title: Towards a Reference Architecture for Model-Driven Business Apps

abstract: Model-driven development techniques have been proposed for cross-platform app development. Typically, an individual domain-specific language (DSL) is used. The MD² framework consists of a DSL for business apps and generators, which transform a domain model to native platform code. Prior research on it focused on language and general generator design, this paper accentuates the code generation stage. A reference architecture for the generated apps is proposed to accelerate the development of new generators for which architectural decisions had been made on ad-hoc basis up to now. Moreover, generators are going to expose similar structures that facilitate maintenance. Our proposal takes MD²'s characteristics into account while providing developers with greater flexibility. A code generator for a commercial cross-platform framework has been realized, demonstrating the applicability of our proposal. We not only present steps towards a reference architecture but generalize findings for use beyond the area of mobile computing.


title: Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards

abstract: Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and pr...


title: Dural ectasia in Marfan syndrome: a case control study.

abstract: BACKGROUND AND PURPOSE ::: Dural ectasia (DE) is one of the major criteria of Marfan syndrome (MFS). Our aim was to establish the prevalence of DE in an adult population fulfilling the Ghent criteria for MFS and to assess definitions of DE. ::: ::: ::: MATERIALS AND METHODS ::: One hundred five adults with suspected MFS were included. MR imaging at 1.5T was performed unless contraindicated; then CT was obtained. Lumbosacral anteroposterior vertebral body diameters (VBD) and dural sac diameters (DSD) were measured. Dural sac ratios (DSR = DSD/VBD) at levels L3 through S1 were calculated. Anterior meningoceles, herniations of nerve root sleeves, and scalloping were characterized. One hundred one sex- and age-matched patients were included as controls. ::: ::: ::: RESULTS ::: We identified 3 patient groups: 1) fulfilling Ghent criteria independent of DE (n = 73), 2); fulfilling Ghent criteria dependent on DE (n = 14), and 3); and suspected MFS, not fulfilling Ghent criteria (n = 18). DE was found in 86% of group 1. At levels L4-S1, mean DSRs were significantly higher in group 1 than in group 3 and controls (P < .001). Herniations of the nerve root sleeves were present in 73% in group 1 versus 1% in controls. Anterior meningoceles were found in 37% and 14% in groups 1 and 2, respectively, but not in group 3 or controls. ::: ::: ::: CONCLUSIONS ::: The diagnosis of DE on MR imaging or CT should be based on the presence of at least 1 of the following criteria: anterior meningoceles or nerve root sleeve herniation, DSD at S1 or below larger than DSD at L4, and DSR at S1 >0.59.


title: Scanning Knife-edge Method for Laser Plasma Size Measurements.

abstract: In this Diploma paper the knife edge method is developed to measure the size of a small laser-plasma x-ray source. The method is theoretically discussed, showing that very high spatial resolution ( ~2 !J.m) is feasible. Accurate computer control of a DC motor for knife edge positioning and simultaneous data acquisition has been implemented. Experimental measurements on small (20-25 !J.m) water-window laser plasma x-ray sources demonstrate the method. Compared to other x-ray size measurement methods, the simplicity and high resolution of the knife edge method make it attractive for fast 1-D measurements of plasma size. 2-D imaging can be performed using tomographic methods.


title: Learning to Predict High-Quality Edge Maps for Room Layout Estimation

abstract: The goal of room layout estimation is to predict the three-dimensional box that represents the room spatial structure from a monocular image. In this paper, a deconvolution network is trained first to predict the edge map of a room image. Compared to the previous fully convolutional networks, the proposed deconvolution network has a multilayer deconvolution process that can refine the edge map estimate layer by layer. The deconvolution network also has fully connected layers to aggregate the information of every region throughout the entire image. During the layout generation process, an adaptive sampling strategy is introduced based on the obtained high-quality edge maps. Experimental results prove that the learned edge maps are highly reliable and can produce accurate layouts of room images.


title: IoT based smart irrigation system and nutrient detection with disease analysis

abstract: Agriculture remains the sector which contributes the highest to India's GDP. But, when considering technology that is deployed in this field, we find that the development is not tremendous. After consulting with the Kerala Agricultural University, Mannuthy and Kerala Rice Research Station, Vytilla, we identified a few fundamental issues that are faced by the paddy farmers today. It includes the problem of over or under watering and the need for regular manual irrigation. Furthermore, when it comes to rice, which is the staple crop of Kerala, there does not exist a system for automatically monitoring the diseases associated with the rice species, and checking whether the crop is supplied with ample amount of nutrients. In this paper, we have devised a means for cost-effective automated irrigation and fertigation along with MATLAB based image processing for identifying the rice diseases and nutrient deficiencies. Here, we are focusing on two important nutrients, namely magnesium and nitrogen. The hardware consists of a Raspberry Pi, DHT11 temperature and humidity sensor and solenoid valves. Furthermore, the proposed model enables the farmer to monitor weather conditions using an Android Application, with which he also has a choice to override the system if required.


Meta-analysis of Failure and Survival Rate of Implant-supported Single Crowns, Fixed Partial Denture, and Implant Tooth-supported Prostheses


An Integrated Web-based Interactive Data Platform for Molecular Dynamics Simulations


title: The Management of Scale: Big Organizations, Big Decisions, Big Mistakes

abstract: Managers are above all decision makers. They are constantly being required to make choices and form policies on the basis of the limited information available to them at the time. These choices drive and shape the business, often with consequences that were not forseen and where failures might not be avoided. In this text, David Collingbroke makes case studies of corporations which made decisions that turned out to be costly failures. Above all, his findings show that organizations need, like individuals, to be able to learn from experience. The successful organization is one with the capability to change tack, learn by past mistakes and proceed by small steps. "The Management of Scale" aims to offer readers a challenging new approach to the problems of strategic management. This book should be of interest to undergraduates, postgraduates and academics in management, technology management, production, organizational behaviour and engineering, management consultants and MBA students.


title: Real-Time 6D Object Pose Estimation on CPU

abstract: We propose a fast and accurate 6D object pose estimation from a RGB-D image. Our proposed method is template matching based and consists of three main technical components, PCOF-MOD (multimodal PCOF), balanced pose tree (BPT) and optimum memory rearrangement for a coarse-to-fine search. Our model templates on densely sampled viewpoints and PCOF-MOD which explicitly handles a certain range of 3D object pose improve the robustness against background clutters. BPT which is an efficient tree-based data structures for a large number of templates and template matching on rearranged feature maps where nearby features are linearly aligned accelerate the pose estimation. The experimental evaluation on tabletop and bin-picking dataset showed that our method achieved higher accuracy and faster speed in comparison with state-of-the-art techniques including recent CNN based approaches. Moreover, our model templates can be trained solely from 3D CAD in a few minutes and the pose estimation run in near real-time (23 fps) on CPU. These features are suitable for any real applications.


title: FilterReg: Robust and Efficient Probabilistic Point-Set Registration Using Gaussian Filter and Twist Parameterization

abstract: Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Point-set registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter to improve its performance while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs), which makes it highly efficient even for high DOF systems. The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks.


title: Human leucocyte antigen Class I and II alleles associated with anti-hepatitis C virus-positive patients of North India

abstract: Purpose: Humans are the only known natural hosts of hepatitis C virus (HCV). This study was undertaken to examine the frequencies of human leucocyte antigens (HLAs) Class I and Class II genotype profiles in anti-HCV-infected patients of Northern India. Materials and Methods: From a period of January 2013 to August 2014, 148 anti-HCV-positive patients of North India referred to the Department of Molecular Biology and Transplant Immunology, Indraprastha Apollo Hospitals, New Delhi, for performing HLA typing were included in the study. Results: AFNx0102, AFNx0131 allele frequency decreased significantly in anti-HCV-positive patients. Frequencies for HLA-B loci did not reach any statistical significance. Among the Class II alleles, HLA-DRB1FNx0103 and HLA-DRB1FNx0110 were significantly higher in the patient population, and HLA-DRB1FNx0115 was significantly decreased in the patient population as compared to the controls. Conclusion: HLA-AFNx0133 was significantly increased as compared to control population and showed geographic variation in HCV-infected individuals of India.


title: Mitigating the Power Density and Temperature Problems in the Nano-Era

abstract: This paper introduces the power-density and temperature induced issues in the modern on-chip systems. In particular, the emerging Dark Silicon problem is discussed along with critical research challenges. Afterwards, an overview of key research efforts and concepts is presented that leverage dark silicon for performance and reliability optimization. In case temperature constraints are violated, an efficient dynamic thermal management technique is employed.


