title: Variant Callers for Next-Generation Sequencing Data: A Comparison Study

abstract: Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a “gold-standard” method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.


title: Identification of Mammalian microRNA Host Genes and Transcription Units

abstract: To derive a global perspective on the transcription of microRNAs (miRNAs) in mammals, we annotated the genomic position and context of this class of noncoding RNAs (ncRNAs) in the human and mouse genomes. Of the 232 known mammalian miRNAs, we found that 161 overlap with 123 defined transcription units (TUs). We identified miRNAs within introns of 90 protein-coding genes with a broad spectrum of molecular functions, and in both introns and exons of 66 mRNA-like noncoding RNAs (mlncRNAs). In addition, novel families of miRNAs based on host gene identity were identified. The transcription patterns of all miRNA host genes were curated from a variety of sources illustrating spatial, temporal, and physiological regulation of miRNA expression. These findings strongly suggest that miRNAs are transcribed in parallel with their host transcripts, and that the two different transcription classes of miRNAs (`exonic' and `intronic') identified here may require slightly different mechanisms of biogenesis.


title: The design of low power 11.6mW high speed 1.8Gb/s stand-alone LVDS driver in 0.18-µm CMOS

abstract: This paper presents a design of power efficient and high speed transmitter implemented in CMOS 180nm UMC technology, fully compatible with low voltage differential signaling (LVDS) standard specified by IEEE. The main stand-alone driver's functional blocks: LVDS core, common mode feedback (CMFB), control buffer and band-gap reference source are described in detail. Three architectures of LVDS transmitter cores: bridged-switched current source (BSCS), double current source (DCS) and open drain (OD) are compared and finally the BSCS architecture is selected due to project's requirements. The designed LVDS driver characterizes a very low level of static and dynamic power dissipation, P stat = 7.5mW and P dyn = 11.6mW respectively, at data speed transmission 1.8Gb/s with receiver's input capacitance C R = 1pF and P dyn = 8.5mW, data rates equals 400Mb/s at C R = 5pF.


title: Cloudlets: at the leading edge of cloud-mobile convergence

abstract: Since the dawn of mobile computing two decades ago, the unique constraints of mobility have shaped the software architectures of systems. We now stand at the threshold of the next major transformation in computing: one in which the rich sensing and interaction capabilities of mobile devices are seamlessly fused with compute-intensive and data-intensive processing in the cloud. This heralds a new genre of software that augments human perception and cognition in a mobile context. A major obstacle to realizing this vision is the large and variable end-to-end WAN latency between mobile device and cloud, and the possibility of WAN disruptions. Cloudlets have emerged as an architectural solution to this problem. A cloudlet represents the middle tier of a 3-tier hierarchy: mobile device -- cloudlet -- cloud, and can be viewed as a "data center in a box" whose goal is to "bring the cloud closer". A cloudlet-based hardware/software ecosystem inspires futuristic visions such as cognitive assistance for attention-challenged mobile users, scalable crowd-sourcing of first-person video, and ubiquitous mobile access to one's legacy world. Realizing these visions will require many technical challenges to be overcome. It will also require us to rethink a wide range of issues in areas such as privacy, software licensing, and business models.


Extraction of synonyms and semantically related words from chat logs


title: A Compact Dual-Band Dual-Polarized Loop-Slot Planar Antenna

abstract: This letter presents a compact dual-band dual-polarized loop-slot planar antenna. The antenna consists of a rectangular loop, a coplanar waveguide (CPW), and a microstrip line. The dual-band characteristic is realized using two different type resonant modes of the loop-slot antenna, namely, a loop basic mode in the lower band and a slot basic mode in the higher band. A CPW is used to excite vertical polarization modes and a microstrip line is used to excite horizontal polarization modes to achieve dual-polarized performance. The overall dimension of the prototype is only $55 \times 57~\hbox{mm}^{2}$ . The proposed antenna is built and tested. It has two common $-10~\hbox{dB}$ impedance bandwidths of 420 MHz (2.34-2.76 GHz) and 660 MHz (3.22-3.88 GHz) to cover 2.4 GHz WLAN band and 2.5/3.5 GHz WiMAX bands, respectively, and the isolation between the two ports is better than 21 dB in the dual-band. The antenna has stable radiation patterns and gains in both bands. The antenna also has the merits of compact dimension, low cross-polarization, and low cost.


title: Towards multilateral secure digital rights distribution infrastructures

abstract: Digital Rights Management (DRM) systems and applications appear to increasingly attract the interest of e-commerce business developers. DRM systems aim at secure distribution of digital content and commonly comprise a huge variety of different technologies. Current DRM systems focus mainly on right-holder's security needs and commonly neglect those of consumers. In particular, these systems even lack reliable means for users to verify that they purchase usage-rights on works (licenses) from the rightful authors (rights-holder). This seems to be simply achievable in centralistic systems where only global players or a few large collecting societies control the distribution of licenses. However, in a large distributed system with many unknown and potentially untrusted sellers/distributors of digital content it is crucial from the legal and security perspectives that any user/device of the DRM platform can reliably verify that a purchased license is authorised by the rightful author/rights-holder.In this paper we introduce a formal model and the corresponding schemes for establishing a multilateral secure rights-distribution infrastructure. The key features of our proposal are: firstly, consumers can directly verify that a seller is indeed authorised to grant certain usage-rights. Secondly, the authorship and rights licenses are invariant with respect to perceptibly similar works, i.e., they are not only valid for the original work but also for a set of closely related similar works which are modifications of the original work (e.g., different resolutions or encodings of an image or a video). The reason for providing this property is that these works are still considered to be under the copyright of the author.Our scheme provides an enabling technology for decentralised right distribution infrastructures for intellectual property with multilateral security.


title: Transmission Electron Microscopy for the Diagnosis of Epidermolysis Bullosa

abstract: Since the early application of transmission electron microscopy (TEM) to the study of epidermolysis bullosa (EB), TEM has for many years retained the premier position among diagnostic techniques for establishing or confirming the diagnosis of the major types of EB. Even today, TEM remains the most powerful method for determining the levels of skin separation which underlie the current classification of EB and certain related disorders. Other laboratory techniques, including immunofluorescence (IF) microscopy and DNA analysis, have become increasingly important in EB diagnosis. However, no single method has yet fully superseded TEM, although it is now used mainly in a reference laboratory and is less applicable as a primary diagnostic procedure. This article aims to provide a summary of the main TEM findings in the major types of EB and to describe briefly the ultrastructural features of some of the disorders that have been recently added to the EB classification. Finally, it includes a short discussion on the current advantages and disadvantages of using TEM for EB diagnosis.


title: A method of error correction for digital class D power amplifier

abstract: Because of the nonlinearity of digital PWM generator and the effect of power supply noise in power stage, the error is introduced into digital class D power amplifier. A method used to eliminate the error is presented in this paper, and it is easy to implement. Based on this method, a digital class D power amplifier is designed and simulated, the simulation results indicate this method can basically eliminate the error produced by digital PWM generator and power stage, and improve the performance of the system.


title: Consolidation of unorganized point clouds for surface reconstruction

abstract: We consolidate an unorganized point cloud with noise, outliers, non-uniformities, and in particular interference between close-by surface sheets as a preprocess to surface generation, focusing on reliable normal estimation. Our algorithm includes two new developments. First, a weighted locally optimal projection operator produces a set of denoised, outlier-free and evenly distributed particles over the original dense point cloud, so as to improve the reliability of local PCA for initial estimate of normals. Next, an iterative framework for robust normal estimation is introduced, where a priority-driven normal propagation scheme based on a new priority measure and an orientation-aware PCA work complementarily and iteratively to consolidate particle normals. The priority setting is reinforced with front stopping at thin surface features and normal flipping to enable robust handling of the close-by surface sheet problem. We demonstrate how a point cloud that is well-consolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data.


