title: Simulator Sickness Depends on Frequency of the Simulator Motion Mismatch: An Observation

abstract: In this study we describe a new approach to relate simulator sickness ratings with the main frequency component of the simulator motion mismatch, that is, the computed difference between the time histories of simulator motion and vehicle motion, respectively. During two driving simulator experiments in the TNO moving-base driving simulator---that were performed for other reasons than the purpose of this study---we collected simulator sickness questionnaires from in total 58 subjects. The main frequency component was computed by means of the power spectrum density of the computed mismatch signal. We hypothesized that simulator sickness incidence depends on this frequency component, in a similar way as the incidence of “real” motion sickness, such as sea sickness, depends on motion frequency. The results show that the simulator sickness ratings differed between both driving simulator experiments. The experiment with its main frequency component of the mismatch signal of 0.08 Hz had significantly higher simulator sickness incidence than the experiment with its main frequency at 0.46 Hz. Since the experimental design differed between both experiments, we cannot exclusively attribute the difference in sickness ratings to the frequency component, but the observation does suggest that quantitative analysis of the mismatch between the motion profiles of the simulator and the vehicle may greatly improve our understanding of the causal mechanism of simulator sickness.


title: Design considerations and performance evaluation of 1200 V, 100 a SiC MOSFET based converter for high power density application

abstract: Silicon Carbide (SiC) MOSFET is capable of achieving better efficiency, power density and reliability of power converters due to its low on-state resistance, high temperature operation capability and lower switching losses compared to silicon (Si) IGBT. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200 V, 100 A SiC MOSFET module and compares efficiency of a Two Level Voltage Source Converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200 V, 100 A SiC MOSFET based 2L-VSC including gate drive design, bus bar packaging and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20 kHz switching frequency with DC bus voltage at 800 V and the experimental results are presented.


title: A primitive-based 3D segmentation algorithm for mechanical CAD models

abstract: This paper presents a novel segmentation algorithm for mechanical CAD models (represented by either mesh or point cloud) constructed from planes, cylinders, cones, spheres, tori and easily extendable to surfaces of revolution. Our proposed approach differs from existing techniques in the following aspects. First, by assuming that common mechanical models only have a limited number of dominant orientations that their primitives are either parallel or orthogonal to, we narrow down the search space for detecting the primitives to the automatically estimated major orientations of the input model. Second, we employ a dimension reduction method which transforms the problem of detecting 3D primitives into the classical 2D problems such as circle and line detection in images. Third, we generate an over-complete set of primitives and formulate the segmentation as a set cover optimization problem. We demonstrate our method's robustness to noise and show that it compares favorably with state-of-the-art solutions such as the RANSAC-based (Schnabel et al., 2007) and GlobFit (Li et al., 2011) approaches on many synthetic and real scanned examples.


Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding.


Blind Image Deconvolution: An Algorithmic Approach to Practical Image Restoration


title: An aeronautical data link security overview

abstract: This paper reviews existing security mechanisms for aeronautical data link communication: current support and availability of such features are described. With an Open Systems Interconnection (OSI) reference model-driven analysis, each solution is classified and analyzed according to the layer where security is deployed and a relevant taxonomy is proposed. Moreover, advantages, drawbacks, and possible threats of every security mechanisms previously introduced are discussed. According to this security infrastructure overview, a proposal for an efficient security architecture adapted to the aeronautical context is made for future studies. Satellite communication-based system specific problematic is taken into account with a constraint bandwidth and the need of reduced overhead for any additional mechanisms.


title: Design and analysis of an artificial finger joint for anthropomorphic robotic hands

abstract: In order to further understand what physiological characteristics make a human hand irreplaceable for many dexterous tasks, it is necessary to develop artificial joints that are anatomically correct while sharing similar dynamic features. In this paper, we address the problem of designing a two degree of freedom metacarpophalangeal (MCP) joint of an index finger. The artificial MCP joint is composed of a ball joint, crocheted ligaments, and a silicon rubber sleeve which as a whole provides the functions required of a human finger joint. We quantitatively validate the efficacy of the artificial joint by comparing its dynamic characteristics with that of two human subjects' index fingers by analyzing their impulse response with linear regression. Design parameters of the artificial joint are varied to highlight their effect on the joint's dynamics. A modified, second-order model is fit which accounts for non-linear stiffness and damping, and a higher order model is considered. Good fits are observed both in the human (R2 = 0.97) and the artificial joint of the index finger (R2 = 0.95). Parameter estimates of stiffness and damping for the artificial joint are found to be similar to those in the literature, indicating our new joint is a good approximation for an index finger's MCP joint.


title: Multilevel buck dc-dc converter for high voltage application

abstract: This paper presents a nonisolated multilevel step-down dc-dc converter suitable for high voltage application. The main features of this topology are: low voltage across the semiconductors; low switching losses and reduced volume of output filter. This paper focuses on the five-level structure of the proposed converter, in which the theoretical analysis is carried out and discussed. The five-level proposed dc-dc converter has four capacitors and their voltages should be balanced for its correct operation. Therefore, a capacitor voltage balancing active control is presented and analyzed in detail herein. In order to demonstrate the performance of this converter, experimental results for a prototype of the 700-V input voltage to 450-V output voltage, 5-kW rated power and switching frequency of 20 kHz are reported herein.


title: Design FOPID Control of an Automatic Voltage Regulator (AVR) System Imperialist Competitive Algorithm

abstract: This paper addresses a robust method for optimal Fractional order PID (FOPID) control of automatic Voltage Regulation (AVR) system to damp terminal voltage oscillation following disturbances in power systems. The optimization is carried out by the Imperialist Competitive algorithm Optimization (ICA) to improve their situation. The optimal tuning problem of the FOPID gains to control of AVR system against parametric uncertainties is formulated as an optimization problem according to time domain based objective function. It is carried out under multiple operation conditions to achieve the desired level of terminal voltage regulation. The results analysis reveals that the ICA based FOPID type AVR control system is effective and provides good terminal voltage oscillations damping ability.


Momentum, Reversals, and other Puzzles in Fama-MacBeth Cross-Sectional Regressions


title: 'Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors

abstract: Electronic medical record (EMR) systems can deliver many benefits to healthcare organizations and the patients they serve. However, one of the biggest stumbling blocks in garnering these benefits is the limited adoption and use by doctors. We employ the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation and adapt the theory to the context of EMR system adoption and use by doctors. Specifically, we suggest that age will be the only significant moderator, and gender, voluntariness and experience will not play significant moderating roles. We tested our model in a longitudinal study over a 7-month period in a hospital implementing a new EMR system. We collected 3 waves of survey data from 141 doctors and used system logs to measure use. While the original UTAUT only predicted about 20% of the variance in intention, the modified UTAUT predicted 44%. Both models were comparable in their prediction of use. In addition to contributing to healthcare IT and UTAUT research, we hope this work will serve as a foundation for future work that integrates UTAUT with other theoretical perspectives.


