e-commerce, social media marketing, criminal justice and even counter-terrorism.


title: Accelerated, Parallel and Proximal Coordinate Descent

abstract: We propose a new stochastic coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only. Our method (APPROX) is simultaneously Accelerated, Parallel and PROXimal; this is the first time such a method is proposed. In the special case when the number of processors is equal to the number of coordinates, the method converges at the rate $2\bar{\omega}\bar{L} R^2/(k+1)^2 $, where $k$ is the iteration counter, $\bar{\omega}$ is an average degree of separability of the loss function, $\bar{L}$ is the average of Lipschitz constants associated with the coordinates and individual functions in the sum, and $R$ is the distance of the initial point from the minimizer. We show that the method can be implemented without the need to perform full-dimensional vector operations, which is the major bottleneck of existing accelerated coordinate descent methods. The fact that the method depends on the average degree of separability, and not on the maximum degree of separability, can be attributed to the use of new safe large stepsizes, leading to improved expected separable overapproximation (ESO). These are of independent interest and can be utilized in all existing parallel stochastic coordinate descent algorithms based on the concept of ESO.


title: Crowdsourcing based Description of Urban Emergency Events using Social Media Big Data

abstract: Crowdsourcing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human. Especially, nowadays, no countries, no communities, and no person are immune to urban emergency events. Detection about urban emergency events, e.g., fires, storms, traffic jams is of great importance to protect the security of humans. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media usually includes references to urban emergency events occurring at, or affecting specific locations. In this paper, in order to detect and describe the real time urban emergency event, the 5W (What, Where, When, Who, and Why) model is proposed. Firstly, users of social media are set as the target of crowd sourcing. Secondly, the spatial and temporal information from the social media are extracted to detect the real time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive case studies based on real urban emergency events. The results show the accuracy and efficiency of the proposed method.


title: 1.0 Introduction Linearization Techniques for Mixers

abstract: An improved modular window assembly includes a window glass and peripheral frame having a plurality of threaded attached studs spaced therealong extending through matching grommet lined holes spaced inwardly from the peripheral edge of the glass. The frictional engagement of the threaded studs with the grommets serves to retain the glass and frame together as a separately handled unit with no necessity of an injection molded gasket. The window unit may be attached to the pinchweld of a vehicle window opening by dropping the studs through matching holes and threading fasteners to the studs to draw down the window frame. This serves to compress the grommets further to seal the holes through the window glass. A bead of sealant or adhesive may be used in conjunction with the window assembly if desired, and the inwardly spacing of the studs creates an unobstructed space therefor.


title: Mining and recommending software features across multiple web repositories

abstract: The "Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. The effectiveness of feature-related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommending software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). Then, we mine the hidden affinities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. The results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. The result of feature recommendation is effective and interesting.


LC Snubber Designing for DC-DC Converter by Genetic Algorithm and Taguchi Method


title: A holistic account of the own-race effect in face recognition: evidence from a cross-cultural study

abstract: A robust finding in the cross-cultural research is that people's memories for faces of their own race are superior to their memories for other-race faces. However, the mechanisms underlying the own-race effect have not been well defined. In this study, a holistic explanation was examined in which Caucasian and Asian participants were asked to recognize features of Caucasian and Asian faces presented in isolation and in the whole face. The main finding was that Caucasian participants recognized own-race faces more holistically than Asian faces whereas Asian participants demonstrated holistic recognition for both own-race and other-race faces. The differences in holistic recognition between Caucasian and Asian participants mirrored differences in their relative experience with own-race and other-race faces. These results suggest that the own-race effect may arise from the holistic recognition of faces from a highly familiar racial group.


title: System-level max power (SYMPO): a systematic approach for escalating system-level power consumption using synthetic benchmarks

abstract: To effectively design a computer system for the worst case power consumption scenario, system architects often use hand-crafted maximum power consuming benchmarks at the assembly language level. These stressmarks, also called power viruses, are very tedious to generate and require significant domain knowledge. In this paper, we propose SYMPO, an automatic SYstem level Max POwer virus generation framework, which maximizes the power consumption of the CPU and the memory system using genetic algorithm and an abstract workload generation framework. For a set of three ISAs, we show the efficacy of the power viruses generated using SYMPO by comparing the power consumption with that of MPrime torture test, which is widely used by industry to test system stability. Our results show that the usage of SYMPO results in the generation of power viruses that consume 14-41% more power compared to MPrime on SPARC ISA. The genetic algorithm achieved this result in about 70 to 90 generations in 11 to 15 hours when using a full system simulator. We also show that the power viruses generated in the Alpha ISA consume 9-24% more power compared to the previous approach of stressmark generation. We measure and provide the power consumption of these benchmarks on hardware by instrumenting a quad-core AMD Phenom II X4 system. The SYMPO power virus consumes more power compared to various industry grade power viruses on x86 hardware. We also provide a microarchitecture independent characterization of various industry standard power viruses.