title: Robot Navigation by Gesture Recognition with ROS and Kinect

abstract: This project aims to use the ROS (Robot Operating System) framework and its tools to classify simple gestures developed by the upper limbs of the user. The capture of the data movement is made by the Kinect sensor through ROS packages that allow the calibration and recognition of users. The obtained data is processed by fuzzy logic control, converting them into acceptable values for the robot movement control.


title: A new fingerprint matching approach using level 2 and level 3 features

abstract: Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~ 1000dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most Automated Fingerprint Identification Systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement. We develop a matcher that utilizes Level 3 features, including pores and ridge contours, in conjunction with level 2 features (minutiae) for matching. The aim is to reduce the error rates, namely FAR (False Acceptance Rate) and FRR (False Rejection Rate) in the existing minutiae based systems. The hierarchical matcher has been tested on two diverse databases in public domain. The obtained results are promising and verify our claim.


title: Performance analysis of PF, M-LWDF and EXP/PF packet scheduling algorithms in 3GPP LTE downlink

abstract: In this paper, the performance of Proportional Fair (PF), Maximum-Largest Weighted Delay First (M-LWDF) and Exponential/Proportional Fair (EXP/PF) packet scheduling algorithms has been investigated in the downlink Long Term Evolution (LTE) system. The performance evaluation is conducted in single cell with interference environment while increasing number of users in the range of 10 to 80 (Case 1) and varying users' speed in the range [3, 120] km/h (Case 2). Results show that in Case 1, M-LWDF outperforms other two algorithms in terms of average throughput, spectral efficiency, packet delay, packet loss ratio (PLR), and fairness index, and in Case 2, the performance of simulated scheduling algorithms drops noticeably with increased users' speed and M-LWDF outperforms other two algorithms in terms of average throughput, spectral efficiency, PLR, and delay.


Accurate indoor navigation using Smartphone , Bluetooth Low Energy and Visual Tags


title: IEC 61850/61499 Control of Distributed Energy Resources: Concept, Guidelines, and Implementation

abstract: The increasing installation of distributed energy resources during the last years has lead to a fundamental change of the power system structure. As a consequence, utility operators are faced with new challenges in grid planning and operation. New and intelligent approaches—like smart grids—show promising results in increasing the hosting capacity for distributed and renewable resources. Standardized automation, control, and communication systems are important keys to realize such intelligent methods. In this paper, a standard-based control approach for distributed energy resources is introduced and implemented. It uses the IEC 61850 interoperability approach as well as the IEC 61499 reference model for distributed automation. Elementary implementation guidelines are provided to handle the hierarchical architecture of distributed control applications. In order to show the advantages of the proposed approach, a simulation example and a laboratory test are demonstrated using a prototypical open-source-based implementation.


title: Hands-on Killing of Intimate Partners as a Function of Sex and Relationship Status/State

abstract: Guided by evolutionary psychology and a situational perspective on violence, we generated three hypotheses to investigate whether the percentage of intimate partner homicides by beating, a hands-on homicide method, varies with the victim-offender relationship. We tested these hypotheses with a national database that includes incident-level information on over 50,000 intimate partner homicides. Results indicate that: (1) men are more likely than women to kill a partner by beating, and (2) men are more likely to kill their partners by beating when the relationship is dating or non-marital cohabiting (versus legal marriage). We argue that the lack of commitment in these non-marital relationships may produce greater jealousy in men, driving the perpetrator to kill his victim in a more violent manner (i.e., beating), relative to men who kill their wives.


title: Deep Convolutional Networks on Graph-Structured Data

abstract: Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. ::: In this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.


title: Position control of a 3 DOF compliant micro-motion stage

abstract: This paper focuses on the open-loop and closed-loop position control of a three degree-of-freedom (DOF) compliant micro-motion stage with flexure hinges. This micro-motion stage has a parallel structure for better stiffness and accuracy than serial structures and is driven by three PZT stack actuators. It was discussed in the authors' previous paper that due to the limitation of current modeling methods for compliant mechanisms the derived kinematic model is not able to accurately describe the behavior of the investigated micro-motion stage. Therefore, an experimentally determined constant Jacobian matrix is used to relate the extensions of PZTs and the end-effector movement for position control. With the use of the experimentally determined Jacobian in open-loop control, the micro-motion stage is proved able to achieve good positioning accuracy. Nevertheless, the positioning accuracy is significantly improved by incorporating end-effector closed-loop control.


title: Personalizing robot tutors to individuals' learning differences

abstract: In education research, there is a widely-cited result called “Bloom’s two sigma” that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction [1]. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot’s actions to an individual’s strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants’ puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot’s personalizations were less sophisticated than what a human tutor can do, we still witnessed a “one-sigma” improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics; J.4 [Computer Applications]: Social And Behavioral Sciences—Psychology General Terms Experimentation


title: A Wideband Minkowski Fractal Dielectric Resonator Antenna

abstract: A CPW slot loop fed Minkowski shaped fractal dielectric resonator antenna is proposed. Self similar property of fractal geometry is utilized to bring higher order modes close together to realize a wide impedance bandwidth. A comparative study of the resonant frequency behavior between the well established fractal electrical boundary antennas and fractal magnetic boundary antennas is presented to provide insight into the functionality of the proposed antenna. The comparison highlights the fact that electrical boundary lowers resonance frequency, whereas fractal magnetic boundary increases the resonant frequency. Minkowski fractal is also compared with Koch fractal and Sierpinski curve geometries. It is observed that the Minkowski fractal DRA yields the widest impedance bandwidth along with stable gain amongst the three proposed geometries. The proposed antenna exhibits a fractional bandwidth of 64% (5.52 -10.72 GHz) and a maximum gain of 4.9 dBi.


title: Re-writing the natural history of pain and related symptoms in the joint hypermobility syndrome/Ehlers-Danlos syndrome, hypermobility type.

abstract: Joint hypermobility syndrome (JHS) and Ehlers-Danlos syndrome, hypermobility type (EDS-HT) are two clinically overlapping connective tissue disorders characterized by chronic/recurrent pain, joint instability complications, and minor skin changes. Fatigue and headache are also common, although are not yet considered diagnostic criteria. JHS/EDS-HT is a unexpectedly common condition that remains underdiagnosed by most clinicians and pain specialists. This results in interventions limited to symptomatic and non-satisfactory treatments, lacking reasonable pathophysiologic rationale. In this manuscript the fragmented knowledge on pain, fatigue, and headache in JHS/EDS is presented with review of the available published information and a description of the clinical course by symptoms, on the basis of authors' experience. Pathogenic mechanisms are suggested through comparisons with other functional somatic syndromes (e.g., chronic fatigue syndrome, fibromyalgia, and functional gastrointestinal disorders). The re-writing of the natural history of JHS/EDS-HT is aimed to raise awareness among clinical geneticists and specialists treating chronic pain conditions about pain and other complications of JHS/EDS-HT. Symptoms' clustering by disease stage is proposed to investigate both the molecular causes and the symptoms management of JHS/EDS-HT in future studies.


title: Tracer particles and seeding for particle image velocimetry

abstract: The size specifications for suitable tracer particles for particle image velocimetry (PIV), particularly with respect to their flow tracking capability, are discussed and quantified for several examples. A review of a wide variety of tracer materials used in recent PIV experiments in liquids and gases indicates that appropriately sized particles have normally been used. With emphasis on gas flows, methods of generating seeding particles and for introducing the particles into the flow are described and their advantages are discussed.


title: Speech Emotion Recognition Using CNN

abstract: Deep learning systems, such as Convolutional Neural Networks (CNNs), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose to learn affect-salient features for Speech Emotion Recognition (SER) using semi-CNN. The training of semi-CNN has two stages. In the first stage, unlabeled samples are used to learn candidate features by contractive convolutional neural network with reconstruction penalization. The candidate features, in the second step, are used as the input to semi-CNN to learn affect-salient, discriminative features using a novel objective function that encourages the feature saliency, orthogonality and discrimination. Our experiment results on benchmark datasets show that our approach leads to stable and robust recognition performance in complex scenes (e.g., with speaker and environment distortion), and outperforms several well-established SER features.