title: GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support

abstract: MOTIVATION ::: Modern biology is shifting from the 'one gene one postdoc' approach to genomic analyses that include the simultaneous monitoring of thousands of genes. The importance of efficient access to concise and integrated biomedical information to support data analysis and decision making is therefore increasing rapidly, in both academic and industrial research. However, knowledge discovery in the widely scattered resources relevant for biomedical research is often a cumbersome and non-trivial task, one that requires a significant amount of training and effort. ::: ::: ::: RESULTS ::: To develop a model for a new type of topic-specific overview resource that provides efficient access to distributed information, we designed a database called 'GeneCards'. It is a freely accessible Web resource that offers one hypertext 'card' for each of the more than 7000 human genes that currently have an approved gene symbol published by the HUGO/GDB nomenclature committee. The presented information aims at giving immediate insight into current knowledge about the respective gene, including a focus on its functions in health and disease. It is compiled by Perl scripts that automatically extract relevant information from several databases, including SWISS-PROT, OMIM, Genatlas and GDB. Analyses of the interactions of users with the Web interface of GeneCards triggered development of easy-to-scan displays optimized for human browsing. Also, we developed algorithms that offer 'ready-to-click' query reformulation support, to facilitate information retrieval and exploration. Many of the long-term users turn to GeneCards to quickly access information about the function of very large sets of genes, for example in the realm of large-scale expression studies using 'DNA chip' technology or two-dimensional protein electrophoresis. ::: ::: ::: AVAILABILITY ::: Freely available at http://bioinformatics.weizmann.ac.il/cards/ ::: ::: ::: CONTACT ::: cards@bioinformatics.weizmann.ac.il


title: Security Vulnerabilities of Unmanned Aerial Vehicles and Countermeasures: An Experimental Study

abstract: Drones present a novel airborne platform for new commercial and consumer tasks. Due to their increasing use, security analysis has become necessary. In this paper, we present a collection of fatal attacks, minor observations and associated security vulnerabilities for DJI Phantom 4 Pro(P4P) and Parrot Bebop 2 drones. We also propose some countermeasures to bolster the security of the analyzed drones. In addition to this, we provided a brief overview of the system and embedded architecture of these vehicles. Although DJI has tried to improve security by introducing radio communication and partially encrypted firmware, the P4P still remains vulnerable to GPS spoofing, jamming and other vulnerabilities through DJI-SDK has been exposed in our experimental study. In contrast, Bebop drones are more vulnerable to wireless attacks and we have experimentally validated this for Bebop 2 drone.


title: Abnormally malicious autonomous systems and their internet connectivity

abstract: While many attacks are distributed across botnets, investigators and network operators have recently identified malicious networks through high profile autonomous system (AS) depeerings and network shutdowns. In this paper, we explore whether some ASs indeed are safe havens for malicious activity. We look for ISPs and ASs that exhibit disproportionately high malicious behavior using 10 popular blacklists, plus local spam data, and extensive DNS resolutions based on the contents of the blacklists. We find that some ASs have over 80% of their routable IP address space blacklisted. Yet others account for large fractions of blacklisted IP addresses. Several ASs regularly peer with ASs associated with significant malicious activity. We also find that malicious ASs as a whole differ from benign ones in other properties not obviously related to their malicious activities, such as more frequent connectivity changes with their BGP peers. Overall, we conclude that examining malicious activity at AS granularity can unearth networks with lax security or those that harbor cybercrime.


title: Control of attendance applied in higher education through mobile NFC technologies

abstract: This paper presents an improved attendance control system, required for continuous evaluation which has become compulsory following the Bologna Process. It provides a solution based on NFC technology and is based on a real project developed and pilot tested at the ''Universidad Pontificia de Salamanca, Campus Madrid''.


title: Numerical study of wind-driven natural ventilation in a greenhouse with screens

abstract: Abstract This paper is devoted to the CFD study of wind-driven ventilation in a greenhouse, with particular focus to the effect of screens on the inner airflow distribution. Although the use of shading screens to cover agricultural crops has been constantly increased to reduce high radiation loads, their effect on airflow distribution within the greenhouse is still not fully understood. In this paper, CFD simulations of the ventilation in a greenhouse with and without screens are performed, by means of a finite volume CFD code (Ansys-Fluent 17.2), with a standard k- e turbulence model, together with proper user defined functions (UDF) for the inlet velocity and turbulent profiles. The screens have been modeled as porous surfaces and the porosity and the permeability have been obtained experimentally and set into the model. The code has been validated by a comparison with velocity measurements performed in a greenhouse owned by the University of Bologna. Comparisons between the airflow velocity patterns obtained within the greenhouse with screens and without screens have been obtained for different external airflow velocities. The cases with screens show a more uniform distribution of velocity field inside the greenhouse than the cases without screens, especially near the crops. All the cases show that screens strongly affect the airflow velocity distribution inside the greenhouse and the distribution of volume flow rates through the vents. This work shows how the characteristics of the screens and their positioning near the vents are critical for the ventilation within a greenhouse.


title: An Unsupervised Aspect-Sentiment Model for Online Reviews

abstract: With the increase in popularity of online review sites comes a corresponding need for tools capable of extracting the information most important to the user from the plain text data. Due to the diversity in products and services being reviewed, supervised methods are often not practical. We present an unsuper-vised system for extracting aspects and determining sentiment in review text. The method is simple and flexible with regard to domain and language, and takes into account the influence of aspect on sentiment polarity, an issue largely ignored in previous literature. We demonstrate its effectiveness on both component tasks, where it achieves similar results to more complex semi-supervised methods that are restricted by their reliance on manual annotation and extensive knowledge sources.


title: A Randomized Controlled Trial of Postcrisis Suicide Prevention

abstract: OBJECTIVE ::: This study tested the hypothesis that professionals' maintenance of long-term contact with persons who are at risk of suicide can exert a suicide-prevention influence. This influence was hypothesized to result from the development of a feeling of connectedness and to be most pertinent to high-risk individuals who refuse to remain in the health care system. ::: ::: ::: METHODS ::: A total of 3,005 persons hospitalized because of a depressive or suicidal state, populations known to be at risk of subsequent suicide, were contacted 30 days after discharge about follow-up treatment. A total of 843 patients who had refused ongoing care were randomly divided into two groups; persons in one group were contacted by letter at least four times a year for five years. The other group-the control group-received no further contact. A follow-up procedure identified patients who died during the five-year contact period and during the subsequent ten years. Suicide rates in the contact and no-contact groups were compared. ::: ::: ::: RESULTS ::: Patients in the contact group had a lower suicide rate in all five years of the study. Formal survival analyses revealed a significantly lower rate in the contact group (p=.04) for the first two years; differences in the rates gradually diminished, and by year 14 no differences between groups were observed. ::: ::: ::: CONCLUSIONS ::: A systematic program of contact with persons who are at risk of suicide and who refuse to remain in the health care system appears to exert a significant preventive influence for at least two years. Diminution of the frequency of contact and discontinuation of contact appear to reduce and eventually eliminate this preventive influence.


title: CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF

abstract: This paper addresses the problem of video object segmentation, where the initial object mask is given in the first frame of an input video. We propose a novel spatio-temporal Markov Random Field (MRF) model defined over pixels to handle this problem. Unlike conventional MRF models, the spatial dependencies among pixels in our model are encoded by a Convolutional Neural Network (CNN). Specifically, for a given object, the probability of a labeling to a set of spatially neighboring pixels can be predicted by a CNN trained for this specific object. As a result, higher-order, richer dependencies among pixels in the set can be implicitly modeled by the CNN. With temporal dependencies established by optical flow, the resulting MRF model combines both spatial and temporal cues for tackling video object segmentation. However, performing inference in the MRF model is very difficult due to the very high-order dependencies. To this end, we propose a novel CNN-embedded algorithm to perform approximate inference in the MRF. This algorithm proceeds by alternating between a temporal fusion step and a feed-forward CNN step. When initialized with an appearance-based one-shot segmentation CNN, our model outperforms the winning entries of the DAVIS 2017 Challenge, without resorting to model ensembling or any dedicated detectors.


title: Landmark-Based Homing Navigation Using Omnidirectional Depth Information

abstract: A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation.


title: Design, Modeling, and Demonstration of a MEMS Repulsive-Force Out-of-Plane Electrostatic Micro Actuator

abstract: An analytical model is developed for a two-layer repulsive-force out-of-plane micro electrostatic actuator by using conformal mapping techniques. The model provides the means to establish the performance characteristics in terms of stroke and generated force of the actuator and is used to develop design and optimization rules for the actuator. Numerical simulations were conducted in order to verify the analytical model. A simple physical model is also presented that explains the mechanism for generating the repulsive force. A Multi-User-MEMS-Processes repulsive-force out-of-plane rotation micromirror is developed to experimentally verify the analytical model and to demonstrate the repulsive-force actuator's capability of driving large-size rotation plates by using surface micromachining technology. Experimental measurements show that the repulsive-force rotation micromirror with a size of 312 mum times 312 mum achieved a mechanical rotation of 0deg-2.1deg at a dc driving voltage of 0-200 V. The micromirror achieved an open-loop settling time of 2.9 ms for a mechanical rotation of 2.3deg and an open-loop bandwidth of 150 Hz (-3 dB).