A Wavelet Tour of Signal Processing, 2nd Edition


title: Evolving Object-Oriented Designs with Refactorings

abstract: Refactorings are behaviour-preserving program transformations that automate design-level changes in object-oriented applications. Our previous research established that many schema transformations, design patterns and hot-spot meta-patterns are automatable. This research evaluates whether refactoring technology can be transferred to the mainstream by restructuring non-trivial C++ applications. The applications that we examine were evolved manually by software engineers. We show that an equivalent evolution could be reproduced significantly faster and cheaper by applying a handful of general-purpose refactorings. In one application, over 14 K lines of code, that otherwise would have been coded by hand, were transformed automatically. Our experiments identify the benefits, limitations and topics of further research related to the transfer of refactoring technology to a production environment.


title: Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

abstract: This paper studies gradient-based schemes for image denoising and deblurring problems based on the discretized total variation (TV) minimization model with constraints. We derive a fast algorithm for the constrained TV-based image deburring problem. To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA) we have recently introduced. The resulting gradient-based algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known gradient projections-based methods. Our results are applicable to both the anisotropic and isotropic discretized TV functionals. Initial numerical results demonstrate the viability and efficiency of the proposed algorithms on image deblurring problems with box constraints.


title: Comparing workflow specification languages: A matter of views

abstract: We address the problem of comparing the expressiveness of workflow specification formalisms using a notion of view of a workflow. Views allow to compare widely different workflow systems by mapping them to a common representation capturing the observables relevant to the comparison. Using this framework, we compare the expressiveness of several workflow specification mechanisms, including automata, temporal constraints, and pre-and postconditions, with XML and relational databases as underlying data models. One surprising result shows the considerable power of static constraints to simulate apparently much richer workflow control mechanisms.


title: Classification of Audio Data using Support Vector Machine 1

abstract: Audio mining is to extract audio signals for indicating patterns and features of audio data to get data mining results. Various audio features like Mel frequency Cepstral Coefficient (MFCC), Linear Predictive Coefficient (LPC), Compactness, Spectral Flux (SF), Band Periodicity (BP), Zero Crossing Rate (ZCR) etc are used to classify audio data into various classes. Various classification algorithms such as Naive Bayes, FT, J48, ID3 and LibSVM are used to classify audio data into defined classes. Using various performance parameters such as True Positive (TP) Rate, False Positive (FP) Rate etc., results of various classification algorithms are compared.


title: Building 3D mosaics from an Autonomous Underwater Vehicle, Doppler velocity log, and 2D imaging sonar

abstract: This paper reports on a 3D photomosaicing pipeline using data collected from an autonomous underwater vehicle performing simultaneous localization and mapping (SLAM). The pipeline projects and blends 2D imaging sonar data onto a large-scale 3D mesh that is either given a priori or derived from SLAM. Compared to other methods that generate a 2D-only mosaic, our approach produces 3D models that are more structurally representative of the environment being surveyed. Additionally, our system leverages recent work in underwater SLAM using sparse point clouds derived from Doppler velocity log range returns to relax the need for a prior model. We show that the method produces reasonably accurate surface reconstruction and blending consistency, with and without the use of a prior mesh. We experimentally evaluate our approach with a Hovering Autonomous Underwater Vehicle (HAUV) performing inspection of a large underwater ship hull.


title: A Review on Link Prediction in Social Network

abstract: Social network analysis is an evolving field of research and link prediction problem shows a vital role for prediction of social network structure. This paper emphases on prevailing research on link prediction problem. Prevailing researches reveal that link prediction problem complexity, available solutions effective group communication management and social link consciousness. The link prediction problem across associated networks can include anchor link prediction problem and link transfer through associated heterogeneous networks. This paper summarizes recent growth about link prediction algorithms and survey of all the prevailing link prediction techniques.


title: The effect of social media communication on consumer perceptions of brands

abstract: Researchers and brand managers have limited understanding of the effects of firm-created and user-generated social media communication on brand equity, brand attitude, and purchase intention. Thus, we investigated 504 Facebook users using a standardized online survey across Poland. To test the proposed model, we analyzed 60 brands across three different industries: non-alcoholic beverages, clothing, and mobile operators. In the data analysis, we applied the structural equation modeling technique. The results of our empirical studies showed that user-generated social media communication had a positive influence on brand equity and brand attitude. In addition, the analysis indicated that firm-created social media communication affected only brand attitude. Both brand equity and brand attitude showed a positive influence on purchase intention. Moreover, measurement invariance was assessed using a multi-group structural modeling equation. The findings revealed that the proposed model was invariant across the researched industries


title: Feasibility of a DC network for commercial facilities

abstract: This paper analyzes the feasibility of direct current for the supply of offices and commercial facilities. This is done by analyzing a case study, i.e. the supply to the Department of Electric Power Engineering, Chalmers University of Technology, Gothenburg, Sweden. Voltage drop calculations have been carried out for different voltage levels. A backup system for reliable power supply is designed based on commercially available batteries. Finally, an economic evaluation of AC versus DC is performed and protection of the proposed system is briefly addressed.


title: Emotional Intelligence and Social Interaction

abstract: Two studies found positive relationships between the ability to manage emotions and the quality of social interactions, supporting the predictive and incremental validity of an ability measure of emotional intelligence, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). In a sample of 118 American college students (Study 1), higher scores on the managing emotions subscale of the MSCEIT were positively related to the quality of interactions with friends, evaluated separately by participants and two friends. In a diary study of social interaction with 103 German college students (Study 2), managing emotions scores were positively related to the perceived quality of interactions with opposite sex individuals. Scores on this subscale were also positively related to perceived success in impression management in social interactions with individuals of the opposite sex. In both studies, the main findings remained statistically significant after controlling for Big Five personality traits.


title: An isolated bridgeless AC-DC converter with high power factor

abstract: This paper introduce a new isolated bridgeless AC to DC converter with power factor correction capability. This power factor correction (PFC) circuit is capable to perform the AC to DC conversion without the diode bridge rectifier and able to obtain low output voltage which is more suitable to most applications. The detail small signal and steady state analysis of the converter operated in discontinuous conduction mode (DCM) are presented using the current injected equivalent circuit approach (CIECA). The dynamic and steady state response at the input current and output voltage are included to verify the converter capability as a PFC circuit.


title: Integrated Modular Motor Drive Design With GaN Power FETs

abstract: This paper explores the use of GaN power FETs to realize an integrated modular motor drive (IMMD) with an induction motor. A structure in which inverter modules are connected in series is proposed to reduce the module maximum voltages and to offer an opportunity to utilize low-voltage wide-band-gap GaN devices. With the superb switching performance of GaN power FETs, a reduction in IMMD size is achieved by eliminating inverter heat sink and optimizing dc-link capacitors. Gate signals of the IMMD modules are interleaved to suppress the total voltage ripple of dc-link capacitors and to further reduce the capacitor size. Motor winding configurations and their coupling effect are also investigated as a part of the IMMD design. The proposed structure and design methods are verified by experimental results.


