title: Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques

abstract: PurposeBreathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing the breathing sounds collected from a large number of subjects during entire overnight sleep.MethodsThe participants were patients who presented at a sleep center with snoring or cessation of breathing during sleep. They were subjected to full-night polysomnography (PSG) during which the breathing sound was recorded using a microphone. Then, audio features were extracted and a group of features differing significantly between different SDB severity groups was selected as a potential acoustic biomarker. To assess the validity of the acoustic biomarker, classification tasks were performed using several machine learning techniques. Based on the apnea–hypopnea index of the subjects, four-group classification and binary classification were performed.ResultsUsing tenfold cross validation, we achieved an accuracy of 88.3% in the four-group classification and an accuracy of 92.5% in the binary classification. Experimental evaluation demonstrated that the models trained on the proposed acoustic biomarkers can be used to estimate the severity of SDB.ConclusionsAcoustic biomarkers may be useful to accurately predict the severity of SDB based on the patient’s breathing sounds during sleep, without conducting attended full-night PSG. This study implies that any device with a microphone, such as a smartphone, could be potentially utilized outside specialized facilities as a screening tool for detecting SDB.


title: AR Tabletop Interface using a Head-Mounted Projector

abstract: In this paper, we propose a tabletop interface in which a user wears a projector with a depth camera on his or her head and can perform touch operations on an image projected on a flat surface. By using the head-mounted projector, images are always projected in front of the user in the direction of the user's gaze. By changing the image to be projected based on the user's head movement, this interface realizes a large effective screen size. The system superimposes an image on the flat surface by performing plane detection, placing the image on the detected plane, performing perspective projection to obtain a 2D image, and projecting the 2D image using the projector. Registration between the real world and the image is performed by estimating the user's head pose using the detected plane information. Furthermore, touch input is recognized by detecting the user's finger on the plane using the depth camera. We implemented some application examples into the system to demonstrate the usefulness of the proposed interface.


title: Fully complex deep neural network for phase-incorporating monaural source separation

abstract: Deep neural network (DNN) have become a popular means of separating a target source from a mixed signal. Most of DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the input signal. However, recent studies have revealed that incorporating phase information can improve the quality of separated sources. To estimate simultaneously the magnitude and the phase of STFT coefficients, this work paper developed a fully complex-valued deep neural network (FCDNN) that learns the nonlinear mapping from complex-valued STFT coefficients of a mixture to sources. In addition, to reinforce the sparsity of the estimated spectra, a sparse penalty term is incorporated into the objective function of the FCDNN. Finally, the proposed method is applied to singing source separation. Experimental results indicate that the proposed method outperforms the state-of-the-art DNN-based methods.


title: Technical realization of the MIMO radar MIRA-CLE Ka

abstract: This paper presents the technical realization of the MIMO (Multiple Input Multiple Output) radar MIRA-CLE Ka (MIMO radar-configurable in Ka-band). This system is a stationary imaging radar without any mechanical moving parts. It is highly portable, ready to use in only a couple of minutes, and it is able to process one radar image per second in the current stage of development. The MIMO system operates in Ka band and consists of 16 receive and 16 transmit elements so that 256 virtual elements are generated. The size of the antenna frontend is about 80 centimeters in width and only a bit larger than the real array. Due to its compact size the system offers a wide field of applications.


title: Realtime Anomaly Detection Using Trajectory-Level Crowd Behavior Learning

abstract: We present an algorithm for realtime anomaly detection in low to medium density crowd videos using trajectorylevel behavior learning. Our formulation combines online tracking algorithms from computer vision, non-linear pedestrian motion models from crowd simulation, and Bayesian learning techniques to automatically compute the trajectory-level pedestrian behaviors for each agent in the video. These learned behaviors are used to segment the trajectories and motions of different pedestrians or agents and detect anomalies. We demonstrate the interactive performance on the PETS 2016 ARENA dataset as well as indoor and outdoor crowd video benchmarks consisting of tens of human agents.


title: LBP maps for improving fractal based texture classification

abstract: Abstract This paper presents an innovative manner of obtaining discriminative texture signatures by using the LBP approach to extract additional sources of information from an input image and by using fractal dimension to calculate features from these sources. Four strategies, called Min, Max, Diff Min and Diff Max , were tested, and the best success rates were obtained when all of them were employed together, resulting in an accuracy of 99.25%, 72.50% and 86.52% for the Brodatz, UIUC and USPTex databases, respectively, using Linear Discriminant Analysis. These results surpassed all the compared methods in almost all the tests and, therefore, confirm that the proposed approach is an effective tool for texture analysis.


title: 3D-models of the Human Habitat for the Internet.

abstract: The Internet has inspired an enormous appetite for 3-dimensional geo-data of the urban environment to support location-aware applications. This has in fact become the surprising "killer application" of such 3-dimensional data. In March 2005, at the occasion of his 50th birthday, Bill Gates went public with his "Virtual Earth Vision" for local search in the Internet and stated: "You'll be walking around in downtown London and be able to see the shops, the stores, see what the traffic is like. Walk in a shop and navigate the merchandise. Not in the flat, 2D interface that we have on the web today, but in a virtual reality walkthrough." This paper seeks to evangelize the current capabilities of the Virtual Earth system, focuses on the creation of 3D data, but also points to some pieces of new science in the 3D-analysis of overlapping imagery of the human habitat at sub-pixel accuracies.


title: 3D Facial model construction and expressions synthesis from a single frontal face image

abstract: In this paper we present a system which automatically generates 3D face model from a single frontal image of a face with the help of generic 3D model. Our system consists of three components. The first component detects the features like eyes, mouth, eyebrow and contour of face. After detecting features the second component automatically adapts the generic 3d model into face specific 3D model using geometric transformations. Once the model is ready six basic facial expressions are generated with the help of MPEG-4 facial animation parameters (FAPS). To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models and blending the corresponding textures. Our system has the advantage that it is fully automatic, robust and fast. It can be used in a variety of applications for which the accuracy of depths are not critical such as games, avatars, face recognition etc. Our morphing results show the accuracy of system. We have tested and evaluated using database BU-3DFE.


title: On the analysis of large-dimension reconfigurable suspended cable-driven parallel robots

abstract: In this paper, a new type of large-dimension reconfigurable suspended cable-driven parallel robots (CDPR) is introduced as a means to substitute for conventional methods of handing large and heavy parts across wide workspaces. The reconfigurability of the proposed CDPR offers better performances in term of workspace, flexibility and power consumption. A systematic procedure to solve a complex nonlinear optimization problem to find optimal reconfiguration for the robot is presented. Critical issues regarding various constraints and performance criteria are addressed. The robot can operate in offline reconfiguration or online reconfiguration modes which offer wide range of solutions to the end-users.


title: Research on leadership in a cross-cultural context: Making progress, and raising new questions

abstract: Abstract It is almost cliche to say that there has been an explosion in the amount of research on leadership in a cross-cultural context. In this review, we describe major advances and emerging patterns in this research domain over the last several years. Our starting point for this update is roughly 1996–1997, since those are the dates of two important reviews of the cross-cultural leadership literature [specifically, House, Wright, and Aditya (House, R. J., Wright, N. S., & Aditya, R. N. (1997). Cross-cultural research on organizational leadership: A critical analysis and a proposed theory. In: P. C. Earley, & M. Erez (Eds.), New perspectives on international industrial/organizational psychology (pp. 535–625). San Francisco, CA) and Dorfman (Dorfman, P. W. (1996). International and cross-cultural leadership research. In: B. J. Punnett, & O. Shenkar (Eds.), Handbook for international management research , pp. 267–349, Oxford, UK: Blackwell)]. We describe the beginnings of the decline in the quest for universal leadership principles that apply equivalently across all cultures, and we focus on the increasing application of the dimensions of culture identified by Hofstede [Hofstede, G. (1980). Culture's consequences: International differences in work-related values (Abridged ed.). Newbury Park, CA: Sage] and others to describe variation in leadership styles, practices, and preferences. We also note the emergence of the field of cross-cultural leadership as a legitimate and independent field of endeavor, as reflected in the emergence of publication outlets for this research, and the establishment of long-term multinational multi-investigator research programs on the topic. We conclude with a discussion of progress made since the two pieces that were our departure point, and of progress yet to be made.