title: Improving high frequency DC-DC converter performance with monolithic half bridge GaN ICs

abstract: The rapid maturation of GaN power transistors continues to enable new capabilities in high frequency power conversion. In this paper we will evaluate one of the latest technological advancements in eGaN® FETs, monolithic integration. The benefits of monolithic integration for GaN power transistors with regards to parasitic reduction, die size optimization, and thermal performance will be discussed. Experimental results for a 12 V IN to 1 V OUT buck converter operating at a switching frequency of 1 MHz and up to 40 A of output current will be demonstrated with 30 V eGaN monolithic half bridge (HB) ICs. For an 80 V eGaN monolithic HB IC, 48 V IN to 1 V OUT and 1.8 V OUT point-of-load (POL) converters will be demonstrated at switching frequencies up to 500 kHz and output currents up to 30 A.


title: Comparison of oral and inhaled metaproterenol for prevention of exercise-induced asthma.

abstract: The effectiveness of inhaled versus oral metaproterenol in preventing exercise-induced asthma (EIA) was studied. Inhaled metaproterenol given 10 min before the exercise significantly reduced the degree of EIA in a group of twenty-four patients, and in 75% of them completely prevented it. The mean percentage decrease in FEV1 was 6.5% with the inhaler and 30.1% with placebo. When inhaled 1 hr before the exercise, metaproterenol was still better than placebo but its effectiveness was considerably lower. Metaproterenol tablets had a slight protective effect given 1 hr before, and none when administered 2 hr before exercise. There was no correlation between the protective effect against EIA and the bronchodilating effect obtained before exercise. Metaproterenol administered by metered-dose inhaler is a very effective prophylactic medication against clinically troublesome EIA, while metaproterenol tablets should not be recommended for this purpose.


Indonesian News Classification using Support Vector Machine


title: The Study of Biometrics Technology Applied in Attendance Management System

abstract: Research on i§How to create a fair, convenient attendance management systemi¨, is being pursued by academics and government departments fervently. This study is based on the biometric recognition technology. The hand geometry machine captures the personal hand geometry data as the biometric code and applies this data in the attendance management system as the attendance record. The attendance records that use this technology is difficult to replicate by others. It can improve the reliability of the attendance records and avoid fraudulent issues that happen when you use a register. This research uses the social survey method-questionnaire to evaluate the theory and practice of introducing biometric recognition technology-hand geometry capturing into the attendance management system.


title: Hierarchical Clustering for Software Architecture Recovery

abstract: Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.


title: Non-Orthogonal Multiple Access (NOMA) for Cellular Future Radio Access

abstract: This paper presents a non-orthogonal multiple access (NOMA) concept for cellular future radio access (FRA) towards the 2020s information society. Different from the current LTE radio access scheme (until Release 11), NOMA superposes multiple users in the power domain although its basic signal waveform could be based on the orthogonal frequency division multiple access (OFDMA) or the discrete Fourier transform (DFT)-spread OFDM the same as LTE baseline. In our concept, NOMA adopts a successive interference cancellation (SIC) receiver as the baseline receiver scheme for robust multiple access, considering the expected evolution of device processing capabilities in the future. Based on system-level evaluations, we show that the downlink NOMA with SIC improves both the capacity and cell-edge user throughput performance irrespective of the availability of the frequency-selective channel quality indicator (CQI) on the base station side. Furthermore, we discuss possible extensions of NOMA by jointly applying multi-antenna/site technologies with a proposed NOMA/MIMO scheme using SIC and an interference rejection combining (IRC) receiver to achieve further capacity gains, e.g., a three-fold gain in the spectrum efficiency representing a challenging target for FRA.


title: Significance and Challenges of Big Data Research

abstract: In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significance and opportunities that big data brings to us. Next, we present representative big data initiatives all over the world. We describe the grand challenges (namely, data complexity, computational complexity, and system complexity), as well as possible solutions to address these challenges. Finally, we conclude the paper by presenting several suggestions on carrying out big data projects.


BioCreative II Gene Mention Tagging System at IBM Watson


title: Personal servers as digital keys

abstract: Personal servers are an attractive concept: people carry around a device that takes care of computing, storage and communication on their behalf in a pervasive computing environment. So far personal servers have mainly been considered for accessing personal information. In this paper, we consider personal servers in the context of a digital key system. Digital keys are an interesting alternative to physical keys for mail or good delivery companies whose employees access tens of private buildings every day. We present a digital key system tailored for the current incarnation of personal servers, i.e., a Bluetooth-enabled mobile phone. We describe how to use Bluetooth for this application, we present a simple authentication protocol and we provide a detailed analysis of response time and energy consumption on the mobile phone.


title: Proteomics: a pragmatic perspective

abstract: The evolution of mass spectrometry-based proteomic technologies has advanced our understanding of the complex and dynamic nature of proteomes while concurrently revealing that no 'one-size-fits-all' proteomic strategy can be used to address all biological questions. Whereas some techniques, such as those for analyzing protein complexes, have matured and are broadly applied with great success, others, such as global quantitative protein expression profiling for biomarker discovery, are still confined to a few expert laboratories. In this Perspective, we attempt to distill the wide array of conceivable proteomic approaches into a compact canon of techniques suited to asking and answering specific types of biological questions. By discussing the relationship between the complexity of a biological sample and the difficulty of implementing the appropriate analysis approach, we contrast areas of proteomics broadly usable today with those that require significant technical and conceptual development. We hope to provide nonexperts with a guide for calibrating expectations of what can realistically be learned from a proteomics experiment and for gauging the planning and execution effort. We further provide a detailed supplement explaining the most common techniques in proteomics.


title: FPGA-based accelerator for long short-term memory recurrent neural networks

abstract: Long Short-Term Memory Recurrent neural networks (LSTM-RNNs) have been widely used for speech recognition, machine translation, scene analysis, etc. Unfortunately, general-purpose processors like CPUs and GPGPUs can not implement LSTM-RNNs efficiently due to the recurrent nature of LSTM-RNNs. FPGA-based accelerators have attracted attention of researchers because of good performance, high energy-efficiency and great flexibility. In this work, we present an FPGA-based accelerator for LSTM-RNNs that optimizes both computation performance and communication requirements. The peak performance of our accelerator achieves 7.26 GFLOP/S, which significantly outperforms previous approaches.


title: The design of personal mobile technologies for lifelong learning

abstract: Abstract This paper sets out a framework for the design of a new genre of educational technology — personal (handheld or wearable) computer systems that support learning from any location throughout a lifetime. We set out a theory of lifelong learning mediated by technology and indicate how it can provide requirements for the software, hardware, communications and interface design of a handheld learning resource, or HandLeR. The paper concludes with a description and formative evaluation of a demonstrator system for children aged 7–11.


title: Keyphrase Extraction Based on Prior Knowledge

abstract: Keyphrase is an important way to quickly get the topic of a document by providing highly-summative information. The previous approaches for keyphrase extraction simply rank keyphrases according to statistics-based model or graph-based model, which ignore the influence of external knowledge. In this paper, we take prior knowledge, which contains controlled vocabulary of keyphrases and their prior probability, into consideration to enhance previous methods. First, we build a controlled vocabulary of keyphrases introduced by keyphrases from existing collections and a keyphrase candidate set is filtered from a given document by it. Then, we use prior probability to represent the importance of keyphrases candidate with TF-IDF and TextRank. Finally, a supervised learning algorithm is used to learn optimal weights of these three features. Experiments on four benchmark datasets show the great advantages of prior knowledge on keyphrase extraction. Furthermore, we achieve competitive performance compared with the state-of-the art methods.


title: Automatic recommendation of API methods from feature requests

abstract: Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, and Struts 2. Our experiments show that our approach is able to recommend the right methods from 10 libraries with an average recall-rate@5 of 0.690 and recall-rate@10 of 0.779 respectively. We also show that the state-of-the-art approach by Chan et al., that recommends API methods based on precise text phrases, is unable to handle feature requests.


title: Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank

abstract: Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliche of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank based in Ireland, we propose a new data mining-based approach for AML. In this paper, we present this approach and some preliminary results associated with this method when applied to transaction datasets.