title: Recognition of facial expression and visual P300 in schizophrenic patients: Differences between paranoid type patients and non‐paranoid patients

abstract: This study compared the effects of facial affective stimuli on visual event-related potentials (ERP) in schizophrenic patients and healthy subjects using photographs of babies depicting sadness (crying face), neutrality (neutral face), and pleasure (smiling face). Visual ERP were recorded using an oddball paradigm in 32 schizophrenic patients (16 paranoid type and 16 non-paranoid patients) and 32 age-matched healthy subjects. The P300 amplitude, latency, and the subject's reaction time were recorded. The P300 amplitude when viewing a photograph of a smiling baby was the smallest registered of three photographs for healthy subjects and paranoid type patients with successively greater amplitudes for neutrality and sadness. However, the P300 amplitude was the smallest while viewing crying photographs and was the largest while viewing a smiling photograph for non-paranoid patients. These results suggest that the P300 amplitude is influenced by viewing emotionally moving facial expressions and that the effect is different for different subtypes of schizophrenia. These differences may reflect differences in information processing resulted from emotional influences caused by visual-affective stimuli.


title: Self-learning-based post-processing for image/video deblocking via sparse representation

abstract: Abstract Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm.


title: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

abstract: The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.


title: Semantic data mining: A survey of ontology-based approaches

abstract: Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.


title: Collision avoidance in the presence of a virtual agent in small-scale virtual environments

abstract: Computer-controlled, human-like virtual agents (VAs), are often embedded into immersive virtual environments (IVEs) in order to enliven a scene or to assist users. Certain constraints need to be fulfilled, e.g., a collision avoidance strategy allowing users to maintain their personal space. Violating this flexible protective zone causes discomfort in real-world situations and in IVEs. However, no studies on collision avoidance for small-scale IVEs have been conducted yet. Our goal is to close this gap by presenting the results of a controlled user study in a CAVE. 27 participants were immersed in a small-scale office with the task of reaching the office door. Their way was blocked either by a male or female VA, representing their co-worker. The VA showed different behavioral patterns regarding gaze and locomotion. Our results indicate that participants preferred collaborative collision avoidance: they expect the VA to step aside in order to get more space to pass while being willing to adapt their own walking paths.


title: Design, Additive Manufacture, and Control of a Pneumatic MR-Compatible Needle Driver

abstract: This paper reports the design, modeling, and control of an MR-compatible actuation unit comprising pneumatic stepper mechanisms. One helix-shaped bellows and one toroid-shaped bellows were designed to actuate in pure rotation and pure translation, respectively. The actuation unit is a 2-degree-of-freedom (DOF) needle driver that translates and rotates the base of one tube of a steerable needle like a concentric tube robot. For safety, mechanical stops limit needle motion to maximum unplanned step sizes of 0.5 mm and 0.5°. Additively manufactured by selective laser sintering, the flexible fluidic actuating (FFA) mechanism achieves 2-DOF motion as a monolithic, compact, and hermetically sealed device. A second novel contribution is substep control for precise translations and rotations less than full-step increments; steady-state errors of 0.013 mm and 0.018° were achieved. The linear FFA produced peak forces of 33 and –26.5 N for needle insertion and retraction, respectively. The rotary FFA produced bidirectional peak torques of 68 N·mm. With the FFAs in full motion in a 3-T scanner, no loss in signal-to-noise ratio of MR images observed.


title: Workload analysis of a large-scale key-value store

abstract: Key-value stores are a vital component in many scale-out enterprises, including social networks, online retail, and risk analysis. Accordingly, they are receiving increased attention from the research community in an effort to improve their performance, scalability, reliability, cost, and power consumption. To be effective, such efforts require a detailed understanding of realistic key-value workloads. And yet little is known about these workloads outside of the companies that operate them. This paper aims to address this gap. To this end, we have collected detailed traces from Facebook's Memcached deployment, arguably the world's largest. The traces capture over 284 billion requests from five different Memcached use cases over several days. We analyze the workloads from multiple angles, including: request composition, size, and rate; cache efficacy; temporal patterns; and application use cases. We also propose a simple model of the most representative trace to enable the generation of more realistic synthetic workloads by the community. Our analysis details many characteristics of the caching workload. It also reveals a number of surprises: a GET/SET ratio of 30:1 that is higher than assumed in the literature; some applications of Memcached behave more like persistent storage than a cache; strong locality metrics, such as keys accessed many millions of times a day, do not always suffice for a high hit rate; and there is still room for efficiency and hit rate improvements in Memcached's implementation. Toward the last point, we make several suggestions that address the exposed deficiencies.


title: Engineering Cheerful Robots: An Ethical Consideration

abstract: Socially interactive robots in a variety of forms and function are quickly becoming part of everyday life and bring with them a host of applied ethical issues. This paper concerns meta-ethical implications at the interface among robotics, ethics, psychology, and the social sciences. While guidelines for the ethical design and use of robots are necessary and urgent, meeting this exigency opens up the issue of whose values and vision of the ideal society inform public policies. The paper is organized as a sequence of questions: Can robots be agents of cultural transmission? Is a cultural shift an issue for roboethics? Should roboethics be an instrument of (political) social engineering? How could biases of the technological imagination be avoided? Does technological determinism compromise the possibility of moral action? The answers to these questions are not straightforwardly affirmative or negative, but their contemplation leads to heeding C. Wright Mills’ metaphor of the cheerful robot.