title: Analytical Approximation-Based Machine Learning Methods for User Positioning in Distributed Massive MIMO

abstract: We propose a machine learning approach, based on analytical inference in Gaussian process regression (GP), to locate users from their uplink received signal strength (RSS) data in a distributed massive multiple-input-multiple-output setup. The training RSS data is considered noise-free, while the test RSS data is assumed to be noisy due to shadowing effects of the wireless channel. We first apply an analytical moment matching-based GP method, namely, the Gaussian approximation GP (GaGP), and make the necessary extensions to suit the problem under study. The GaGP method learns from the stochastic nature of the test RSS data to provide more realistic $2\sigma$ error-bars on the estimated locations than the conventional GP (CGP) method. Despite the improvement in $2\sigma$ error-bars, simulation studies reveal that the GaGP method achieves similar root-mean-squared estimation error (RMSE) performance as the CGP method. To address this concern, we propose a new GP method, namely the reconstruction-cum-Gaussian-approximation GP (RecGaGP) method. RecGaGP not only achieves lower RMSE values than the CGP and GaGP methods, but also provides realistic $2\sigma$ error-bars on the estimated locations. This ability is achieved by first reconstructing the test RSS from a low-dimensional principal subspace of the noise-free training RSS and then learning from the statistical properties of the residual noise present. For both the GaGP and RecGaGP methods, closed-form expressions are derived for the estimated user locations and the associated $2\sigma$ error-bars. Numerical studies reveal that the GaGP and RecGaGP methods indeed provide realistic $2\sigma$ error-bars on the estimated user locations and their RMSE performances are very close to the Cramer–Rao lower bounds. Also, their RMSE performances saturate beyond a certain point when the number of BS antennas and/or the number of training locations are increased.


title: HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks

abstract: Transductive classification TC using a small labeled data to help classifying all the unlabeled data in information networks. It is an important data mining task on information networks. Various classification methods have been proposed for this task. However, most of these methods are proposed for homogeneous networks but not for heterogeneous ones, which include multi-typed objects and relations and may contain more useful semantic information. In this paper, we firstly use the concept of meta path to represent the different relation paths in heterogeneous networks and propose a novel meta path selection model. Then we extend the transductive classification problem to heterogeneous information networks and propose a novel algorithm, named HetPathMine. The experimental results show that: 1 HetPathMine can get higher accuracy than the existing transductive classification methods and 2 the weight obtained by HetPathMine for each meta path is consistent with human intuition or real-world situations.


title: The implementation of a cryptography-based secure office system

abstract: A cryptography-based secure office system is discussed, including design criteria and a specific implementation. The system is intended to be practical, simple, and inexpensive, but also highly secure. The implementation uses a hybrid scheme of conventional (DES) and public-key (RSA) cryptography. Randomly generated DES keys encrypt messages and files, and the DES keys themselves and a one-way hash of the messages are encrypted and signed by RSA keys. The system provides secure electronic mail (including electronic registered mail and an electronic notary public), secure two-way channels, and secure user files. Timestamps and a special signed file of public keys help decrease the need for an online central authority involved in all transactions.


PhTM: Phased transactional memory


title: Does Overconfidence Affect Corporate Investment? CEO Overconfidence Measures Revisited

abstract: "This article presents the growing research area of Behavioural Corporate Finance in the context of one specific example: distortions in corporate investment due to CEO overconfidence. We first review the relevant psychology and experimental evidence on overconfidence. We then summarise the results of Malmendier and Tate l2005ar on the impact of overconfidence on corporate investment. We present supplementary evidence on the relationship between CEOs' press portrayals and overconfident investment decisions. This alternative approach to measuring overconfidence, developed in Malmendier and Tate l2005br, relies on the perception of outsiders rather than the CEO's own actions. The robustness of the results across such diverse proxies jointly corroborates previous findings and suggests new avenues to measuring executive overconfidence". Copyright Blackwell Publishers Ltd, 2005.


title: Appearance-Based QR Code Beautifier

abstract: Quick Response (QR) code is a widely used matrix bar code with the increasing population of smart phones. However, QR code usually consists of random textures which are not suitable for incorporating with other visual designs (e.g. name card and business advertisement poster). In order to overcome the shortcomings of noise-like looks of QR codes, we propose a systematic QR code beautification framework where the visual appearance of QR code is composed of visually meaningful patterns selected by users, and more importantly, the correctness of message decoding is kept intact. Our work makes QR code from machine decodable only (i.e. standardized random texture) to a personalized form with human visual pleasing appearance. We expect the proposed QR code beautifier can inspire more visual-pleasant mobile multimedia applications.


Building a Student At-Risk Model: An End-to-End Perspective From User to Data Scientist.


title: A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling

abstract: In this paper, a novel inverse random under sampling (IRUS) method is proposed for class imbalance problem. The main idea is to severely under sample the negative class (majority class), thus creating a large number of distinct negative training sets. For each training set we then find a linear discriminant which separates the positive class from the negative class. By combining the multiple designs through voting, we construct a composite between the positive class and the negative class. The proposed methodology is applied on 11 UCI data sets and experimental results indicate a significant increase in Area Under Curve (AUC) when compared with many existing class-imbalance learning methods.


title: Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

abstract: Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.


title: OCF: An Open Cloud Forensics Model for Reliable Digital Forensics

abstract: The rise of cloud computing has changed the way computing services and resources are used. However, existing digital forensics science cannot cope with the black-box nature of clouds nor with multi-tenant cloud models. Because of the fundamental characteristics of clouds, many assumptions of digital forensics are invalidated in clouds. In the digital forensics process involving clouds, the role of cloud service providers (CSP) is utterly important, a role which needs to be considered in the science of cloud forensics. In this paper, we define cloud forensics considering the role of the CSP and propose the Open Cloud Forensics (OCF) model. Based on this OCF model, we propose a cloud computing architecture and validate our proposed model using a case study, which is inspired from an actual civil lawsuit.


title: “Analyzing Costs and Accuracy of Validation Mechanisms for Crowdsourcing Platforms.”

abstract: Abstract Crowdsourcing is becoming more and more important for commercial purposes. With the growth of crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, a huge work force and a large knowledge base can be easily accessed and utilized. But due to the anonymity of the workers, they are encouraged to cheat the employers in order to maximize their income. In this paper, we analyze two widely used crowd-based approaches to validate the submitted work. 1 Both approaches are evaluated with regard to their detection quality, their costs and their applicability to different types of typical crowdsourcing tasks.


title: Hardware design of a neural processing unit for bio-inspired computing

abstract: Unsupervised artificial neural networks are now considered as a likely alternative to classical computing models in many application domains. For example, recent neural models defined by neuro-scientists exhibit interesting properties for an execution in embedded and autonomous systems: distributed computing, unsupervised learning, self-adaptation, self-organisation, tolerance. But these properties only emerge from large scale and fully connected neural maps that result in intensive computation coupled with high synaptic communications. We are interested in deploying these powerful models in the embedded context of an autonomous bio-inspired robot learning its environment in realtime. So we study in this paper in what extent these complex models can be simplified and deployed in hardware accelerators compatible with an embedded integration. Thus we propose a Neural Processing Unit designed as a programmable accelerator implementing recent equations close to self-organizing maps and neural fields. The proposed architecture is validated on FPGA devices and compared to state of the art solutions. The trade-off proposed by this dedicated but programmable neural processing unit allows to achieve significant improvements and makes our architecture adapted to many embedded systems.


