title: An AI Approach to Automatic Natural Music Transcription

text: Automatic music transcription (AMT) remains a fundamental and difficult problem in music information research, and current music transcription systems are still unable to match human performance. AMT aims to automatically generate a score representation given a polyphonic acoustical signal. In our project, we approach the AMT problem on two fronts: acoustic modeling to identify pitches from a frame of audio, and establishing a score generation model to convert exact piano roll representations of audio into more ‘natural’ sheet music. We build an end to end pipeline that aims to convert .wav classical piano audio files into a ‘natural’ score representation.


title: COMPARISON OF LOSSLESS DATA COMPRESSION ALGORITHMS FOR TEXT DATA

text: Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.


title: A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

text: We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted partof-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.


title: Walking on foot to explore a virtual environment with uneven terrain

text: In this work we examine people's ability to maintain spatial awareness of an HMD--based immersive virtual environment when the terrain of the virtual world does not match the real physical world in which they are locomoting. More specifically, we examine what happens when a subject explores a hilly virtual environment while physically walking in a flat room. In our virtual environments, subjects maintain their height (or distance to the ground plane) at all times. In the experiment described in this work, we directly compare spatial orientation in a flat virtual environment to two variations of a hilly virtual environment. Interestingly, we find that subjects' spatial awareness of the environment is improved with the addition of uneven terrain.


title: The emergence of lncRNAs in cancer biology.

text: The discovery of numerous noncoding RNA (ncRNA) transcripts in species from yeast to mammals has dramatically altered our understanding of cell biology, especially the biology of diseases such as cancer. In humans, the identification of abundant long ncRNA (lncRNA) >200 bp has catalyzed their characterization as critical components of cancer biology. Recently, roles for lncRNAs as drivers of tumor suppressive and oncogenic functions have appeared in prevalent cancer types, such as breast and prostate cancer. In this review, we highlight the emerging impact of ncRNAs in cancer research, with a particular focus on the mechanisms and functions of lncRNAs.


title: Action Reaction Learning : Analysis and Synthesis of Human Behaviour

text: We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically uncovers a mapping between gestures from one human participant (an action) and a subsequent gesture (a reaction) from another participant. A probabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The system drives a graphical interactive character which probabilistically predicts the most likely response to the user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user.


title: Vehicle licence plate recognition using super-resolution technique

text: Due to the development of economy and technology, the people's demands on cars are growing and so are the problems, such as finding stolen car, banning violation and parking lot management. It will be time-consuming and low-efficiency if we do those jobs only by human because of the limitation of human being's concentration. Therefore, it has been a popular topic to develop intelligent monitoring system under new video technology within a decade. There are still many rooms for future development and application in the car license detection and recognition fields. For finding stolen car, we can integrate car license detection system and road monitoring system to analyze the videos and trace the objects, so we can gain high-efficiency and low-cost results. For parking lot management system, we can have the result of access management and automatic charge to reduce the human resource through car license recognition system. Automated toll of highway can be done through car license recognition system as well. Because the car license recognition system is mostly applied to security monitoring field and business purposes, the demand of the accuracy is quite strict. There are causes which make the inaccuracy of the license recognition, such as the lack of video resolution, too small license due to the distance and the light and shadow. We will discuss the license recognition system and how to use apply the super-resolution to overcome those above problems.


PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.


title: Adam: A Method for Stochastic Optimization

text: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.


title: Probabilistic Principal Component Analysis

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: Stochastic Variational Inference

text: We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.


title: PILCO: A Model-Based and Data-Efficient Approach to Policy Search

text: In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.


title: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

text: We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.


title: A 14-mW 6.25-Gb/s Transceiver in 90-nm CMOS

text: This paper describes a 6.25-Gb/s 14-mW transceiver in 90-nm CMOS for chip-to-chip applications. The transceiver employs a number of features for reducing power consumption, including a shared LC-PLL clock multiplier, an inductor-loaded resonant clock distribution network, a low- and programmable-swing voltage-mode transmitter, software-controlled clock and data recovery (CDR) and adaptive equalization within the receiver, and a novel PLL-based phase rotator for the CDR. The design can operate with channel attenuation of -15 dB or greater at a bit-error rate of 10-15 or less, while consuming less than 2.25 mW/Gb/s per transceiver.


title: Limbic-cortical dysregulation: a proposed model of depression.

text: A working model of depression implicating failure of the coordinated interactions of a distributed network of limbic-cortical pathways is proposed. Resting state patterns of regional glucose metabolism in idiopathic depressed patients, changes in metabolism with antidepressant treatment, and blood flow changes with induced sadness in healthy subjects were used to test and refine this hypothesis. Dorsal neocortical decreases and ventral paralimbic increases characterize both healthy sadness and depressive illness; concurrent inhibition of overactive paralimbic regions and normalization of hypofunctioning dorsal cortical sites characterize disease remission. Normal functioning of the rostral anterior cingulate, with its direct connections to these dorsal and ventral areas, is postulated to be additionally required for the observed reciprocal compensatory changes, since pretreatment metabolism in this region uniquely predicts antidepressant treatment response. This model is offered as an adaptable framework to facilitate continued integration of clinical imaging findings with complementary neuroanatomical, neurochemical, and electrophysiological studies in the investigation of the pathogenesis of affective disorders.


title: Image Steganography Using LSB and Edge – Detection Technique

text: 217  Abstract-Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using " Edge detection Filter ". In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way.


title: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.

text: We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5' to 3' decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.


title: An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds

text: We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.


title: Clustal W and Clustal X version 2.0

text:
SUMMARY
The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems.

AVAILABILITY
The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/


title: Evaluating three treatments for borderline personality disorder: a multiwave study.

text:
OBJECTIVE
The authors examined three yearlong outpatient treatments for borderline personality disorder: dialectical behavior therapy, transference-focused psychotherapy, and a dynamic supportive treatment.