title: Things they would not teach me of in college: what Microsoft developers learn later

abstract: There has always been a gap between what college graduates in any field are taught and what they need to know to work in industry. However, today the gap in computer science has grown into a chasm. Current college hires who join Microsoft development teams only know a small fraction of their jobs and cannot be trusted to write new code until they have received months of in-depth training. The cause of this growing gap is a fundamental shift in the software industry, which now demands higher quality and greater attention to customer needs. This paper presents five new courses to add to computer science curriculums to help close this gap.


title: Interpersonal Rejection as a Determinant of Anger and Aggression

abstract: This article reviews the literature on the relationship between interpersonal rejection and aggression. Four bodies of research are summarized: laboratory experiments that manipulate rejection, rejection among adults in everyday life, rejection in childhood, and individual differences that may moderate the relationship. The theoretical mechanisms behind the effect are then explored. Possible explanations for why rejection leads to anger and aggression include: rejection as a source of pain, rejection as a source of frustration, rejection as a threat to self-esteem, mood improvementfollowing aggression, aggression as social influence, aggression as a means of reestablishing control, retribution, disinhibition, and loss of self-control.


title: Patient recruitment in paediatric clinical trials

abstract: Trials in young people with diabetes are commonly perceived to be problematic because of participants' rejection of authority and issues with adherence. Attitudes were examined in patients aged 6–17 years (from the UK and Ireland) who had participated in a trial comparing insulin detemir (Levemir, Novo Nordisk, Denmark) with NPH in a multi-injection therapy for type 1 diabetes. Most patients needed to increase injection frequency (by at least one per day — usually at lunchtime, often at school), clinic attendance and glucose monitoring, and make multiple diary entries. A bespoke, 23-item, child-friendly questionnaire, which gave patients the option to remain anonymous to encourage honesty, was distributed to 44 patients. Of these, 36 completed the questionnaire: 58% aged 12–14, 25% aged 15–17, and 17% aged 6–11 years. The most popular reasons for trial entry were desire to improve glycaemic control (30%), ‘interesting prospect’ (21%) and a desire to help others (19%). Only 9% entered because of parental persuasion; 86% scored increased injection frequency and frequent visits positively, and 81% reported that they would participate in another trial if asked. Notwithstanding the possible self-selecting nature of this small cohort, experience of trial involvement was positive, implying that patients of all ages can be motivated to participate in trials to learn and help others, and they appreciate the inclusion of new technology, information supplied and additional support given by site staff. Copyright © 2005 John Wiley & Sons, Ltd.


title: Understanding Dropouts in MOOCs

abstract: Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).What are the major factors that cause the users to drop out?What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high correlation between dropouts of different courses and strong influence between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.


title: MV-FTL: An FTL That Provides Page-Level Multi-Version Management

abstract: In this paper, we propose MV-FTL, a multi-version flash transition layer (FTL) that provides page-level multi-version management. By extending a unique characteristic of solid-state drives (SSDs), the out-of-place (OoP) update to multi-version management, MV-FTL can both guarantee atomic page updates from each transaction and provide concurrency without requiring redundant log data writes as well. For evaluation, we first modified SQLite, a lightweight database management system (DBMS), to cooperate with MV-FTL. Owing to the architectural simplicity of SQLite, we clearly show that MV-FTL improves both the performance and the concurrency aspects of the system. In addition, to prove the effectiveness in a full-fledged enterprise-level DBMS, we modified MyRocks, a MySQL variant by Facebook, to use our new Patch Compaction algorithm, which deeply relies on MV-FTL. The TPC-C and LinkBench benchmark tests demonstrated that MV-FTL reduces the overall amount of writes, implying that MV-FTL can be effective in such DBMSs.


title: Ultra wideband antennas: past and present

abstract: Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.


Designs : Selection and Implementation


title: Quadruped robot obstacle negotiation via reinforcement learning

abstract: Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the problem of negotiating obstacles with a quadruped robot. Our algorithm is based on a two-level hierarchical decomposition of the task, in which the high-level controller selects the sequence of foot-placement positions, and the low-level controller generates the continuous motions to move each foot to the specified positions. The high-level controller uses an estimate of the value function to guide its search; this estimate is learned partially from supervised data. The low-level controller is obtained via policy search. We demonstrate that our robot can successfully climb over a variety of obstacles which were not seen at training time


title: A Short Survey on Data Clustering Algorithms

abstract: With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end.


title: Vision-Based Predictive Path Tracking Control of a Wheeled Mobile Robot

abstract: This paper develops methodologies and techniques for control architecture design, path tracking laws and posture estimation of a vision-based wheeled mobile robot (WMR). To solve the problem of position/orientation tracking control of the WMR, two kinematical predictive control laws are developed to manipulate the vehicle to asymptotically follow the desired trajectories. A Kalman filtering scheme is used to reduce the bad effect of the imagine nose, thereby improving the accuracy of pose estimation. Simulation and experimental results are included to illustrate the feasibility and effectiveness of the proposed control laws.


title: Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance

abstract: Abstract One of the key challenges for research in wireless sensor networks is the development of routing protocols that provide application-specific service guarantees. This paper presents a new cluster-based Route Optimisation and Load-balancing protocol, called ROL, that uses various Quality of Service (QoS) metrics to meet application requirements. ROL combines several application requirements, specifically it attempts to provide an inclusive solution to prolong network life, provide timely message delivery and improve network robustness. It uses a combination of routing metrics that can be configured according to the priorities of user-level applications to improve overall network performance. To this end, an optimisation tool for balancing the communication resources for the constraints and priorities of user applications has been developed and Nutrient-flow-based Distributed Clustering (NDC), an algorithm for load balancing is proposed. NDC works seamlessly with any clustering algorithm to equalise, as far as possible, the diameter and the membership of clusters. This paper presents simulation results to show that ROL/NDC gives a higher network lifetime than other similar schemes, such Mires++. In simulation, ROL/NDC maintains a maximum of 7% variation from the optimal cluster population, reduces the total number of set-up messages by up to 60%, reduces the end-to-end delay by up to 56%, and enhances the data delivery ratio by up to 0.98% compared to Mires++.


title: A portable image-based cytometer for rapid malaria detection and quantification

abstract: Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However, this is often challenged by low levels of infection (referred to as parasitemia) and presence of predominantly young parasitic forms in the patients' peripheral blood. Herein, we developed a simple, inexpensive and portable image-based cytometer that detects and numerically counts Plasmodium falciparum infected red blood cells (iRBCs) from Giemsa-stained smears derived from infected blood. Our cytometer is able to classify all parasitic subpopulations by quantifying the area occupied by the parasites within iRBCs, with high specificity, sensitivity and negligible false positives (~ 0.0025%). Moreover, we demonstrate the application of our image-based cytometer in testing anti-malarial efficacy against a commercial flow cytometer and demonstrate comparable results between the two methods. Collectively, these results highlight the possibility to use our image-based cytometer as a cheap, rapid and accurate alternative for antimalarial testing without compromising on efficiency and minimal processing time. With appropriate filters applied into the algorithm, to rule out leukocytes and reticulocytes, our cytometer may also be used for field diagnosis of malaria.


title: Developing a Java based RFID application to automate student attendance monitoring

abstract: Research has proved that there is a high correlation between high class attendance and academic performance. Learning is a progressive activity which builds upon those of the previous day(s). Reading material and working independently do not compensate for the loss of insight gained by being physically present in classroom environment. As a result attendance monitoring should be an integral part of any institution especially a tertiary institution.