title: Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition

abstract: There has been a clear distinction between induction or training time and diagnosis time active information acquisition. While active learning during induction focuses on acquiring data that promises to provide the best classification model, the goal at diagnosis time focuses completely on next features to observe about the test case at hand in order to make better predictions about the case. We introduce a model and inferential methods that breaks this distinction. The methods can be used to extend case libraries under a budget but, more fundamentally, provide a framework for guiding agents to collect data under scarce resources, focused by diagnostic challenges. This extension to active learning leads to a new class of policies for real-time diagnosis, where recommended information-gathering sequences include actions that simultaneously seek new data for the case at hand and for cases in the training set.


title: Auto-Encoding Scene Graphs for Image Captioning

abstract: We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation ``person on bike'', it is natural to replace ``on'' with ``ride'' and infer ``person riding bike on a road'' even the ``road'' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely to overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph (G) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image (I) and sentence (S). In the textual domain, we use SGAE to learn a dictionary (D) that helps to reconstruct sentences in the S -> G -> D -> S pipeline, where D encodes the desired language prior; in the vision-language domain, we use the shared D to guide the encoder-decoder in the I -> G -> D -> S pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, \eg, our SGAE-based single-model achieves a new state-of-the-art 127.8 CIDEr-D on the Karpathy split, and a competitive 125.5 CIDEr-D (c40) on the official server even compared to other ensemble models. Code has been made available at: https://github.com/yangxuntu/SGAE.


title: TensorLog: A Differentiable Deductive Database

abstract: Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into "deep" gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are "unrolled" into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.


title: RGB-D object tracking: A particle filter approach on GPU

abstract: This paper presents a particle filtering approach for 6-DOF object pose tracking using an RGB-D camera. Our particle filter is massively parallelized in a modern GPU so that it exhibits real-time performance even with several thousand particles. Given an a priori 3D mesh model, the proposed approach renders the object model onto texture buffers in the GPU, and the rendered results are directly used by our parallelized likelihood evaluation. Both photometric (colors) and geometric (3D points and surface normals) features are employed to determine the likelihood of each particle with respect to a given RGB-D scene. Our approach is compared with a tracker in the PCL both quantitatively and qualitatively in synthetic and real RGB-D sequences, respectively.


title: A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression

abstract: Robustness is a crucially important issue in watermarking. Robustness against geometric distortion and JPEG compression at the same time with blind extraction remains especially challenging. A blind discrete wavelet transform-discrete Fourier transform (DWT-DFT) composite image watermarking algorithm that is robust against both affine transformation and JPEG compression is proposed. The algorithm improves robustness by using a new embedding strategy, watermark structure, 2D interleaving, and synchronization technique. A spread-spectrum-based informative watermark with a training sequence is embedded in the coefficients of the LL subband in the DWT domain while a template is embedded in the middle frequency components in the DFT domain. In watermark extraction, we first detect the template in a possibly corrupted watermarked image to obtain the parameters of an affine transform and convert the image back to its original shape. Then, we perform translation registration using the training sequence embedded in the DWT domain, and, finally, extract the informative watermark. Experimental work demonstrates that the proposed algorithm generates a more robust watermark than other reported watermarking algorithms. Specifically it is robust simultaneously against almost all affine transform related testing functions in StirMark 3.1 and JPEG compression with quality factor as low as 10. While the approach is presented for gray-level images, it can also be applied to color images and video sequences.


title: An ontological ubiquitous city information platform provided with Cyber-Physical-Social-Systems

abstract: Aim of the paper is to illustrate the methodology used to implement an ubiquitous city platform called Wi-City-Plus provided with mobile and centralized Decision Support Systems (DSSs) taking advantage of all the data of city interest, including social data, and those sensed by networked ambient Cyber-Physical-Systems (CPSs). The paper proposes to model such data by a suitable RDF ontology specifically designed to support the main user scenarios in smart cities and to implement both the technical and the service interoperability envisaged by the CPS ideal model. This ontology is obtained by reusing existing ontologies and allows the DSSs to access by SPARQL queries all the relevant data independent of the proprietary technology of the CPSs and of the DBMS adopted by the data owners. Examples derived from the pilot version of the proposed platform allows us to point out advantages and perspectives of our approach.


title: An overview of the treatment of severe narcissistic pathology

abstract: This paper provides an overview of narcissistic personality disorders as they present clinically along a spectrum of severity ranging from the best functioning forms of pathological narcissism to t...


title: Hypernetwork Knowledge Graph Embeddings

abstract: Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated subject and relation vectors. Whilst results are impressive, the method is unintuitive and poorly understood. We propose a hypernetwork architecture that generates simplified relation-specific convolutional filters that (i) outperforms ConvE and all previous approaches across standard datasets; and (ii) can be framed as tensor factorization and thus set within a well established family of factorization models for link prediction. We thus demonstrate that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.


INVESTIGATION OF ANTIMICROBIAL ACTIVITY AND MORPHOLOGICAL PROPERTIES OF METAL COATED TEXTILE SURFACES


title: The Effect of Teacher Interpersonal Behaviour on Students' Subject-Specific Motivation.

abstract: This study brings together insights from research on teaching and learning in specific subjects, learning environments research and effectiveness research by linking teacher interpersonal behaviour to students’ subject-related attitudes. Teaching was studied in terms of a model originating from clinical psychology that was adapted to education. Teacher interpersonal behaviour was analysed interms of two, independent behaviour dimensions called Influence and Proximity. This study investigated the added value of students’ perceptions of teacher interpersonal behaviour (after correction for covariates such as gender, report card grade, class size, etc.) on students’ subjectspecific motivation. Data of 52 third-year EFL-classes (English as a Foreign Language; 1041 students), taught by 32 secondary teachers, were included in the analyses. The study used multilevel analysis of variance to investigate the effect of teaching on motivation and included several covariates as well. For all of the discerned subject-related attitude variables – pleasure, relevance, confidence and effort – a positive and strong effect was found for teacher Proximity. In addition, for three of the outcome variables – pleasure, relevance and effort – Influence also had a positive effect. Overall, however, proximity seemed to be of greater importance than teacher influence. The results demonstrate the significant role of teacher interpersonal behaviour in student motivation and the importance of combining insights from various educational research disciplines.


title: An Underfrequency Load Shedding Scheme for Hybrid and Multiarea Power Systems

abstract: The purpose of underfrequency load shedding (UFLS) is to maintain the frequency of an interconnected or island power system within permissible limits in case of severe power deficits. Thus, the proper performance of UFLS scheme is of great importance. In hybrid power systems, disconnecting and connecting the power generation sources and loads result in variations in the system parameters. Moreover, the output power of some distributed energy resources, such as photovoltaic (PV) or wind turbine generator (WTG), might change during the load shedding process. These factors may affect the performance of the load shedding scheme and decrease the reliability of power system. Hence, in this paper, a UFLS scheme is proposed that has two advantages: 1) it is independent of power system parameters; and 2) it considers power generation variations during the load shedding process. To verify the effectiveness of the proposed load shedding method, simulation studies are carried out on a hybrid power system including PV, WTG, fuel cell, diesel generator, and battery energy storage system. Finally, the performance of the proposed method on a multiarea power system is evaluated.