METHOD
Ninety patients who were diagnosed with borderline personality disorder were randomly assigned to transference-focused psychotherapy, dialectical behavior therapy, or supportive treatment and received medication when indicated. Prior to treatment and at 4-month intervals during a 1-year period, blind raters assessed the domains of suicidal behavior, aggression, impulsivity, anxiety, depression, and social adjustment in a multiwave study design.

RESULTS
Individual growth curve analysis revealed that patients in all three treatment groups showed significant positive change in depression, anxiety, global functioning, and social adjustment across 1 year of treatment. Both transference-focused psychotherapy and dialectical behavior therapy were significantly associated with improvement in suicidality. Only transference-focused psychotherapy and supportive treatment were associated with improvement in anger. Transference-focused psychotherapy and supportive treatment were each associated with improvement in facets of impulsivity. Only transference-focused psychotherapy was significantly predictive of change in irritability and verbal and direct assault.

CONCLUSIONS
Patients with borderline personality disorder respond to structured treatments in an outpatient setting with change in multiple domains of outcome. A structured dynamic treatment, transference-focused psychotherapy was associated with change in multiple constructs across six domains; dialectical behavior therapy and supportive treatment were associated with fewer changes. Future research is needed to examine the specific mechanisms of change in these treatments beyond common structures.


title: A Corpus and Model Integrating Multiword Expressions and Supersenses

text: This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.


title: The neural basis of rationalization: cognitive dissonance reduction during decision-making.

text: People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.


title: Sparse Non-rigid Registration of 3D Shapes

text: Non-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an ℓ2-norm regularization on the local transformation differences. However, the ℓ2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodness-of-fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an ℓ1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise.


title: THE STRUCTURE ( S ) OF PARTICLE VERBS

text: • complex heads are formed either in the lexicon (cf. Booij 1990 , Johnson 1991 , Koizumi 1993, Neeleman 1994, Neeleman & Weerman 1993, Stiebels 1996, Stiebels & Wunderlich 1994, Wiese 1996, Ackerman & Webelhuth 1998 ) or by some kind of overt or covert incorporation of the particle into the verb (cf. van Riemsdijk 1978, Baker 1988, Koopman 1995, Olsen 1995a, 1995b, Zeller 1997a, 1997b, 1998) • small clause structure (cf. Kayne 1985, Guéron 1987 , 1990, Hoekstra 1988, Grewendorf 1990, Bennis 1992, Mulder 1992 , den Dikken 1992, 1995 , Zwart 1997 among others) 1


title: State of the Art in Stereoscopic and Autostereoscopic Displays

text: Underlying principles of stereoscopic direct-view displays, binocular head-mounted displays, and autostereoscopic direct-view displays are explained and some early work as well as the state of the art in those technologies are reviewed. Stereoscopic displays require eyewear and can be categorized based on the multiplexing scheme as: 1) color multiplexed (old technology but there are some recent developments; low-quality due to color reproduction and crosstalk issues; simple and does not require additional electronics hardware); 2) polarization multiplexed (requires polarized light output and polarization-based passive eyewear; high-resolution and high-quality displays available); and 3) time multiplexed (requires faster display hardware and active glasses synchronized with the display; high-resolution commercial products available). Binocular head-mounted displays can readily provide 3-D, virtual images, immersive experience, and more possibilities for interactive displays. However, the bulk of the optics, matching of the left and right ocular images and obtaining a large field of view make the designs quite challenging. Some of the recent developments using unconventional optical relays allow for thin form factors and open up new possibilities. Autostereoscopic displays are very attractive as they do not require any eyewear. There are many possibilities in this category including: two-view (the simplest implementations are with a parallax barrier or a lenticular screen), multiview, head tracked (requires active optics to redirect the rays to a moving viewer), and super multiview (potentially can solve the accommodation-convergence mismatch problem). Earlier 3-D booms did not last long mainly due to the unavailability of enabling technologies and the content. Current developments in the hardware technologies provide a renewed interest in 3-D displays both from the consumers and the display manufacturers, which is evidenced by the recent commercial products and new research results in this field.


title: Importance of audio feature reduction in automatic music genre classification

text: Multimedia database retrieval is rapidly growing and its popularity in online retrieval systems is consequently increasing. Large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, a robust automatic music-genre classification method is needed for organizing this music data into different classes according to specific viable information. Two fundamental components are to be considered for genre classification: audio feature extraction and classifier design. In this paper, we propose diverse audio features to precisely characterize the music content. The feature sets belong to four groups: dynamic, rhythmic, spectral, and harmonic. From the features, five statistical parameters are considered as representatives, including the fourth-order central moments of each feature as well as covariance components. Ultimately, insignificant representative parameters are controlled by minimum redundancy and maximum relevance. This algorithm calculates the score level of all feature attributes and orders them. Only high-score audio features are considered for genre classification. Moreover, we can recognize those audio features and distinguish which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficient statistical parameters, such as covariance components and variance, are more frequently selected than the feature attributes of other groups. This approach does not transform the original features as do principal component analysis and linear discriminant analysis. In addition, other feature reduction methodologies, such as locality-preserving projection and non-negative matrix factorization are considered. The performance of the proposed system is measured based on the reduced features from the feature pool using different feature reduction techniques. The results indicate that the overall classification is competitive with existing state-of-the-art frame-based methodologies.


title: A Dual-Band Dual-Circular-Polarization Antenna for Ka-Band Satellite Communications

text: A novel Ka-band dual-band dual-circularly-polarized antenna array is presented in this letter. A dual-band antenna with left-hand circular polarization for the Ka-band downlink frequencies and right-hand circular polarization for the Ka-band uplink frequencies is realized with compact annular ring slots. By applying the sequential rotation technique, a 2 × 2 subarray with good performance is obtained. This letter describes the design process and presents simulation and measurement results.


title: Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data

text: House sales are determined based on the Standard & Poor’s Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction. 2014 Elsevier Ltd. All rights reserved.