Overview of the ImageCLEF 2018 Caption Prediction Tasks


title: Electromagnetic Coupling of Twisted Pair Cables

abstract: Increasing deployment of digital subscriber line (DSL) technology operating in the long wave, medium wave and sometimes short wave frequency ranges, has raised the question of how such systems interact with rival radio frequency sources and equipment. Two of the major standards bodies in the field, ITU and ETSI, imposed by the standardization of such promising technologies like ADSL, SHDSL and VDSL, have begun to elaborate performance tests for radio frequency interference (RFI) scenarios for inclusion into their standards documents. Existing out-door loops of the copper telephone network can have large dimensions, are often unshielded and sometimes not even twisted, thus, they can act like antennas for both RFI egress and ingress. The RFI egress issue requires basic knowledge of the relationship between a given current distribution I(l) on a twisted pair communication cable and the electromagnetic fleld E, H generated by that current distribution at a given distance d. This problem is reciprocal to the RFI ingress issue, which requires knowledge of the relationship between a disturbing high-frequency electric field E in the surroundings of a twisted pair cable, and the terminal voltage of that cable across a load impedance. This contribution provides the theoretical background to the field theoretical problem and supplies simple yet accurate approximation formulae for the prediction of RFI egress and RFI ingress.


title: Spatial Awareness in Synthetic Vision Systems: Using Spatial and Temporal Judgments to Evaluate Texture and Field of View

abstract: OBJECTIVE ::: This work introduced judgment-based measures of spatial awareness and used them to evaluate terrain textures and fields of view (FOVs) in synthetic vision system (SVS) displays. ::: ::: ::: BACKGROUND ::: SVSs are cockpit technologies that depict computer-generated views of terrain surrounding an aircraft. In the assessment of textures and FOVs for SVSs, no studies have directly measured the three levels of spatial awareness with respect to terrain: identification of terrain, its relative spatial location, and its relative temporal location. ::: ::: ::: METHODS ::: Eighteen pilots made four judgments (relative azimuth angle, distance, height, and abeam time) regarding the location of terrain points displayed in 112 noninteractive 5-s simulations of an SVS head-down display. There were two between-subject variables (texture order and FOV order) and five within-subject variables (texture, FOV, and the terrain point's relative azimuth angle, distance, and height). ::: ::: ::: RESULTS ::: Texture produced significant main and interaction effects for the magnitude of error in the relative angle, distance, height, and abeam time judgments. FOV interaction effects were significant for the directional magnitude of error in the relative distance, height, and abeam time judgments. ::: ::: ::: CONCLUSION ::: Spatial awareness was best facilitated by the elevation fishnet (EF), photo fishnet (PF), and photo elevation fishnet (PEF) textures. ::: ::: ::: APPLICATION ::: This study supports the recommendation that the EF, PF, and PEF textures be further evaluated in future SVS experiments. Additionally, the judgment-based spatial awareness measures used in this experiment could be used to evaluate other display parameters and depth cues in SVSs.


title: Site-directed labeling of a monoclonal antibody: targeting to a disulfide bond.

abstract: We have designed and synthesized crabescein, the first member of a class of fluorescent labels that add across disulfide bonds. Crabescein is a fluorescein derivative that reports the rotational correlation time of the immunoglobulin G (IgG) segment to which it is covalently bound. Chemical analysis of the IgG labeled with crabescein indicates that the fluorophore is inserted into the third disulfide bond (cysteine-229 of mouse IgG2a) in the hinge region. The rotational correlation time of this labeled macromolecule was measured as a single exponential with a decay constant of 26.8 ns. This is in contrast to the double exponential with decay constants of 14.3 and 0.2 ns for the same IgG when labeled with fluorescein via a conventional labeling reagent in which the probe is bound to the macromolecule by one-point attachments. Thus, crabescein is the prototype of a class of fluorescent and phosphorescent probes that, by virtue of their two-point attachments to proteins, faithfully report on the dynamics of the segment of macromolecule to which they are covalently bound.


title: Cognitive systems for intelligent business information management in cognitive economy

abstract: In the paper was described cognitive information systems for business information management.This system is designed to semantically analyse some economic data with selected financial ratios.Semantic reasoning algorithms based on cognitive resonance was applied for such analysis.An idea of cognitive economy was presented. This paper will present new theoretical and applied solutions for intelligent data analysis and information management in the fields of cognitive economics. Intelligent data analysis and information management are performed by information systems called cognitive systems, dedicated for semantic interpretation of acquired business information. To interpret the meaning of the analysed data, complex linguistic algorithms must be used, based on which it is possible to find the core information elements for business processes forecasting and economical knowledge management. The presentation of selected methods of semantic data analysis in cognitive economy, which allow to perform both local and global information management forms the main subject of this paper. Here, semantic analysis methods are dedicated to cognitive economics problems, namely the interpretation, analysis and assessment of the meaning of selected sets of economic/financial ratios. The meaning of the interpreted data sets is assessed by analysing the layers of meaning contained in data analysed sets. Obtained semantic information may be used in future business processes evaluation and forecasting.


title: One evaluation of model-based testing and its automation

abstract: Model-based testing has become increasingly popular in recent years. Major reasons include: (1) the need for quality assurance for increasingly complex systems, (2) the emerging model-centric development paradigm, e.g., UML and MDA, with its seemingly direct connection to testing, and (3) the advent of test-centered development methodologies. Model-based testing relies on execution traces of behavior models. They are used as test cases for an implementation: input and expected output. This complements the ideas of model-driven testing. The latter uses static models to derive test drivers to automate test execution. This assumes the existence of test cases, and is, like the particular intricacies of OO testing, not in the focus of this tutorial. We cover major methodological and technological issues: the business case of model-based testing within model-based development, the need for abstraction and inverse concretization, test selection, and test case generation. We (1) discuss different scenarios of model-based testing, (2) present common abstractions when building models, and their consequences for testing, (3) explain how to use functional, structural, and stochastic test selection criteria, and (4) describe today's test generation technology. We provide both practical guidance and a discussion of the state-of-the-art. Potentials of model-based testing in practical applications and future research are highlighted.


abstract: Attention Modeling for Face Recognition via Deep Learning Sheng-hua Zhong (csshzhong@comp.polyu.edu.hk) Department of Computing, Hung Hom, Kowloon Hong Kong, 999077 CHINA Yan Liu (csyliu@comp.polyu.edu.hk) Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA Yao Zhang (csyaozhang@comp.polyu.edu.hk) Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA Fu-lai Chung (cskchung@comp.polyu.edu.hk) Department of Computing, Hung Hom, Kowloon Hong Kong, 99907 CHINA Abstract Face recognition is an important area of research in cognitive science and machine learning. This is the first paper utilizing deep learning techniques to model human’s attention for face recognition. In our attention model based on bilinear deep belief network (DBDN), the discriminant information is maximized in a frame of simulating the human visual cortex and human’s perception. Comparative experiments demonstrate that from recognition accuracy our deep learning model outperforms both representative benchmark models and existing bio-inspired models. Furthermore, our model is able to automatically abstract and emphasize the important facial features and patterns which are consistent with the human’s attention map. Keywords: face recognition; attention model; deep learning. Introduction Face recognition plays an important role in the social life and attracts interest from a very broad range of researchers and scientists (Anderson, 1998). In machine learning and computer vision areas, face recognition using computational models is a classical problem. The representative models include: Eigenface (Turk, et al., 1991), Fisherfaces (Belhumer, et al., 1997), support vector machine (SVM) (Muller, et al., 2001), and so on. In cognitive science, face recognition is a vividly researched area (Gauthier, et al., 2000) (Afraz, et al., 2006) (Civile, et al. 2011). It is argued that face perception is involved in a unique cognitive process compared with non- face object or scene perception. Researchers in cognitive science seek to understand how the visual system transforms a face image from an initial, pixel-like representation, to a new powerful form of representation, and finally induce the selectively response of the neurons in inferior temporal cortex (Afraz, et al., 2006). Hence, the researchers have utilized some signal processing techniques to simulate the response of human visual system, such as human’s attention allocation. Computational attention model is utilized to measure of the conspicuity and provide the predictions about which regions are likely to attract observers’ attention (Koch et al., 1985) (Parkhurst et al., 2002). Many empirical validations have demonstrated that attention models have notable ability in various tasks, such as content aware resizing (Avidan et al., 2007), quality assessment (Zhong et al., 2010), and face recognition (Cappelli, et al., 2007) (Fang, et al., 2011). This paper models human’s attention for face recognition via deep learning technique. Deep learning models the learning tasks using deep architectures composed of multiple layers of parameterized nonlinear modules. Deep model is selected in this paper because of two considerations. First, the multiple layers deep architecture is consistent with the laminar structure of human’s brain cortex and the information delivery in deep model simulates human’s visual cortex. Second, deep learning has demonstrated distinguished ability of information abstraction and robust performance of data classification in various visual data analysis tasks (Hinton, et al., 2006). This is the first paper utilizing deep learning techniques to model human’s attention for face recognition. Compared with existing face recognition models, our proposed bilinear deep belief network (BDBN) has several attractive characters: 1) BDBN maximizes the discriminant information in a frame of simulating the human visual cortex and human’s perception. As we known, nearly all existing machine learning models aims to find the discriminant solution to face recognition applications. Existing computational cognitive model emphasizes the identity between the model and the human visual system. Our model attempts to integrate the advantages of both techniques and provide a new thought of this problem. 2) Compared with existing computational face recognition models or representative attention models, BDBN has the ability to automatically extract and emphasize the important facial features and patterns which are consistent with the human attention map. 3) BDBN includes three learning stages: semiconducting bilinear discriminant initialization, greedy layer-wise reconstruction, and global fine-turning. The rational of