title: Aripiprazole-induced priapism

abstract: Priapism is a urologic emergency representing a true disorder of penile erection that persists beyond or is unrelated to sexual interest or stimulation. A variety of psychotropic drugs are known to produce priapism, albeit rarely, through their antagonistic action on alpha-1 adrenergic receptors. We report such a case of priapism induced by a single oral dose of 10 mg aripiprazole, a drug with the least affinity to adrenergic receptors among all atypical antipsychotics. Polymorphism of alpha-2A adrenergic receptor gene in schizophrenia patients is known to be associated with sialorrhea while on clozapine treatment. Probably, similar polymorphism of alpha-1 adrenergic receptor gene could contribute to its altered sensitivity and resultant priapism. In future, pharmacogenomics-based approach may help in personalizing the treatment and effectively prevent the emergence of such side effects.


title: A practical and linguistically-motivated approach to compositional distributional semantics

abstract: Distributional semantic methods to approximate word meaning with context vectors have been very successful empirically, and the last years have seen a surge of interest in their compositional extension to phrases and sentences. We present here a new model that, like those of Coecke et al. (2010) and Baroni and Zamparelli (2010), closely mimics the standard Montagovian semantic treatment of composition in distributional terms. However, our approach avoids a number of issues that have prevented the application of the earlier linguistically-motivated models to full-fledged, real-life sentences. We test the model on a variety of empirical tasks, showing that it consistently outperforms a set of competitive rivals. 1 Compositional distributional semantics The research of the last two decades has established empirically that distributional vectors for words obtained from corpus statistics can be used


title: Implementation of personalized recommendation system using demograpic data and RFM method in e-commerce

abstract: This paper proposes the recommendation system which is used the implicit method without onerous question and answer to the users based on the data from purchasing, unlike the other evaluation techniques. We applied demographic variable of the user information and RFM technique which can analyze the tendency of the various personalization and the exact customer. It could be proved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall.


title: Adaptive Disengagement Buffers Self-Esteem From Negative Social Feedback:

abstract: The degree to which self-esteem hinges on feedback in a domain is known as a contingency of self-worth, or engagement. Although previous research has conceptualized engagement as stable, it would be advantageous for individuals to dynamically regulate engagement. The current research examined whether the tendency to disengage from negative feedback accounts for variability in self-esteem. We created the Adaptive Disengagement Scale (ADS) to capture individual differences in the tendency to disengage self-esteem from negative outcomes. Results demonstrated that the ADS is reliable and valid (Studies 1 and 2). Furthermore, in response to negative social feedback, higher scores on the ADS predicted greater state self-esteem (Study 3), and this relationship was mediated by disengagement (Study 4). These findings demonstrate that adaptive disengagement protects self-esteem from negative outcomes and that the ADS is a valid measure of individual differences in the implementation of this process.


title: Development of a polymer-based tendon-driven wearable robotic hand

abstract: This paper presents the development of a polymer-based tendon-driven wearable robotic hand, Exo-Glove Poly. Unlike the previously developed Exo-Glove, a fabric-based tendon-driven wearable robotic hand, Exo-Glove Poly was developed using silicone to allow for sanitization between users in multiple-user environments such as hospitals. Exo-Glove Poly was developed to use two motors, one for the thumb and the other for the index/middle finger, and an under-actuation mechanism to grasp various objects. In order to realize Exo-Glove Poly, design features and fabrication processes were developed to permit adjustment to different hand sizes, to protect users from injury, to enable ventilation, and to embed Teflon tubes for the wire paths. The mechanical properties of Exo-Glove Poly were verified with a healthy subject through a wrap grasp experiment using a mat-type pressure sensor and an under-actuation performance experiment with a specialized test set-up. Finally, performance of the Exo-Glove Poly for grasping various shapes of object was verified, including objects needing under-actuation.


title: Thoracolumbar burst fractures without neurological deficit: the role for conservative treatment

abstract: Burst fractures are common in the thoracolumbar junction and account for 17% of all major spinal fractures. There is a considerable controversy on the efficacy of conservative treatment and the need for surgical intervention. Need for additional stability, prevention of neurological deterioration, attainment of canal clearance, prevention of kyphosis and early relief of pain are the commonly quoted reasons for surgical intervention. However, a careful review of literature does not validate any of the above arguments. The available randomised control trials prove that the results of conservative treatment for burst fractures are equal to that of surgery and also with lesser complications. Surgery for burst fractures may, however, have definite advantages in patients with polytrauma or in the rare event of deteriorating neurology. It is also important for the treating surgeon to clearly distinguish a burst fracture from other inherently unstable injuries like fracture dislocations, chance fractures and flexion rotation injuries which require surgical stabilisation.


title: Design and evaluation of GaN-based dual-phase interleaved MHz critical mode PFC converter

abstract: This paper presents the design consideration and performance evaluation of gallium nitride (GaN) high electron mobility transistor (HEMT) based dual-phase interleaved MHz critical conduction mode (CRM) power factor correction (PFC) converter. A 1.2kW 1-3MHz interleaved boost PFC converter prototype is built with 97.9% peak efficiency and 120W/in3 power density. The significant impact of MHz frequency is demonstrated as dramatically size reduction of boost inductor and electro-magnetic interference (EMI) filter. Several inductor designs are discussed. The corner frequency of EMI filter is pushed to several hundreds of kHz. Finally, the limitation of conventional boost PFC converter is discussed as high conduction loss on diode rectifier bridge and high switching loss caused by valley switching, which is negligible in other low frequency PFC converter but significant in MHz PFC converter. The totem-pole bridgeless PFC converter is introduced to further improve the efficiency with no rectifier bridge and zero-voltage switching (ZVS) extension strategy.


title: Automatic Attribute Discovery and Characterization from Noisy Web Data

abstract: It is common to use domain specific terminology - attributes - to describe the visual appearance of objects. In order to scale the use of these describable visual attributes to a large number of categories, especially those not well studied by psychologists or linguists, it will be necessary to find alternative techniques for identifying attribute vocabularies and for learning to recognize attributes without hand labeled training data. We demonstrate that it is possible to accomplish both these tasks automatically by mining text and image data sampled from the Internet. The proposed approach also characterizes attributes according to their visual representation: global or local, and type: color, texture, or shape. This work focuses on discovering attributes and their visual appearance, and is as agnostic as possible about the textual description.


title: Mammoth A Massively Multiplayer Game Research Framework

abstract: This paper presents Mammoth, a massively multiplayer game research framework designed for experimentation in an academic setting. Mammoth provides a modular architecture where different components, such as the network engine, the replication engine, or interest management, can easily be replaced. Subgames allow a researcher to define different game goals, for instance, in order to evaluate the effects of different team-play tactics on the game performance. Mammoth also offers a modular and flexible infrastructure for the definition of non-player characters with behavior controlled by complex artificial intelligence algorithms. This paper focuses on the Mammoth architecture, demonstrating how good design practices can be used to create a modular framework where researchers from different research domains can conduct their experiments. The effectiveness of the architecture is demonstrated by several successful research projects accomplished using the Mammoth framework.


title: How to evaluate technologies for health behavior change in HCI research

abstract: New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.


title: Effects of a 3-D video game on middle school student achievement and attitude in mathematics

abstract: Today's student has unprecedented access to media, information, and even global interaction that was unheard of only a few years ago. With all of the various media our students are exposed to daily, engaging student learning in a traditional classroom might prove challenging. The purpose of this study was to determine whether or not a highly interactive, 3-D video game, Dimension-M, can achieve the goal of not only positively influencing middle school student achievement in mathematics, but also positively influencing their attitude. In addition, this study examined whether or not gender interacts to influence this hypothesized impact on achievement and attitude. ::: Cape Fear Middle School created a mathematics remediation course called Virtual Math for students who achieved below proficient levels on their state end-of-grade mathematics exams. The Virtual Math class provided a setting for the study. Achievement and attitude data was collected for 21 girls and 8 boys in grades six through eight prior to their exposure to the video game environment. Following a pre-test and pre-attitude survey, students played the game?s Tutorial mission and the Xeno Island mission, which addressed the concepts of prime numbers, even and odd patterns, and perfect squares. Students received no direct mathematics instruction during the treatment period in order to test the impact of the game alone. Following the treatment, a post-test and post-attitude survey were administered. ::: Students displayed a significant gain in achievement (t(27) = -3.96, p < .05), but no significant differences were detected between their pre- and post-attitude surveys. An interview with the tutor assigned to the students, the school?s math coach, and the principal of the school, indicated a positive impact on students? mathematics performance in their regular math classes as well as a positive impact on students? self-efficacy in mathematics. The results of the study imply that Dimension-M can potentially have a positive impact on student achievement and that students respond enthusiastically to the Dimension-M environment.