title: Robust prediction of fault-proneness by random forests

abstract: Accurate prediction of fault prone modules (a module is equivalent to a C function or a C+ + method) in software development process enables effective detection and identification of defects. Such prediction models are especially beneficial for large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a novel methodology for predicting fault prone modules, based on random forests. Random forests are an extension of decision tree learning. Instead of generating one decision tree, this methodology generates hundreds or even thousands of trees using subsets of the training data. Classification decision is obtained by voting. We applied random forests in five case studies based on NASA data sets. The prediction accuracy of the proposed methodology is generally higher than that achieved by logistic regression, discriminant analysis and the algorithms in two machine learning software packages, WEKA [I. H. Witten et al. (1999)] and See5. The difference in the performance of the proposed methodology over other methods is statistically significant. Further, the classification accuracy of random forests is more significant over other methods in larger data sets.


title: Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective

abstract: This paper presents a novel semi-supervised joint dictionary learning (S2JDL) algorithm for hyperspectral image classification. The algorithm jointly minimizes the reconstruction and classification error by optimizing a semi-supervised dictionary learning problem with a unified objective loss function. To this end, we construct a semi-supervised objective loss function which combines the reconstruction term from unlabeled samples and the reconstruction–discrimination term from labeled samples to leverage the unsupervised and supervised information. In addition, a soft-max loss is used to build the reconstruction–discrimination term. In the training phase, we randomly select the unlabeled samples and loop through the labeled samples to comprise the training pairs, and the first-order stochastic gradient descents are calculated to simultaneously update the dictionary and classifier by feeding the training pairs into the objective loss function. The experimental results with three popular hyperspectral datasets indicate that the proposed algorithm outperforms the other related methods.


title: Effective reinforcement learning for mobile robots

abstract: Programming mobile robots can be a long, time-consuming process. Specifying the low-level mapping from sensors to actuators is prone to programmer misconceptions, and debugging such a mapping can be tedious. The idea of having a robot learn how to accomplish a task, rather than being told explicitly, is an appealing one. It seems easier and much more intuitive for the programmer to specify what the robot should be doing, and to let it learn the fine details of how to do it. In this paper, we introduce a framework for reinforcement learning on mobile robots and describe our experiments using it to learn simple tasks.


title: Mapping the structure of semantic memory

abstract: Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual’s semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals have a small-world structure with short distances between words and high clustering. The distribution of links follows a power law truncated by an exponential cutoff, meaning that most words are poorly connected and a minority of words has a high, although bounded, number of connections. Existing aggregate networks mirror the individual link distributions, and so they are not scale-free, as has been previously assumed; still, there are properties of individual structure that the aggregate networks do not reflect. A simulation of the new sampling process suggests that it can uncover the true structure of an individual’s semantic memory.


title: Region-based strategies for active contour models

abstract: The variational method has been introduced by Kass et al. (1987) in the field of object contour modeling, as an alternative to the more traditional edge detection-edge thinning-edge sorting sequence. since the method is based on a pre-processing of the image to yield an edge map, it shares the limitations of the edge detectors it uses. in this paper, we propose a modified variational scheme for contour modeling, which uses no edge detection step, but local computations instead—only around contour neighborhoods—as well as an “anticipating” strategy that enhances the modeling activity of deformable contour curves. many of the concepts used were originally introduced to study the local structure of discontinuity, in a theoretical and formal statement by leclerc & zucker (1987), but never in a practical situation such as this one. the first part of the paper introduces a region-based energy criterion for active contours, and gives an examination of its implications, as compared to the gradient edge map energy of snakes. then, a simplified optimization scheme is presented, accounting for internal and external energy in separate steps. this leads to a complete treatment, which is described in the last sections of the paper (4 and 5). the optimization technique used here is mostly heuristic, and is thus presented without a formal proof, but is believed to fill a gap between snakes and other useful image representations, such as split-and-merge regions or mixed line-labels image fields.


title: Assessment of Play and Leisure: Delineation of the Problem

abstract: This paper discusses current perspectives on play and leisure and proposes that if play and leisure are to be accepted as viable occupations, then (a) valid and reliable measures of play must be developed, (b) interventions must be examined for inclusion of the elements of play, and (c) the promotion of play and leisure must be an explicit goal of occupational therapy intervention. Existing tools used by occupational therapists to assess clients' play and leisure are evaluated for the aspects of play and leisure they address and the aspects they fail to address. An argument is presented for the need for an assessment of playfulness, rather than of play or leisure activities. A preliminary model for the development of such an assessment is proposed.