title: Volume xx (200y), Number z, pp. 1–12 Physically Based Real-Time Translucency for Leaves Abstract

abstract: This paper presents a new shading model for real-time rendering of plant leaves that reproduces all important attributes of a leaf and allows for a large number of leaves to be shaded. In particular, we use a physically based model for accurate subsurface scattering on the translucent side of directly lit leaves. For real-time rendering of this model, we formulate it as an image convolution process and express the result in an efficient directional basis that is fast to evaluate. We also propose a data acquisition method for leaves that uses off-the-shelf devices.


title: Underwater image enhancement via dark channel prior and luminance adjustment

abstract: Underwater images are degraded mainly by light scattering and color distortion. Light scattering occurs because the light is reflected and deflected multiple times by the suspended particles in the water before reaching the camera. Color distortion is caused due to the absorption degrees vary according to the light wavelength. At last, the underwater images are dominated by a bluish tone. In this paper, we propose a novel underwater image enhancement approach based on dark channel prior and luminance adjustment. The dehazed underwater image obtained by dark channel prior characterizes with low brightness. Then, we utilize a novel luminance adjustment method to enhance image contrast. The enhanced image shows improved global contrast and better preserved image details and edges. Experimental results demonstrate the effectiveness of our method compared to the state-of-the-art methods.


title: Dual-band circularly polarized monopole antenna

abstract: This paper proposes a dual-band circular polarization (CP) monopole antenna. This antenna can generate a broadband impedance-bandwidth from 2.17 to 8.47 GHz, and excite a left-hand circular polarization (LHCP) at 2.48 GHz and right-hand circular polarization (RHCP) at 3.90 GHz. The measured 3-dB axial ratio (AR) bandwidths are about 140 MHz from 2.41 to 2.55 GHz approximately 5.6% with respect to the center frequency at 2.48 GHz and 900 MHz from 3.45 to 4.35 GHz approximately 23.1% with respect to the center frequency at 3.90 GHz.


title: Generalized Equalization Model for Image Enhancement

abstract: In this paper, we propose a generalized equalization model for image enhancement. Based on our analysis on the relationships between image histogram and contrast enhancement/white balancing, we first establish a generalized equalization model integrating contrast enhancement and white balancing into a unified framework of convex programming of image histogram. We show that many image enhancement tasks can be accomplished by the proposed model using different configurations of parameters. With two defining properties of histogram transform, namely contrast gain and nonlinearity, the model parameters for different enhancement applications can be optimized. We then derive an optimal image enhancement algorithm that theoretically achieves the best joint contrast enhancement and white balancing result with trading-off between contrast enhancement and tonal distortion. Subjective and objective experimental results show favorable performances of the proposed algorithm in applications of image enhancement, white balancing and tone correction. Computational complexity of the proposed method is also analyzed.


title: Coeliac disease

abstract: Coeliac disease occurs in about 1% of people in most populations. Diagnosis rates are increasing, and this seems to be due to a true rise in incidence rather than increased awareness and detection. Coeliac disease develops in genetically susceptible individuals who, in response to unknown environmental factors, develop an immune response that is subsequently triggered by the ingestion of gluten. The disease has many clinical manifestations, ranging from severe malabsorption to minimally symptomatic or non-symptomatic presentations. Diagnosis requires the presence of duodenal villous atrophy, and most patients have circulating antibodies against tissue transglutaminase; in children, European guidelines allow a diagnosis without a duodenal biopsy provided that strict symptomatic and serological criteria are met. Although a gluten-free diet is an effective treatment in most individuals, a substantial minority develop persistent or recurrent symptoms. Difficulties adhering to a gluten-free diet have led to the development of non-dietary therapies, several of which are undergoing trials in human beings.


title: Resistance to forgetting associated with hippocampus-mediated reactivation during new learning

abstract: The authors find that during the encoding of new memories, responses in the human hippocampus are predictive of the retention of memories for previously experienced, overlapping events. They report that this is accomplished by reactivating the neural representation of older memories as new memories are formed.


title: Injectable treatments for the aging face.

abstract: The use of injectable agents, specifically soft tissue fillers and botulinum toxin type A, has risen dramatically over recent years, due to the increased demand for minimally invasive techniques. In fact, today they represent the most commonly performed cosmetic procedures in the United States, with botulinum type A injections topping the list. In the treatment of the aging face, these agents, when used individually or in combination, can effectively decrease rhytids and restore lost volume. The result is a fuller, smoother, more youthful appearance. This article provides an overview of botulinum type A (Botox Cosmetic; Allergan, Inc., Irvine, CA) and the two injectable fillers most commonly used in our practice, namely hyaluronic acid (Restalyne; Medicis Aesthetics, Inc., Scottsdale, AZ) and human-derived collagen (Cosmoderm and Cosmoplast; Inamed Aesthetics, Inc., Santa Barbara, CA). Although we commonly use autologous fat as an injectable filler for facial-volume augmentation, its discussion is beyond the scope of this article. Conceptually, the aging face can be divided into upper, middle, and lower thirds. Using this framework, we will discuss our treatment strategies for addressing each facial region. General principles, preinjection evaluation, and specific approaches and techniques for each anatomic region will be discussed, with particular emphasis on the benefit of using dermal fillers in conjunction with botulinum toxin type A to achieve optimal aesthetic results for facial rejuvenation.


title: Using Statistical Testing in the Evaluation of Retrieval Experiments

abstract: The standard strategies for evaluation based on precision and recall are examined and their relative advantages and disadvantages are discussed. In particular, it is suggested that relevance feedback be evaluated from the perspective of the user. A number of different statistical tests are described for determining if differences in performance between retrieval methods are significant. These tests have often been ignored in the past because most are based on an assumption of normality which is not strictly valid for the standard performance measures. However, one can test this assumption using simple diagnostic plots, and if it is a poor approximation, there are a number of non-parametric alternatives.


title: Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery

abstract: We consider the use of deep convolutional neural networks (CNNs) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts of data to facilitate a complex end-to-end feature extraction and classification process. Within the context of X-ray security screening, limited availability of object of interest data examples can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be optimized explicitly as a later secondary process towards this application domain. To provide a consistent feature-space comparison between this approach and traditional feature space representations, we also train support vector machine (SVM) classifier on CNN features. We empirically show that fine-tuned CNN features yield superior performance to conventional hand-crafted features on object classification tasks within this context. Overall we achieve 0.994 accuracy based on AlexNet features trained with SVM classifier. In addition to classification, we also explore the applicability of multiple CNN driven detection paradigms, such as sliding window-based CNN (SW-CNN), Faster region-based CNNs (F-RCNNs), region-based fully convolutional networks (R-FCN), and YOLOv2. We train numerous networks tackling both single and multiple detections over SW-CNN/ F-RCNN/R-FCN/YOLOv2 variants. YOLOv2, Faster-RCNN, and R-FCN provide superior results to the more traditional SW-CNN approaches. With the use of YOLOv2, using input images of size $544\times 544$ , we achieve 0.885 mean average precision (mAP) for a six-class object detection problem. The same approach with an input of size $416\times 416$ yields 0.974 mAP for the two-class firearm detection problem and requires approximately 100 ms per image. Overall we illustrate the comparative performance of these techniques and show that object localization strategies cope well with cluttered X-ray security imagery, where classification techniques fail.


title: Beyond tumorigenesis: cancer stem cells in metastasis

abstract: The importance of cancer stem cells (CSCs) in tumor-initiation has been firmly established in leukemia and recently reported for a variety of solid tumors. However, the role of CSCs in multistage cancer progression, particularly with respect to metastasis, has not been well-defined. Cancer metastasis requires the seeding and successful colonization of specialized CSCs at distant organs. The biology of normal stem cells and CSCs share remarkable similarities and may have important implications when applied to the study of cancer metastasis. Furthermore, overlapping sets of molecules and pathways have recently been identified to regulate both stem cell migration and cancer metastasis. These molecules constitute a complex network of cellular interactions that facilitate both the initiation of the pre-metastasis niche by the primary tumor and the formation of a nurturing organ microenvironment for migrating CSCs. In this review, we surveyed the recent advances in this dynamic field and propose a unified model of cancer progression in which CSCs assume a central role in both tumorigenesis and metastasis. Better understanding of CSCs as a fundamental component of the metastatic cascade will lead to novel therapeutic strategies against metastatic cancer.