title: Current trends in the development of intelligent unmanned autonomous systems

abstract: Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.


title: HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

abstract: Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances. State-of-the-art region proposal methods usually need several thousand proposals to get high recall, thus hurting the detection efficiency. Although the latest Region Proposal Network method gets promising detection accuracy with several hundred proposals, it still struggles in small-size object detection and precise localization (e.g., large IoU thresholds), mainly due to the coarseness of its feature maps. In this paper, we present a deep hierarchical network, namely HyperNet, for handling region proposal generation and object detection jointly. Our HyperNet is primarily based on an elaborately designed Hyper Feature which aggregates hierarchical feature maps first and then compresses them into a uniform space. The Hyper Features well incorporate deep but highly semantic, intermediate but really complementary, and shallow but naturally high-resolution features of the image, thus enabling us to construct HyperNet by sharing them both in generating proposals and detecting objects via an end-to-end joint training strategy. For the deep VGG16 model, our method achieves completely leading recall and state-of-the-art object detection accuracy on PASCAL VOC 2007 and 2012 using only 100 proposals per image. It runs with a speed of 5 fps (including all steps) on a GPU, thus having the potential for real-time processing.


title: The qualitative research proposal

abstract: Qualitative research in the health sciences has had to overcome many prejudices and a number of misunderstandings, but today qualitative research is as acceptable as quantitative research designs and is widely funded and published. Writing the proposal of a qualitative study, however, can be a challenging feat, due to the emergent nature of the qualitative research design and the description of the methodology as a process. Even today, many sub-standard proposals at post-graduate evaluation committees and application proposals to be considered for funding are still seen. This problem has led the researcher to develop a framework to guide the qualitative researcher in writing the proposal of a qualitative study based on the following research questions: (i) What is the process of writing a qualitative research proposal? and (ii) What does the structure and layout of a qualitative proposal look like? The purpose of this article is to discuss the process of writing the qualitative research proposal, as well as describe the structure and layout of a qualitative research proposal. The process of writing a qualitative research proposal is discussed with regards to the most important questions that need to be answered in your research proposal with consideration of the guidelines of being practical, being persuasive, making broader links, aiming for crystal clarity and planning before you write. While the structure of the qualitative research proposal is discussed with regards to the key sections of the proposal, namely the cover page, abstract, introduction, review of the literature, research problem and research questions, research purpose and objectives, research paradigm, research design, research method, ethical considerations, dissemination plan, budget and appendices.


title: Millimeter-Wave MultiBeam Aperture-Coupled Magnetoelectric Dipole Array With Planar Substrate Integrated Beamforming Network for 5G Applications

abstract: A modified topology of the 2-D multibeam antenna array fed by a passive beamforming network (BFN) is proposed by introducing two sets of vertical interconnections into the conventional array configuration. Different from the traditional design, the new array structure can be integrated into multilayered planar substrates conveniently, which has advantages of low loss characteristics, ease of realization, and low fabrication cost for millimeter-wave (mmWave) applications. A $4\times4$ multibeam antenna array that can generate 16 beams is then designed, fabricated, and measured in order to demonstrate the correctness of the proposed topology. As the crucial components of the design, two kinds of aperture-coupled magnetoelectric dipole antenna elements with multilayered feed structures and three types of vertical interconnections consisting of the vertical substrate integrated waveguides are investigated, respectively. Wide bandwidth of 16.4%, stable radiation beams, and gain up to 14.7 dBi are achieved by the fabricated prototype. The proposed array configuration provides a new mean to implement the relatively large size 2-D multibeam antenna arrays with planar passive BFNs, which would be attractive for future mmWave wireless systems used for 5G communications and other applications.


title: Quantifying Mental Health from Social Media with Neural User Embeddings

abstract: Mental illnesses adversely affect a significant proportion of the population worldwide. However, the methods traditionally used for estimating and characterizing the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of mental health conditions are often years out of date. Automated approaches to supplement these survey methods with broad, aggregated information derived from social media content provides a potential means for near real-time estimates at scale. These may, in turn, provide grist for supporting, evaluating and iteratively improving upon public health programs and interventions. ::: We propose a novel model for automated mental health status quantification that incorporates user embeddings. This builds upon recent work exploring representation learning methods that induce embeddings by leveraging social media post histories. Such embeddings capture latent characteristics of individuals (e.g., political leanings) and encode a soft notion of homophily. In this paper, we investigate whether user embeddings learned from twitter post histories encode information that correlates with mental health statuses. To this end, we estimated user embeddings for a set of users known to be affected by depression and post-traumatic stress disorder (PTSD), and for a set of demographically matched `control' users. We then evaluated these embeddings with respect to: (i) their ability to capture homophilic relations with respect to mental health status; and (ii) the performance of downstream mental health prediction models based on these features. Our experimental results demonstrate that the user embeddings capture similarities between users with respect to mental conditions, and are predictive of mental health.


title: Examining characteristics of resilience among university students: An international study

abstract: Attending university is a particularly stressful time due to unique emergent stressors such as changes in environment, loss or diminishment of social support networks, academic pressures, developing peer relationships, and financial management. There is growing recognition that these common stressors may have deleterious effects on the mental health of students. Resilience, a personality characteristic that moderates the negative effects of stress and promotes adaptation, has been associated with increased psychological well-being. Despite a growing body of research on resilience and its clinical significance in preventing mental health problems, relatively little is known about contributing factors for resilience in well-adjusted university students. This current study examined the characteristics of university students reporting high and low resilience for elucidating its clinical implications in preventing mental health problem, primarily focusing on potentially modifiable psychosocial variables. An international sample of 214 university students recruited from Australia, the United States of America, and Hong Kong universities completed measures of resilience, perceived social support, campus connectedness, and psychological distress. Results of a one-way between groups multivariate analysis of variance revealed that perceived social support, campus connectedness, and psychological distress accounted for a significant proportion (36%) of the variance between the high and low resilience groups of university students. University students with low levels of resilience reported significantly lower levels of perceived social support, campus connectedness, and higher levels of psychological distress, in comparison to university students with high levels of resilience. Findings offer important implications for the development of resilience-based interventions among university students.


title: An optimal minimum spanning tree algorithm

abstract: We establish that the algorithmic complexity of the minimumspanning tree problem is equal to its decision-tree complexity.Specifically, we present a deterministic algorithm to find aminimum spanning tree of a graph with n vertices andm edges that runs in timeO(T*(m,n)) whereT* is the minimum number of edge-weightcomparisons needed to determine the solution. The algorithm isquite simple and can be implemented on a pointer machine.Althoughour time bound is optimal, the exact function describing it is notknown at present. The current best bounds known forT* are T*(m,n) =Ω(m) and T*(m,n) =O(m ∙ α(m,n)), where α is acertain natural inverse of Ackermann's function.Even under theassumption that T* is superlinear, we show thatif the input graph is selected from Gn,m,our algorithm runs in linear time with high probability, regardlessof n, m, or the permutation of edge weights. Theanalysis uses a new martingale for Gn,msimilar to the edge-exposure martingale forGn,p.