title: Evolution of regulatory networks: nematode vulva induction as an example of developmental systems drift.

abstract: Changes in the developmental processes and developmental mechanisms can result in the modification of morphological structures and in the evolution of phenotypic novelty. But how do developmental processes evolve? One striking finding in modern biology is the confrontation of morphological diversity in multicellular organisms with the conserved blueprint of life—the small number of conserved signaling pathways and transcriptional regulators. Evolutionary developmental biology (evo-devo) tries to explain this discrepancy between macroscopic diversity and molecular uniformity. Selected case studies in evo-devo models allowed detailed insight into the mechanisms of evolutionary changes and might help solving this problem. Here, I compare the formation of vulva development between Caenorhabditis elegans and the evo-devo model Pristionchus pacificus. More than 3 decades of work in C. elegans and 15 years in P. pacificus provide an insight into the molecular mechanisms of developmental change during vulva evolution. C. elegans and P. pacificus differ first, in the type of the signaling system used for vulva induction; second, the cells required for the inductive interactions; third, the logic of the signal system, and finally, the sequence and structure of peptide domains in otherwise conserved proteins. Nonetheless, the vulva is formed from the same three cells in both nematodes. I discuss redundancy as an evolutionary mechanism to explain developmental systems drift, a theory predicting conserved morphological structures to be generated by diverse molecular regulatory networks.


title: Analysis of a Wideband Circularly Polarized Cylindrical Dielectric Resonator Antenna With Broadside Radiation Coupled With Simple Microstrip Feeding

abstract: In this paper, a wideband circularly polarized cylindrical shaped dielectric resonator antenna (DRA) with simple microstrip feed network has been designed and investigated. The proposed design uses dual vertical microstrip lines arranged in a perpendicular fashion to excite fundamental orthogonal hybrid ${HE}_{11\delta }^{x}$ and ${HE}_{11\delta }^{y}$ modes in the cylindrical DR. The Phase quadrature relationships between orthogonal modes have been attained by varying corresponding microstrips heights. To ratify the simulation results, an antenna prototype is fabricated and measured. Measured input reflection coefficient and axial ratio bandwidth (at $\Phi =0^{\circ }$ , $\theta =0^{\circ }$ ) of 30.37% (2.82–3.83 GHz) and 24.6% (2.75–3.52 GHz) has been achieved, respectively. This antenna design achieves an average gain of 5.5 dBi and radiation efficiency of above 96% over operational frequency band. Justifiable agreement between simulated and fabricated antenna results are obtained.


title: An Interactive Method for Inferring Demographic Attributes in Twitter

abstract: Twitter data offers an unprecedented opportunity to study demographic differences in public opinion across a virtually unlimited range of subjects. Whilst demographic attributes are often implied within user data, they are not always easily identified using computational methods. In this paper, we present a semi-automatic solution that combines automatic classification methods with a user interface designed to enable rapid resolution of ambiguous cases. TweetClass employs a two-step, interactive process to support the determination of gender and age attributes. At each step, the user is presented with feedback on the confidence levels of the automated analysis and can choose to refine ambiguous cases by examining key profile and content data. We describe how a user-centered design approach was used to optimise the interface and present the results of an evaluation which suggests that TweetClass can be used to rapidly boost demographic sample sizes in situations where high accuracy is required.


title: Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types

abstract: This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.


title: Performance Improvement in High-Performance Brushless Rare-Earth Magnet Motors for Hybrid Vehicles by Use of High Flux-Density Steel

abstract: Hybrid and electrical vehicles use mostly high-efficiency brushless permanent-magnet AC motors and sinewave drives. The motors utilize rare-earth magnet material and also are required to have a very high torque-per-rotor volume. The designs are now very well refined and often use interior permanent-magnet (IPM) rotors for a better field weakening control. This configuration has been shown to increase the efficiency. In this paper, the Toyota Prius 2004 motor is modeled using finite element analysis with different laminated steel materials in order to assess the effectiveness of using high flux-density steel versus the material volume. Additionally to the IPM rotor in the manufactured design, a spoke magnet rotor is also investigated. It is found that using high flux-density laminated steel will either increase the performance of the motor or allow the motor to be reduced in size (in this case, reduction in axial length).


title: Application of bioreactor systems for large scale production of horticultural and medicinal plants

abstract: AbstractAutomation of micropropagation via organogenesis or somatic embryogenesis in a bioreactor has been advanced as a possible way of reducing costs. Micropropagation by conventional techniques is typically a labour-intensive means of clonal propagation. The paper describes lower cost and less labour-intensive clonal propagation through the use of modified air-lift, bubble column, bioreactors (a balloon-type bubble bioreactor), together with temporary immersion systems for the propagation of shoots, bud-clusters and somatic embryos. Propagation of Anoectochilus, apple, Chrysanthemum, garlic, ginseng, grape, Lilium, Phalaenopsis and potato is described. In this chapter, features of bioreactors and bioreactor process design specifically for automated mass propagation of several plant crops are described, and recent research aimed at maximizing automation of the bioreactor production process is highlighted.


title: Reconfigurable Battery Techniques and Systems: A Survey

abstract: Battery packs with a large number of battery cells are becoming more and more widely adopted in electronic systems, such as robotics, renewable energy systems, energy storage in smart grids, and electronic vehicles. Therefore, a well-designed battery pack is essential for battery applications. In the literature, the majority of research in battery pack design focuses on battery management system, safety circuit, and cell-balancing strategies. Recently, the reconfigurable battery pack design has gained increasing attentions as a promising solution to solve the problems existing in the conventional battery packs and associated battery management systems, such as low energy efficiency, short pack lifespan, safety issues, and low reliability. One of the most prominent features of reconfigurable battery packs is that the battery cell topology can be dynamically reconfigured in the real-time fashion based on the current condition (in terms of the state of charge and the state of health) of battery cells. So far, there are several reconfigurable battery schemes having been proposed and validated in the literature, all sharing the advantage of cell topology reconfiguration that ensures balanced cell states during charging and discharging, meanwhile providing strong fault tolerance ability. This survey is undertaken with the intent of identifying the state-of-the-art technologies of reconfigurable battery as well as providing review on related technologies and insight on future research in this emerging area.


title: Cypider: building community-based cyber-defense infrastructure for android malware detection

abstract: The popularity of Android OS has dramatically increased malware apps targeting this mobile OS. The daily amount of malware has overwhelmed the detection process. This fact has motivated the need for developing malware detection and family attribution solutions with the least manual intervention. In response, we propose Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building an efficient and scalable similarity network infrastructure of malicious apps. Our detection method is based on a novel concept, namely malicious community, in which we consider, for a given family, the instances that share common features. Under this concept, we assume that multiple similar Android apps with different authors are most likely to be malicious. Cypider leverages this assumption for the detection of variants of known malware families and zero-day malware. It is important to mention that Cypider does not rely on signature-based or learning-based patterns. Alternatively, it applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and most likely malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a learning model for each malicious community. Cypider shows excellent results by detecting about 50% of the malware dataset in one detection iteration. Besides, the preliminary results of the community fingerprint are promising as we achieved 87% of the detection.


title: Applying Ricci Flow to Manifold Learning

abstract: Traditional manifold learning algorithms often bear an assumption that the local neighborhood of any point on embedded manifold is roughly equal to the tangent space at that point without considering the curvature. The curvature indifferent way of manifold processing often makes traditional dimension reduction poorly neighborhood preserving. To overcome this drawback we propose a new algorithm called RF-ML to perform an operation on the manifold with help of Ricci flow before reducing the dimension of manifold.