title: Factors influencing an organisation's ability to manage innovation: a structured literature review and conceptual model

abstract: Management literature prescribes innovation as a stimulus for sustained competitive advantage in companies; however, the nature of the development in this field has resulted in the literature being broad and fragmented. This paper focuses on the body of literature concerned with the factors which influence innovation management in organisations. The aim of this research is to present a holistic view of the factors that affect innovation management. Using a systematic literature review approach, using over 100 papers, this research identifies nine key factors that impact on an organisation's ability to manage innovation. These nine factors have been identified as management style and leadership, resources, organisational structure, corporate strategy, technology, knowledge management, employees and innovation process. This paper then discusses the inductively derived model that presents the important relationships identified between the factors to present a holistic view of innovation management. From this, we open up the debate on innovation management as a systemic approach rather than being focused on the singular factors. We can therefore conclude that a number of dominant relationships exist between the factors with the innovation process being the only endogenous factor within the model.


title: Online gaming addiction in children and adolescents: A review of empirical research

abstract: Abstract Background and aims: Research suggests that excessive online gaming may lead to symptoms commonly experienced by substance addicts. Since games are particularly appealing to children and adolescents, these individuals may be more at risk than other groups of developing gaming addiction. Methods: Given these potential concerns, a literature review was undertaken in order (i) to present the classification basis of online gaming addiction using official mental disorder frameworks, (ii) to identify empirical studies that assess online gaming addiction in children and adolescents, and (iii) to present and evaluate the findings against the background of related and established mental disorder criteria. Results: Empirical evidence comprising 30 studies indicates that for some adolescents, gaming addiction exists and that as the addiction develops, online gaming addicts spend increasing amounts of time preparing for, organizing, and actually gaming. Conclusions: Evidence suggests that problematic online ...


title: Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

abstract: Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since keyword-based text mining has the limitation whereby, when screening useful keywords, it frequently omits meaningful keywords, analyzers therefore need to repeat the careful scrutiny of the derived keywords to clarify the meaning of keywords. In this research, we structure meaningful keyword sets related to technological information from patent documents; then we layer the keywords, depending on the level of information. This research involves two steps. First, the characteristics of technological information are analyzed by reviewing the patent law and investigating the description of patent documents. Second, the technological information is structured by considering the information types, and the keywords in each type are layered through natural language processing. Consequently, the structured and layered keyword set does not omit useful keywords and the analyzer can easily understand the meaning of each keyword.


Agile Project Manager Competencies in Software Development Projects : A Systematic Literature Review


title: Flight PID controller design for a UAV quadrotor

abstract: This paper presents the modeling of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as the quad rotor aircraft. The paper presents a new model design method for the flight control of an autonomous quad rotor. The paper describes the controller architecture for the quad rotor as well. The dynamic model of the quad-rotor, which is an under actuated aircraft with fixed four pitch angle rotors was described. The Modeling of a quad rotor vehicle is not an easy task because of its complex structure. The aim is to develop a model of the vehicle as realistic as possible. The model is used to design a stable and accurate controller. This paper explains the developments of a PID (proportional-integral-derivative) control method to obtain stability in flying the Quad-rotor flying object. The model has four input forces which are basically the thrust provided by each propeller connected to each rotor with fixed angle. Forward (backward) motion is maintained by increasing (decreasing) speed of front (rear) rotor speed while decreasing (increasing) rear (front) rotor speed simultaneously which means changing the pitch angle. Left and right motion is accomplished by changing roll angle by the same way. The front and rear motors rotate counter-clockwise while other motors rotate clockwise so that the yaw command is derived by increasing (decreasing) counter-clockwise motors speed while decreasing (increasing) clockwise motor speeds.


title: TabEL: Entity Linking in Web Tables

abstract: Web tables form a valuable source of relational data. The Web contains an estimated 154 million HTML tables of relational data, with Wikipedia alone containing 1.6 million high-quality tables. Extracting the semantics of Web tables to produce machine-understandable knowledge has become an active area of research. ::: ::: A key step in extracting the semantics of Web content is entity linking EL: the task of mapping a phrase in text to its referent entity in a knowledge base KB. In this paper we present TabEL, a new EL system for Web tables. TabEL differs from previous work by weakening the assumption that the semantics of a table can be mapped to pre-defined types and relations found in the target KB. Instead, TabEL enforces soft constraints in the form of a graphical model that assigns higher likelihood to sets of entities that tend to co-occur in Wikipedia documents and tables. In experiments, TabEL significantly reduces error when compared to current state-of-the-art table EL systems, including a $$75\%$$ error reduction on Wikipedia tables and a $$60\%$$ error reduction on Web tables. We also make our parsed Wikipedia table corpus and test datasets publicly available for future work.