Do "Future Work" sections have a purpose? Citation links and entailment for global scientometric questions


title: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

abstract: Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.


title: Using Machine Learning to Detect Cyberbullying

abstract: Cyber bullying is the use of technology as a medium to bully someone. Although it has been an issue for many years, the recognition of its impact on young people has recently increased. Social networking sites provide a fertile medium for bullies, and teens and young adults who use these sites are vulnerable to attacks. Through machine learning, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyber bullying content. The data we used for our project was collected from the website Formspring.me, a question-and-answer formatted website that contains a high percentage of bullying content. The data was labeled using a web service, Amazon's Mechanical Turk. We used the labeled data, in conjunction with machine learning techniques provided by the Weka tool kit, to train a computer to recognize bullying content. Both a C4.5 decision tree learner and an instance-based learner were able to identify the true positives with 78.5% accuracy.


title: Elapsed Time in Human Gait Recognition: A New Approach

abstract: Human gait is an effective biometric source for human identification and visual surveillance; therefore human gait recognition becomes to be a hot topic in recent research. However, the elapsed time problem, which is in its infancy, still receives poor performance. In this paper, we introduce a novel discriminant analysis method to improve the performance. The new model inherits the merits from the tensor rank one analysis, which handles the small samples size problem naturally, and the linear discriminant analysis, which is optimal for classification. Although 2 DLDA and DATR also benefit from these two methods, they cannot converge during the training procedure. This means they can be hardly utilized for practical applications. Based on a lot of experiments on elapsed time problem in human gait recognition, the new method is demonstrated to significantly outperform the existing appearance-based methods, such as the principle component analysis, the linear discriminant analysis, and the tensor rank one analysis


title: LiveRAC: interactive visual exploration of system management time-series data

abstract: We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.


title: Two collaborative filtering recommender systems based on sparse dictionary coding

abstract: This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.


title: Internet of Things: A Scientometric Review

abstract: Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002–2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author’s country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.


title: ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

abstract: Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.


title: Electronic frog eye: Counting crowd using WiFi

abstract: Crowd counting, which count or accurately estimate the number of human beings within a region, is critical in many applications, such as guided tour and crowd control. A crowd counting solution should be scalable and be minimally intrusive (i.e., device-free) to users. Image-based solutions are device-free, but cannot work well in a dim or dark environment. Non-image based solutions usually require every human being carrying device, and are inaccurate and unreliable in practice. In this paper, we present FCC, a device-Free Crowd Counting approach based on Channel State Information (CSI). Our design is motivated by our observation that CSI is highly sensitive to environment variation, like a frog eye. We theoretically discuss the relationship between the number of moving people and the variation of wireless channel state. A major challenge in our design of FCC is to find a stable monotonic function to characterize the relationship between the crowd number and various features of CSI. To this end, we propose a metric, the Percentage of nonzero Elements (PEM), in the dilated CSI Matrix. The monotonic relationship can be explicitly formulated by the Grey Verhulst Model, which is used for crowd counting without a labor-intensive site survey. We implement FCC using off-theshelf IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenarios. Our results demonstrate that FCC outperforms the state-of-art approaches with much better accuracy, scalability and reliability.


title: A Case for a Flexible Scalar Unit in SIMT Architecture

abstract: The wide availability and the Single-Instruction Multiple-Thread (SIMT)-style programming model have made graphics processing units (GPUs) a promising choice for high performance computing. However, because of the SIMT style processing, an instruction will be executed in every thread even if the operands are identical for all the threads. To overcome this inefficiency, the AMD's latest Graphics Core Next (GCN) architecture integrates a scalar unit into a SIMT unit. In GCN, both the SIMT unit and the scalar unit share a single SIMT style instruction stream. Depending on its type, an instruction is issued to either a scalar or a SIMT unit. In this paper, we propose to extend the scalar unit so that it can either share the instruction stream with the SIMT unit or execute a separate instruction stream. The program to be executed by the scalar unit is referred to as a scalar program and its purpose is to assist SIMT-unit execution. The scalar programs are either generated from SIMT programs automatically by the compiler or manually developed by expert developers. We make a case for our proposed flexible scalar unit through three collaborative execution paradigms: data prefetching, control divergence elimination, and scalar-workload extraction. Our experimental results show that significant performance gains can be achieved using our proposed approaches compared to the state-of-art SIMT style processing.


abstract: We consider the problem of learning sparsely used dictionaries with an arbitrary square dictionary and a random, sparse coefficient matrix. We prove that $O (n \log n)$ samples are sufficient to uniquely determine the coefficient matrix. Based on this proof, we design a polynomial-time algorithm, called Exact Recovery of Sparsely-Used Dictionaries (ER-SpUD), and prove that it probably recovers the dictionary and coefficient matrix when the coefficient matrix is sufficiently sparse. Simulation results show that ER-SpUD reveals the true dictionary as well as the coefficients with probability higher than many state-of-the-art algorithms.


title: Instructional Design Models for Well-Structured and Ill-Structured Problem-Solving Learning Outcomes

abstract: Although problem solving is regarded by most educators as among the most important learning outcomes, few instructional design prescriptions are available for designing problem-solving instruction and engaging learners. This paper distinguishes between well-structured problems and ill-structured problems. Well-structured problems are constrained problems with convergent solutions that engage the application of a limited number of rules and principles within well-defined parameters. Ill-structured problems possess multiple solutions, solution paths, fewer parameters which are less manipulable, and contain uncertainty about which concepts, rules, and principles are necessary for the solution or how they are organized and which solution is best. For both types of problems, this paper presents models for how learners solve them and models for designing instruction to support problem-solving skill development. The model for solving well-structured problems is based on information processing theories of learning, while the model for solving ill-structured problems relies on an emerging theory of ill-structured problem solving and on constructivist and situated cognition approaches to learning.


title: A hakka text-to-speech system

abstract: In this paper, the implementation of a Hakka text-to-speech (TTS) system is presented. The system is designed based on the same principle of developing a Mandarin and a Min-Nan TTS systems proposed previously. It takes 671 base-syllables as basic synthesis units and uses a recurrent neural network (RNN)-based prosody generator to generate proper prosodic parameters for synthesizing natural output speech. The whole system is implemented by software and runs in real-time on PC. Informal subjective listening test confirmed that the system performed well. All synthetic speeches sounded well for well-tokenized texts and fair for texts with automatic tokenization.


title: Classifying depression patients and normal subjects using machine learning techniques

abstract: Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients.


title: Optical character recognition ― A survey

abstract: In order to highlight the interesting problems and actual results on the state of the art in optical character recognition (OCR), this paper describes and compares preprocessing, feature extraction and postprocessing techniques for commercial reading machines. Problems related to handwritten and printed character recognition are pointed out, and the functions and operations of the major components of an OCR system are described. Historical background on the development of character recognition is briefly given and the working of an optical scanner is explained. The specifications of several recognition systems that are commercially available are reported and compared.


title: Issues and Perspectives in Meditation Research: In Search for a Definition

abstract: Despite the growing interest in the neurobiological correlates of meditation, most research has omitted to take into account the underlying philosophical aspects of meditation and its wider implications. This, in turn, is reflected in issues surrounding definition, study design, and outcomes. Here, I highlight the often ignored but important aspect of definition in the existing scholarship on neuroscience and meditation practice. For a satisfactory account of a neuroscience of meditation, we must aim to retrieve an operational definition that is inclusive of a traditional ontological description as well as the modern neurocognitive account of the phenomena. Moving beyond examining the effects of meditation practice, to take a potential step forward in the direction to establish how meditation works, it becomes crucial to appraise the philosophical positions that underlie the phenomenology of meditation in the originating traditions. This endeavor may challenge our intuitions and concepts in either directions, but issues pertaining to definition, design, and validity of response measures are extremely important for the evolution of the field and will provide a much-needed context and framework for meditation based interventions.