title: Outperformance of Cu pillar flip chip bumps in electromigration testing

abstract: Standard NiAu/SAC (SnAgCu) solder bumps are compared with Cu pillar bumps in terms of their electromigration behavior. Both bump configurations are flip chipped onto package substrates with a thick Cu finish. The Cu pillar bumps, which are soldered with a thin SnAg cap, outperform the standard solder flip chip bumps due to the fast formation of an intermetallic phase which covers the full solder stand-off height. These bumps do not show any significant electromigration damage and do not fail within reasonable testing times and test conditions. For the standard solder flip chip bumps, micro-structural degradation at the NiAu chip metallization coincides with an according bump resistance increase which can be clearly monitored for all different test conditions. The electromigration parameters are calculated to be 0.90eV for the activation energy Q and 1.69 for the current density exponential factor n. A TaN temperature sensor is incorporated in the test chip which allows in-situ measurements of the actual device temperature. This value may deviate from the ambient temperature due to Joule heating. Fuse tests in which the current is gradually ramped up to a maximum of 3A while the resulting temperature rise is simultaneously monitored, are performed. In general, the thermal performance of the pillar bump exceeds the one of the solder bump.


title: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

abstract: Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.


title: Design of a novel harmonic-suppressed wideband microstrip bandstop filter

abstract: A systematic procedure for designing harmonic-suppressed wideband microstrip bandstop filter (BSF) based on the frequency-selecting coupling structure (FSCS) is presented. This filter is added with shunt open stubs to introduce several attenuation poles to achieve a deeper rejection in the first and second stopband. The simulation result shows that the BSF has a wide 3 dB fractional bandwidth which is more than 120% at the center frequency of 11.5 GHz. From 5.5 to 17 GHz in the first stopband, the proposed BSF has a rejection of better than 20 dB and the maximum rejection level of 50 dB, from 27.5 to 36 GHz in the second stopband, the proposed BSF has a rejection of better than 20 dB and the maximum rejection level of 40 dB. Application of this filter is also investigated. Obviously, it can be used to suppress the unwanted fundamental frequency from 11 to 12 GHz and the third harmonic from 33 to 36 GHz caused by a doubler, and let the corresponding second harmonic from 22 to 24 GHz pass through.


title: Methods and tools for corporate knowledge management

abstract: This article??This article is an extension of a paper presented at the specialized workshop KAW?98.is a survey of some methods, techniques and tools aimed at managing corporate knowledge from a corporate memory designer's perspective. In particular, it analyses problems and solutions related to the following steps: detection of needs of corporate memory, construction of the corporate memory, its diffusion (specially using the Internet technologies), use, evaluation and evolution


title: Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)

abstract: Massive Online Open Courses (MOOCs) which were introduced in 2008 has since drawn attention around the world for both its advantages as well as criticism on its drawbacks. One of the issues in MOOCs which is the lack of interactivity with the instructor has brought conversational bot into the picture to fill in this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is being developed and integrated into MOOCs website to respond to the learner inquiries using text or speech input. MOOC-bot is using the popular Artificial Intelligence Markup Language (AIML) to develop its knowledge base, leverage from AIML capability to deliver appropriate responses and can be quickly adapted to new knowledge domains. The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter, chat interface, MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability. The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner - ALICE, frequent asked questions, and a content offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of MOOC-bot based on the past competition questions from Chatterbox Challenge (CBC) and Loebner Prize has shown that it was able to provide correct answers most of the time during the test and demonstrated the capability to prolong the conversation. The advantages of MOOC-bot such as able to provide 24-hour service that can serve different time zones, able to have knowledge in multiple domains, and can be shared by multiple sites simultaneously have outweighed its existing limitations.


Detection of Suspicious Patterns in Secure Physical Environments Index Terms—data mining, RFID, security.


title: Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

abstract: Ensuring ultra-reliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture, and topology (across access, edge, and core) and decision-making under uncertainty is sorely lacking. The overarching goal of this article is a first step to fill this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a plethora of techniques and methodologies pertaining to the requirements of ultra-reliable and low-latency communication, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliable wireless networks.


Acoustic Scene Classification Using a CNN-SuperVector System Trained with Auditory and Spectrogram Image Features.


The Impact of Agile Methodology ( DSDM ) on Software Project


Monopole antennas


title: Double-spending fast payments in bitcoin

abstract: Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to verify payments. Nowadays, Bitcoin is increasingly used in a number of fast payment scenarios, where the time between the exchange of currency and goods is short (in the order of few seconds). While the Bitcoin payment verification scheme is designed to prevent double-spending, our results show that the system requires tens of minutes to verify a transaction and is therefore inappropriate for fast payments. An example of this use of Bitcoin was recently reported in the media: Bitcoins were used as a form of \emph{fast} payment in a local fast-food restaurant. Until now, the security of fast Bitcoin payments has not been studied. In this paper, we analyze the security of using Bitcoin for fast payments. We show that, unless appropriate detection techniques are integrated in the current Bitcoin implementation, double-spending attacks on fast payments succeed with overwhelming probability and can be mounted at low cost. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast payments are not always effective in detecting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we propose and implement a modification to the existing Bitcoin implementation that ensures the detection of double-spending attacks against fast payments.


Molecular systematics of the Cyprinoidea (Teleostei: Cypriniformes), the world's largest clade of freshwater fishes: further evidence from six nuclear genes.


title: Elevations in ostensibly anabolic hormones with resistance exercise enhance neither training-induced muscle hypertrophy nor strength of the elbow flexors

abstract: The aim of our study was to determine whether resistance exercise-induced elevations in endogenous hormones enhance muscle strength and hypertrophy with training. Twelve healthy young men (21.8 ± 1.2 yr, body mass index = 23.1 ± 0.6 kg/m2) trained their elbow flexors independently for 15 wk on separate days and under different hormonal milieu. In one training condition, participants performed isolated arm curl exercise designed to maintain basal hormone concentrations (low hormone, LH); in the other training condition, participants performed identical arm exercise to the LH condition followed immediately by a high volume of leg resistance exercise to elicit a large increase in endogenous hormones (high hormone, HH). There was no elevation in serum growth hormone (GH), insulin-like growth factor (IGF-1), or testosterone after the LH protocol but significant (P < 0.001) elevations in these hormones immediately and 15 and 30 min after the HH protocol. The hormone responses elicited by each respective exercise protocol late in the training period were similar to the response elicited early in the training period, indicating that a divergent postexercise hormone response was maintained over the training period. Muscle cross-sectional area (CSA) increased by 12% in LH and 10% in HH (P < 0.001) with no difference between conditions (condition × training interaction, P = 0.25). Similarly, type I (P < 0.01) and type II (P < 0.001) muscle fiber CSA increased with training with no effect of hormone elevation in the HH condition. Strength increased in both arms, but the increase was not different between the LH and HH conditions. We conclude that exposure of loaded muscle to acute exercise-induced elevations in endogenous anabolic hormones enhances neither muscle hypertrophy nor strength with resistance training in young men.


title: HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions

abstract: In this paper, we describe HeidelTime, a system for the extraction and normalization of temporal expressions. HeidelTime is a rule-based system mainly using regular expression patterns for the extraction of temporal expressions and knowledge resources as well as linguistic clues for their normalization. In the TempEval-2 challenge, HeidelTime achieved the highest F-Score (86%) for the extraction and the best results in assigning the correct value attribute, i.e., in understanding the semantics of the temporal expressions.


title: Intelligent multi-agent system for smart home energy management

abstract: Smart grid literature shows a potential way to implement smart grid using Multi-Agent System (MAS) which is a distributed computational intelligence technique that comprises of multiple interactive intelligent agents in an environment. This paper presents an application of MAS technique for improving the efficiency and optimizing the energy usage of smart homes. The smart appliances in a smart home are modeled as agents and optimization algorithms are used in decision making of the agents. These agents work together to reduce energy consumption while striking a balance between consumers comfort, energy cost and peak energy saving in the distribution grid. Such a way, a Home Energy Management System (HEMS) was designed and developed using MAS. This research enables smart homes to communicate, interact and negotiate with energy sources and devices in smart homes that achieve maximum overall energy efficiency and minimum electricity bill. Simulation studies carried out on the developed MAS have shown that it has the potential to provide the optimum solutions. How MAS communication influences on decision making of the agents and getting the optimum solution is also demonstrated in this paper.