title: Using plane + parallax for calibrating dense camera arrays

text: A light field consists of images of a scene taken from different viewpoints. Light fields are used in computer graphics for image-based rendering and synthetic aperture photography, and in vision for recovering shape. In this paper, we describe a simple procedure to calibrate camera arrays used to capture light fields using a plane + parallax framework. Specifically, for the case when the cameras lie on a plane, we show (i) how to estimate camera positions up to an affine ambiguity, and (ii) how to reproject light field images onto a family of planes using only knowledge of planar parallax for one point in the scene. While planar parallax does not completely describe the geometry of the light field, it is adequate for the first two applications which, it turns out, do not depend on having a metric calibration of the light field. Experiments on acquired light fields indicate that our method yields better results than full metric calibration.


title: Performance Analysis, Mapping, and Multiobjective Optimization of a Hybrid Robotic Machine Tool

text: A serial-parallel hybrid machine tool is expected to integrate the respective features of pure serial/parallel mechanism. The traditional method of hybridization is to connect n (n ≥ 2) mechanisms bottom to head, in which at least one should be a parallel mechanism. One unique approach called Mechanism Hybridization is to embed one serial mechanism inside of a pure parallel mechanism, which greatly changes its overall performance. Based on this idea, an X-Y gantry system including a five-axis hybrid manipulator is developed, which is expected to be applied as the next generation of computer numerical control machine. The inverse kinematics and Jacobian matrix are derived. Since performance improvement is one of the most important factors that greatly affect the application potential of hybrid manipulators in different industry fields, to deeply investigate the comprehensive features, the local/global performance indexes of stiffness, dexterity, and manipulability are mathematically modeled and mapped. A discrete-boundary-searching method is developed to calculate and visualize the workspace. Pareto-based evolutionary multiobjective performance optimization is implemented to simultaneously improve the four indexes, and the representative nondominated solutions are listed. The proposed methodologies are generic and applicable for the design, modeling, and improvement of other parallel/hybrid robotic machine tools.


title: Simulation in software engineering training

text:
Simulation is frequently used for training in many application areas like aviation and economics, but not in software engineering. We present the SESAM project which focuses on software engineering education using simulation. In the SESAM project a simulator was developed. Using this simulator, a student can take the role of a software project manager. The simulated software project can be finished within a couple of hours because it is simulated in “quick-motion” mode.
In this paper, the background and goals of the SESAM project are presented. A new simulation model, the so called QA model, is introduced. The model behavior is demonstrated by investigating and comparing different strategies for software development. The results of experiments based on the QA model are reported. Finally, conclusions are drawn from the experiments and future work is outlined.


title: Effects of Transformational Leadership Training on Attitudinal and Financial Outcomes : A Field Experiment

text: A pretest-posttest control-group design (N = 20) was used to assess the effects of transformational leadership training, with 9 and 11 managers assigned randomly to training and control groups, respectively. Training consisted of a 1-day group session and 4 individual booster sessions thereafter on a monthly basis. Multivariate analyses of covariance, with pretest scores as the covariate, showed that the training resulted in significant effects on subordinates' perceptions of leaders' transformational leadership, subordinates' own organizational commitment, and 2 aspects of branch-level financial performance.


title: An approach to finite-time controller design for a class of T-S fuzzy systems

text: This paper studies the finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel non-Lipschitz continuous state feedback control scheme with augmented dynamics is developed. It is shown that finite-time convergence of the closed loop system can be achieved, and the potential controller singularity problem can be avoided. Finally, one example is given to illustrate the effectiveness of the proposed control approach.


title: The Human Hippocampus and Spatial and Episodic Memory

text: Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.


title: 3D Sampling Textures for Creative Design and Manufacturing

text: 1 Texture synthesized from 3D scans of snake-skin and mushroom laminae. ABSTRACT 3D sampling is a robust new strategy for exploring and creating designs. 3D textures are sampled and mixed almost like music to create new multifunctional surfaces and material microstructures (Figure 1). This paper presents several design cases performed using 3D sampling techniques, demonstrating how they can be used to explore and enhance product ideas, variations, and functions in subtle and responsive ways. In each case, design variations are generated and their mechanical behavior is evaluated against performance criteria using computational simulation or empirical testing. This work aims to promote creativity and discovery by introducing irregular geometric structures within trial-and-error feedback loops. Sayjel Vijay Patel SUTD DManD Centre


title: Large-Scale Liquid Simulation on Adaptive Hexahedral Grids

text: Regular grids are attractive for numerical fluid simulations because they give rise to efficient computational kernels. However, for simulating high resolution effects in complicated domains they are only of limited suitability due to memory constraints. In this paper we present a method for liquid simulation on an adaptive octree grid using a hexahedral finite element discretization, which reduces memory requirements by coarsening the elements in the interior of the liquid body. To impose free surface boundary conditions with second order accuracy, we incorporate a particular class of Nitsche methods enforcing the Dirichlet boundary conditions for the pressure in a variational sense. We then show how to construct a multigrid hierarchy from the adaptive octree grid, so that a time efficient geometric multigrid solver can be used. To improve solver convergence, we propose a special treatment of liquid boundaries via composite finite elements at coarser scales. We demonstrate the effectiveness of our method for liquid simulations that would require hundreds of millions of simulation elements in a non-adaptive regime.


title: Power optimization for FinFET-based circuits using genetic algorithms

text: Reducing power consumption is one of the important design goals for circuit designers. Power optimization techniques for bulk CMOS-based circuit designs have been extensively studied. As technology scales, FinFET has been proposed as an alternative for bulk CMOS when technology scales beyond 32 nm technology (E.J. Nowak et al., 2004). In this paper, we propose a power optimization framework for FinFET based circuit design, based on genetic algorithms. We exploit the unique feature of independent gate (IG) controls for FinFET devices to reduce the power consumption, and combine with other low power techniques such as multi-Vdd and gate sizing to achieve power optimization for FinFET-based circuits. We use 32 nm PTM FinFET device model (W. Zhao and Y. Cao, 2006) and conduct experiments on ISCAS benchmarks. The experimental results show that our methodology can achieve over 80% power reduction while satisfying the same performance constraints, comparing to the case that all the FinFET transistors are tuned to be the fastest ones.


title: Improving the intrinsic calibration of a Velodyne LiDAR sensor

text: LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.