title: A Compact Transregional Model for Digital CMOS Circuits Operating Near Threshold

abstract: Power dissipation is currently one of the most important design constraints in digital systems. In order to reduce power and energy demands in the foremost technology, namely CMOS, it is necessary to reduce the supply voltage to near the device threshold voltage. Existing analytical models for MOS devices are either too complex, thus obscuring the basic physical relations between voltages and currents, or they are inaccurate and discontinuous around the region of interest, i.e., near threshold. This paper presents a simple transregional compact model for analyzing digital circuits around the threshold voltage. The model is continuous, physically derived (by way of a simplified inversion-charge approximation), and accurate over a wide operational range: from a few times the thermal voltage to approximately twice the threshold voltage in modern technologies.


title: Parameter Generation Methods With Rich Context Models for High-Quality and Flexible Text-To-Speech Synthesis

abstract: In this paper, we propose parameter generation methods using rich context models as yet another hybrid method combining Hidden Markov Model (HMM)-based speech synthesis and unit selection synthesis. Traditional HMM-based speech synthesis enables flexible modeling of acoustic features based on a statistical approach. However, the speech parameters tend to be excessively smoothed. To address this problem, several hybrid methods combining HMM-based speech synthesis and unit selection synthesis have been proposed. Although they significantly improve quality of synthetic speech, they usually lose flexibility of the original HMM-based speech synthesis. In the proposed methods, we use rich context models, which are statistical models that represent individual acoustic parameter segments. In training, the rich context models are reformulated as Gaussian Mixture Models (GMMs). In synthesis, initial speech parameters are generated from probability distributions over-fitted to individual segments, and the speech parameter sequence is iteratively generated from GMMs using a parameter generation method based on the maximum likelihood criterion. Since the basic framework of the proposed methods is still the same as the traditional framework, the capability of flexibly modeling acoustic features remains. The experimental results demonstrate: (1) the use of approximation with a single Gaussian component sequence yields better synthetic speech quality than the use of EM algorithm in the proposed parameter generation method, (2) the state-based model selection yields quality improvements at the same level as the frame-based model selection, (3) the use of the initial parameters generated from the over-fitted speech probability distributions is very effective to further improve speech quality, and (4) the proposed methods for spectral and F0 components yields significant improvements in synthetic speech quality compared with the traditional HMM-based speech synthesis.


title: Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency

abstract: Climate change is recognised as one of the key challenges humankind is facing. The Information and Communication Technology (ICT) sector including data centres generates up to 2% of the global CO 2 emissions, a number on par to the aviation sector contribution, and data centres are estimated to have the fastest growing carbon footprint from across the whole ICT sector, mainly due to technological advances such as the cloud computing and the rapid growth of the use of Internet services. There are no recent estimations of the total energy consumption of the European data centre and of their energy efficiency. The aim of this paper is to evaluate, analyse and present the current trends in energy consumption and efficiency in data centres in the European Union using the data submitted by companies participating in the European Code of Conduct for Data Centre Energy Efficiency programme, a voluntary initiative created in 2008 in response to the increasing energy consumption in data centres and the need to reduce the related environmental, economic and energy supply security impacts. The analysis shows that the average Power Usage Effectiveness (PUE) of the facilities participating in the programme is declining year after year. This confirms that voluntary approaches could be effective in addressing climate and energy issue.


title: A cloud computing solution for sharing healthcare information

abstract: In recent years, sharing healthcare information becomes one of essential requirements of e-health development. To cover this gap, different solutions are presented through different technologies. In this paper, we proposed a cloud computing solution for sharing healthcare information based on Google App Engine (GAE). With the experiment test results, we achieve interoperability among different healthcare centers and between healthcare providers and receivers with high stability and availability.


MATLAB MODELS FOR PNEUMATIC ARTIFICIAL MUSCLES


title: The design of a new X band antenna element

abstract: A micro-strip quasi-Yagi antenna is improved in this paper. The antenna unit for X band is designed. The number of the directors is increased in order to improve the gain of the antenna. The length of the directors, the width of the directors and the space of each directors are simulated and analyzed by the simulation software HFSS. The results show that: the bandwidth of the antenna is greater than 30%, the gain of the antenna is 3.57dB. The smaller size of the antenna is easy to design antenna array, and it has a good application.


Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit


title: CT sinogram‐consistency learning for metal‐induced beam hardening correction

abstract: PURPOSE ::: This paper proposes a sinogram-consistency learning method to deal with beam hardening-related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform METHODS: The proposed learning method aims to repair inconsistent sinogram by removing the primary metal-induced beam hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data and a patient's implant type-specific learning model is used to simplify the learning process. ::: ::: ::: RESULTS ::: The feasibility of the proposed method is investigated using a dataset, consisting of real CT scans of pelvises containing simulated hip prostheses. The anatomical areas in training and test data are different, in order to demonstrate that the proposed method extracts the beam hardening features, selectively. The results show that our method successfully corrects sinogram inconsistency by extracting beam hardening sources by means of deep learning. ::: ::: ::: CONCLUSION ::: This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time. Conventional methods for beam hardening reduction are based on regularizations, and have the fundamental drawback of being not easily able to use manifold CT images, while a deep learning approach has the potential to do so.


title: Experimental Security Analysis of a Modern Automobile

abstract: Modern automobiles are no longer mere mechanical devices; they are pervasively monitored and controlled by dozens of digital computers coordinated via internal vehicular networks. While this transformation has driven major advancements in efficiency and safety, it has also introduced a range of new potential risks. In this paper we experimentally evaluate these issues on a modern automobile and demonstrate the fragility of the underlying system structure. We demonstrate that an attacker who is able to infiltrate virtually any Electronic Control Unit (ECU) can leverage this ability to completely circumvent a broad array of safety-critical systems. Over a range of experiments, both in the lab and in road tests, we demonstrate the ability to adversarially control a wide range of automotive functions and completely ignore driver input\dash including disabling the brakes, selectively braking individual wheels on demand, stopping the engine, and so on. We find that it is possible to bypass rudimentary network security protections within the car, such as maliciously bridging between our car's two internal subnets. We also present composite attacks that leverage individual weaknesses, including an attack that embeds malicious code in a car's telematics unit and that will completely erase any evidence of its presence after a crash. Looking forward, we discuss the complex challenges in addressing these vulnerabilities while considering the existing automotive ecosystem.


title: Anti-forensics of median filtering

abstract: A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is median filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several median filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of median filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains median filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing median filtering detectors under realistic conditions.


title: SegGAN: Semantic Segmentation with Generative Adversarial Network

abstract: Semantic segmentation is a long standing challenging issue in computer vision. In this paper, a novel method named SegGAN is proposed, in which a pre-trained deep semantic segmentation network is fitted into a generative adversarial framework for computing better segmentation masks. The composited networks are jointly fine-tuned end-to-end to get better segmentation masks. In the pre-training of Generative Adversarial Network (GAN), we try to minimize the loss between the generated images from the generator with the ground truth masks as input and the original images. Our motivation is that the learned GAN shows the relationship between the ground truth masks and the original images, thus the predicted masks of the semantic segmentation model should have the same distribution or relationship with the original images. Concretely, GAN is treated as a kind of loss for semantic segmentation to achieve better performance. Numerous experiments conducted on two publicly available datasets demonstrate the effectiveness of the proposed SegGAN.


title: ABiRCNN with neural tensor network for answer selection

abstract: Answer selection is a very important task in domain question answering. However, because of the word variety between questions and answers, there exists the lexical gap between questions and answers, which is the major challenge in question answer matching. In this work, in order to overcome the lexical gap, we propose an attention based bidirectional gated convolution with neural tensor network (ABiRCNN+NTN), which can improve the representations for both questions and answers and model their interactions with a neural tensor network. We carry out large-scale experiments on answer selection dataset, InsuranceQA and achieve new state-of-the-art results on InsuranceQA dataset. The experimental results demonstrate that our model can effectively capture the complex semantic relations between questions and answers and encode them in a more effective way. The source code of our work can be obtained from https://github.com/paperstudy/AnswerSelection.