title: Partitioning Trees with Supply, Demand and Edge-Capacity

abstract: Let T be a given tree. Each vertex of T is either a supply vertex or a demand vertex, and is assigned a positive number, called the supply or demand. Each demand vertex v must be supplied an amount of “power,” equal to the demand of v, from exactly one supply vertex through edges in T . Each edge is assigned a positive number called the capacity. One wishes to partition T into subtrees by deleting edges from T so that each subtree contains exactly one supply vertex whose supply is no less than the sum of all demands in the subtree and the power flow through each edge is no more than capacity of the edge. The “partition problem” is a decision problem to ask whether T has such a partition. The “maximum partition problem” is an optimization version of the partition problem. In this paper, we give three algorithms for the problems. First is a linear-time algorithm for the partition problem. Second is a pseudo-polynomial-time algorithm for the maximum partition problem. Third is a fully polynomial-time approximation scheme (FPTAS) for the maximum partition problem.


title: A statistical approach to predict flight delay using gradient boosted decision tree

abstract: Supervised machine learning algorithms have been used extensively in different domains of machine learning like pattern recognition, data mining and machine translation. Similarly, there has been several attempts to apply the various supervised or unsupervised machine learning algorithms to the analysis of air traffic data. However, no attempts have been made to apply Gradient Boosted Decision Tree, one of the famous machine learning tools to analyse those air traffic data. This paper investigates the effectiveness of this successful paradigm in the air traffic delay prediction tasks. By combining this regression model based on the machine learning paradigm, an accurate and sturdy prediction model has been built which enables an elaborated analysis of the patterns in air traffic delays. Gradient Boosted Decision Tree has shown a great accuracy in modeling sequential data. With the help of this model, day-to-day sequences of the departure and arrival flight delays of an individual airport can be predicted efficiently. In this paper, the model has been implemented on the Passenger Flight on-time Performance data taken from U.S. Department of Transportation to predict the arrival and departure delays in flights. It shows better accuracy as compared to other methods.


title: Automatic English Question Generation System Based on Template Driven Scheme

abstract: Today, automatic generation of questions is a considerable problem by many researchers. Automatic question generation from text has two major activity domains: dialogue/interactive question answering systems and educational assessment. In this paper, a proposed system is designed, implemented and tested to automate question generation process. The proposed system generates questions by selecting one sentence at a time, extracts sections of the source sentence, then applies transformation rules or patterns for constructing a question. It uses pure syntactic pattern-matching approach to generate content-related questions in order to improve the independent study of any textual material. The proposed system is powered by OpenNLP open source statistical parser to generate the questions using pattern matching strategy. The system is able to learn dynamically, ready to use in ease manner and accepts large-scale number of rules.


title: Development of an integrated torque sensor-motor module for haptic feedback in teleoperated robot-assisted surgery

abstract: In Robot-assisted surgery, haptic feedback is ongoing issue. The breaking of surgical suture material and the damage of tissue are avoidable with haptic feedback. This paper is proposing to add haptic feedback in the da Vinci like robot using unmodified da Vinci surgical instrument. To overcome the restriction of space and difficulty in measuring the force at the end-effector, the torque sensor-motor integrated module is developed. The proposed torque sensor-motor module has a totally integrated design that can replace a nominal motor. It also shows linear stress-strain relation and inherent high sensitivity.


title: An efficient algorithm for enumerating all possible input nodes in controlling complex networks

abstract: Controlling complex networks is of great importance in many application regions. Recent works found that the minimum number of input nodes (MIS) used to control a network can be obtained by the maximum matching. However, the maximum matchings of a network are not unique, and there may exist numerous MISs. Finding all possible input nodes is difficult for the large-scale networks because of high computational costs. Here we present an efficient algorithm to enumerating all possible input nodes of a network. We rigorously prove that all possible input nodes can be obtained by a simple modification of the maximum matching algorithm, which allows us to obtain all possible input nodes (the union of all MISs) by only computing one MIS. The experiment results on both synthetic and real networks show that the computational complexity of the proposed algorithm is significantly improved contrast to the previous one. Speedup of up 100000x compared with the previous algorithm was observed for large-scale synthetic networks and real networks.


title: Abnormal Use of Facial Information in High-Functioning Autism

abstract: Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.


title: Speeding up web page loads with Shandian

abstract: Web page loads are slow due to intrinsic inefficiencies in the page load process. Our study shows that the inefficiencies are attributable not only to the contents and structure of the Web pages (e.g., three-fourths of the CSS resources are not used during the initial page load) but also the way that pages are loaded (e.g., 15% of page load times are spent waiting for parsing-blocking resources to be loaded). ::: ::: To address these inefficiencies, this paper presents Shandian (which means lightening in Chinese) that restructures the page load process to speed up page loads. Shandian exercises control over what portions of the page gets communicated and in what order so that the initial page load is optimized. Unlike previous techniques, Shandian works on demand without requiring a training period, is compatible with existing latency-reducing techniques (e.g., caching and CDNs), supports security features that enforce same-origin policies, and does not impose additional privacy risks. Our evaluations show that Shandian reduces page load times by more than half for both mobile phones and desktops while incurring modest overheads to data usage.