Machine Learning Algorithms for Characterization of EMG Signals


title: Intrinsic motivation, curiosity, and learning

abstract: This article studies the bi-directional causal interactions between curiosity and learning, and discusses how understanding these interactions can be leveraged in educational technology applications. First, we review recent results showing how state curiosity, and more generally the experience of novelty and surprise, can enhance learning and memory retention. Then, we discuss how psychology and neuroscience have conceptualized curiosity and intrinsic motivation, studying how the brain can be intrinsically rewarded by novelty, complexity or other measures of information. We explain how the framework of computational reinforcement learning can be used to model such mechanisms of curiosity. Then, we discuss the learning progress (LP) hypothesis, which posits a positive feedback loop between curiosity and learning. We outline experiments with robots that show how LP-driven attention and exploration can self-organize a developmental learning curriculum scaffolding efficient acquisition of multiple skills/tasks.. Finally, we discuss recent work exploiting these conceptual and computational models in educational technologies, showing in particular how Intelligent Tutoring Systems can be designed to foster curiosity and learning.


title: Depth Map Inpainting under a Second-Order Smoothness Prior

abstract: Many 3D reconstruction methods produce incomplete depth maps. Depth map inpainting can generate visually plausible structures for the missing areas. We present an inpainting method that encourages flat surfaces without favouring fronto-parallel planes. Moreover, it uses a color image to guide the inpainting and align color and depth edges. We implement the algorithm efficiently through graph-cuts. We compare the performance of our method with another inpainting approach used for large datasets and we show the results using several datasets. The depths inpainted with our method are visually plausible and of higher quality.


title: Distorting the visual size of the hand affects hand pre-shaping during grasping

abstract: Vision of the body is known to affect somatosensory perception (e.g. proprioception or tactile discrimination). However, it is unknown whether visual information about one’s own body size can influence bodily action. We tested this by measuring the maximum grip aperture (MGA) parameter of grasping while eight subjects viewed a real size, enlarged or shrunken image of their hand reaching to grasp a cylinder. In the enlarged view condition, the MGA decreased relative to real size view, as if the grasping movement was actually executed with a physically larger hand, thus requiring a smaller grip aperture to grasp the cylinder. Interestingly, MGA remained smaller even after visual feedback was removed. In contrast, no effect was found for the reduced view condition. This asymmetry may reflect the fact that enlargement of body parts is experienced more frequently than shrinkage, notably during normal growth. In conclusion, vision of the body can significantly and persistently affect the internal model of the body used for motor programming.


title: Isolation, Synthesis and AChE Inhibitory Potential of Some Novel Cinnamyl Esters of Taraxerol, the Major Metabolite of the Mangrove Bruguiera cylindrica.

abstract: Systematic chemical screening of the leaves of Bruguiera cylindrica, the tree mangrove of Rhizophoraceae family, afforded five single and pure compounds. The structures of the isolated compounds were established by their spectroscopic data as taraxerol (1), 3β-(E)-coumaroyltaraxerol (2), 3β-(Z)-coumaroyltaraxerol (3), β-sitosterol (4), and eicosanol (5). In view of significant accumulation and interesting biological activities, taraxerol (1) was chemically transformed to synthesize a series of ten cinnamyl esters in very good to excellent yields. The synthesized analogues along with the parent compound were evaluated for their AChE inhibitory potential, BBB permeability and cytotoxicity against Neuro 2A cell line. Among the tested samples, compound 9 showed promising AChE inhibition with significantly low IC50 values, low cytotoxicity and high BBB permeability. Hence, compound 9 can be considered as a lead molecule for further development as potent AChE inhibitor.


title: Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks

abstract: While there are optimal TSP solvers, as well as recent learning-based approaches, the generalization of the TSP to the Multiple Traveling Salesmen Problem is much less studied. Here, we design a neural network solution that treats the salesmen, cities and depot as three different sets of varying cardinalities. We apply a novel technique that combines elements from recent architectures that were developed for sets, as well as elements from graph networks. Coupled with new constraint enforcing output layers, a dedicated loss, and a search method, our solution is shown to outperform all the meta-heuristics of the leading solver in the field.


title: Text analysis as a tool for analyzing conversation in online support groups

abstract: In this paper we describe a software tool that allows investigators to make comparisons across different online forums and media by analyzing word counts in user-specified categories. Using a large sample of messages from a bipolar support chatroom, we demonstrate how this tool can be used to characterize the nature of the discourse and compare it to other media, to analyze relationships among different word categories, and to characterize changes in visitors' discourse over time. Future plans for adding functionality to the software and using external data for additional validation are also discussed.


Deep Learning Techniques for Classification of P300 Component.


title: A rotatable reconfigurable antenna for cognitive radio applications

abstract: This paper presents an antenna scheme suitable for cognitive radio applications. A UWB sensing antenna and a frequency reconfigurable communicating antenna are incorporated together into the same antenna substrate. The UWB antenna searches for available spectrum holes while the reconfigurable antenna simultaneously tunes its operating frequency to the corresponding frequency determined by the sensing antenna. A new technique to achieve the required frequency reconfigurability is implemented via a rotational motion of a series of antenna patches. This eliminates the use of any bias lines as with the case of RF MEMs, PIN diodes and lumped elements. A prototype antenna was fabricated to test the proposed method. In the fabricated prototype, a stepper motor is incorporated and controlled via a computer. The use of this antenna structure in a cognitive radio communication link is demonstrated.


title: Parameter Control in Evolutionary Algorithms: Trends and Challenges

abstract: More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state-of-the-art. We briefly summarize the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview, we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research.


title: Bayesian active learning-based robot tutor for children's word-reading skills

abstract: Effective tutoring requires personalization of the interaction to each student. Continuous and efficient assessment of the student's skills are a prerequisite for such personalization. We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot. We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot. The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child. We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range. We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring. Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned. The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.


title: DEFINING ARTIFICIAL INTELLIGENCE

abstract: This chapter contains sections titled: Background The Turing Test Simulation of Human Expertise Neural Networks Definition of Intelligence Intelligence, the Scientific Method, and Evolution Evolving Artificial Intelligence References Chapter 1 Exercises


title: Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multilane highways

abstract: In this paper, we introduce a novel stereo-monocular fusion approach to on-road localization and tracking of vehicles. Utilizing a calibrated stereo-vision rig, the proposed approach combines monocular detection with stereo-vision for on-road vehicle localization and tracking for driver assistance. The system initially acquires synchronized monocular frames and calculates depth maps from the stereo rig. The system then detects vehicles in the image plane using an active learning-based monocular vision approach. Using the image coordinates of detected vehicles, the system then localizes the vehicles in real-world coordinates using the calculated depth map. The vehicles are tracked both in the image plane, and in real-world coordinates, fusing information from both the monocular and stereo modalities. Vehicles' states are estimated and tracked using Kalman filtering. Quantitative analysis of tracks is provided. The full system takes 46ms to process a single frame.


title: Software engineering for security: a roadmap

abstract: Is there such a thing anymore as a software system that doesn’t need to be secure? Almost every softwarecontrolled system faces threats from potential adversaries, from Internet-aware client applications running on PCs, to complex telecommunications and power systems accessible over the Internet, to commodity software with copy protection mechanisms. Software engineers must be cognizant of these threats and engineer systems with credible defenses, while still delivering value to customers. In this paper, we present our perspectives on the research issues that arise in the interactions between software engineering and security.


title: Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems

abstract: Artificial neural networks often achieve high classification accuracy rates, but they are considered as black boxes due to their lack of explanation capability. This paper proposes the new rule extraction algorithm RxREN to overcome this drawback. In pedagogical approach the proposed algorithm extracts the rules from trained neural networks for datasets with mixed mode attributes. The algorithm relies on reverse engineering technique to prune the insignificant input neurons and to discover the technological principles of each significant input neuron of neural network in classification. The novelty of this algorithm lies in the simplicity of the extracted rules and conditions in rule are involving both discrete and continuous mode of attributes. Experimentation using six different real datasets namely iris, wbc, hepatitis, pid, ionosphere and creditg show that the proposed algorithm is quite efficient in extracting smallest set of rules with high classification accuracy than those generated by other neural network rule extraction methods.


title: Smart drip irrigation system using raspberry pi and arduino

abstract: This paper proposes a design for home automation system using ready-to-use, cost effective and energy efficient devices including raspberry pi, arduino microcontrollers, xbee modules and relay boards. Use of these components results in overall cost effective, scalable and robust implementation of system. The commands from the user are processed at raspberry pi using python programming language. Arduino microcontrollers are used to receive the on/off commands from the rasperry pi using zigbee protocol. Star zigbee topology serves as backbone for the communication between raspberry pi and end devices. Raspberry pi acts a central coordinator and end devices act as various routers. Low-cost and energy efficient drip irrigation system serves as a proof of concept. The design can be used in big agriculture fields as well as in small gardens via just sending an email to the system to water plants. The use of ultrasound sensors and solenoid valves make a smart drip irrigation system. The paper explains the complete installation of the system including hardware and software aspects. Experimental set-up is also tested and explained for an automatic drip irrigation system to water 50 pots.


title: An Approach to Checking Consistency between UML Class Model and Its Java Implementation

abstract: Model Driven Engineering (MDE) aims to expedite the software development process by providing support for transforming models to running systems. Many modeling tools provide forward engineering features, which automatically translate a model into a skeletal program that developers must complete. Inconsistencies between a design model and its implementation, however, can arise, particularly when a final implementation is developed dependently on the code from which it was generated. Manually checking that an implementation conforms to its model is a daunting task. Thus, an MDE tool that developers can use to check that implementations conform to their models can significantly improve a developer's productivity. This paper presents a model-based approach for testing whether or not an implementation satisfies the constraints imposed by its design model. Our model-based testing approach aims to efficiently reduce the test input space while supporting branch coverage criteria. To evaluate the approach's ability to uncover inconsistencies, we developed a prototypical tool and applied it to the Eclipse UML2 projects. We were able to uncover inconsistencies between the models and their implementations using the tool.


title: An Efficient and Stable Ray Tracing Algorithm for Parametric Surfaces

abstract: In this paper, we propose an efficient and stable algorithm for finding the ray-surface intersections. Newton's method and Bezier clipping are adapted to form the core of our algorithm. Ray coherence is used to find starting points for Newton itera- tion. We introduce an obstruction detection technique to verify whether an intersection point found by using Newton's method is the closest one. When Newton's method fails to achieve convergence, we use Bezier clipping as the substitution to find the intersec- tion points. This combination achieves significant improvement in tracing primary rays. A similar approach also successfully improves the performance in tracing secondary rays. Ray tracing is one of the most important techniques for realistic image generation. It is capable of producing very impressive images for highly specular scenes. The kernel of the ray tracing algorithm is an intersection finding routine for computing the ray-surface intersection points. Whitted (1) estimated that a ray tracing algorithm will spend up to 95% of its computation time on intersection tests for scenes of moderate complexity. It takes plenty of time during the whole process, efficient intersection finding is one of the greatest challenges in ray tracing. Newton's method is a popular numerical method because of its efficiency and convenience in implementation. The critical task when using Newton's method is finding a good initial point. Toth (2) proposed an elegant theoretical results to locate the initial points. Lischinski and Gonczarowski (3) also proposed an improved technique based on Toth's results (2). Other researchers (4-10) subdivided surfaces into flat patches and lo- cated the initial points from the intersections of rays and tighter bounding volumes that enclosed the patches. However, the primary shortcoming of these methods is that they do not use the ray coherence property finding the ray-surface intersection points. Taking the intersection point of a previous adjacent ray as the initial point, Joy and Bhetanabhotla


title: Near Perfect Protein Multi-Label Classification with Deep Neural Networks

abstract: Biological sequences can be considered as data items of high-, non-fixed dimensions, corresponding to the length of those sequences. The comparison and the classification of biological sequences in their relations to large databases are important areas of research today. Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here we present two new ANNs with multi-label classification ability, showing impressive accuracy when classifying protein sequences into 698 UniProt families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).


title: Angel-Eye: A Complete Design Flow for Mapping CNN onto Customized Hardware

abstract: Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many applications. However, for embedded platforms, CNN-based solutions are still too complex to be applied if only CPU is utilized for computation. Various dedicated hardware designs on FPGA and ASIC have been carried out to accelerate CNN, while few of them explore the whole design flow for both fast deployment and high power efficiency. In this paper, we propose Angel-Eye, a programmable and flexible CNN processor architecture, together with compilation tool and runtime environment. Evaluated on Zynq XC7Z045 platform, Angel-Eye is 8× faster and 7× better in power efficiency than peer FPGA implementation on the same platform. A demo of face detection on XC7Z020 is also 20× and 15× more energy efficient than counterparts on mobile CPU and mobile GPU respectively.


title: Efficiently mining frequent trees in a forest

abstract: Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semistructured data, and so on. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present T REE M INER , a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. We contrast T REE M INER with a pattern matching tree mining algorithm (P ATTERN M ATCHER ). We conduct detailed experiments to test the performance and scalability of these methods. We find that T REE M INER outperforms the pattern matching approach by a factor of 4 to 20, and has good scaleup properties. We also present an application of tree mining to analyze real web logs for usage patterns.


title: History and highlights of the teratological collection in the Museum Anatomicum of Leiden University, The Netherlands

abstract: The anatomical collection of the Anatomical Museum of Leiden University Medical Center (historically referred to as Museum Anatomicum Academiae Lugduno-Batavae) houses and maintains more than 13,000 unique anatomical, pathological and zoological specimens, and include the oldest teratological specimens of The Netherlands. Throughout four centuries hundreds of teratological specimens were acquired by more than a dozen collectors. Due to the rich history of this vast collection, teratological specimens can be investigated in a unique retrospective sight going back almost four centuries. The entire 19th century collection was described in full detail by Eduard Sandifort (1742-1814) and his son Gerard Sandifort (1779-1848). Efforts were made to re-describe, re-diagnose and re-categorize all present human teratological specimens, and to match them with historical descriptions. In the extant collection a total of 642 human teratological specimens were identified, including exceptional conditions such as faciocranioschisis and conjoined twins discordant for cyclopia, and sirenomelia. Both father and son Sandifort differed in their opinion regarding the causative explanation of congenital anomalies. Whereas, their contemporaries Wouter Van Doeveren (1730-1783) and Andreas Bonn (1738-1817) both presented an interesting view on how congenital anomalies were perceived and explained during the 18th and 19th centuries; the golden age of descriptive teratology. Although this enormous collection is almost 400 years old, it still impresses scientists, (bio)medical students, and laymen visiting and exploring the collections of the Museum Anatomicum in Leiden, The Netherlands.


title: Automatic dance generation from music annotation

abstract: Automatic dance generation from music is useful for various entertainment applications including computer games. In this paper, we propose the automatic dance generation technique from music annotation.


JOINT DESIGN OF ACOUSTIC ECHO CANCELLATION AND ADAPTIVE BEAMFORMING FOR MICROPHONE ARRAYS


title: Fuzzy Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark

abstract: A huge amount of digital data containing useful information, called Big Data, is generated everyday. To mine such useful information, clustering is widely used data analysis technique. A large number of Big Data analytics frameworks have been developed to scale the clustering algorithms for big data analysis. One such framework called Apache Spark works really well for iterative algorithms by supporting in-memory computations, scalability etc. We focus on the design and implementation of partitional based clustering algorithms on Apache Spark, which are suited for clustering large datasets due to their low computational requirements. In this paper, we propose Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) implemented on an Apache Spark Cluster to handle the challenges associated with big data clustering. Experimental studies on various big datasets have been conducted. The performance of SRSIO-FCM is judged in comparison with the proposed scalable version of the Literal Fuzzy c-Means (LFCM) and Random Sampling plus Extension Fuzzy c-Means (rseFCM) implemented on the Apache Spark cluster. The comparative results are reported in terms of time and space complexity, run time and measure of clustering quality, showing that SRSIO-FCM is able to run in much less time without compromising the clustering quality.


title: Opinion Recommendation using Neural Memory Model

abstract: We present opinion recommendation, a novel task of jointly predicting a custom review with a rating score that a certain user would give to a certain product or service, given existing reviews and rating scores to the product or service by other users, and the reviews that the user has given to other products and services. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning, which is the strength of neural models. We use a single neural network to model users and products, capturing their correlation and generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp's own ratings, and our methods give better results compared to several pipelines baselines using state-of-the-art sentiment rating and summarization systems.


title: Overview of oversampling clock and data recovery circuits

abstract: Phase-locked loop (PLL) based clock and data recovery (CDR) circuits use a 2x-oversampling (2XO) of the incoming non-return to zero (NRZ) data stream to recover the data. As an extension of the idea, 3x-oversampling (3XO) CDR circuits provide improved performance in the presence of total asymmetric jitter. This paper presents an overview of the oversampling CDR circuits with an emphasis on digital architectures. These include, but are not limited to, the 3XO jitter-tolerant variable-interval 3XO architecture, the 3XO eye-tracking architecture, and the blind oversampling architecture. We propose a modified architecture that utilizes multiple rotating phases to improve the performance of the 3XO eye-tracking architecture


title: Sleep‐based memory processing facilitates grammatical generalization: Evidence from targeted memory reactivation

abstract: Abstract Generalization—the ability to abstract regularities from specific examples and apply them to novel instances—is an essential component of language acquisition. Generalization not only depends on exposure to input during wake, but may also improve offline during sleep. Here we examined whether targeted memory reactivation during sleep can influence grammatical generalization. Participants gradually acquired the grammatical rules of an artificial language through an interactive learning procedure. Then, phrases from the language (experimental group) or stimuli from an unrelated task (control group) were covertly presented during an afternoon nap. Compared to control participants, participants re-exposed to the language during sleep showed larger gains in grammatical generalization. Sleep cues produced a bias, not necessarily a pure gain, suggesting that the capacity for memory replay during sleep is limited. We conclude that grammatical generalization was biased by auditory cueing during sleep, and by extension, that sleep likely influences grammatical generalization in general.


title: The role of rumination in depressive disorders and mixed anxiety/depressive symptoms.

abstract: Several studies have shown that people who engage in ruminative responses to depressive symptoms have higher levels of depressive symptoms over time, after accounting for baseline levels of depressive symptoms. The analyses reported here showed that rumination also predicted depressive disorders, including new onsets of depressive episodes. Rumination predicted chronicity of depressive disorders before accounting for the effects of baseline depressive symptoms but not after accounting for the effects of baseline depressive symptoms. Rumination also predicted anxiety symptoms and may be particularly characteristic of people with mixed anxiety/depressive symptoms. People with a ruminative response style think repetitively and passively about their negative emotions, focuaing on their symptoms of distress ("I feel so lousy," "I just can't concentrate") and worrying about the meanings of their distress ("Will I ever get over this?"; Lyubomirsky, Ca/dwell, & Nolen-Hoeksema, 1998; NolenHoeksema, 1991). Several longitudinal studies have shown that people who engage in more ruminative responses when they are sad, blue, or depressed have higher levels of depressive symptoms over time, even after accounting for their baseline levels of de


title: Heterogeneous architecture design with emerging 3D and non-volatile memory technologies

abstract: Energy becomes the primary concern in nowadays multi-core architecture designs. Moore's law predicts that the exponentially increasing number of cores can be packed into a single chip every two years, however, the increasing power density is the obstacle to continuous performance gains. Recent studies show that heterogeneous multi-core is a competitive promising solution to optimize performance per watt. In this paper, different types of heterogeneous architecture are discussed. For each type, current challenges and latest solutions are briefly introduced. Preliminary analyses are performed to illustrate the scalability of the heterogeneous system and the potential benefits towards future application requirements. Moreover, we demonstrate the advantages of leveraging three-dimensional (3D) integration on heterogeneous architectures. With 3D die stacking, disparate technologies can be integrated on the same chip, such as the CMOS logic and emerging non-volatile memory, enabling a new paradigm of architecture design.1


title: Structured Feature Learning for Pose Estimation

abstract: In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation. Different from existing approaches of modeling structures on score maps or predicted labels, feature maps preserve substantially richer descriptions of body joints. The relationships between feature maps of joints are captured with the introduced geometrical transform kernels, which can be easily implemented with a convolution layer. Features and their relationships are jointly learned in an end-to-end learning system. A bi-directional tree structured model is proposed, so that the feature channels at a body joint can well receive information from other joints. The proposed framework improves feature learning substantially. With very simple post processing, it reaches the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of learning features at each joint separately with ConvNet, the mean PCP has been improved by 18% on FLIC. The code is released to the public. 1


title: Automating Proofs in Category Theory

abstract: We introduce a semi-automated proof system for basic category-theoretic reasoning. It is based on a first-order sequent calculus that captures the basic properties of categories, functors and natural transformations as well as a small set of proof tactics that automate proof search in this calculus. We demonstrate our approach by automating the proof that the functor categories Fun [C x D, E] and Fun [C, Fun [D, E] ] are naturally isomorphic.


title: A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts

abstract: The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.


title: Cross Temporal Recurrent Networks for Ranking Question Answer Pairs

abstract: Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients. This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner. In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks. Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.e., pairwise temporal gating. Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset.


title: Coupled Functional Maps

abstract: Classical formulations of the shape matching problem involve the definition of a matching cost that directly depends on the action of the desired map when applied to some input data. Such formulations are typically one-sided – they seek for a mapping from one shape to the other, but not vice versa. In this paper we consider an unbiased formulation of this problem, in which we solve simultaneously for a low-distortion map relating the two given shapes and its inverse. We phrase the problem in the spectral domain using the language of functional maps, resulting in an especially compact and efficient optimization problem. The benefits of our proposed regularization are especially evident in the scarce data setting, where we demonstrate highly competitive results with respect to the state of the art.


title: Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks

abstract: We present ULSA, a novel stochastic local search algorithm for random binary constraint satisfaction problems (CSP). ULSA is many times faster than the prior state of the art on a widely-studied suite of random CSP benchmarks. Unlike the best previous methods for these benchmarks, ULSA is a simple unweighted method that does not require dynamic adaptation of weights or penalties. ULSA obtains new record best solutions satisfying 99 of 100 variables in the challenging frb100-40 benchmark instance.


title: Dynamic Provisioning of Resources in Data Centers

abstract: Data centers make decisions about the allocation of resources to application providers based on policies. Policies are any type of formal behavioural guide and are input to the component that makes resource allocation decisions. This paper describes a system that use policies from different source e.g., SLAs (negotiated between the application provider and the data center), and business objectives and rules (from the data center). Decisions are based on a model that integrates all the policies together. Policy changes can be accommodated without recompiling the system.


Penile Mondor’s disease


title: Antecedents and Consequences of Work Engagement Among Nurses

abstract: Background: Engaged nurses have high levels of energy and are enthusiastic about their work which impacts quality of health care services. However, in the context of Iran, due to observed burnout, work engagement among nurses necessitates immediate exploration. Objectives: This investigation aimed to identify a suitable work engagement model in nursing profession in hospitals according to the hypothesized model and to determine antecedents and consequences related to work engagement among nurses. Patients and Methods: In this cross-sectional study, a questionnaire was given to 279 randomly-selected nurses working in two general teaching hospitals of Shiraz University of Medical Sciences (Shiraz, Iran) to measure antecedents and consequences of work engagement using the Saks’s (2005) model. Structural Equation Modeling was used to examine the model fitness. Results: Two paths were added using LISREL software. The resulting model showed good fitness indices (χ 2 = 23.62, AGFI = 0.93, CFI = 0.97, RMSEA = 0.07) and all the coefficients of the paths were significant (t ≥ 2, t ≤ -2). A significant correlation was found between work engagement and model variables. Conclusions: Paying adequate attention to the antecedents of work engagement can enhance the quality of performance among nurses. Additionally, rewards, organizational and supervisory supports, and job characteristics should be taken into consideration to establish work engagement among nurses. Further researches are required to identify other probable antecedents and consequences of nursing work engagement, which might be related to specific cultural settings.


Leukaemia and lymphoma in Kenya


title: Construction of a controllable β-carotene biosynthetic pathway by decentralized assembly strategy in Saccharomyces cerevisiae.

abstract: Saccharomyces cerevisiae is an important platform organism for the synthesis of a great number of natural products. However, the assembly of controllable and genetically stable heterogeneous biosynthetic pathways in S. cerevisiae still remains a significant challenge. Here, we present a strategy for reconstructing controllable multi-gene pathways by employing the GAL regulatory system. A set of marker recyclable integrative plasmids (pMRI) was designed for decentralized assembly of pathways. As proof-of-principle, a controllable β-carotene biosynthesis pathway (∼16 kb) was reconstructed and optimized by repeatedly using GAL10-GAL1 bidirectional promoters with high efficiency (80-100%). By controling the switch time of the pathway, production of 11 mg/g DCW of total carotenoids (72.57 mg/L) and 7.41 mg/g DCW of β-carotene was achieved in shake-flask culture. In addition, the engineered yeast strain exhibited high genetic stability after 20 generations of subculture. The results demonstrated a controllable and genetically stable biosynthetic pathway capable of increasing the yield of target products. Furthermore, the strategy presented in this study could be extended to construct other pathways in S. cerevisisae.


title: Unique in the Crowd: The privacy bounds of human mobility

abstract: We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.


title: Detection of cough signals in continuous audio recordings using hidden Markov models

abstract: Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.


title: Analysis of execution log files

abstract: Log analysis can be used to find problems, define operational profiles, and even pro-actively prevent issues. The goal of my dissertation research is to investigate log management and analysis techniques suited for very large and very complex logs, such as those we might expect in a computational cloud system.


title: PathGraph: A Path Centric Graph Processing System

abstract: Large scale iterative graph computation presents an interesting systems challenge due to two well known problems: (1) the lack of access locality and (2) the lack of storage efficiency. This paper presents PathGraph, a system for improving iterative graph computation on graphs with billions of edges. First, we improve the memory and disk access locality for iterative computation algorithms on large graphs by modeling a large graph using a collection of tree-based partitions. This enables us to use path-centric computation rather than vertex-centric or edge-centric computation. For each tree partition, we re-label vertices using DFS in order to preserve consistency between the order of vertex ids and vertex order in the paths. Second, a compact storage that is optimized for iterative graph parallel computation is developed in the PathGraph system. Concretely, we employ delta-compression and store tree-based partitions in a DFS order. By clustering highly correlated paths together as tree based partitions, we maximize sequential access and minimize random access on storage media. Third but not the least, our path-centric computation model is implemented using a scatter/gather programming model. We parallel the iterative computation at partition tree level and perform sequential local updates for vertices in each tree partition to improve the convergence speed. To provide well balanced workloads among parallel threads at tree partition level, we introduce the concept of multiple stealing points based task queue to allow work stealings from multiple points in the task queue. We evaluate the effectiveness of PathGraph by comparing with recent representative graph processing systems such as GraphChi and X-Stream etc. Our experimental results show that our approach outperforms the two systems on a number of graph algorithms for both in-memory and out-of-core graphs. While our approach achieves better data balance and load balance, it also shows better speedup than the two systems with the growth of threads.


title: Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

abstract: Saliency prediction typically relies on hand-crafted (multiscale) features that are combined in different ways to form a "master" saliency map, which encodes local image conspicuity. Recent improvements to the state of the art on standard benchmarks such as MIT1003 have been achieved mostly by incrementally adding more and more hand-tuned features (such as car or face detectors) to existing models. In contrast, we here follow an entirely automatic data-driven approach that performs a large-scale search for optimal features. We identify those instances of a richly-parameterized bio-inspired model family (hierarchical neuromorphic networks) that successfully predict image saliency. Because of the high dimensionality of this parameter space, we use automated hyperparameter optimization to efficiently guide the search. The optimal blend of such multilayer features combined with a simple linear classifier achieves excellent performance on several image saliency benchmarks. Our models outperform the state of the art on MIT1003, on which features and classifiers are learned. Without additional training, these models generalize well to two other image saliency data sets, Toronto and NUSEF, despite their different image content. Finally, our algorithm scores best of all the 23 models evaluated to date on the MIT300 saliency challenge, which uses a hidden test set to facilitate an unbiased comparison.


title: Collaborative filtering algorithm based on rating distance

abstract: Collaborative filtering, a successful and wildly used technique in personalized recommender systems, generates recommendations by similar users. Cosine similarity and Pearson correlation coefficient are widely used in collaborative filtering to calculate the similarity; however, the similarity is not accurate in some cases because of the defects of the algorithm. To solve these issues, this paper proposes a novel similarity calculation method which combined information entropy with compressive distance weight based on the probability distribution of rating distance. Experiment results show that the proposed method get better performance than conventional Pearson correlation coefficient method.


title: A Proactive Multi-type Context-Aware Recommender System in the Environment of Internet of Things

abstract: Currently recommender systems are incorporating context and social information of the user, producing context aware recommender systems. In the future, they will use implicit, local and personal information of the user from the Internet of Things, where anyone and anything will be connected at anytime and anywhere. Most recommender systems follow a request-response approach in which the recommendations are provided to the user upon his request. Recently a proactive recommender system - that pushes recommendations to the user when the current situation seems appropriate, without explicit user request - has been introduced in the research area of recommender systems. The fact that the future is for Internet of Things, and the emergence of proactivity concept leads to our system design, in which multi-type rather than one type of recommendations will be recommended proactively to the user in real time. In this paper, a design of a context aware recommender system that recommends different types of items proactively under the Internet of Things paradigm is proposed. A major part of this design is the context aware management system. In this system, we have used a neural network that will do the reasoning of the context to determine whether to push a recommendation or not and what type of items to recommend. The neural network inputs are derived virtually from the Internet of Things, and its outputs are scores for three types of recommendations, they are: gas stations, restaurants and attractions. These scores have been used to decide whether to push a recommendation or not, and what type of recommendations to push among these three types. The results of 5000 random contexts were tested. For an average of 98% of them, our trained neural network generated correct recommendation types in the correct times and contexts.


title: Postal address block location in real time

abstract: The CEDAR real-time address block location system, which determines candidates for the location of the destination address from a scanned mail piece image, is described. For each candidate destination address block (DAB), the address block location (ABL) system determines the line segmentation, global orientation, block skew, an indication of whether the address appears to be handwritten or machine printed, and a value indicating the degree of confidence that the block actually contains the destination address. With 20-MHz Sparc processors, the average time per mail piece for the combined hardware and software system components is 0.210 seconds. The system located 89.0% of the addresses as the top choice. Recent developments in the system include the use of a top-down segmentation tool, address syntax analysis using only connected component data, and improvements to the segmentation refinement routines. This has increased top choice performance to 91.4%. >


title: Forecasting Fraudulent Financial Statements using Data Mining

abstract: This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. To sum up, this study indicates that the investigation of financial information can be used in the identification of FFS and underline the importance of financial ratios. Keywords—machine learning, stacking, classifier