title: Towards Fast Computation of Certified Robustness for ReLU Networks

abstract: Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17]. Although finding the exact minimum adversarial distortion is hard, giving a certified lower bound of the minimum distortion is possible. Current available methods of computing such a bound are either time-consuming or delivering low quality bounds that are too loose to be useful. In this paper, we exploit the special structure of ReLU networks and provide two computationally efficient algorithms Fast-Lin and Fast-Lip that are able to certify non-trivial lower bounds of minimum distortions, by bounding the ReLU units with appropriate linear functions Fast-Lin, or by bounding the local Lipschitz constant Fast-Lip. Experiments show that (1) our proposed methods deliver bounds close to (the gap is 2-3X) exact minimum distortion found by Reluplex in small MNIST networks while our algorithms are more than 10,000 times faster; (2) our methods deliver similar quality of bounds (the gap is within 35% and usually around 10%; sometimes our bounds are even better) for larger networks compared to the methods based on solving linear programming problems but our algorithms are 33-14,000 times faster; (3) our method is capable of solving large MNIST and CIFAR networks up to 7 layers with more than 10,000 neurons within tens of seconds on a single CPU core. ::: In addition, we show that, in fact, there is no polynomial time algorithm that can approximately find the minimum $\ell_1$ adversarial distortion of a ReLU network with a $0.99\ln n$ approximation ratio unless $\mathsf{NP}$=$\mathsf{P}$, where $n$ is the number of neurons in the network.


title: Non-Intrusive Signature Extraction for Major Residential Loads

abstract: This paper presents a technique to extract load signatures non-intrusively by using the smart meter data. Load signature extraction is different from load activity identification. It is a new and important problem to solve for the applications of non-intrusive load monitoring (NILM). For a target appliance whose signatures are to be extracted, the proposed technique first selects the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. It then applies a clustering algorithm to identify the authentic events of this appliance. In the third step, the operation cycles of appliances are estimated using an association algorithm. Finally, the electric signatures are extracted from these operation cycles. The results can have various applications. One is to create signature databases for the NILM applications. Another is for load condition monitoring. Validation results based on the data collected from three actual houses and a laboratory experiment have shown that the proposed method is a promising solution to the problem of load signature collection.


title: Adaptively secure threshold cryptography: Introducing concurrency, removing erasures

abstract: We put forward two new measures of security for threshold schemes secure in the adaptive adversary model: security under concurrent composition; and security without the assumption of reliable erasure. Using novel constructions and analytical tools, in both these settings, we exhibit efficient secure threshold protocols for a variety of cryptographic applications. In particular, based on the recent scheme by Cramer-Shoup, we construct adaptively secure threshold cryptosystems secure against adaptive chosen ciphertext attack under the DDH intractability assumption. Our techniques are also applicable to other cryptosystems and signature schemes, like RSA, DSS, and ElGamal. ::: ::: Our techniques include the first efficient implementation, for a wide but special class of protocols, of secure channels in erasure-free adaptive model. Of independent interest, we present the notion of a committed proof.


title: Essential Oils and Their Constituents Targeting the GABAergic System and Sodium Channels as Treatment of Neurological Diseases

abstract: Essential oils and the constituents in them exhibit different pharmacological activities, such as antinociceptive, anxiolytic-like, and anticonvulsant effects. They are widely applied as a complementary therapy for people with anxiety, insomnia, convulsion, pain, and cognitive deficit symptoms through inhalation, oral administration, and aromatherapy. Recent studies show that essential oils are emerging as a promising source for modulation of the GABAergic system and sodium ion channels. This review summarizes the recent findings regarding the pharmacological properties of essential oils and compounds from the oils and the mechanisms underlying their effects. Specifically, the review focuses on the essential oils and their constituents targeting the GABAergic system and sodium channels, and their antinociceptive, anxiolytic, and anticonvulsant properties. Some constituents target transient receptor potential (TRP) channels to exert analgesic effects. Some components could interact with multiple therapeutic target proteins, for example, inhibit the function of sodium channels and, at the same time, activate GABAA receptors. The review concentrates on perspective compounds that could be better candidates for new drug development in the control of pain and anxiety syndromes.


title: Estimation of IMU orientation using linear Kalman Filter based on correntropy criterion

abstract: Human motion is an important issue in various medical analyses. In this paper, the application of interest is estimating the orientation of the wrist in indoor and outdoor activities using low cost Inertial Measurement Sensor IMU. We present an orientation estimation using Kalman Filter based on Correntropy Criterion (KFCC). The proposed method uses only the acceleration data in the aim to reduce the computing load with accurate orientation estimation. Optical measurement system has been used to benchmark the performance of the proposed method. Moreover, the KFCC is evaluated in indoor and outdoor activities with static and dynamic motion conditions. The proposed method proves its efficiency and reliability against existing methods.


title: Large-Scale Visual Relationship Understanding

abstract: Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of 〈subject, relation, object〉 triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories.


title: Nomenclature of genetic movement disorders: Recommendations of the international Parkinson and movement disorder society task force

abstract: The system of assigning locus symbols to specify chromosomal regions that are associated with a familial disorder has a number of problems when used as a reference list of genetically determined disorders,including (I) erroneously assigned loci, (II) duplicated loci, (III) missing symbols or loci, (IV) unconfirmed loci and genes, (V) a combination of causative genes and risk factor genes in the same list, and (VI) discordance between phenotype and list assignment. In this article, we report on the recommendations of the International Parkinson and Movement Disorder Society Task Force for Nomenclature of Genetic Movement Disorders and present a system for naming genetically determined movement disorders that addresses these problems. We demonstrate how the system would be applied to currently known genetically determined parkinsonism, dystonia, dominantly inherited ataxia, spastic paraparesis, chorea, paroxysmal movement disorders, neurodegeneration with brain iron accumulation, and primary familial brain calcifications. This system provides a resource for clinicians and researchers that, unlike the previous system, can be considered an accurate and criterion-based list of confirmed genetically determined movement disorders at the time it was last updated.


title: Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

abstract: Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.


title: Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

abstract: Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.


title: A Systematic Review of Cloud Computing, Big Data and Databases on the Cloud

abstract: Cloud computing has emerged as an initiative which offers great promise in improving access to computational resources that would otherwise be unattainable due to sheer cost. While some cloud computing concepts date back to the 1950s, it is recent new cloud architecture and platforms that shape the way that resources are leased using service-based models. However, some confusion exists regarding the relationship between cloud-based models and challenges in managing big data. Some attempt to solve the problem by replacing and upgrading physical infrastructures, while others look to intelligent software to improve the scalability of data analytics. What also remains unclear is the definition and positioning of cloud-orientated paradigms. This is important to establish as it gets to the heart of where the underlying challenges exist in terms of availability, virtualisation, partitioning and distribution, scalability and elasticity, and performance bottlenecks when managing data. The goal of this systematic review is to provide insight into the current state of cloud computing and big data research. We find that challenges have been gaining momentum in this area from 2008 to 2013. In this study, using a systematic review framework, 129 publications are evaluated. We conclude that the current cloud-computing based frameworks are potentially neglecting fundamental database properties regarding atomicity and durability issues.