title: Combining template tracking and laser peak detection for 3D reconstruction and grasping in underwater environments

text: Autonomous grasping of unknown objects by a robot is a highly challenging skill that is receiving increasing attention in the last years. This problem becomes still more challenging (and less explored) in underwater environments, with highly unstructured scenarios, limited availability of sensors and, in general, adverse conditions that affect in different degree the robot perception and control systems. This paper describes an approach for semi-autonomous grasping and recovery on underwater unknown objects from floating vehicles. A laser stripe emitter is attached to a robot forearm that performs a scan of a target of interest. This scan is captured by a camera that also estimates the motion of the floating vehicle while doing the scan. The scanned points are triangulated and transformed according to the motion estimation, thus recovering partial 3D structure of the scene with respect to a fixed frame. A user then indicates the part where to grab the object, and the final grasp is automatically planned on that area. The approach herein presented is tested and validated in water tank conditions.


title: Clear underwater vision

text: Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.


title: Girona 500 AUV: From Survey to Intervention

text: This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.


title: ROS: an open-source Robot Operating System

text: This paper gives an overview of ROS, an opensource robot operating system. ROS is not an operating system in the traditional sense of process management and scheduling; rather, it provides a structured communications layer above the host operating systems of a heterogenous compute cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.


title: Design and Control of Autonomous Underwater Robots: A Survey

text: During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.


title: Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge

text: We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google’s TENSORFLOW primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge comple-


title: Survey on Data Classification and Data Encryption Techniques Used in Cloud Computing

text: Cloud computing is an imminent revolution in information technology (IT) industry because of its performance, accessibility, low cost and many other luxury features. Security of data in the cloud is one of the major issues which acts as barrier in the implementation of cloud computing. In past years, a number of research works have targeted this problem. In this paper discuss some of the data classification techniques widely used in cloud computing. The objective of data classification is to find out the required level of security for data and to protect data by providing sufficient level of security according to the risk levels of data. In this paper also discuss a survey of existing solutions for security problem, discuss their advantages, and point out any disadvantages for future research. Specifically, focus on the use of encryption techniques, and provide a comparative study of the major encryption techniques.


title: Metaphor in the Mind : The Cognition of Metaphor

text: The most sustained and innovative recent work on metaphor has occurred in cognitive science and psychology. Psycholinguistic investigation suggests that novel, poetic metaphors are processed differently than literal speech, while relatively conventionalized and contextually salient metaphors are processed more like literal speech. This conflicts with the view of “cognitive linguists” like George Lakoff that all or nearly all thought is essentially metaphorical. There are currently four main cognitive models of metaphor comprehension: juxtaposition, category-transfer, feature-matching, and structural alignment. Structural alignment deals best with the widest range of examples; but it still fails to account for the complexity and richness of fairly novel, poetic metaphors. 1. General Issues in the Study of Metaphor Philosophers have often adopted a dismissive attitude toward metaphor. Hobbes (ch. 8) advocated excluding metaphors from rational discourse because they “openly profess deceit,” while Locke (Bk. 3, ch. 10) claimed that figurative uses of language serve only “to insinuate wrong ideas, move the passions, and thereby mislead the judgment; and so indeed are perfect cheats.” Later, logical positivists like Ayer and Carnap assumed that because metaphors like (1) How sweet the moonlight sleeps upon this bank!2 involve category mistakes, they have no real meaning or verification conditions. Thus, they too mentioned metaphor only to place it beyond the pale of rational discourse. Starting in the 1960s and 70s, philosophers and linguists began to take more positive interest in metaphor. Black argued forcefully that metaphors do have a distinctive, essentially non-propositional meaning or cognitive significance, which is produced by the “interaction” of the “systems of associated commonplaces” for the metaphor’s “primary” and “secondary” subjects (e.g., with moonlight and sleeping sweetly). Other theorists were more friendly to the idea that metaphorical and literal meaning are of the same essential kind. Many of them proposed that the literal absurdity of metaphors © Blackwell Publishing 2006 Philosophy Compass 1/2 (2006): 154–170, 10.1111/j.1747-9991.2006.00013.x


title: Faculty at Saudi Electronic University attitudes toward using augmented reality in education

text: This study aims to examine the possibility of implementing the Augmented Reality (AR) application in higher education by answering three questions: Which extended faculty at the Saudi Electronic University are familiar with such applications, what the perceptions do they hold toward using it in education, and what barriers do they believe may hinder implementing this technology. An online survey was designed and distributed to faculty members at two colleges selected randomly to collect data from participants. Even though the faculty were at an accepted level of familiarity with this technology, they did not use it in their classes. Results showed that faculty have a positive attitude toward the use of AR and have confidence in its potential to enrich the learning environment. This result is connected to the AR advantages the faculty were in agreement with. Results also showed that faculty have concerns about some barriers that might impact implementation of AR in the education environment, such as the lack of technical support.


title: Impact of supply chain integration and selective disintegration on supply chain efficiency and organization performance

text: An efficient business system integration (BSI) is the key determinant factor for the organization to stay competitive. The goal of this study is to develop and execute a research project to study BSI in the oil & gas industry and how an efficient BSI in supply chain (SCI) can be used to gain advantage. A mixed (qualitative & quantitative) survey method was employed and structural equation modeling (SEM) is applied to test the theoretical framework and hypotheses. Findings reveal that, total integration do not offer optimum performance and selective disintegration (DIS) is necessary for supply chain efficiency (SCE) and organization performance (PER). This study helps to better understand the degree of integration required for optimum supply chain performance and came up with new construct DIS. Specifically, this study investigated the effect of SCI and DIS on SCE and PER. The compelled sample size precludes evaluating and testing of additional broad models of the relationship amongst the constructs. The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, selective disintegration is necessary for optimum supply chain efficiency.