title: Light field compression using disparity-compensated lifting and shape adaptation

abstract: We propose disparity-compensated lifting for wavelet compression of light fields. With this approach, we obtain the benefits of wavelet coding, such as scalability in all dimensions, as well as superior compression performance. Additionally, the proposed approach solves the irreversibility limitations of previous light field wavelet coding approaches, using the lifting structure. Our scheme incorporates disparity compensation into the lifting structure for the transform across the views in the light field data set. Another transform is performed to exploit the coherence among neighboring pixels, followed by a modified SPIHT coder and rate-distortion optimized bitstream assembly. A view-sequencing algorithm is developed to organize the views for encoding. For light fields of an object, we propose to use shape adaptation to improve the compression efficiency and visual quality of the images. The necessary shape information is efficiently coded based on prediction from the existing geometry model. Experimental results show that the proposed scheme exhibits superior compression performance over existing light field compression techniques.


title: A Scalable and Explicit Event Delivery Mechanism for UNIX

abstract: UNIX applications not wishing to block when doing I/O often use the select() system call, to wait for events on multiple file descriptors. The select() mechanism works well for small-scale applications, but scales poorly as the number of file descriptors increases. Many modern applications, such as Internet servers, use hundreds or thousands of file descriptors, and suffer greatly from the poor scalability of select(). Previous work has shown that while the traditional implementation of select() can be improved, the poor scalability is inherent in the design. We present a new event-delivery mechanism, which allows the application to register interest in one or more sources of events, and to efficiently dequeue new events. We show that this mechanism, which requires only minor changes to applications, performs independently of the number of file descriptors.


title: Supply Chain Design and Analysis: Models and Methods

abstract: For years, researchers and practitioners have primarily investigated the various processes within manufacturing supply chains individually. Recently, however, there has been increasing attention placed on the performance, design, and analysis of the supply chain as a whole. This attention is largely a result of the rising costs of manufacturing, the shrinking resources of manufacturing bases, shortened product life cycles, the leveling of the playing field within manufacturing, and the globalization of market economies. The objectives of this paper are to: (1) provide a focused review of literature in multi-stage supply chain modeling and (2) define a research agenda for future research in this area.


title: Gauging Similarity with n-Grams : Language-Independent Categorization of Text

abstract: A language-independent means of gauging topical similarity in unrestricted text is described. The method combines information derived from n-grams (consecutive sequences of n characters) with a simple vector-space technique that makes sorting, categorization, and retrieval feasible in a large multilingual collection of documents. No prior information about document content or language is required. Context, as it applies to document similarity, can be accommodated by a well-defined procedure. When an existing document is used as an exemplar, the completeness and accuracy with which topically related documents are retrieved is comparable to that of the best existing systems. The results of a formal evaluation are discussed, and examples are given using documents in English and Japanese.


title: AWL: Turning Spatial Aliasing From Foe to Friend for Accurate WiFi Localization

abstract: Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known "bad" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.


Bilinguals ’ Recall and Recognition of Emotion Words


title: Comment Mining, Popularity Prediction, and Social Network Analysis

abstract: 7 Chapter 1: Introduction 8 1.1. Motivation 9 1.2. Problem Hypothesis 11 1.3. Contributions 11 1.4. Thesis Outline 11 Chapter 2 : Background 13 2.1. Social Networks 13 2.2. Implicit versus Explicit Social Network Formations 14 2.3. Social Network Analysis versus Dynamic Network Analysis 15 2.4. Related Research 16 2.5. Social Bookmarking and Digg Network 18 Chapter 3: User Characterization 21 3.1. Network Description and Statistics 21 3.2. Degree Distribution 22 3.3. Egonet Analysis 23 3.4. User Membership Analysis 27 Chapter 4: Comparative Analysis of Network Formations 31 4.


title: Survey research.

abstract: For the first time in decades, conventional wisdom about survey methodology is being challenged on many fronts. The insights gained can not only help psychologists do their research better but also provide useful insights into the basics of social interaction and cognition. This chapter reviews some of the many recent advances in the literature, including the following: New findings challenge a long-standing prejudice against studies with low response rates; innovative techniques for pretesting questionnaires offer opportunities for improving measurement validity; surprising effects of the verbal labels put on rating scale points have been identified, suggesting optimal approaches to scale labeling; respondents interpret questions on the basis of the norms of everyday conversation, so violations of those conventions introduce error; some measurement error thought to have been attributable to social desirability response bias now appears to be due to other factors instead, thus encouraging different approaches to fixing such problems; and a new theory of satisficing in questionnaire responding offers parsimonious explanations for a range of response patterns long recognized by psychologists and survey researchers but previously not well understood.


title: A personalized recommender system for the cosmetic business

abstract: Abstract In order to have an effective command of the relationship between customers and products, we have constructed a personalized recommender system which incorporates content-based, collaborative filtering, and data mining techniques. We have also introduced a new scoring approach to determine customers' interest scores on products. To demonstrate how our system works, we used it to analyze real cosmetic data and generate a recommender score table for sellers to refer to. After tracking its performance for 1 year, we have obtained quite impressive results.


title: Comparison of Dimension Reduction Methods for Automated Essay Grading

abstract: Automatic Essay Assessor (AEA) is a system that utilizes information retrieval techniques such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) for automatic essay grading. The system uses learning materials and relatively few teacher-graded essays for calibrating the scoring mechanism before grading. We performed a series of experiments using LSA, PLSA and LDA for document comparisons in AEA. In addition to comparing the methods on a theoretical level, we compared the applicability of LSA, PLSA, and LDA to essay grading with empirical data. The results show that the use of learning materials as training data for the grading model outperforms the k-NN-based grading methods. In addition to this, we found that using LSA yielded slightly more accurate grading than PLSA and LDA. We also found that the division of the learning materials in the training data is crucial. It is better to divide learning materials into sentences than paragraphs.


title: (S)-4-(phenylmethyl)-2-oxazolidinone

abstract: (S)-4-(Phenylmethyl)-2-oxazolidinone ::: ::: ::: ::: intermediate: (S)-Phenylalanol ::: ::: ::: ::: ::: product: (S)-4-(Phenylmethyl)-2-oxazolidinone ::: ::: ::: ::: ::: ::: Keywords: ::: ::: cyclization, condensation; ::: reduction, CO2H CH2OH; ::: assay methods, of enantiomeric purity of alcohols by GLC analysis of mosher ester; ::: boron trifluoride etherate; ::: l-phenylalanine; ::: tetrahydrofuran; ::: benzene


title: DLR's torque-controlled light weight robot III-are we reaching the technological limits now?

abstract: A third generation of torque-controlled light weight robots has been developed in DLR's robotics and mechatronics lab which is based on all the experiences that have been had with the first two generations. It aims at reaching the limits of what seems achievable with present day technologies not only with respect to light-weight, but also with respect to minimal power consumption and losses. One of the main gaps we tried to close in version III was the development of a new, robot-dedicated high energy motor designed with the best available techniques of concurrent engineering, and the renewed efforts to save weight in the links by using ultralight carbon fibres.


title: Human Action Classification Using Temporal Slicing for Deep Convolutional Neural Networks

abstract: Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed on three-dimensional media such as video clips. This can be attributed to a lack of adequate resources, available training datasets, hardware constraints, and appropriate frameworks for implementing such networks. This paper attempts to provide an alternate methodology of feeding three-dimensional video data by preprocessing instead of directly inputting to a deep convolutional neural network. By taking sequential segments from multiple frames of a single video clip and combining them into a single image, the temporal dimension of the video can be encoded as a two-dimensional image. This process is called as temporal slicing and repeated for the entire spatial dimension of the video. The end result is spatio-temporal data encoded in a spatial format, which is then propagated through a convolutional neural network as image data. This method is less resource-intensive and is remarkably faster than pre-existing three-dimensional convolutional methods, while achieving significantly higher accuracy compared to the aforementioned network architectures.


title: FiLM: Visual Reasoning with a General Conditioning Layer

abstract: We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.


title: Feature extraction from handwritten documents for personality analysis

abstract: Handwriting can be used to predict or analyze a person's behavioral or personality traits. Characteristics of a handwriting are studied for that. In this work various characteristics like size, spacing, slant, skew, pressure, etc are studied. Since signature reflects important characteristics of human being, we analyze them also. As our objective is to build an automated or computerized handwriting analysis system, we discuss the features from algorithmic view points. For experimental purpose, various handwriting of different writers are collected. Identical as well as different texts is used for that. Implementing these algorithms on the data sets, we obtain experimental results which are given here.


title: Analysis of rain clutter detections in commercial 77 GHz automotive radar

abstract: This paper investigates the impact of rain on performance of 77 GHz radar. The experiments performed in a rain-fog tunnel show radar detections, attributed to rain, at very short ranges for high rain intensities. The theoretical analysis of rain clutter demonstrates a possibility of rain detection by a radar with narrow beam at short ranges.


title: Sentence similarity detection in Malayalam language using cosine similarity

abstract: Identifying paraphrase in Malayalam language is difficult task because it is a highly agglutinative language and the linguistic structure in Malayalam language is complex compared to other languages. Here we use individual words synonyms to find the similarity between two sentences. In this paper, cosine similarity method is used to find the paraphrases in Malayalam language. In this paper we present the observations on sentence similarity between two Malayalam sentences using cosine similarity method, we used test data of 900 and 1400 sentence pairs of FIRE 2016 Malayalam corpus that used in two iterations to present and obtained an accuracy of 0.8 and 0.59.


title: A computational theory of awareness and decision making

abstract: We exhibit a new computational-based definition of awareness, informally that our level of unawareness of an object is the amount of time needed to generate that object within a certain environment. We give several examples to show this notion matches our intuition in scenarios where one organizes, accesses and transfers information. We also give a formal process-independent definition of awareness based on Levin's universal enumeration. We show the usefulness of computational awareness by showing how it relates to decision making, and how others can manipulate our decision making with appropriate advertising, in particular, we show connections to sponsored search and brand awareness. Understanding awareness can also help rate the effectiveness of various user interfaces designed to access information.