title: A hybrid community detection based on evolutionary algorithms in social networks

abstract: In social network analysis, community detection is an optimization problem of finding out partitions of maximum modularity density from a network. It is a NP-hard problem which can be done using evolutionary algorithms such as Particle Swarm Optimization, Cat Swarm Optimization, Genetic Algorithm and Genetic Algorithm with Simulated Annealing. In this work, we proposed an algorithm based on Genetic Algorithm with Simulated annealing for not being trapped into local optimal solution which is giving more better results. The main motto of our work is to get better communities with low computation cost. We tested our proposed algorithm on three standard datasets such as Zachary's Karate Club Dataset, American College Football and Dolphin Social Network Dataset. Experimental results demonstrate that our proposed algorithm outperforms state of the art approaches.


title: Beyond 3D culture models of cancer

abstract: The mechanisms underlying the spatiotemporal evolution of tumor ecosystems present a challenge in evaluating drug efficacy. In this Perspective, we address the use of three-dimensional in vitro culture models to delineate the dynamic interplay between the tumor and the host microenvironment in an effort to attain realistic platforms for assessing pharmaceutical efficacy in patients.


title: A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks

abstract: In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-N content-based recommendation scenario. Specifically, we propose a deep architecture which adopts Long Short Term Memory (LSTM) networks to jointly learn two embeddings representing the items to be recommended as well as the preferences of the user. Next, given such a representation, a logistic regression layer calculates the relevance score of each item for a specific user and we returns the top-N items as recommendations. In the experimental session we evaluated the effectiveness of our approach against several baselines: first, we compared it to other shallow models based on neural networks (as Word2Vec and Doc2Vec), next we evaluated it against state-of-the-art algorithms for collaborative filtering. In both cases, our methodology obtains a significant improvement over all the baselines, thus giving evidence of the effectiveness of deep learning techniques in content-based recommendation scenarios and paving the way for several future research directions.


Untangling wicked problems


title: Can We Predict a Riot? Disruptive Event Detection Using Twitter

abstract: In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to “real world” events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale “disruptive events,” smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases.


title: Euthanasia and assisted suicide for people with an intellectual disability and/or autism spectrum disorder: an examination of nine relevant euthanasia cases in the Netherlands (2012–2016)

abstract: Euthanasia and assisted suicide (EAS) have been legally possible in the Netherlands since 2001, provided that statutory due care criteria are met, including: (a) voluntary and well-considered request; (b) unbearable suffering without prospect of improvement; (c) informing the patient; (d) lack of a reasonable alternative; (e) independent second physician’s opinion. ‘Unbearable suffering’ must have a medical basis, either somatic or psychiatric, but there is no requirement of limited life expectancy. All EAS cases must be reported and are scrutinised by regional review committees (RTE). The purpose of this study was to investigate whether any particular difficulties arise when the EAS due care criteria are applied to patients with an intellectual disability and/or autism spectrum disorder. The 416 case summaries available on the RTE website (2012–2016) were searched for intellectual disability (6) and autism spectrum disorder (3). Direct content analysis was used on these nine cases. Assessment of decisional capacity was mentioned in eight cases, but few details given; in two cases, there had been uncertainty or disagreement about capacity. Two patients had progressive somatic conditions. For most, suffering was due to an inability to cope with changing circumstances or increasing dependency; in several cases, suffering was described in terms of characteristics of living with an autism spectrum disorder, rather than an acquired medical condition. Some physicians struggled to understand the patient’s perspective. Treatment refusal was a common theme, leading physicians to conclude that EAS was the only remaining option. There was a lack of detail on social circumstances and how patients were informed about their prognosis. Autonomy and decisional capacity are highly complex for patients with intellectual disabilities and difficult to assess; capacity tests in these cases did not appear sufficiently stringent. Assessment of suffering is particularly difficult for patients who have experienced life-long disability. The sometimes brief time frames and limited number of physician-patient meetings may not be sufficient to make a decision as serious as EAS. The Dutch EAS due care criteria are not easily applied to people with intellectual disabilities and/or autism spectrum disorder, and do not appear to act as adequate safeguards.


title: An empirical analysis of the distribution of unit test smells and their impact on software maintenance

abstract: Unit testing represents a key activity in software development and maintenance. Test suites with high internal quality facilitate maintenance activities, such as code comprehension and regression testing. Several guidelines have been proposed to help developers write good test suites. Unfortunately, such rules are not always followed resulting in the presence of bad test code smells (or simply test smells). Test smells have been defined as poorly designed tests and their presence may negatively affect the maintainability of test suites and production code. Despite the many studies that address code smells in general, until now there has been no empirical evidence regarding test smells (i) distribution in software systems nor (ii) their impact on the maintainability of software systems. This paper fills this gap by presenting two empirical studies. The first study is an exploratory analysis of 18 software systems (two industrial and 16 open source) aimed at analyzing the distribution of test smells in source code. The second study, a controlled experiment involving twenty master students, is aimed at analyzing whether the presence of test smells affects the comprehension of source code during software maintenance. The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.


title: Bayesian modeling of dependency trees using hierarchical Pitman-Yor priors

abstract: In this paper, we describe two hierarchical Bayesian models for dependency trees. First, we show that Eisner’s classic generative dependency model [1996] can be substantially improved by (a) using a hierarchical Pitman-Yor process as a prior over the distribution over dependents of a word, and (b) sampling the model hyperparameters (section 3). These changes alone yield a signicant increase in parse accuracy over Eisner’s model. Second, we present a Bayesian dependency parsing model in which latent state variables mediate the relationships between words and their dependents. This model clusters dependencies into states using a similar approach to that employed by Bayesian topic models when clustering words into topics (section 4). The inferred states have a syntactic avor and lead to modestly improved accuracy when substituted for part-of-speech tags in the parsing model.


title: An Open Framework for Human-Like Autonomous Driving Using Inverse Reinforcement Learning

abstract: In this paper, a global optimization methodology is described to pre-design an electric vehicle powertrain in order to find the best compromises between components. The modeled system includes a transmission, an electric machine, an inverter and a battery pack. The challenge is to find the dedicated formulations, with the vehicle performance requirements, electric range, and cost calculation that include the whole system without exploding computational time. Bi-objective, range/costs, optimizations with performance constraints are performed to find the potential gain with the system model.


title: Synthesized Policies for Transfer and Adaptation across Tasks and Environments

abstract: The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simultaneously transfer across both environments (ENV) and tasks (TASK), probably more importantly, by learning from only sparse (ENV, TASK) pairs out of all the possible combinations. We propose a novel compositional neural network architecture which depicts a meta rule for composing policies from the environment and task embeddings. Notably, one of the main challenges is to learn the embeddings jointly with the meta rule. We further propose new training methods to disentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies. Experiments on GridWorld and Thor, of which the agent takes as input an egocentric view, show that our approach gives rise to high success rates on all the (ENV, TASK) pairs after learning from only 40\% of them.


title: Election fraud and privacy related issues: Addressing electoral integrity

abstract: Nowadays, in a highly computerized system, people have tendency to have not expect privacy to be maintained and continue in its current form if individual control is required and monitored. Especially, in contemporary campaigns for higher office, the candidates often use mass media to deliver political message or having certain campaign developed by expert in the efficient use of providing personal information for the purpose of influencing the voters while some of them may be hidden or exploited by certain parties. Practically, it is impossible to have total control over personal information, once it has been collected and dispersed but it is possible to have privacy without control and vice versa. In election, huge amount of data is stored and transfer from multiple location to other numerous or single location to facilitate tabulation and calculation analysis. Thus, it is critical to conduct monitoring process to prevent crime such as theft, fraud and illegal use, to prevent unauthorized disclosure of confidential information, to comply with the obligation, law and regulation in avoiding discrimination, to maintain productivity and quality of service and to ensure integrity of the systems. This paper explores the election fraud and privacy related issues occurred during the election period in various countries to address the electoral integrity in Indonesia.


Bayesian Aggregation for Hierarchical Classification


title: MobiContext: A Context-Aware Cloud-Based Venue Recommendation Framework

abstract: In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement. However, performance of most of the existing collaborative filtering-based recommendation system suffers due to the challenges, such as: (a) cold start, (b) data sparseness, and (c) scalability. Moreover, recommendation problem is often characterized by the presence of many conflicting objectives or decision variables, such as users’ preferences and venue closeness. In this paper, we proposed MobiContext , a hybrid cloud-based bi-objective recommendation framework (BORF) for mobile social networks. The MobiContext utilizes multi-objective optimization techniques to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data preprocessing by using the Hub-Average (HA) inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for scalar optimization and an evolutionary algorithm (NSGA-II) is applied for vector optimization to provide optimal suggestions to the users about a venue. The results of comprehensive experiments on a large-scale real dataset confirm the accuracy of the proposed recommendation framework.


title: Facebook in higher education promotes social but not academic engagement.

abstract: Although there is evidence that academically successful students are engaged with their studies, it has proved difficult to define student engagement clearly. Student engagement is commonly construed as having two dimensions, social and academic. The rapid adoption of social media and digital technologies has ensured increasing interest in using them for improving student engagement. This paper examines Facebook usage among a first year psychology student cohort and reports that although the majority of students (94%) had Facebook accounts and spent an average of one hour per day on Facebook, usage was found to be predominantly social. Personality factors influenced usage patterns, with more conscientious students tending to use Facebook less than less conscientious students. This paper argues that, rather than promoting social engagement in a way that might increase academic engagement, it appears that Facebook is more likely to operate as a distracting influence.


title: End of Moore's law: thermal (noise) death of integration in micro and nano electronics

abstract: The exponential growth of memory size and clock frequency in computers has a great impact on everyday life. The growth is empirically described by Moore’s law of miniaturization. Physical limitations of this growth would have a serious impact on technology and economy. A thermodynamical effect, the increasing thermal noise voltage (Johnson–Nyquist noise) on decreasing characteristic capacitances, together with the constrain of using lower supply voltages to keep power dissipation manageable on the contrary of increasing clock frequency, has the potential to break abruptly Moore’s law within 6–8 years, or earlier.  2002 Elsevier Science B.V. All rights reserved.


title: An introduction to random indexing

abstract: Word space models enjoy considerable attention in current research on semantic indexing. Most notably, Latent Semantic Analysis/Indexing (LSA/LSI; Deerwester et al., 1990, Landauer & Dumais, 1997) has become a household name in information access research, and deservedly so; LSA has proven its mettle in numerous applications, and has more or less spawned an entire research field since its introduction around 1990. Today, there is a rich flora of word space models available, and there are numerous publications that report exceptional results in many different applications, including information retrieval (Dumais et al., 1988), word sense disambiguation (Schutze, 1993), various semantic knowledge tests (Lund et al., 1995, Karlgren & Sahlgren, 2001), and text categorization (Sahlgren & Karlgren, 2004). This paper introduces the Random Indexing word space approach, which presents an efficient, scalable and incremental alternative to standard word space methods. The paper is organized as follows: in the next section, we review the basic word space methodology. We then look at some of the problems that are inherent in the basic methodology, and also review some of the solutions that have been proposed in the literature. In the final section, we introduce the Random Indexing word space approach, and briefly review some of the experimental results that have been achieved with Random Indexing.


Multi-Task Feature Learning Via Efficient 2 , 1-Norm Minimization


Implementation of Image Processing Algorithms on FPGA


title: Language Independent Single Document Image Super-Resolution using CNN for improved recognition

abstract: Recognition of document images have important applications in restoring old and classical texts. The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text. The image enhancement and quality improvement constitute important steps as subsequent recognition depends upon the quality of the input image. There are scenarios when high resolution images are not available and our experiments show that the OCR accuracy reduces significantly with decrease in the spatial resolution of document images. Thus the only option is to improve the resolution of such document images. The goal is to construct a high resolution image, given a single low resolution binary image, which constitutes the problem of single image super-resolution. Most of the previous work in super-resolution deal with natural images which have more information-content than the document images. Here, we use Convolution Neural Network to learn the mapping between low and the corresponding high resolution images. We experiment with different number of layers, parameter settings and non-linear functions to build a fast end-to-end framework for document image super-resolution. Our proposed model shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images, resulting in a 3 % improvement of word level accuracy by the OCR. It takes less time than the recent sparse based natural image super-resolution technique, making it useful for real-time document recognition applications.


title: Sampling algorithms for stochastic graphs

abstract: Stochastic graph as a graph model for complex social networks.Four sampling algorithms for stochastic graphs in which edge weights are random variables.Analyze complex networks using stochastic network measures and sampling algorithms.Study the performance of the sampling algorithms on the stochastic networks. Recently, there has been growing interest in social network analysis. Graph models for social network analysis are usually assumed to be a deterministic graph with fixed weights for its edges or nodes. As activities of users in online social networks are changed with time, however, this assumption is too restrictive because of uncertainty, unpredictability and the time-varying nature of such real networks. The existing network measures and network sampling algorithms for complex social networks are designed basically for deterministic binary graphs with fixed weights. This results in loss of much of the information about the behavior of the network contained in its time-varying edge weights of network, such that is not an appropriate measure or sample for unveiling the important natural properties of the original network embedded in the varying edge weights. In this paper, we suggest that using stochastic graphs, in which weights associated with the edges are random variables, can be a suitable model for complex social network. Once the network model is chosen to be stochastic graphs, every aspect of the network such as path, clique, spanning tree, network measures and sampling algorithms should be treated stochastically. In particular, the network measures should be reformulated and new network sampling algorithms must be designed to reflect the stochastic nature of the network. In this paper, we first define some network measures for stochastic graphs, and then we propose four sampling algorithms based on learning automata for stochastic graphs. In order to study the performance of the proposed sampling algorithms, several experiments are conducted on real and synthetic stochastic graphs. The performances of these algorithms are studied in terms of Kolmogorov-Smirnov D statistics, relative error, Kendall's rank correlation coefficient and relative cost.


title: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition - Part 11

abstract: For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms.


title: Intelligent Car Braking System with Collision Avoidance and ABS

abstract: This paper provides an efficient way to design an automatic car braking system using Fuzzy Logic. The system could avoid accidents caused by the delays in driver reaction times at critical situations. The proposed Fuzzy Logic Controller is able to brake a car when the car approaches for an obstacle in the very near range. Collision avoidance is achieved by steering the car if the obstacle is in the tolerable range and hence there is no necessity to apply the brakes. Another FLC (which is cascaded with the first FLC for collision avoidance) implements the Anti-lock Braking capability during heavy braking condition. Thus the system is made intelligent since it could take decisions automatically depending upon the inputs from ultrasonic sensors. A simulative study is done using MATLAB and LabVIEW software. The results obtained by the simulation model are compared with the existing system and the proposed model conveys a satisfactory result which has high consumer acceptance. ATMega controller is used for implementation of the proposed system.


title: Generalized Knight's Tour Problem and Its Solutions Algorithm

abstract: Knight's tour matrix, the solution of knight's tour problem (KTP), can be applied to digital image information security. Inspired by the solution of KTP and the require of information security, in this paper, the KTP is generalized to m-dimensional space to get generalized knight's tour problem (GKTP). We give the GKTP's mathematic definitions and investigate its solutions algorithm. When (x1, x2 ,middotmiddot,middot, xm) is a beginning point of the generalized m-dimensional knight want to visit, by focusing on its move, we show that nm chessboard does not have a knight tour if n and x1+x2+middotmiddotmiddotxm are odd. Based on this and minimal outlet number, an intelligence algorithm is proposed to find solutions of the m-dimensional GKTP. The proposed algorithm can also be applied to generalized 2-dimensional [a 1, a2]-knight's tour problem. The experimental results show that presented algorithm is able to find quickly the generalized knight's tour matrix (KTM), namely the solutions of GKTP


Biologically Inspired Algorithms for Financial Modelling


title: Generalization Error in Deep Learning

abstract: Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.


title: Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes

abstract: Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images. On the one hand, Generative Adversarial Networks (GANs) have contributed a highly effective adversarial learning procedure, but still suffer from stability issues. On the other hand, Conditional Variational Auto-Encoders (CVAE) models provide a sound way of conditional modeling but suffer from mode-mixing issues. Therefore, recent work has turned back to simple and stable regression models that are effective at generation but give up on the sampling mechanism and the latent code representation. We propose a novel and efficient stochastic regression approach with latent drop-out codes that combines the merits of both lines of research. In addition, a new training objective increases coverage of the training distribution leading to improvements over the state of the art in terms of accuracy as well as diversity.


title: Simultaneous Edge Alignment and Learning

abstract: Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to considerable improvements in this area. Many state of the art edge detection models are learned with fully convolutional networks (FCNs). However, FCN-based edge learning tends to be vulnerable to misaligned labels due to the delicate structure of edges. While such problem was considered in evaluation benchmarks, similar issue has not been explicitly addressed in general edge learning. In this paper, we show that label misalignment can cause considerably degraded edge learning quality, and address this issue by proposing a simultaneous edge alignment and learning framework. To this end, we formulate a probabilistic model where edge alignment is treated as latent variable optimization, and is learned end-to-end during network training. Experiments show several applications of this work, including improved edge detection with state of the art performance, and automatic refinement of noisy annotations.


title: Plagiarism Detection in Programming Assignments Using Deep Features

abstract: This paper proposes a method for detecting plagiarism in source-codes using deep features. The embeddings for programs are obtained using a character-level Recurrent Neural Network (char-rnn), which is pre-trained on Linux Kernel source-code. Many popular plagiarism detection tools are based on n-gram techniques at syntactic level. However, these approaches to plagiarism detection fail to capture long term dependencies (non-contiguous interaction) present in the source-code. Contrarily, the proposed deep features capture non-contiguous interaction within n-grams. These are generic in nature and there is no need to fine-tune the char-rnn model again to program submissions from each individual problem-set. Our experiments show the effectiveness of deep features in the task of classifying assignment program submissions as copy, partial-copy and non-copy. Comparing our proposed features with handcrafted features (source-code metrics and textual features), we report f1-score improvement of 9.5% for binary classification and 5% for three-way classification tasks respectively.


title: Learning to Predict Code-Switching Points

abstract: Predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech recognition systems and syntactic analyzers. We present in this paper exploratory results on learning to predict potential code-switching points in Spanish-English. We trained different learning algorithms using a transcription of code-switched discourse. To evaluate the performance of the classifiers, we used two different criteria: 1) measuring precision, recall, and F-measure of the predictions against the reference in the transcription, and 2) rating the naturalness of artificially generated code-switched sentences. Average scores for the code-switched sentences generated by our machine learning approach were close to the scores of those generated by humans.


title: Multi Bit Plane Image Steganography

abstract: This paper addresses a novel steganography method for images. Most statistical steganalysis algorithms are strong to defeat previous steganography algorithms. RS steganalysis and pixel difference histogram analysis are two well-known statistical steganalysis algorithms which detect non-random changes caused by embedding a secret message into cover image. In this paper, we first explain how two steganalysis algorithms exploit the effect of the non-random changes and then propose a new steganography method that avoids the non-random changes to evade statistical analysis methods. For this purpose, we adjust the embedding process to be more adaptive to cover image by considering embedding in Gray code bit planes, not natural binary bit planes, of cover images, and two parameters: (1) similarity threshold for selecting non-flat area in lower bit planes, and (2) size of flat blocks n x n in embedding bit planes. Experimental results show that the secret messages embedded by our method are undetectable under RS steganalysis and pixel difference histogram analysis.


title: A survey on DNA and image steganography

abstract: Steganography is the classical technique of securing the confidential data. The data is hidden in an object called cover medium. The encoder (sender) and the decoder (receiver) only have the knowledge of secret data hidden in the cover object. An image is a cover object that hides the secret data. DNA is another cover object which has a huge data storage capacity and it hides a very large secret data by replacing the bases which are of huge numbers to a particular DNA. In this study, the various techniques that are applied for hiding a secret message in different cover objects like image and DNA are summarized and the integrity of the secret message in various mediums are discussed. The combination of DNA and Images used together to hide a secret message or data is known as dual cover steganography; here the cover objects are DNA and Image. This work also provides all the existing methods on Image steganography, DNA-based steganography and Dual Cover steganography, their merits, and demerits.


title: Coupled Item-based Matrix Factorization

abstract: The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations. To solve these challenges, the objective item attributes are incorporated as complementary information. However, most of the existing methods for inferring the relationships between items assume that the attributes are “independently and identically distributed (iid)”, which does not always hold in reality. In fact, the attributes are more or less coupled with each other by some implicit relationships. Therefore, in this paper we propose an attribute-based coupled similarity measure to capture the implicit relationships between items. We then integrate the implicit item coupling into MF to form the Coupled Item-based Matrix Factorization (CIMF) model. Experimental results on two open data sets demonstrate that CIMF outperforms the benchmark methods.


title: A new speaker change detection method in a speaker identification system for two-speakers segmentation

abstract: Speaker change detection is done in many speaker and speech identification applications that the speech is from two speakers. However, the standard metric-based methods performance is not suitable and stable owing to the amid window distance calculation stability. Therefore, a new method is proposed to improve the stability and enhance the performance of the system according to speakers' characteristics using between window correlations. Moreover, reference speaker models set that shows the space of the entire speaker model are trained in this approach. A metric is defined as the between window correlation of scores likelihood vectors versus the reference models. The Peak and Valley information and gender information are also used. In this paper, we look at telephone conversations where it is known a priori that there are two speakers, but the identity of the speakers is not known. Experiments over Farsdat Database show better performance In comparison with the GLR and the BIC approaches. This new approach has more effect rather than the GLR and the BIC approaches in the broader value of defined thresholds.


title: Invariant Manifold Approach for the Stabilization of Nonholonomic Chained Systems: Application to a Mobile Robot

abstract: In this paper it is shown that a class ofn-dimensional nonholonomic chained systems can bestabilized using the invariant manifold approach. First, we derive aninvariant manifold for this class of systems and we show that, once onit, all the closed-loop trajectories tend to the origin under a linearsmooth time-invariant state feedback. Thereafter, it is shown that thismanifold can be made attractive by means of a discontinuoustime-invariant state feedback. Finally, a mobile robot is taken as anexample demonstrating the effectiveness of our study.


title: Scenario-based interactive UI design

abstract: Clearly picturing user behavior is one of the key requirements when designing successful interactive software. However, covering all possible user behaviors with one UI is a complex challenge. The Scenario-based Interactive UI Design tool is designed to support the characterization of user behavior based on scenarios and then using the information in UI design. Scenarios make it easy to understand and share user behavior even if we have little design knowledge. However, they have two big weaknesses; 1) integrating several scenarios in one UI is difficult, even if we can create appropriate scenarios, 2) maintaining the links between scenarios and the UI is a heavy task in iterative design. Our tool solves the above problems through its hierarchical scenario structure and visualized overview of scenarios. It enhances the designer's skill in writing scenarios and designing UIs smoothly and easily.


title: Video from a single coded exposure photograph using a learned over-complete dictionary

abstract: Cameras face a fundamental tradeoff between the spatial and temporal resolution - digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution.


title: Data mining for credit card fraud: A comparative study

abstract: Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly due to the lack of available data for research. This paper evaluates two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, as part of an attempt to better detect (and thus control and prosecute) credit card fraud. The study is based on real-life data of transactions from an international credit card operation.


title: Baseline holder for CMOS readout circuits with feedback extension to the charge preamplifier

abstract: We are developing a CMOS circuit to be used with silicon drift detectors (SDDs) for high-resolution and high peak stability X-ray spectroscopy. The single analog channel of the circuit is composed by a low-noise charge preamplifier and by a 6th order semiGaussian shaping amplifier. The preamplifier operates with the input JFET and the feedback capacitor directly integrated on the detector itself. The discharge of the leakage current is performed by an avalanche mechanism on the drain-gate junction of the JFET. A low-frequency current-mode feedback loop adjusts the operating point of JFET drain with respect to background and leakage current variations. Because JFET drain, preamplifier output and shaper output are all DC coupled, any variation of the drain voltage causes a baseline shift at the shaper output. We have designed a baseline holder (BLH) which senses the baseline voltage shifts at the output of the circuit and provides a feedback loop back to the preamplifier to stabilize the output baseline. The BLH feedback signal is fed into a suitable point of the preamplifier in order to allow the JFET drain to be adjusted for the discharge of the leakage current and at the same time minimizing the shift of the preamplifier and shaper output voltages. The impact of the BLH on the noise performances of the whole circuit has been verified to be negligible. Baseline shifts limited to less than 1 mV have been obtained.


Network Effects and Data Breaches: Investigating the Impact of Information Sharing and the Cyber Black Market


title: Current sensor in PCB technology

abstract: A novel DC/AC current sensor works on the fluxgate principle. The core in the form of a 7/10 mm ring made of electrodeposited Permalloy is sandwiched into the middle of the printed circuit board (PCB), while the sensor excitation winding is also integrated in the copper layers of the PCB. In order to lower the sensor power consumption, the excitation winding was tuned by a parallel capacitor. The peak-peak/rms ratio of 5.2 was achieved for the excitation frequency of 25 kHz. The open-loop sensor has 100 mA/A sensitivity characteristics with linearity error of 10% and hysteresis below 0.1% in the 1A range.


title: Text-based communication influences self-esteem more than face-to-face or cellphone communication

abstract: Diary data was collected for 76 participants over 6day intervals.3649 social interactions were randomly sampled across communication channels.Self-esteem was measured at baseline and follow-up.Face-to-face communication was the most frequent form of communication.Text-based communication was most influential on self-esteem. Meaningful social interactions are positively associated with improvements in self-esteem, but this phenomenon has largely been unexplored in digital media despite the prevalence of new, text-based communication (e.g. Facebook, texting, email, etc.). To address this gap in the literature the frequency and quality, or meaningfulness, of communication was measured in mediated and non-mediated channels across a random sample of 3649 social interactions using Experience Sampling Methods. Results revealed that most communication took place face-to-face (62%), with less text-based (about 22%) and cell phone voice (14%) communication. Meaningful face-to-face and text-based communication were associated with changes in self-esteem according to a marginally significant and significant finding, respectively. Text-based communication was more important for self-esteem than face-to-face or phone communication, which is consistent with research on the magnifying effect of text-based communication on interpersonal processes. According to the Internet enhanced self-disclosure hypothesis, the psychological benefits of text-based communication stems from enhanced self-disclosure, which is also supported in the data. Additional work is needed to better understand the mechanisms underlying the positive relationship between meaningful text-based interactions and self-esteem, but findings point to the important role of digital communication for psychological health.


title: Brain-Computer Interfaces Using Electrocorticographic Signals

abstract: Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single- neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electro-corticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG- based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.


title: Segmented electrodes electrodynamic destruction in metallized film capacitors

abstract: This paper presents the results of the experimental investigation and numerical simulation of electrodynamic destruction of metallized film capacitors electrodes during self-healing process. The degradation of intersegment gates of Al metallized patterned electrodes under high current load was studied. The numerical simulation was conducted by means of COMSOL Multiphysics software. The model takes into account the heat flow from metal layer to polymer film since it has significant influence on destruction process dynamics. The simulation results are in good agreement with the experimental data.


title: Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

abstract: Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.


title: Towards Real-Time People Recognition on Aerial Imagery Using Convolutional Neural Networks

abstract: People recognition in aerial imagery is a complex problem since it involves cameras angles, which are subject to six degrees of freedom. Traditional classifiers used in pattern matching algorithms are not robust enough to deal with such issues. Thus it is necessary to use other techniques such as deep learning methods, e.g. Convolutional Neural Networks (CNN), in order to cope with such a complex problem. However, in spite of providing potentially better recognition results, deep learning methods demand greater processing time, hindering its use on on-board embedded systems of small Unmanned Aerial Vehicles (UAV). This work is a step towards real-time (and possibly on-board) people recognition on aerial imagery. We propose a new approach based on CNN for real-time people recognition on aerial images obtained from cameras attached to small UAVs. This approach combines images from a low-cost thermal camera to detect candidate objects and CNN for the classification task. We have compared our approach with two traditional pattern matching approaches: saliency map detection and cascade classifiers. The obtained results show that, by using CNN, higher rates of correct classification within 95% of precision are obtained. These results have been obtained within real-time processing of 1.08 fps in the worst case on a hardware without any GPU processing.


title: IPM Machine Drive Design and Tests for an Integrated Starter–Alternator Application

abstract: This paper deals with an integrated starter-alternator (ISA) drive able to satisfy the requirements of exhibiting high torque, driving start, and a wide constant-power speed range driving generation. It illustrates the design, analysis and test of an interior permanent magnet (IPM) motor and drive for an ISA, developed for the IEEE International Future Energy Challenge (IFEC) 2007. The paper reports the design of the stator and rotor geometry, the results of finite elements computations, the description of control algorithms and the experimental results of the tests on the prototype.


title: Size miniaturized fractal antenna for 2.5GHz application

abstract: In this paper fractal technique is analysed for antenna miniaturization. The space filling property of fractals is used for size reduction of patch antenna. By the application of fractal technique, the effective electrical length of the antenna is increased for the same resonant frequency. However, the fractal antenna has the same radiation pattern and input impedance as a conventional antenna. In our work, we use the Koch fractal for size reduction of circular patch antenna. 45% reduction in operating frequency and 21% reduction in size has been achieved in proposed design. Analysis of antenna is done in CST Microwave Studio.


title: HIGH PERFORMANCE MULTI-SECTION CORRUGATED SLOT-COUPLED DIRECTIONAL COUPLERS

abstract: In this paper a robust technique for the design of high performance directional couplers is proposed. It combines the advantages of wiggly coupled lines and slot-coupled lines but overcomes their main limitations. The key to this novel technique is a new corrugated slot that allows perfect compensation of the even and odd mode phase velocities and can be easily designed using Bloch- Floquet theory, yielding outstanding performance. To demonstrate the validity of the proposed technique, the design of two difierent wideband directional couplers is presented. The flrst design consists of a 10dB asymmetric directional coupler with a one decade bandwidth (1.2{ 12GHz) that exhibits a coupling accuracy of 10 § 0:6dB, a return loss better than 23dB and an isolation better than 28dB across the complete frequency band. The second design consists of a symmetric quadrature hybrid that operates over the complete UWB band (3.1 to 10.6GHz) showing an amplitude and phase imbalance between the output ports lower than §0:5dB and §0:7 - , respectively.


title: Secure Routing for Mobile Ad Hoc Networks

abstract: Buttyan found out a security flaw in Ariadne (Y.C. Hu, A. Perrig, andD. B. Johnson, "Ariadne: a secure on-demand routing protocol for ad hoc networks," in Proc. of the Eighth ACM hitl Conf. on Mobile Computing and Networking (MOBI.COM 2002), pp.23-28. Atlanta, GA.2002.) and proposed a secure routing protocol, EndairA (L. Buttyan, and I. Vajda, "Towards provable security for ad hoc routing protocols", in Proc. of the 2nd ACM Workshop on Security of ad hoc and Sensor Networks, 2005 and G. Acs, L. Buttyan, and I. Vajda, "Provably secure on-demand source routing in mobile ad hoc networks," IEEE Transactions on Mobile Computing, Vol. 5, No. 11, November 2006.), with the ability to resist active-1-1 attacks. But unfortunately we discover an as yet unknown active-0- 1 attack which we call man-in-the-middle attack and EndairA couldn 't resist. Accordingly we propose a new secure routing protocol, EndairALoc. Analysis shows that EndairALoc can resist not only active-1-1 attacks but also the wormhole attack. Furthermore EndairALoc uses pairwise secret keys instead of public keys used in EndairA. Compared with EndairA, EndairALoc can save more energy in the routing establishment.


title: Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces

abstract: Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and invivo, in two downstream text classification tasks.