title: Learning to summarize web image and text mutually

text:
We consider the problem of learning to summarize images by text and visualize text utilizing images, which we call Mutual-Summarization. We divide the web image-text data space into three subspaces, namely pure image space (PIS), pure text space (PTS) and image-text joint space (ITJS). Naturally, we treat the ITJS as a knowledge base.
 For summarizing images by sentence issue, we map images from PIS to ITJS via image classification models and use text summarization on the corresponding texts in ITJS to summarize images. For text visualization problem, we map texts from PTS to ITJS via text categorization models and generate the visualization by choosing the semantic related images from ITJS, where the selected images are ranked by their confidence. In above approaches images are represented by color histograms, dense visual words and feature descriptors at different levels of spatial pyramid; and the texts are generated according to the Latent Dirichlet Allocation (LDA) topic model. Multiple Kernel (MK) methodologies are used to learn classifiers for image and text respectively. We show the Mutual-Summarization results on our newly collected dataset of six big events ("Gulf Oil Spill", "Haiti Earthquake", etc.) as well as demonstrate improved cross-media retrieval performance over existing methods in terms of MAP, Precision and Recall.


title: A-PNR: Automatic Plate Number Recognition

text: Automatic Plate Number Recognition (APNR) has important applications in traffic surveillance, toll booth, protected parking lot, No parking zone, etc. It is a challenging problem, especially when the license plates have varying sizes, the number of lines, fonts, background diversity etc. This work aims to address APNR using deep learning method for real-time traffic images. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall using one class SVM. The verified candidates are used for NP recognition purpose along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic images with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Also, the background was very challenging. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition.


title: Virtual reality job interview training in adults with autism spectrum disorder.

text: The feasibility and efficacy of virtual reality job interview training (VR-JIT) was assessed in a single-blinded randomized controlled trial. Adults with autism spectrum disorder were randomized to VR-JIT (n = 16) or treatment-as-usual (TAU) (n = 10) groups. VR-JIT consisted of simulated job interviews with a virtual character and didactic training. Participants attended 90 % of laboratory-based training sessions, found VR-JIT easy to use and enjoyable, and they felt prepared for future interviews. VR-JIT participants had greater improvement during live standardized job interview role-play performances than TAU participants (p = 0.046). A similar pattern was observed for self-reported self-confidence at a trend level (p = 0.060). VR-JIT simulation performance scores increased over time (R(2) = 0.83). Results indicate preliminary support for the feasibility and efficacy of VR-JIT, which can be administered using computer software or via the internet.


title: Event-based stock market prediction

text: There are various studies on the behavior of the market. In particular, derivatives such as futures and options have taken a lot of attentions, lately. Predicting these derivatives is not only important for the risk management purposes, but also for price speculative activities. Besides that accurate prediction of the market’s direction can help investors to gain enormous profits with small amount of capital [Tsaih et al., 1998]. Stock market prediction can be viewed as a challenging time-series prediction [Kim, 2003]. There are many factors that are influential on the financial markets, including political events, natural disasters, economic conditions, and so on. Despite the complexity of the movements in market prices, the market behavior is not completely random. Instead, it is governed by a highly nonlinear dynamical system [Blank, 1991]. Forecasting the future prices is carried out based on the technical analysis, which studies the market’s action using past prices and the other market information. Market analysis is in contradiction with the Efficient Market Hypothesis (EMH). EMH was developed in 1970 by economist Eugene Fama [Fama, 1965a, Fama, 1965b] whose theory stated that it is not possible for an investor to outperform the market because all available information is already built into all stock prices. If the EMH was true, it would not be possible to use machine learning techniques for market prediction. Nevertheless, there are many successful technical analysis in the financial world and number of studies appearing in academic literature that are using machine learning techniques for market prediction [Choudhry and Garg, 2008]. One way to forecast the market movement is by analyzing the special events of the market such as earnings announcements. Earnings announcement for each stock is an official public statement of a company’s profitability for a specific time period, typically a quarter or a year. Each company has its specific earnings announcement dates. Stock price of a company is affected by the earnings announcement event. Equity analysts usually predict the earnings per share (EPS) prior to the announcement date. In this project, using machine-learning techniques, we want to predict whether a given stock will be rising in the following day after earnings announcement or not. This will lead to a binary classification problem, which can be tackled based on the huge amount of available public data. This project consists of two major tasks: data collection and application of machine learning algorithms. In §2, we will discuss our efforts to collect the required data. In continuation in §3, features definitions and selections are described. We have considered and discussed different machine learning algorithms in §4. In §5, the summary of our results and possible extensions are explained.


title: Effects of early-life abuse differ across development: infant social behavior deficits are followed by adolescent depressive-like behaviors mediated by the amygdala.

text: Abuse during early life, especially from the caregiver, increases vulnerability to develop later-life psychopathologies such as depression. Although signs of depression are typically not expressed until later life, signs of dysfunctional social behavior have been found earlier. How infant abuse alters the trajectory of brain development to produce pathways to pathology is not completely understood. Here we address this question using two different but complementary rat models of early-life abuse from postnatal day 8 (P8) to P12: a naturalistic paradigm, where the mother is provided with insufficient bedding for nest building; and a more controlled paradigm, where infants undergo olfactory classical conditioning. Amygdala neural assessment (c-Fos), as well as social behavior and forced swim tests were performed at preweaning (P20) and adolescence (P45). Our results show that both models of early-life abuse induce deficits in social behavior, even during the preweaning period; however, depressive-like behaviors were observed only during adolescence. Adolescent depressive-like behavior corresponds with an increase in amygdala neural activity in response to forced swim test. A causal relationship between the amygdala and depressive-like behavior was suggested through amygdala temporary deactivation (muscimol infusions), which rescued the depressive-like behavior in the forced swim test. Our results indicate that social behavior deficits in infancy could serve as an early marker for later psychopathology. Moreover, the implication of the amygdala in the ontogeny of depressive-like behaviors in infant abused animals is an important step toward understanding the underlying mechanisms of later-life mental disease associated with early-life abuse.


title: Neural correlates of cognitive processing in monolinguals and bilinguals.