title: Design of a novel adaptive variable structure control law for industrial heat exchanger process representing a class of non minimum process plant

abstract: This paper presents the development of a novel adaptive backstepping sliding mode control law for temperature control of an industrial heat exchanger system. Backstepping control law has been utilized during the design of reaching mode of the sliding mode controller to ensure a smoother convergence towards sliding surface. However, in order to mitigate the problems of over-conservative approach of robust design, an adaptive mechanism has also been incorporated to find out the appropriate value of the disturbance bound. The proposed control law is robust against the effects of model uncertainties and can address the issues related to tracking problem of a non-minimum phase nonlinear systems. It has been devised in such a generalized manner that it can be used to address the control problem of any other non-minimum phase nonlinear systems.


title: Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design

abstract: Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the re- quester to iterate on the design before publishing it. We report a field experiment in which tasks that underwent prototype task iteration produced higher-quality work results than the original task designs. With this research, we suggest that a simple and rapid iteration cycle can improve crowd work, and we provide empirical evidence that requester "quality" directly impacts result quality.


title: Network-Based Intrusion Detection Using Adaboost Algorithm

abstract: Intrusion detection on the Internet is a heated research field in computer science, where much work has been done during the past two decades. In this paper, we build a network-based intrusion detection system using Adaboost, a prevailing machine learning algorithm. The experiments demonstrate that our system can achieve an especially low false positive rate while keeping a preferable detection rate, and its computational complexity is extremely low, which is a very attractive property in practice.


title: Identifying the potential of human-robot collaboration in automotive assembly lines using a standardised work description

abstract: The increased availability of sensitive and compliant lightweight robots for use in assembly lines collaborating with the human promises significant improvements of different socio-technical aspects of work. Workplaces can be reorganized to assign monotonous or unergonomic tasks to the robot. Also unproductive jobs currently done by the human can be minimized by an improved work distribution. Since there is only little experience with the new generation of collaborating robots, the implementation of workplaces shared by human and robot is often influenced by subjective perspectives. In this paper, an approach to assess the collaboration potential of workplaces is presented. Based on existing standardised work descriptions, the suitability for human-robot collaboration can be derived and therefore a more objective evaluation and comparison of the whole assembly can be achieved.


title: Automatic Generation of the Domain Module from Electronic Textbooks: Method and Validation

abstract: Technology-supported learning systems have proved to be helpful in many learning situations. These systems require an appropriate representation of the knowledge to be learned, the Domain Module. The authoring of the Domain Module is cost and labor intensive, but its development cost might be lightened by profiting from semiautomatic Domain Module authoring techniques and promoting knowledge reuse. DOM-Sortze is a system that uses natural language processing techniques, heuristic reasoning, and ontologies for the semiautomatic construction of the Domain Module from electronic textbooks. To determine how it might help in the Domain Module authoring process, it has been tested with an electronic textbook, and the gathered knowledge has been compared with the Domain Module that instructional designers developed manually. This paper presents DOM-Sortze and describes the experiment carried out.


title: Adversarial Feature Sampling Learning for Efficient Visual Tracking

abstract: The tracking-by-detection framework usually consist of two stages: drawing samples around the target object in the first stage and classifying each sample as the target object or background in the second stage. Current popular trackers based on tracking-by-detection framework typically draw samples in the raw image as the inputs of deep convolution networks in the first stage, which usually results in high computational burden and low running speed. In this paper, we propose a new visual tracking method using sampling deep convolutional features to address this problem. Only one cropped image around the target object is input into the designed deep convolution network and the samples is sampled on the feature maps of the network by spatial bilinear resampling. In addition, a generative adversarial network is integrated into our network framework to augment positive samples and improve the tracking performance. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves a comparable performance to state-of-the-art trackers and accelerates tracking-by-detection trackers based on raw-image samples effectively.


title: Multimodal Recurrent Model with Attention for Automated Radiology Report Generation

abstract: Radiologists routinely examine medical images such as X-Ray, CT, or MRI and write reports summarizing their descriptive findings and conclusive impressions. A computer-aided radiology report generation system can lighten the workload for radiologists considerably and assist them in decision making. Although the rapid development of deep learning technology makes the generation of a single conclusive sentence possible, results produced by existing methods are not sufficiently reliable due to the complexity of medical images. Furthermore, generating detailed paragraph descriptions for medical images remains a challenging problem. To tackle this problem, we propose a novel generative model which generates a complete radiology report automatically. The proposed model incorporates the Convolutional Neural Networks (CNNs) with the Long Short-Term Memory (LSTM) in a recurrent way. It is capable of not only generating high-level conclusive impressions, but also generating detailed descriptive findings sentence by sentence to support the conclusion. Furthermore, our multimodal model combines the encoding of the image and one generated sentence to construct an attention input to guide the generation of the next sentence, and henceforth maintains coherence among generated sentences. Experimental results on the publicly available Indiana U. Chest X-rays from the Open-i image collection show that our proposed recurrent attention model achieves significant improvements over baseline models according to multiple evaluation metrics.


title: Adaptive Sampling Towards Fast Graph Representation Learning

abstract: Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.