title: Ohtaekwangia koreensis gen. nov., sp. nov. and Ohtaekwangia kribbensis sp. nov., isolated from marine sand, deep-branching members of the phylum Bacteroidetes

abstract: Two Gram-stain-negative, non-motile, non-spore-forming, rod-shaped bacterial strains, designated 3B-2(T) and 10AO(T), were isolated from a sand sample collected from the west coast of the Korean peninsula by using low-nutrient media, and their taxonomic positions were investigated in a polyphasic study. The strains did not grow on marine agar. They grew optimally at 30 °C and pH 6.5-7.5. Strains 3B-2(T) and 10AO(T) shared 97.5 % 16S rRNA gene sequence similarity and mean level of DNA-DNA relatedness of 12 %. In phylogenetic trees based on 16S rRNA gene sequences, strains 3B-2(T) and 10AO(T), together with several uncultured bacterial clones, formed independent lineages within the evolutionary radiation encompassed by the phylum Bacteroidetes. Strains 3B-2(T) and 10AO(T) contained MK-7 as the predominant menaquinone and iso-C(15 : 0) and C(16 : 1)ω5c as the major fatty acids. The DNA G+C contents of strains 3B-2(T) and 10AO(T) were 42.8 and 44.6 mol%, respectively. Strains 3B-2(T) and 10AO(T) exhibited very low levels of 16S rRNA gene sequence similarity (<85.0 %) to the type strains of recognized bacterial species. These data were sufficient to support the proposal that the novel strains should be differentiated from previously known genera of the phylum Bacteroidetes. On the basis of the data presented, we suggest that strains 3B-2(T) and 10AO(T) represent two distinct novel species of a new genus, for which the names Ohtaekwangia koreensis gen. nov., sp. nov. (the type species; type strain 3B-2(T) = KCTC 23018(T) = CCUG 58939(T)) and Ohtaekwangia kribbensis sp. nov. (type strain 10AO(T) = KCTC 23019(T) = CCUG 58938(T)) are proposed.


title: Hekaton: Efficient and Practical Large-Scale MIMO

abstract: Large-scale multiuser MIMO (MU-MIMO) systems have the potential for multi-fold scaling of network capacity. The research community has recognized this theoretical potential and developed architectures [1,2] with large numbers of RF chains. Unfortunately, building the hardware with a large number of RF chains is challenging in practice. CSI data transport and computational overhead of MU-MIMO beamforming can also become prohibitive under large network scale. Furthermore, it is difficult to physically append extra RF chains on existing communication equipments to support such large-scale MU-MIMO architectures. In this paper, we present Hekaton, a novel large-scale MU-MIMO framework that combines legacy MU-MIMO beamforming with phased-array antennas. The core of Hekaton is a two-level beamforming architecture. First, the phased-array antennas steer spatial beams toward each downlink user to reduce channel correlation and suppress the cross-talk interference in the RF domain (for beamforming gain), then we adopt legacy digital beamforming to eliminate the interference between downlink data streams (for spatial multiplexing gain). In this way, Hekaton realizes a good fraction of potential large-scale MU-MIMO gains even under the limited RF chain number on existing communication equipments. We evaluate the performance of Hekaton through over-the-air testbed built over the WARPv3 platform and trace-driven emulation. In the evaluations, Hekaton can improve single-cell throughput by up to 2.5X over conventional MU-MIMO with a single antenna per RF chain, while using the same transmit power.


title: Single controller current balance (SCCB) technique for voltage-mode multi-phase buck converter

abstract: A single controller current balance (SCCB) technique is proposed to achieve the time-multiplexing current balance in voltage-mode multi-phase DC-DC buck converter with only one modulation controller. Thus, the N-1 controllers can be removed when the SCCB technique is implemented in the monolithic N-phase DC-DC buck converters for largely decreasing the silicon area and power consumption. In addition, the time-multiplexing current balance scheme can further enhance the current sharing accuracy since the mismatches between different controllers does not exist. Simulated results show that the current difference between each phase can be decreased by over 80 % at both heavy load and light load conditions.


title: A Study on Low-Rate DDoS Attacks in Real Networks

abstract: DDoS attacks are becoming more advanced to attack with a small traffic that is difficult to detect. In this paper, we propose two DDoS attacks that send a small traffic. One named SQL Search attack that is modified DoS attack using SQL wildcards. The other named Mass Contents Request attack that request large files. We prove experimentally that they are possible for the real homepages to DDoS attack with 10Megabytes/sec to 1Gigabytes/sec traffic. Also, we prove that proposed DDoS attacks have a small traffic and no patterns that not only it is difficult to detect of threshold-based but also it is hardly possible to detect of pattern-based through an experiment.


Free and Open Source Software for Industrial Process Control Systems


title: Coupling Matrix Synthesis of Nonreciprocal Lossless Two-Port Networks using Gyrators and Inverters

abstract: A coupling matrix technique is presented for the synthesis of nonreciprocal lossless two-port networks. This technique introduces complex inverters to build the nonreciprocal transversal network corresponding to the nonreciprocal coupling matrix. It subsequently transforms this matrix into canonical topologies through complex similarity transformations. The complex inverters in the final topology are transformed into real inverters and gyrators for implementation simplicity. A second-order example is then given to illustrate the proposed technique. Possible implementations of the gyrators involved in the final design are also discussed.


Data Analysis Project : Using Knowledge Graphs for Image Classification


title: A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics

abstract: We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observed data. We derive new finite sample minimax lower bounds for the estimation of A, as well as new upper bounds for our proposed estimator. We describe the scenarios where our estimator is minimax adaptive. Our finite sample analysis is valid for any number of documents (n), individual document length (N_i), dictionary size (p) and number of topics (K), and both p and K are allowed to increase with n, a situation not handled well by previous analyses. We complement our theoretical results with a detailed simulation study. We illustrate that the new algorithm is faster and more accurate than the current ones, although we start out with a computational and theoretical disadvantage of not knowing the correct number of topics K, while we provide the competing methods with the correct value in our simulations.


title: Dilated Deep Residual Network for Image Denoising

abstract: Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean images. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. We utilize appropriate zero padding to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines.


title: Parent Involvement, Academic Achievement and the Role of Student Attitudes and Behaviors as Mediators.

abstract: Previous research shows inconsistent relationships between parent involvement and academic achievement and often asks why such inconsistencies occur. The research proposes a theoretical model that separates parent involvement into those practices linking parents to children and those practices linking parents to other adults in the school environment. The researcher hypothesizes that parent-child (i.e. discussion and monitoring) and parent-school (i.e. educational support strategies and Parent Teacher Organization involvement) practices will differentially affect student attitudes (educational expectations), behaviors (absenteeism, homework, truancy), and achievement (math and science). Using a national survey conducted in the United States of schools and students, the National Education Longitudinal Study (NELS:88), The research estimates a series of hierarchical models to test the direct and indirect effects of parent involvement on student attitudinal, behavioral and academic outcomes. Findings confirm that parent-child and parent-school involvement practices differentially influence student attitudes and behaviors, thereby indirectly affecting student achievement - to varying degrees.


title: An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis

abstract: With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.


title: Information Technology Governance Role in Enhancing Performance: A Case Study on Jordan Public Sector

abstract: Currently information technology covers almost all aspects in our life, so that it considered as an essential requirement for organizations to be in harmony with e- government concept. Information technology considered as an essential way to enhance performance. E-government in Jordan still need an effective e-governance model to work in harmony with the corporate governance model, so as to improve the performance of organizations. This research investigates Jordanian public sector readiness to implement IT Governance concepts, in order to enhance Performance in Jordanian public sector organizations. Quantitative method is adopted for answering the research questions. For data collection, questionnaire was used after the selection of the targeted population and samples. Different statistics methods were utilized to analyze the collected data; also different statistical tools were used, such as SPSS 15 and SmartPLS 2.0, through a field survey of 26 organizations in Jordanian public sector. The first finding of this research indicates that there is an existence of the relationship between IT Governance and its Pillars: accountability, transparency, participation, and predictability, the second finding indicates that there is a relationship between IT Governance and Performance. This kind of research is very important for Jordanian public sector to take benefits from implementation of the above concepts in order to increase performance.


title: Validation of Heart Rate Monitor Polar RS800 for Heart Rate Variability Analysis During Exercise

abstract: Heart rate variability (HRV) analysis during exercise is an interesting noninvasive tool to measure the cardiovascular response to the stress of exercise. Wearable heart rate monitors are a comfortable option to measure interbeat (RR) intervals while doing physical activities. It is necessary to evaluate the agreement between HRV parameters derived from the RR series recorded by wearable devices and those derived from an electrocardiogram (ECG) during dynamic exercise of low to high intensity. Twenty-three male volunteers performed an exercise stress test on a cycle ergometer. Subjects wore a Polar RS800 device, whereas ECG was also recorded simultaneously to extract the reference RR intervals. A time–frequency spectral analysis was performed to extract the instantaneous mean heart rate (HRM), and the power of low-frequency (PLF) and high-frequency (PHF) components, the latter centered on the respiratory frequency. Analysis was done in intervals of different exercise intensity based on oxygen consumption. Linear correlation, reliability, and agreement were computed in each interval. The agreement between the RR series obtained from the Polar device and from the ECG is high throughout the whole test although the shorter the RR is, the more differences there are. Both methods are interchangeable when analyzing HRV at rest. At high exercise intensity, HRM and PLF still presented a high correlation ([rho] > 0.8) and excellent reliability and agreement indices (above 0.9). However, the PHF measurements from the Polar showed reliability and agreement coefficients around 0.5 or lower when the level of the exercise increases (for levels of O2 above 60%).


title: ASSESSMENT OF KNOWLEDGE, ATTITUDE AND PRACTICE OF HAND WASHING AMONG HEALTH CARE WORKERS IN AIN SHAMS UNIVERSITY HOSPITALS IN CAIRO

abstract: Most nosocomial infections are thought to be transmitted by the hands of health care workers. The aim of this work was to assess the knowledge, attitude and practice of hand washing among health care workers (HCW) in Ain-Shams University hospitals and to investigate the presence of the necessary facilities and supplies required for hand washing (HW) in ten wards. A cross- sectional descriptive and observational study was conducted for six months from June till November 2006. Observation of the HCW for hand washing practice was done at any opportunity of contact with the patients in the different wards by members of the infection control team. Knowledge & attitude of HCW towards hand hygiene was done through self-administered questionnaire to HCW in 10 different departments. The total opportunities observed were 2189 opportunities. Doctors showed a signifi- cantly higher compliance (37.5%) than other groups of HCW (P = 0.000), however only 11.6% of the opportunities observed for doctors were done appropriately. The most common type of HW practiced among HCW was the routine HW (64.2%) and the least was the antiseptic HW (3.9%). Having a short contact time and improper drying (23.2%) were the most common errors that lead to inappropriate HW. Most of the wards had available sinks (80%) but none of them had available paper towels. The mean knowledge score was higher in nurses compared to doc- tors (42.6 Â± 11.7 versus 39.1 Â± 10.5). Most of the nurses (97.3%) believe that administrative orders and continuous observation can improve hand washing practices. Implementation of mul- tifaceted interventional behavioral hand hygiene program with continuous monitoring and performance feedback, increasing the supplies necessary for HW and institutional support are impor- tant for improving the compliance of hand hygiene guidelines.


title: Dynamics of Thin-film Piezoelectric Microactuators with Large Vertical Stroke Subject to Multi-axis Coupling and Fabrication Asymmetries.

abstract: The high performance and small size of MEMS based scanners has allowed various optical imaging techniques to be realized in a small form factor. Many such devices are resonant scanners, and thus their linear and nonlinear dynamic behaviors have been studied in the past. Thin-film piezoelectric materials, in contrast, provide sufficient energy density to achieve both large static displacements and high-frequency resonance, but large deformation can in turn influence dynamic scanner behavior. This paper reports on the influence of very large stroke translation of a piezoelectric vertical actuator on its resonant behavior, which may not be otherwise explained fully by common causes of resonance shift such as beam stiffening or nonlinear forcing. To examine the change of structural compliance over the course of scanner motion, a model has been developed that includes internal forces from residual stress and the resultant additional multi-axis coupling among actuator leg structures. Like some preceding vertical scanning micro-actuators, the scanner of this work has four legs, with each leg featuring four serially connected thin-film PZT unimorphs that allow the scanner to generate larger than 400μm of vertical displacement at 14VDC. Using an excitation near one or more resonances, the input voltage can be lowered, and complementary multi-axis rotations can be also generated, but change of the resonant frequencies with scanner height needs to be understood to maximize scanner performance. The presented model well predicts the experimental observation of the decrease of the resonant frequencies of the scanner with the increase of a dc bias voltage. Also, the effects of the magnitude and uniformity of residual stress across the scanner structure on the natural frequencies have been studied.


title: Is More Better?: The Relationship Between Nurse Staffing and the Quality of Nursing Care in Hospitals

abstract: OBJECTIVE ::: The objective of this study was to examine the effects of nurse staffing and process of nursing care indicators on assessments of the quality of nursing care. ::: ::: ::: RESEARCH DESIGN ::: This study examined the variation in inpatient hospital staff nurses' assessments of the quality of nursing care and the effects of nurse staffing (patient workload), patient safety problems (medication errors and patient falls with injuries), and unfinished care (number of nursing tasks left undone) on the variation in those assessments. Secondary analysis of a survey of nurses using multivariate regression models was undertaken. ::: ::: ::: SUBJECTS ::: Data were derived from a 1999 statewide survey of 8670 inpatient staff nurses working in acute care hospitals in Pennsylvania. ::: ::: ::: RESULTS ::: Quality of nursing care ratings were significantly associated with the number of patients who nurses care for, rates of unfinished care for those patients, and the frequency of patient safety problems. The effect of patient workload on quality ratings was attenuated substantially by the effects of unfinished care and patient safety problems. Unfinished care had the strongest relationship of all, with over 40% of the variation in quality ratings associated with the number of tasks left undone. ::: ::: ::: CONCLUSIONS ::: Assessments of the quality of nursing are associated with both structural (workload) and process of care indicators (unfinished clinical care and patient safety problems), with the relationship strongest between process of care and quality. Explicating the interrelationship between structure and process of care is key to understanding the influence of both on quality. Studies that assess the causal influence of these features on quality of care and patient outcomes are warranted.


title: Wavelet-Based Energy Features for Glaucomatous Image Classification

abstract: Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naive Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.


IN GOOGLE WE TRUST: USERS ’ DECISIONS ON RANK, POSITION AND RELEVANCY


title: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback

abstract: We introduce an extended representation of time series that allows fast, accurate classification and clustering in addition to the ability to explore time series data in a relevance feedback framework. The representation consists of piece-wise linear segments to represent shape and a weight vector that contains the relative importance of each individual linear segment. In the classification context, the weights are learned automatically as part of the training cycle. In the relevance feedback context, the weights are determined by an interactive and iterative process in which users rate various choices presented to them. Our representation allows a user to define a variety of similarity measures that can be tailored to specific domains. We demonstrate our approach on space telemetry, medical and synthetic data.


title: Dynamic Trust Management for Delay Tolerant Networks and Its Application to Secure Routing

abstract: Delay tolerant networks (DTNs) are characterized by high end-to-end latency, frequent disconnection, and opportunistic communication over unreliable wireless links. In this paper, we design and validate a dynamic trust management protocol for secure routing optimization in DTN environments in the presence of well-behaved, selfish and malicious nodes. We develop a novel model-based methodology for the analysis of our trust protocol and validate it via extensive simulation. Moreover, we address dynamic trust management, i.e., determining and applying the best operational settings at runtime in response to dynamically changing network conditions to minimize trust bias and to maximize the routing application performance. We perform a comparative analysis of our proposed routing protocol against Bayesian trust-based and non-trust based (PROPHET and epidemic) routing protocols. The results demonstrate that our protocol is able to deal with selfish behaviors and is resilient against trust-related attacks. Furthermore, our trust-based routing protocol can effectively trade off message overhead and message delay for a significant gain in delivery ratio. Our trust-based routing protocol operating under identified best settings outperforms Bayesian trust-based routing and PROPHET, and approaches the ideal performance of epidemic routing in delivery ratio and message delay without incurring high message or protocol maintenance overhead.


title: Psychological stress detection from cross-media microblog data using Deep Sparse Neural Network

abstract: Long-term stress may lead to many severe physical and mental problems. Traditional psychological stress detection usually relies on the active individual participation, which makes the detection labor-consuming, time-costing and hysteretic. With the rapid development of social networks, people become more and more willing to share moods via microblog platforms. In this paper, we propose an automatic stress detection method from cross-media microblog data. We construct a three-level framework to formulate the problem. We first obtain a set of low-level features from the tweets. Then we define and extract middle-level representations based on psychological and art theories: linguistic attributes from tweets' texts, visual attributes from tweets' images, and social attributes from tweets' comments, retweets and favorites. Finally, a Deep Sparse Neural Network is designed to learn the stress categories incorporating the cross-media attributes. Experiment results show that the proposed method is effective and efficient on detecting psychological stress from microblog data.


title: A differential approach to inference in Bayesian networks

abstract: We present a new approach to inference in Bayesian networks, which is based on representing the network using a polynomial and then retrieving answers to probabilistic queries by evaluating and differentiating the polynomial. The network polynomial itself is exponential in size, but we show how it can be computed efficiently using an arithmetic circuit that can be evaluated and differentiated in time and space linear in the circuit size. The proposed framework for inference subsumes one of the most influential methods for inference in Bayesian networks, known as the tree-clustering or jointree method, which provides a deeper understanding of this classical method and lifts its desirable characteristics to a much more general setting. We discuss some theoretical and practical implications of this subsumption.


title: Global and Local Structure Preservation for Feature Selection

abstract: The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scenarios. In order to show this, we propose a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection. To demonstrate the generality of our framework, we employ methods that are well known in the literature to model the local geometric data structure and develop three specific GLSPFS-based feature selection algorithms. Also, we develop an efficient optimization algorithm with proven global convergence to solve the resulting feature selection problem. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the-art ones in supervised, unsupervised, and semisupervised learning scenarios. The result indicates that: 1) our framework consistently achieves statistically significant improvement in selection performance when compared with the currently used algorithms; 2) in supervised and semisupervised learning scenarios, preserving global pairwise similarity is more important than preserving local geometric data structure; 3) in the unsupervised scenario, preserving local geometric data structure becomes clearly more important; and 4) the best feature selection performance is always obtained when the two factors are appropriately integrated. In summary, this paper not only validates the advantages of the proposed GLSPFS framework but also gains more insight into the information to be preserved in different feature selection tasks.


title: Marx generator using power mosfets

abstract: A compact Marx generator has been developed by using power MOSFETs as the switches. The objective is to develop repetitive, compact, efficient, short-pulse, high-voltage generator for industrial applications. The initial tests were carried out by using 16 switches, achieving output of ∼ 10 kV and ∼ 250 ns in pulse width at repetition rate of ∼ 400 Hz.


title: Efficient algorithms for multichannel extensions of Itakura-Saito nonnegative matrix factorization

abstract: This paper proposes new algorithms for multichannel extensions of nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We employ Hermitian positive definite matrices for modeling the covariance matrix of a multivariate complex Gaussian distribution. Such matrices are basically estimated for NMF bases, but a source separation task can be performed by introducing variables that relate NMF bases and sources. The new algorithms are derived by using a majorization scheme with properly designed auxiliary functions. The algorithms are in the form of multiplicative updates, and exhibit good convergence behavior. We have succeeded in separating a professionally produced music recording into its vocal and guitar components.


title: Knee biomechanics during walking in recurrent lateral patellar dislocation are normalized by 1 year after medial patellofemoral ligament reconstruction

abstract: Purpose ::: Gait kinematics and kinetics of the knee before and after medial patellofemoral ligament (MPFL) reconstruction in patients with recurrent lateral patellar dislocation (RPD) are unknown. The purpose of this study was to measure knee kinematics and kinetics during gait before and 1 year after anatomical MPFL reconstruction in patients with RPD and compare the results to healthy individuals.


title: Adaptive Stratified Sampling for Precision-Recall Estimation

abstract: We propose a new algorithm for computing a constant-factor approximation of precision-recall (PR) curves for massive noisy datasets produced by generative models. Assessing validity of items in such datasets requires human annotation, which is costly and must be minimized. Our algorithm, AdaStrat, is the first data-aware method for this task. It chooses the next point to query on the PR curve adaptively, based on previous observations. It then selects specific items to annotate using stratified sampling. Under a mild monotonicity assumption, AdaStrat outputs a guaranteed approximation of the underlying precision function, while using a number of annotations that scales very slowly with N, the dataset size. For example, when the minimum precision is bounded by a constant, it issues only log log N precision queries. In general, it has a regret of no more than log log N w.r.t. an oracle that issues queries at data-dependent (unknown) optimal points. On a scaled-up NLP dataset of 3.5M items, AdaStrat achieves a remarkably close approximation of the true precision function using only 18 precision queries, 13x fewer than best previous approaches.


title: Unauthorized origin crossing on mobile platforms: threats and mitigation

abstract: With the progress in mobile computing, web services are increasingly delivered to their users through mobile apps, instead of web browsers. However, unlike the browser, which enforces origin-based security policies to mediate the interactions between the web content from different sources, today's mobile OSes do not have a comparable security mechanism to control the cross-origin communications between apps, as well as those between an app and the web. As a result, a mobile user's sensitive web resources could be exposed to the harms from a malicious origin. In this paper, we report the first systematic study on this mobile cross-origin risk. Our study inspects the main cross-origin channels on Android and iOS, including intent, scheme and web-accessing utility classes, and further analyzes the ways popular web services (e.g., Facebook, Dropbox, etc.) and their apps utilize those channels to serve other apps. The research shows that lack of origin-based protection opens the door to a wide spectrum of cross-origin attacks. These attacks are unique to mobile platforms, and their consequences are serious: for example, using carefully designed techniques for mobile cross-site scripting and request forgery, an unauthorized party can obtain a mobile user's Facebook/Dropbox authentication credentials and record her text input. We report our findings to related software vendors, who all acknowledged their importance. To address this threat, we designed an origin-based protection mechanism, called Morbs, for mobile OSes. Morbs labels every message with its origin information, lets developers easily specify security policies, and enforce the policies on the mobile channels based on origins. Our evaluation demonstrates the effectiveness of our new technique in defeating unauthorized origin crossing, its efficiency and the convenience for the developers to use such protection.


title: CTRANSPORT: Multi-agent-based simulation

abstract: Pollution nowadays is a really important issue that must be solved. Big cities suffer from overcrowding which result in traffic congestion and a lot of air pollution. Adapting to the idea of cities bike lane expansion, we design a Multi-agent simulation to distribute among the users green energy vehicles; concretely bikes, scooters and electric cars.


title: Cognitive Radio: From Theory to Practical Network Engineering

abstract: Under utilization of radio spectrum in traditional wireless communication systems [29], along with the increasing spectrum demand from emerging wireless applications, is driving the development of new spectrum allocation policies for wireless communications. These new spectrum allocation policies, which will allow unlicensed users (i.e., secondary users) to access the radio spectrum when it is not occupied by licensed users (i.e., primary users) will be exploited by the cognitive radio (CR) technology. Cognitive radio will improve spectrum utilization in wireless communication systems while accommodating the increasing amount of services and applications in wireless networks. A cognitive radio transceiver is able to adapt to the dynamic radio environment and the network parameters to maximize the utilization of the limited radio resources while providing flexibility in wireless access [42]. The key features of a CR transceiver include awareness of the radio environment (in terms of spectrum usage, power spectral density of transmitted/received


Author Masking using Sequence-to-Sequence Models


title: Machine Learning Models that Remember Too Much

abstract: Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that"memorize"information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data.


title: Robust Face Alignment for Illumination and Pose Invariant Face Recognition

abstract: In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set. The modified AAM search can now handle both illumination and pose variations in the same epoch, hence it provides better convergence in both point-to-point and point-to-curve senses. We also investigate how face recognition performance is affected by the selection of feature space as well as the proposed alignment method. The experimental results show that the combined pose alignment and illumination normalization methods increase the recognition rates considerably for all feature-spaces.


title: Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

abstract: The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.