text: Here, we review the neural correlates of cognitive control associated with bilingualism. We demonstrate that lifelong practice managing two languages orchestrates global changes to both the structure and function of the brain. Compared with monolinguals, bilinguals generally show greater gray matter volume, especially in perceptual/motor regions, greater white matter integrity, and greater functional connectivity between gray matter regions. These changes complement electroencephalography findings showing that bilinguals devote neural resources earlier than monolinguals. Parallel functional findings emerge from the functional magnetic resonance imaging literature: bilinguals show reduced frontal activity, suggesting that they do not need to rely on top-down mechanisms to the same extent as monolinguals. This shift for bilinguals to rely more on subcortical/posterior regions, which we term the bilingual anterior-to-posterior and subcortical shift (BAPSS), fits with results from cognitive aging studies and helps to explain why bilinguals experience cognitive decline at later stages of development than monolinguals.


title: Low-frequency Fourier analysis of speech rhythm.

text: A method for studying speech rhythm is presented, using Fourier analysis of the amplitude envelope of bandpass-filtered speech. Rather than quantifying rhythm with time-domain measurements of interval durations, a frequency-domain representation is used--the rhythm spectrum. This paper describes the method in detail, and discusses approaches to characterizing rhythm with low-frequency spectral information.


title: Simple sequence repeat markers that identify Claviceps species and strains

text: Claviceps purpurea is a pathogen that infects most members of Pooideae, a subfamily of Poaceae, and causes ergot, a floral disease in which the ovary is replaced with a sclerotium. When the ergot body is accidently consumed by either man or animal in high enough quantities, there is extreme pain, limb loss and sometimes death. This study was initiated to develop simple sequence repeat (SSRs) markers for rapid identification of C. purpurea. SSRs were designed from sequence data stored at the National Center for Biotechnology Information database. The study consisted of 74 ergot isolates, from four different host species, Lolium perenne, Poa pratensis, Bromus inermis, and Secale cereale plus three additional Claviceps species, C. pusilla, C. paspali and C. fusiformis. Samples were collected from six different counties in Oregon and Washington over a 5-year period. Thirty-four SSR markers were selected, which enabled the differentiation of each isolate from one another based solely on their molecular fingerprints. Discriminant analysis of principle components was used to identify four isolate groups, CA Group 1, 2, 3, and 4, for subsequent cluster and molecular variance analyses. CA Group 1 consisting of eight isolates from the host species P. pratensis, was separated on the cluster analysis plot from the remaining three groups and this group was later identified as C. humidiphila. The other three groups were distinct from one another, but closely related. These three groups contained samples from all four of the host species. These SSRs are simple to use, reliable and allowed clear differentiation of C. humidiphila from C. purpurea. Isolates from the three separate species, C. pusilla, C. paspali and C. fusiformis, also amplified with these markers. The SSR markers developed in this study will be helpful in defining the population structure and genetics of Claviceps strains. They will also provide valuable tools for plant breeders needing to identify resistance in crops or for researchers examining fungal movements across environments.


title: Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks

text: In this paper the scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in LTE networks. To enable efficient scheduling for a massive number of machine-type subscribers, a novel distributed scheme is proposed to let machine nodes form local clusters and communicate with the base-station through the cluster-heads. Then, uplink scheduling and power control in LTE networks are introduced and lifetime-aware solutions are investigated to be used for the communication between cluster-heads and the base-station. Beside the exact solutions, low-complexity suboptimal solutions are presented in this work which can achieve near optimal performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended using the proposed protocols.


title: Rumor detection for Persian Tweets

text: Nowadays, striking growth of online social media has led to easier and faster spreading of rumors on cyber space, in addition to tradition ways. In this paper, rumor detection on Persian Twitter community has been addressed for the first time by exploring and analyzing the significances of two categories of rumor features: Structural and Content-based features. While applying both feature sets leads to precision more than 80%, precision around 70% using only structural features has been obtained. Moreover, we show how features of users tending to produce and spread rumors are effective in rumor detection process. Also, the experiments have led to learning of a language model of collected rumors. Finally, all features have been ranked and the most discriminative ones have been discussed.


title: Covalsa: Covariance estimation from compressive measurements using alternating minimization

text: The estimation of covariance matrices from compressive measurements has recently attracted considerable research efforts in various fields of science and engineering. Owing to the small number of observations, the estimation of the covariance matrices is a severely ill-posed problem. This can be overcome by exploiting prior information about the structure of the covariance matrix. This paper presents a class of convex formulations and respective solutions to the high-dimensional covariance matrix estimation problem under compressive measurements, imposing either Toeplitz, sparseness, null-pattern, low rank, or low permuted rank structure on the solution, in addition to positive semi-definiteness. To solve the optimization problems, we introduce the Co-Variance by Augmented Lagrangian Shrinkage Algorithm (CoVALSA), which is an instance of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). We illustrate the effectiveness of our approach in comparison with state-of-the-art algorithms.


title: Control Strategy to Naturally Balance Hybrid Converter for Variable-Speed Medium-Voltage Drive Applications

text: In this paper, a novel control strategy to naturally balance a hybrid converter is presented for medium-voltage induction motor drive applications. Fundamental current of the motor is produced by a three-level quasi-square-wave converter. Several single-phase full-bridge converters are connected in series with this quasi-square-wave converter to eliminate harmonic voltages from the net output voltage of this hybrid converter. Various novel modulation strategies for these series cells are proposed. These lead to a naturally regulated dc-bus voltage of series cells. These also optimize the number of series cells required. Variable fundamental voltage and frequency control of induction motor is adopted with a constant dc-bus voltage of the three-level quasi-square-wave converter. Experimental results are also produced here to validate the proposed modulation strategies for induction motor drives using low-voltage protomodel. Based on the aforementioned, a 2.2-kV induction motor drive has been built, and results are also produced in this paper.


title: Improvement of Intrusion Detection System in Data Mining using Neural Network

text: Many researchers have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS). One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper four different algorithms are used namely as Multilayer Perception, Radial Base Function, Logistic Regression and Voted Perception. All these neural based algorithms are implemented in WEKA data mining tool to evaluate the performance. For experimental work, NSL KDD dataset is used. Each neural based algorithm is tested with conducted dataset. It is shown that the Multilayer Perception neural network algorithm is providing more accurate results than other algorithms. Keywords— Data Mining, Intrusion Detection System, Neural network classifier algorithm, NSL KDD dataset


title: Valid items for screening dysphagia risk in patients with stroke: a systematic review.

text:
BACKGROUND AND PURPOSE
Screening for dysphagia is essential to the implementation of preventive therapies for patients with stroke. A systematic review was undertaken to determine the evidence-based validity of dysphagia screening items using instrumental evaluation as the reference standard.