Content Explorer: Recommending Novel Entities for a Document Writer


title: Radiology Objects in COntext (ROCO): A Multimodal Image Dataset

abstract: This work introduces a new multimodal image dataset, with the aim of detecting the interplay between visual elements and semantic relations present in radiology images. The objective is accomplished by retrieving all image-caption pairs from the open-access biomedical literature database PubMedCentral, as these captions describe the visual content in their semantic context. All compound, multi-pane, and non-radiology images were eliminated using an automatic binary classifier fine-tuned with a deep convolutional neural network system. Radiology Objects in COntext (ROCO) dataset contains over 81k radiology images with several medical imaging modalities including Computer Tomography, Ultrasound, X-Ray, Fluoroscopy, Positron Emission Tomography, Mammography, Magnetic Resonance Imaging, Angiography. All images in ROCO have corresponding caption, keywords, Unified Medical Language Systems Concept Unique Identifiers and Semantic Type. An out-of-class set with 6k images ranging from synthetic radiology figures to digital arts is provided, to improve prediction and classification performance. Adopting ROCO, systems for caption and keywords generation can be modeled, which allows multimodal representation for datasets lacking text representation. Systems with the goal of image structuring and semantic information tagging can be created using ROCO, which is beneficial and of assistance for image and information retrieval purposes.


title: A physically based scalable SPICE model for Shielded-Gate Trench Power MOSFETs

abstract: This paper proposes a novel physical scalable SPICE model for Shielded-Gate Trench Power MOSFETs. The model is based on process and layout parameters, enabling design optimization through a direct link between SPICE and process technology. The model has been validated with Fairchild's state of the art technology. The model is SPICE agnostic, working seamlessly across multiple industry standard simulation platforms.


title: GASTROENTEROLOGY 2006;130:1245–1258 BASIC–LIVER, PANCREAS, AND BILIARY TRACT Targeted Deletion of FATP5 Reveals Multiple Functions in Liver Metabolism: Alterations in Hepatic Lipid Homeostasis

abstract: Background & Aims: Fatty acid transport protein 5 (FATP5/Slc27a5) has been shown to be a multifunctional protein that in vitro increases both uptake of fluorescently labeled long-chain fatty acid (LCFA) analogues and bile acid/coenzyme A ligase activity on overexpression. The aim of this study was to further investigate the diverse roles of FATP5 in vivo. Methods: We studied FATP5 expression and localization in liver of C57BL/6 mice in detail. Furthermore, we created a FATP5 knockout mouse model and characterized changes in hepatic lipid metabolism (this report) and bile metabolism (the accompanying report by Hubbard et al). Results: FATP5 is exclusively expressed by the liver and localized to the basal plasma membrane of hepatocytes, congruent with a role in LCFA uptake from the circulation. Overexpression of FATP5 in mammalian cells increased the uptake of 14 C-oleate. Conversely, FATP5 deletion significantly reduced LCFA uptake by hepatocytes isolated from FATP5 knockout animals. Moreover, FATP5 deletion resulted in lower hepatic triglyceride and free fatty acid content despite increased expression of fatty acid synthetase and also caused a redistribution of lipids from liver to other LCFA-metabolizing tissues. Detailed analysis of the hepatic lipom of FATP5 knockout livers showed quantitative and qualitative alterations in line with a decreased uptake of dietary LCFAs and increased de novo synthesis. Conclusions: Our findings support the hypothesis that efficient hepatocellular uptake of LCFAs, and thus liver lipid homeostasis in general, is largely a protein-mediated process requiring FATP5. These new insights into the physiological role of FATP5 should lead to an improved understanding of liver function and disease.


title: Low-Loss Two-Pole Tunable Filters With Three Different Predefined Bandwidth Characteristics

abstract: Low-loss tunable filters with three different fractional-bandwidth variations were designed and fabricated on epsivr = 2.2, 0.787 mm Duroid substrates for 850-1400-MHz applications. A detailed analysis for realizing predefined bandwidth characteristics is presented, and a design technique to take into account the source and load impedance loading is discussed. It is found that independent electric and magnetic coupling makes it possible to realize three different coupling coefficient variations with the same filter structure. The proposed topology is different from the comb-line design in that all three filters have identical electrical lengths, the same varactors, and the same filter Q values. Three different filters are built using Schottky varactor diodes with a tuning range from ~ 850 to ~ 1400 MHz. The constant fraction-bandwidth filter has a 1-dB bandwidth of 5.4%plusmn0.3% and an insertion loss of 2.88-1.04 dB. The decreasing fractional-bandwidth filter shows a 1-dB bandwidth decrease from 5.2% to 2.9% with an insertion loss of 2.89-1.93 dB (this is effectively a 40-45-MHz constant absolute-bandwidth filter). The increasing fractional-bandwidth filter shows a 1-dB bandwidth increase from 4.3% to 6.5% with an insertion loss of 3.47-1.18 dB. The measured Q of the filters are between 53-152 from ~ 850 to ~ 1400 MHz. The measured third-order intermodulation intercept point ranges from 11.3 to 20.1 dBm depending on the bias voltage. To our knowledge, these planar tunable filters represent state-of-the art insertion-loss performance at this frequency range.


title: Optimal Distributed Online Prediction

abstract: Online prediction methods are typically studied as serial algorithms running on a single processor. In this paper, we present the distributed mini-batch (DMB) framework, a method of converting a serial gradient-based online algorithm into a distributed algorithm, and prove an asymptotically optimal regret bound for smooth convex loss functions and stochastic examples. Our analysis explicitly takes into account communication latencies between computing nodes in a network. We also present robust variants, which are resilient to failures and node heterogeneity in an asynchronous distributed environment. Our method can also be used for distributed stochastic optimization, attaining an asymptotically linear speedup. Finally, we empirically demonstrate the merits of our approach on large-scale online prediction problems.


title: A genetic programming approach to automated software repair

abstract: Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000 lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.


title: Polychlorinated biphenyls (PCBs) exert thyroid hormone-like effects in the fetal rat brain but do not bind to thyroid hormone receptors.

abstract: Polychlorinated biphenyls (PCBs) are ubiquitous environmental contaminants routinely found in human and animal tissues. Developmental exposure to PCBs is associated with neuropsychologic deficits, which may be related to effects on thyroid hormone (TH) signaling in the developing brain. However, PCBs may interfere with TH signaling solely by reducing circulating levels of TH, or they may exert direct effects on TH receptors (TRs). Therefore, we tested whether maternal exposure to a commercial PCB mixture, Aroclor 1254 (A1254), exerts effects in the fetal brain by one or both of these mechanisms. Dams were dosed daily with 0, 1, or 4 mg/kg A1254 from gestational day 6 (GD6) until they were sacrificed on GD16. A1254 significantly reduced circulating levels of triiodothyronine (T3) and thyroxine (T4) in pregnant rats but increased the expression of several TH-responsive genes in the fetal cortex, including neuroendocrine-specific protein A (NSP-A), RC3/neurogranin, and Oct-1. These findings are consistent with a direct action of PCBs on TRs. However, we did not identify parent PCB congeners or metabolites that bound to rat TRs isolated from hepatic nuclei. These findings indicate that PCBs can interfere with TH signaling in the fetal brain by direct actions on the fetus rather than by producing maternal hypothyroidism.


title: MLP and Elman recurrent neural network modelling for the TRMS

abstract: This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology.


Evolutionary Algorithms for Multiobjective Optimization