METHODS
Four databases from 1985 through March 2011 were searched using the terms cerebrovascular disease, stroke deglutition disorders, and dysphagia. Eligibility criteria were: homogeneous stroke population, comparison to instrumental examination, clinical examination without equipment, outcome measures of dysphagia or aspiration, and validity of screening items reported or able to be calculated. Articles meeting inclusion criteria were evaluated for methodological rigor. Sensitivity, specificity, and predictive capabilities were calculated for each item.

RESULTS
Total source documents numbered 832; 86 were reviewed in full and 16 met inclusion criteria. Study quality was variable. Testing swallowing, generally with water, was the most commonly administered item across studies. Both swallowing and nonswallowing items were identified as predictive of aspiration. Neither swallowing protocols nor validity were consistent across studies.

CONCLUSIONS
Numerous behaviors were found to be associated with aspiration. The best combination of nonswallowing and swallowing items as well as the best swallowing protocol remains unclear. Findings of this review will assist in development of valid clinical screening instruments.


title: Distributed Subgradient Methods for Multi-Agent Optimization

text: We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.


title: Monte-Carlo Localization for Mobile Wireless Sensor Networks

text: Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo Localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.


title: On the economic significance of ransomware campaigns: A Bitcoin transactions perspective

text: Bitcoin cryptocurrency system enables users to transact securely and pseudo-anonymously by using an arbitrary number of aliases (Bitcoin addresses). Cybercriminals exploit these characteristics to commit immutable and presumably untraceable monetary fraud, especially via ransomware; a type of malware that encrypts files of the infected system and demands ransom for decryption. In this paper, we present our comprehensive study on all recent ransomware and report the economic impact of such ransomware from the Bitcoin payment perspective. We also present a lightweight framework to identify, collect, and analyze Bitcoin addresses managed by the same user or group of users (cybercriminals, in this case), which includes a novel approach for classifying a payment as ransom. To verify the correctness of our framework, we compared our findings on CryptoLocker ransomware with the results presented in the literature. Our results align with the results found in the previous works except for the final valuation in USD. The reason for this discrepancy is that we used the average Bitcoin price on the day of each ransom payment whereas the authors of the previous studies used the Bitcoin price on the day of their evaluation. Furthermore, for each investigated ransomware, we provide a holistic view of its genesis, development, the process of infection and execution, and characteristic of ransom demands. Finally, we also release our dataset that contains a detailed transaction history of all the Bitcoin addresses we identified for each ransomware.


title: Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

text: Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector  $\ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the  $\ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.


title: Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk

text: We determine optimal trading strategies for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. We take the market impact cost per share to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square-root law that has been proposed based on market microstructure theory. In analogy to the linear model, we define a “characteristic time” for optimal trading, which now depends on the initial portfolio size and decreases as execution proceeds. We also consider a model in which uncertainty of the realized price is increased by demanding rapid execution; we show that optimal trajectories are described by a “critical portfolio size” above which this effect is dominant and below which it may be neglected.


title: Mining Offensive Language on Social Media

text: English. The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions. Italiano. La presente ricerca affronta il tema dell’annotazione e della classificazione automatica dei contenuti volgari e offensivi espressi nei social media. Lo scopo del nostro lavoro consiste nel testare l’efficacia del trattamento computazionale dei contenuti tabù condivisi in rete. L’output che forniamo un corpus di 31,749 commenti generati dagli utenti di Facebook e annotato automaticamente attraverso un metodo basato sul lessico per l’identificazione e la classificazione delle


title: Incremental Algorithms for Hierarchical Classification

text: We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we derive the Bayes-optimal classifier for the H-loss. We then empirically compare two incremental approximations of the Bayes-optimal classifier with a flat SVM classifier and with classifiers obtained by using hierarchical versions of the Perceptron and SVM algorithms. The experiments show that our simplest incremental approximation of the Bayes-optimal classifier performs, after just one training epoch, nearly as well as the hierarchical SVM classifier (which performs best). For the same incremental algorithm we also derive an H-loss bound showing, when data are generated by our probabilistic data model, exponentially fast convergence to the H-loss of the hierarchical classifier based on the true model parameters.


title: An integrated framework for optimizing automatic monitoring systems in large IT infrastructures

text:
The competitive business climate and the complexity of IT environments dictate efficient and cost-effective service delivery and support of IT services. These are largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides an effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes an integrated framework for minimizing false positive tickets and maximizing the monitoring coverage for system faults.
 In particular, the integrated framework defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems. Moreover, a part of misconfigured monitoring conditions can be corrected according to false negative tickets that are discovered by another text classification algorithm in this framework. This paper also provides implementation details of a program product that uses this framework and shows some illustrative examples of successful results.


title: Learning with kernels

text: All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.


title: Fast Exact Inference with a Factored Model for Natural Language Parsing

text: We presenta novel generati ve model for natural languagetree structuresin whichsemantic(lexical dependenc y) andsyntacticstructuresare scoredwith separatemodels.Thisfactorizationprovidesconceptual simplicity, straightforwardopportunitiesfor separatelyimproving the componentmodels,anda level of performancealreadycloseto thatof similar, non-factoredmodels.Most importantly, unlikeothermodernparsing models,thefactoredmodeladmitsanextremelyeffectiveA parsingalgorithm,which makesefficient,exactinferencefeasible.


title: Large margin hierarchical classification

text: We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counterpart. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.


title: A ZigBee -wireless wearable remote physiological monitoring system

text: Wearable health monitoring systems use integrated sensors to monitor vital physiological parameters of the wearer. The paper discusses the preliminary results of a prototype wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG), Heart Rate (HR), Electroencephalogram (EEG) and Body Temperature. The ECG and EEG signals are acquired using a textile electrodes integrated into the fabric of the wearer. The acquired ECG and EEG signals are processed to remove noises and the parameters are extracted and trend analysis is carried out. The physiological parameters are monitored at the remote monitoring station using a ZigBee wireless communication.


title: A survey of datasets for visual tracking

text: For 15 years now, visual tracking has been a very active research area of the computer vision community. But an increasing amount of works can be observed in the last five years. This has led to the development of numerous algorithms that can deal with more and more complex video sequences. Each of them has its own strengths and weaknesses. That is the reason why it becomes necessary to compare those algorithms. For this purpose, some datasets dedicated to visual tracking as well as, sometimes, their ground truth annotation files are regularly made publicly available by researchers. However, each dataset has its own specificities and is sometimes dedicated to test the ability of some algorithms to tackle only one or a few specific visual tracking subproblems. This article provides an overview of some of the datasets that are most used by the visual tracking community, but also of others that address specific tasks. We also propose a cartography of these datasets from a novel perspective, namely that of the difficulties the datasets present for visual tracking.


title: DC Voltage Balancing for PWM Cascaded H-Bridge Converter Based STATCOM

text: This paper presents a new control method for cascaded connected H-bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of power semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-bridges of the converter


title: Emotion recognition from text using semantic labels and separable mixture models

text: This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.


title: Project Aura: Toward Distraction-Free Pervasive Computing

text: A s the effects of Moore's law cause computing systems to become cheaper and more plentiful, a new problem arises: increasingly, the bottleneck in computing is not its disk capacity, processor speed, or communication bandwidth, but rather the limited resource of human attention. Human attention refers to a user's ability to attend to his or her primary tasks, ignoring system-generated distractions such as poor performance and failures. By exploiting plentiful computing resources to reduce user distraction, Project Aura is creating a system whose effectiveness is considerably greater than that of other systems today. Aura is specifically intended for pervasive computing environments involving wireless communication , wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose. To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layer's ability to anticipate requests from a higher layer. In today's systems , each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention. To illustrate the kind of world we are trying to create, we present two hypothetical Aura scenarios. Although these might seem far-fetched today, they represent the kind of scenarios we expect to make commonplace through our research. In the first scenario, Jane is at Gate 23 in the Pitts-burgh airport, waiting for her connecting flight. She has edited many large documents and would like to use her wireless connection to email them. Unfortunately , bandwidth is miserable because many passengers at Gates 22 and 23 are surfing the Web. Aura observes that, at the current bandwidth, Jane won't be able to finish sending her documents before her flight departs. Consulting the airport's wireless network bandwidth service and flight schedule service, Aura discovers that wireless bandwidth is excellent at Gate 15, and that there are …


title: Potassium and sodium transporters of Pseudomonas aeruginosa regulate virulence to barley

text: We investigated the role of three uncharacterized cation transporters of Pseudomonas aeruginosa PAO1 as virulence factors for barley: PA1207, PA5021, and PA2647. PAO1 displayed reduced barley virulence with inactivated PA1207, PA5021, and PA2647 as well as with one known Na+/H+ antiporter, PA1820. Using the Escherichia coli LB2003 mutant lacking three K+ uptake systems, the expression of the PA5021 gene repressed LB2003 growth with low K+, but the strain acquired tolerance to high K+. In contrast, the expression of the PA1207 gene enhanced growth of LB2003 with low K+ but repressed its growth with high K+; therefore, the PA5021 protein exports K+, while the PA1207 protein imports K+. The PA5021 mutant of P. aeruginosa also showed impaired growth at 400 mM KCl and at 400 mM NaCl; therefore, the PA5021 protein may also export Na+. The loss of the PA5021 protein also decreased production of the virulence factor pyocyanin; corroborating this result, pyocyanin production decreased in wild-type PAO1 under high salinity. Whole-genome transcriptome analysis showed that PAO1 induced more genes in barley upon infection compared to the PA5021 mutant. Additionally, PAO1 infection induced water stress-related genes in barley, which suggests that barley may undergo water deficit upon infection by this pathogen.


title: Design of broadband circularly polarized horn antenna using an L-shaped probe

text: This paper deals with a circular horn antenna fed by an L-shaped probe. The design process for broadband matching to a 50 Omega coaxial cable, and the antenna performance in axial ratio and gain are presented. The simulation results of this paper were obtained using Ansoft HFSS 9.2


title: COMPUTATION OF CONDITIONAL PROBABILITY STATISTICS BY 8-MONTH-OLD INFANTS

text: 321 Abstract— A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants' responses to words versus part-words (tri-syllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable discrimination of words from part-words, thereby demonstrating rapid segmentation of continuous speech into words on the basis of transitional probabilities of syllable pairs. Many aspects of the patterns of human languages are signaled in the speech stream by what is called distributional evidence, that is, regularities in the relative positions and order of elements over a corpus of utterances (Bloomfield, 1933; Maratsos & Chalkley, 1980). This type of evidence, along with linguistic theories about the characteristics of human languages, is what comparative linguists use to discover the structure of exotic languages (Harris, 1951). Similarly, this type of evidence , along with tendencies to perform certain kinds of analyses on language input (Chomsky, 1957), could be used by human language learners to acquire their native languages. However, using such evidence would require rather complex distributional and statistical computations , and surprisingly little is known about the abilities of human infants and young children to perform these computations. By using the term computation, we do not mean, of course, that infants are consciously performing a mathematical calculation, but rather that they might be sensitive to and able to store quantitative aspects of distribu-tional information about a language corpus. Recently, we have begun studying this problem by investigating the abilities of human learners to use statistical information to dis-Words are known to vary dramatically from one language to another, so finding the words of a language is clearly a problem that must involve learning from the linguistic environment. Moreover, the beginnings and ends of the sequences of sounds that form words in a …


title: A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws

text: As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.


title: Dual methods for nonconvex spectrum optimization of multicarrier systems

text: The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations