title: MCA: A Multichannel Approach to SAR Autofocus

abstract: We present a new noniterative approach to synthetic aperture radar (SAR) autofocus, termed the multichannel autofocus (MCA) algorithm. The key in the approach is to exploit the multichannel redundancy of the defocusing operation to create a linear subspace, where the unknown perfectly focused image resides, expressed in terms of a known basis formed from the given defocused image. A unique solution for the perfectly focused image is then directly determined through a linear algebraic formulation by invoking an additional image support condition. The MCA approach is found to be computationally efficient and robust and does not require prior assumptions about the SAR scene used in existing methods. In addition, the vector-space formulation of MCA allows sharpness metric optimization to be easily incorporated within the restoration framework as a regularization term. We present experimental results characterizing the performance of MCA in comparison with conventional autofocus methods and discuss the practical implementation of the technique.


title: Personality and Cognitive Ability as Predictors of Effective Performance at Work

abstract: Conclusions about the validity of cognitive ability and personality measures based on meta-analyses published mostly in the past decade are reviewed at the beginning of this article. Research on major issues in selection that affect the use and interpretation of validation data are then discussed. These major issues include the dimensionality of personality, the nature and magnitude of g in cognitive ability measures, conceptualizations of validity, the nature of the job performance domain, trade-offs between diversity and validity, reactions to selection procedures, faking on personality measures, mediator and moderator research on test–performance relationships, multilevel issues, Web-based testing, the situational framing of test stimuli, and the context in which selection occurs.


An Investigation of Recurrent Neural Network Architectures Using Word Embeddings for Phrase Break Prediction


title: Galvanic Replacement-Free Deposition of Au on Ag for Core–Shell Nanocubes with Enhanced Chemical Stability and SERS Activity

abstract: We report a robust synthesis of Ag@Au core–shell nanocubes by directly depositing Au atoms on the surfaces of Ag nanocubes as conformal, ultrathin shells. Our success relies on the introduction of a strong reducing agent to compete with and thereby block the galvanic replacement between Ag and HAuCl4. An ultrathin Au shell of 0.6 nm thick was able to protect the Ag in the core in an oxidative environment. Significantly, the core–shell nanocubes exhibited surface plasmonic properties essentially identical to those of the original Ag nanocubes, while the SERS activity showed a 5.4-fold further enhancement owing to an improvement in chemical enhancement. The combination of excellent SERS activity and chemical stability may enable a variety of new applications.


title: FROM THE GUEST EDITOR Concept Evolution in Sensory Integration: A Proposed Nosology for Diagnosis

abstract: and growing area of practice in occupational therapy. Debate and discussion with colleagues have led us to develop a proposed taxonomy reflecting a new classification scheme to enhance diagnostic specificity. The nosology proposed here is rooted in empirical data first published by Ayres (Ayres, 1972b, 1989) that has evolved based on empirical and theoretical information. This new nosology provides a viewpoint for discussion and research. Two sociopolitical trends contribute to the timeliness of the ideas presented. First, a call exists throughout health and developmental services for evidence-based practice (Sackett, Richardson, Rosenberg, & Haynes, 1997). Diagnostic precision is crucial for homogeneity of samples in empirical research, affecting the validity of the research findings. Second, the condition of sensory processing disorders (SPD) has recently been acknowledged outside the occupational therapy profession in three diagnostic classification references: the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood, Revised (known as the DC: 0–3R) (Zero to Three, 2005), the Diagnostic Manual for Infancy and Early Childhood of the Interdisciplinary Council on Developmental and Learning Disorders (ICDL, 2005), and the Psychodynamic Diagnostic Manual (PDM Task Force, 2006). Both manuals include diagnostic taxonomies with subtypes of SPD suggested by a committee of occupational therapists, who assisted in developing guidelines for diagnostic specificity related to sensory-based disorders (Miller, Cermak, Lane, Anzalone, & Koomar, 2004).


title: Maximum Correntropy Criterion for Robust Face Recognition

abstract: In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l1norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we in particular impose nonnegativity constraint on the variables in the maximum correntropy criterion and develop a half-quadratic optimization technique to approximately maximize the objective function in an alternating way so that the complex optimization problem is reduced to learning a sparse representation through a weighted linear least squares problem with nonnegativity constraint at each iteration. Our extensive experiments demonstrate that the proposed method is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods. In particular, it shows that the proposed method can improve both recognition accuracy and receiver operator characteristic (ROC) curves, while the computational cost is much lower than the SRC algorithms.


title: From cyber-physical systems to Industry 4.0: make future manufacturing become possible

abstract: Recently, 'Industry 4.0' has become the supporting force in the manufacturing field. From CPS to Industry 4.0, they are leading a new round of industrial revolution. To meet the fourth industrial revolution, USA, European countries, Japan, South Korea and China have presented different versions of the so-called 'Industry 4.0' strategies respectively, which are different from each other in the content, but their common goal is to achieve manufacturing structural adjustment and restructuring and upgrading, to seize a place for future manufacturing, to provide users with better service. What is more, we summarised the five major trends of the future manufacturing. Then, we introduce some enabling technologies about Industry 4.0, among them, CPS plays a key role in the Industry 4.0 factory. Based on the customer to business (C2B) mode, we regard everything as a service, and propose a structure of Industry 4.0. Finally, the automotive industry as an example, we make a brief introduction about an Industry 4.0 case. [Received 15 December 2015; Revised 30 May 2016; Accepted 30 May 2016]


title: Providing a Consensus Definition for the Term "Smart Product"

abstract: The term "Smart Product" has become commonly used in recent years. This is because there has been an increasing interest in these kinds of products as part of the consumer goods industry, impacting everyday life and industry. Nevertheless, the term "Smart Product" is used with different meanings in different contexts and application domains. The use of the term "Smart Product" with different meanings and underlying semantics can create important misunderstandings and dissent. The aim of this paper is to analyze the different definitions of Smart Product available in the literature, and to explore and analyze their commonalities and differences, in order to provide a consensus definition that satisfies, and can therefore be used by, all parties. To embrace the identified definitions, the concept of "Smart Thing" is introduced. The methodology used was a systematic literature review. The definition is expressed as an ontology.


title: JSpIRIT: a flexible tool for the analysis of code smells

abstract: Code smells are a popular mechanism to identify structural design problems in software systems. Since it is generally not feasible to f x all the smells arising in the code, some of them are often postponed by developer s to be resolved in the future. One reason for this decision is that the improvement of the code structure, to achieve modifability goals, requires extra effort from developer s. Therefore, they might not always spend this additional effort, particularly when they are focused on delivering customer-visible features. This postponement of code smells are seen as a source of technical debt. Furthermore, not all the code smells may be urgent to f x in the context of the system's modifability and business goals. While there are a number of tools to detect smells, they do not allow developer s to discover the most urgent smells according to their goals. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The tool is fexible to allow developer s to add new smell detection strategies and to prioritize smells, and groups of smells, based on the confguration of their manifold criteria. To illustrate this fexibility, we present an application example of our tool. The results suggest that our tool can be easily extended to be aligned with the developer's goals.


title: High-gain GaN doherty power amplifier for Ka-band satellite communications

abstract: A Doherty power amplifier (DPA) implemented in 100 nm GaN process is presented. It features a peak Power Added Efficiency (PAE) of 37%, an output power of 35 dBm and 25 dB gain. For a noise-to-power ratio (NPR) of 15 dB, an output power of 33 dBm is achieved.


GANerated Hands for Real-Time 3 D Hand Tracking from Monocular RGB – Supplementary Document –


title: News versus Sentiment : Predicting Stock Returns from News Stories

abstract: This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.


title: Analyzing concept drift and shift from sample data

abstract: Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We propose a new data mining task, concept drift mapping—the description and analysis of instances of concept drift or shift. We argue that concept drift mapping is an essential prerequisite for tackling concept drift and shift. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift and shift in marginal distributions. We present quantitative concept drift mapping techniques, along with methods for visualizing their results. We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling.


title: The neurocognitive effects of alcohol on adolescents and college students.

abstract: BACKGROUND ::: Adolescents and college students are at high risk for initiating alcohol use and high-risk (or binge) drinking. There is a growing body of literature on neurotoxic and harmful cognitive effects of drinking by young people. On average, youths take their first drink at age 12 years. ::: ::: ::: METHODS ::: MEDLINE search on neurologic and cognitive effects of underage drinking. ::: ::: ::: RESULTS ::: Problematic alcohol consumption is not a benign condition that resolves with age. Individuals who first use alcohol before age 14 years are at increased risk of developing alcohol use disorders. Underage drinkers are susceptible to immediate consequences of alcohol use, including blackouts, hangovers, and alcohol poisoning and are at elevated risk of neurodegeneration (particularly in regions of the brain responsible for learning and memory), impairments in functional brain activity, and the appearance of neurocognitive deficits. Heavy episodic or binge drinking impairs study habits and erodes the development of transitional skills to adulthood. ::: ::: ::: CONCLUSIONS ::: Underage alcohol use is associated with brain damage and neurocognitive deficits, with implications for learning and intellectual development. Impaired intellectual development may continue to affect individuals into adulthood. It is imperative for policymakers and organized medicine to address the problem of underage drinking.


title: Survey of Contrast Enhancement Techniques based on Histogram Equalization

abstract: This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.


title: Auditory Brainstem Response Wave Amplitude Characteristics as a Diagnostic Tool in Children with Speech Delay with Unknown Causes

abstract: Speech delay with an unknown cause is a problem among children. This diagnosis is the last differential diagnosis after observing normal findings in routine hearing tests. The present study was undertaken to determine whether auditory brainstem responses to click stimuli are different between normally developing children and children suffering from delayed speech with unknown causes. In this cross-sectional study, we compared click auditory brainstem responses between 261 children who were clinically diagnosed with delayed speech with unknown causes based on normal routine auditory test findings and neurological examinations and had >12 months of speech delay (case group) and 261 age- and sex-matched normally developing children (control group). Our results indicated that the case group exhibited significantly higher wave amplitude responses to click stimuli (waves I, III, and V) than did the control group (P=0.001). These amplitudes were significantly reduced after 1 year (P=0.001); however, they were still significantly higher than those of the control group (P=0.001). The significant differences were seen regardless of the age and the sex of the participants. There were no statistically significant differences between the 2 groups considering the latency of waves I, III, and V. In conclusion, the higher amplitudes of waves I, III, and V, which were observed in the auditory brainstem responses to click stimuli among the patients with speech delay with unknown causes, might be used as a diagnostic tool to track patients’ improvement after treatment.


title: Millimeter-wave wideband transition from CPW to substrate integrated waveguide on electrically thick high-permittivity substrates

abstract: A wideband transition from coplanar waveguide (CPW) to substrate integrated waveguide (SIW) is proposed and presented in the 50 GHz frequency range. Electrically thick alumina was used in this case, representative for other high-permittivity substrates such as semiconductors. Simulations predict less than -15 dB return loss within a 35 % bandwidth. CPW probe measurements were carried out and 40 % bandwidth were achieved at -0.5 dB insertion loss for a single transition. Modified SIW via configurations being suitable for simplified fabrication on electrically thick substrates in the upper millimeter-wave spectrum are discussed in the second part.


title: A preliminary radicalisation framework based on social engineering techniques

abstract: The use of online forums and social media sites by extremists for recruiting and radicalising individuals has been covered extensively by researchers. Meanwhile, the social engineering techniques utilised by these extremists to lure marginalised individuals into radicalisation has been neglected. In this article, the social engineering aspects of online radicalisation will be explored. Specifically, the five Principles of Persuasion in Social Engineering (PPSE) will be mapped onto the online radicalisation methods employed by extremists online. Analysing these tactics will aid in gaining a deeper understanding of the process of indoctrination and of the psychology of both the attacker and the target of such attacks. This understanding has enabled the development of a preliminary radicalisation framework based on the social traits of a target that may be exploited during an attack.


title: Anaphylaxis in Children: Clinical and Allergologic Features

abstract: Background. Despite the importance of anaphylaxis, little information is available on its clinical features. Objective. To evaluate the clinical and allergologic features of anaphylaxis in children referred to the aller- gology and immunology unit of A. Meyer Children's Hospital (Florence, Italy) from 1994 to 1996. Results. Ninety-five episodes of anaphylaxis oc- curred in 76 children (50 boys and 26 girls). Sixty-six children (87%) had only one episode of anaphylaxis, while 10 (13%) had two or more episodes. Sixty-two (82%) of the 76 patients had a personal history of atopic symptoms, although 14 (18%) did not. Sixty (79%) of the 76 children studied had at least one positive skin prick test to one or more of the common inhalant and/or food allergens. Children with venom- induced anaphylaxis usually had negative skin tests to the allergens tested. A younger age and eczema were more frequent among children with food-dependent ana- phylaxis, whereas an older age together with urticaria- angioedema were common among those with exercise- induced anaphylaxis. The mean latent period (6SD) of the anaphylaxis episodes was 15.4 6 27.5 minutes. Skin and respiratory manifestations had an earlier onset and were more common than the gastrointestinal and cardio- vascular ones. The most frequent clinical manifestation in children with food anaphylaxis was gastrointestinal symptoms, whereas cardiovascular symptoms were rare. The most probable causative agents in the 95 episodes described were foods (57%), drugs (11%), hymenoptera venom (12%), exercise (9%), additives (1%), specific im- munotherapy (1%), latex (1%), and vaccines (2%), but in 6 cases (6%) the agent was never determined. Among the foods, seafood and milk were the most frequently in- volved. As for location, 57% of the anaphylactic events occurred in the home (54/95), 12% outdoors (11/95); 5% in restaurants (5/95); 3% in the doctor's office (3/95); 3% in hospitals (3/95); 3% on football fields (3/95); 2% on the beach (2/95); 1% in the gym (1/95); 1% at school (1/95); and 1% in the operating room (1/95). In the remaining 12% of cases (11/95) the site remained unknown. Sixty-two per- cent of the patients (59/95) were treated in an emergency room or hospital, while 32% (30/95) were not (this infor- mation is lacking in 6% of the cases (6/95)). Patients were treated with corticosteroids in 72% of the cases (68/95), with antihistamines in 20% (19/95), with epinephrine in 18% (17/95), with b2-agonists in 5% (5/95), and with oxy- gen in 4% (4/95). Conclusions. In our area, foods, particularly seafood and milk, seem to be the most important etiologic factors triggering anaphylaxis. Food-induced anaphy- laxis often occurs in younger children with a severe food allergy, whereas exercise-induced anaphylaxis oc- curs more often in older children with a history of urticaria-angioedema. The venom-induced variant usu- ally presents itself in nonatopic subjects. Given the fact that most of the children had only one anaphylac- tic reaction, prevention is almost impossible. Epineph- rine, although it is the first-choice treatment of ana- phylaxis, often goes unused, even in hospitals and doctors' offices. Pediatrics 1998;101(4). URL: http:// www.pediatrics.org/cgi/content/full/101/4/e8; anaphy- laxis, child, clinical features, allergologic features.


title: Modulation spectrum-based post-filter for GMM-based Voice Conversion

abstract: This paper addresses an over-smoothing effect in Gaussian Mixture Model (GMM)-based Voice Conversion (VC). The flexible use of the statistical approach is one of the major reason why this approach is widely applied to the speech-based systems. However, quality degradation by over-smoothed speech parameter converted is unavoidable problem of statistical modeling. One of common approaches to this over-smoothness in conversion step is to compensate generated features, such as Global Variance (GV), that explicitly express the over-smoothing effect. In statistical Text-To-Speech (TTS) synthesis, we have recently introduced a Modulation Spectrum (MS) which is an extended form of GV, and have proposed MS-based Post-Filter (MSPF) in Hidden Markov Model (HMM)-based TTS synthesis. In this paper, we apply the MSPF to GMM-based VC. Because the MS of speech parameters is degraded through GMM-based conversion process, we perform the post-filter due to MS modification of converted parameters. The experimental evaluation yields the quality benefits by the proposed post-filter.


title: A visual domain adaptation method based on enhanced subspace distribution matching

abstract: One of the challenges in computer vision is how to learn an accurate classifier for a new domain by using labeled images from an old domain under the condition that there is no available labeled images in the new domain. Domain adaptation is an outstanding solution that tackles this challenge by employing available source-labeled datasets, even with significant difference in distribution and properties. However, most prior methods only reduce the difference in subspace marginal or conditional distributions across domains while completely ignoring the source data label dependence information in a subspace. In this paper, we put forward a novel domain adaptation approach, referred to as Enhanced Subspace Distribution Matching. Specifically, it aims to jointly match the marginal and conditional distributions in a kernel principal dimensionality reduction procedure while maximizing the source label dependence in a subspace, thus raising the subspace distribution matching degree. Extensive experiments verify that it can significantly outperform several state-of-the-art methods for cross-domain image classification problems.


title: Understanding face perception by means of prosopagnosia and neuroimaging

abstract: Understanding the human neuro-anatomy of face recognition is a long-standing goal of Cognitive Neuroscience. Studies of patients with face recognition impairment following brain damage (i.e., acquired prosopagnosia) have revealed the specificity of face recognition, the importance and nature of holistic/configural perception of individual faces, and the distribution of this function in the ventral occipito-temporal (VOT) cortex, with a right hemispheric dominance. Yet, neuroimaging studies in this field have essentially focused on a single face-selective area of the VOT and underestimated the right hemisphere superiority. Findings in these studies have also been taken as supporting a hierarchical view of face perception, according to which a face is decomposed into parts in early face-selective areas, these parts being subsequently integrated into a whole representation in higher-order areas. This review takes a historical and current perspective on the study of acquired prosopagnosia and neuroimaging that challenges this latter view. It argues for a combination of these methods, an approach suggesting a coarse-to-fine emergence of the holistic face percept in a non-hierarchical network of cortical face-selective areas.


title: Retrieving actions in movies

abstract: We address recognition and localization of human actions in realistic scenarios. In contrast to the previous work studying human actions in controlled settings, here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance, motion, surrounding scenes, viewing angles and spatio-temporal extents. We introduce a new annotated human action dataset and use it to evaluate several existing methods. We in particular focus on boosted space-time window classifiers and introduce "keyframe priming" that combines discriminative models of human motion and shape within an action. Keyframe priming is shown to significantly improve the performance of action detection. We present detection results for the action class "drinking" evaluated on two episodes of the movie "Coffee and Cigarettes".


title: An Empirical Study on Using the National Vulnerability Database to Predict Software Vulnerabilities

abstract: Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.


title: Off-line signature verification and recognition: Neural network approach

abstract: This paper discusses signature verification and recognition using a new approach that depends on a neural network which enables the user to recognize whether a signature is original or a fraud. The user introduces into the computer the scanned images, modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.


title: Constraint-based models predict metabolic and associated cellular functions

abstract: The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.


title: Revisiting Selectional Preferences for Coreference Resolution

abstract: Selectional preferences have long been claimed to be essential for coreference resolution. However, they are mainly modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. We show that the incorporation of our model improves coreference resolution performance on the CoNLL dataset, matching the state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile such improvements are.


Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry


Using Longitudinal Social Media Analysis to Understand the Effects of Early College Alcohol Use.


The Role of Anonymity in Deindividuated Behavior : A Comparison of Deindividuation Theory and the Social Identity Model of Deindividuation Effects ( SIDE )


title: SN-SVM: a sparse nonparametric support vector machine classifier

abstract: This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND). The proposed model incorporates some near-global variations of the data provided by the KND and, hence, may be viewed as an extension to the KSVM. Similarly, since the support vectors improve the choice of \(\kappa \)-nearest neighbors (\(\kappa -NN\)’s), it can also serve as an extension to the KND. The proposed model is capable of dealing with both heteroscedastic and non-normal data while avoiding the small sample size problem. The model is a convex quadratic optimization problem with one global optimal solution, so it can be estimated easily and efficiently using numerical methods. It can also be reduced to the classical KSVM model and as such existing SVM programs can be used for easy implementation. Through the Bayesian interpretation with the help of a Gaussian prior, we show that our method provides a sparse solution by assigning non-zero weights to only a fraction of the total number of training samples. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SN-SVM model improves the classification accuracy over contemporary classifiers and also provides sparser solution than the KSVM.


title: Theory and Algorithms for Plan Merging

abstract: Abstract Merging operators in a plan can yield significant savings in the cost to execute a plan. This paper provides a formal theory for plan merging and presents both optimal and efficient heuristic algorithms for finding minimum cost merged plans. The optimal plan merging algorithm applies a dynamic programming method to handle multiple linear plans and is extended to partially ordered plans in a novel way. Furthermore, with worst-case and average-case complexity analysis and empirical tests, we demonstrate that efficient and well-behaved approximation algorithms are applicable for optimizing plans with large sizes.


title: Fitting B-Spline Curves to Point Clouds by Squared Distance Minimization

abstract: Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, calledsquared distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, i.e., a set of unorganized, possibly noisy data points. We show that SDM outperforms significantly other optimization methods used currently in common practice of curve fitting. In SDM a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the squared distance (SD) error term, defined by a quadratic approximant of squared distances from data points to a fitting curve. The SD error term measures faithfully the geometric distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent distance (TD) error term, which is adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method, which employs a carefully chosen positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration which is unstable for target shapes with corners, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.


title: The analogous mechanisms of enzymatic inactivation induced by ascorbate and superoxide in the presence of copper.

abstract: The mechanism of enzymatic inactivation of purified and membrane-bound acetylcholine esterase by ascorbate and copper was investigated. While the exposure of the enzyme to either ascorbate or copper did not cause enzymatic inactivation, the incubation of the enzyme with a combination of both ascorbate and copper resulted in a loss in acetylcholine esterase activity, which was time dependent. The enzymatic inactivation required either molecular oxygen or hydrogen peroxide under anaerobic conditions. Scavengers of hydroxyl radicals at concentrations of up to 100 mM did not provide protection to acetylcholine esterase. Only mannitol at very high concentrations (above 1 M) efficiently prevented the inactivation of the enzyme. The kinetics of the aerobic oxidation of reduced ascorbate in the presence of acetylcholine esterase and copper closely followed the rate of enzyme inactivation. Addition of the chelating agents EDTA and diethylenetriaminepentaacetic acid prevented both the oxidation of ascorbate and the inactivation of the enzyme. In the presence of low concentrations of histidine (0.5-2.0 mM), which forms high affinity complexes with copper, the rate of ascorbate oxidation was similar to that recorded in its absence. On the other hand, no enzyme inactivation was indicated in the presence of histidine. Low temperature EPR measurements have demonstrated the binding of copper to the enzyme, and have shown the reduction of the cupric enzyme to the corresponding cuprous complex. In view of these results, a general "site-specific" mechanism for biological damage can be offered, in which copper(II) ions are bound to enzymes or other biological macromolecules. Ascorbate plays a dual role: it reduces the cupric complex to the corresponding cuprous state and serves as a source for H2O2, which, in turn, reacts with the reduced copper complex, in a Fenton reaction. In this reaction, secondary hydroxyl radicals are site specifically formed, and react preferentially with the protein, at the site of their formation, causing its inactivation. This mechanism is analogous to that previously proposed (Samuni, A., Chevion, M., and Czapski, G. (1981) J. Biol. Chem. 256, 12632-12635) for the enhancement of the biological damage caused by superoxide in the presence of copper.


Gradient descent only converges to minimizers


title: Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks

abstract: The application of agricultural machinery in precision agriculture has experienced an increase in investment and research due to the use of robotics applications in the machinery design and task executions. Precision autonomous farming is the operation, guidance, and control of autonomous machines to carry out agricultural tasks. It motivates agricultural robotics. It is expected that, in the near future, autonomous vehicles will be at the heart of all precision agriculture applications [1]. The goal of agricultural robotics is more than just the application of robotics technologies to agriculture. Currently, most of the automatic agricultural vehicles used for weed detection, agrochemical dispersal, terrain leveling, irrigation, etc. are manned. An autonomous performance of such vehicles will allow for the continuous supervision of the field, since information regarding the environment can be autonomously acquired, and the vehicle can then perform its task accordingly.


title: Perceiving personal discrimination: The role of group status and legitimizing ideology

abstract: It was hypothesized that relative group status and endorsement of ideologies that legitimize group status differences moderate attributions to discrimination in intergroup encounters. According to the status-legitimacy hypothesis, the more members of low-status groups endorse the ideology of individual mobility, the less likely they are to attribute negative outcomes from higher status group members to discrimination. In contrast, the more members of high-status groups endorse individual mobility, the more likely they are to attribute negative outcomes from lower status group members to discrimination. Results from 3 studies using 2 different methodologies provide support for this hypothesis among members of different high-status (European Americans and men) and low-status (African Americans, Latino Americans, and women) groups.


title: Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization

abstract: This paper proposes the random drift particle swarm optimization (RDPSO) algorithm to solve economic dispatch (ED) problems from power systems area. The RDPSO is inspired by the free electron model in metal conductors placed in an external electric field, and it employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Many nonlinear characteristics of a power generator, such as the ramp rate limits, prohibited operating zones and nonsmooth cost functions are considered when the proposed method is used in practice for optimizing the generators' operation. The performance of the RDPSO method is evaluated on three different power systems, and compared with that of other optimization methods in terms of the solution quality, robustness, and convergence performance. The experimental results show that the RDPSO method performs better in solving the ED problems than any other tested optimization techniques.


title: Mutual Information for Lucas-Kanade Tracking (MILK): An Inverse Compositional Formulation

abstract: Mutual information (Ml) is popular for registration via function optimization. This work proposes an inverse compositional formulation of Ml for Levenberg-Marquardt optimization. This yields a constant Hessian, which may be precomputed. Speed improvements of 15 percent were obtained, with convergence accuracies similar those of the standard formulation.


title: Microstrip-Fed Circularly Polarized Square-Ring Patch Antenna for GPS Applications

abstract: A novel coupling technique for circularly polarized square-ring patch antenna is developed and discussed. The circular polarization (CP) radiation of the square-ring patch antenna is achieved by a simple microstrip feed line through the coupling of a square patch on the same plane of the antenna. Proper positioning of the coupling square patch excites two orthogonal resonant modes with 90deg phase difference, and a pure circular polarization is obtained. The dielectric material is a square block of ceramic with a permittivity of 58 and that reduces the size of the antenna. The prototype has been designed, fabricated and found to have an impedance bandwidth of 1.1% and a 3-dB axial-ratio bandwidth of about 0.03% at GPS frequency of 1573 MHz. The characteristics of the proposed antenna have been studied by simulation software HFSS and experiments. The measured and simulated results are in good agreement.


title: Text Classification using the Concept of Association Rule of Data Mining

abstract: As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naive Bayes classifier is then used on derived features for final classification.


title: Position and attitude control of a spherical rolling robot equipped with a gyro

abstract: This paper deals with the problem of controlling the position and attitude of a spherical rolling robot. The driving torque for the rolling robot is generated by a new type of mechanism equipped with a gyro. We derive two types of models, a kinematic model and a dynamic model, from the equations of motion for the robot. A feedback controller for the kinematic model is designed by using our previous method that is based on Lyapunov control. And then, from the controller, we obtain a feedback controller for the dynamic model by backstepping technique. The effectiveness of both controllers is demonstrated by numerical simulations


title: Designing concurrent full-body gestures for intense gameplay

abstract: Abstract Full body gestures provide alternative input to video games that are more natural and intuitive. However, full-body game gestures designed by developers may not always be the most suitable gestures available. A key challenge in full-body game gestural interfaces lies in how to design gestures such that they accommodate the intensive, dynamic nature of video games, e.g., several gestures may need to be executed simultaneously using different body parts. This paper investigates suitable simultaneous full-body game gestures, with the aim of accommodating high interactivity during intense gameplay. Three user studies were conducted: first, to determine user preferences, a user-elicitation study was conducted where participants were asked to define gestures for common game actions/commands; second, to identify suitable and alternative body parts, participants were asked to rate the suitability of each body part (one and two hands, one and two legs, head, eyes, and torso) for common game actions/commands; third, to explore the consensus of suitable simultaneous gestures, we proposed a novel choice-based elicitation approach where participants were asked to mix and match gestures from a predefined list to produce their preferred simultaneous gestures. Our key findings include (i) user preferences of game gestures, (ii) a set of suitable and alternative body parts for common game actions/commands, (iii) a consensus set of simultaneous full-body game gestures that assist interaction in different interactive game situations, and (iv) generalized design guidelines for future full-body game interfaces. These results can assist designers and practitioners to develop more effective full-body game gestural interfaces or other highly interactive full-body gestural interfaces.


title: Position estimation and tracking of an autonomous mobile sensor using received signal strength

abstract: In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion.


title: A Review on Brain Stimulation Using Low Intensity Focused Ultrasound

abstract: Brain stimulation techniques are important in both basic and clinical studies. Majority of well-known brain stimulating techniques have low spatial resolution or entail invasive processes. Low intensity focused ultrasound (LIFU) seems to be a proper candidate for dealing with such deficiencies. This review recapitulates studies which explored the effects of LIFU on brain structures and its function, in both research and clinical areas. Although the mechanism of LIFU action is still unclear, its different effects from molecular level up to behavioral level can be explored in animal and human brain. It can also be coupled with brain imaging assessments in future research.


title: Hopscotch hashing

abstract: We present a new class of resizable sequential and concurrent hash map algorithms directed at both uni-processor and multicore machines. The new hopscotch algorithms are based on a novel hopscotchmulti-phased probing and displacement technique that has the flavors of chaining, cuckoo hashing, and linear probing, all put together, yet avoids the limitations and overheads of these former approaches. The resulting algorithms provide tables with very low synchronization overheads and high cache hit ratios. ::: ::: In a series of benchmarks on a state-of-the-art 64-way Niagara II multicore machine, a concurrent version of hopscotch proves to be highly scalable, delivering in some cases 2 or even 3 times the throughput of today's most efficient concurrent hash algorithm, Lea's ConcurrentHashMap from java.concurr.util. Moreover, in tests on both Intel and Sun uni-processor machines, a sequential version of hopscotch consistently outperforms the most effective sequential hash table algorithms including cuckoo hashing and bounded linear probing. ::: ::: The most interesting feature of the new class of hopscotch algorithms is that they continue to deliver good performance when the hash table is more than 90% full, increasing their advantage over other algorithms as the table density grows.


title: Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System

abstract: We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments.


title: Image Content Based Retrieval System using Cosine Similarity for Skin Disease Images

abstract: A content based image retrieval system (CBIR) is proposed to assist the dermatologist for diagnosis of skin diseases. First, after collecting the various skin disease images and their text information (disease name, symptoms and cure etc), a test database (for query image) and a train database of 460 images approximately (for image matching) are prepared. Second, features are extracted by calculating the descriptive statistics. Third, similarity matching using cosine similarity and Euclidian distance based on the extracted features is discussed. Fourth, for better results first four images are selected during indexing and their related text information is shown in the text file. Last, the results shown are compared according to doctor’s description and according to image content in terms of precision and recall and also in terms of a self developed scoring system.


title: Management accounting and integrated information systems: A literature review

abstract: Abstract In order to further advance research within management accounting and integrated information systems (IIS), an understanding of what research has already been done and what research is needed is of particular importance. The purpose of this paper is to uncover, classify and interpret current research within management accounting and IIS. This is done partly to identify research gaps and propose directions for future research and partly to guide researchers and practitioners investigating and making decisions on how to better synthesise the two areas. Based on the strengths of existing frameworks covering elements of management accounting and IIS a new and more comprehensive theoretical framework is developed. This is used as a basis for classifying and presentation of the reviewed literature in structured form. The outcome of the review is an identification of research gaps and a proposal of research opportunities within different research paradigms and with the use of different methods.


title: Auto-tuning Spark Big Data Workloads on POWER8: Prediction-Based Dynamic SMT Threading

abstract: Much research work devotes to tuning big data analytics in modern data centers, since %the truth that even a small percentage of performance improvement immediately translates to huge cost savings because of the large scale. Simultaneous multithreading (SMT) receives great interest from data center communities, as it has the potential to boost performance of big data analytics by increasing the processor resources utilization. For example, the emerging processor architectures like POWER8 support up to 8-way multithreading. However, as different big data workloads have disparate architectural characteristics, how to identify the most efficient SMT configuration to achieve the best performance is challenging in terms of both complex application behaviors and processor architectures. In this paper, we specifically focus on auto-tuning SMT configuration for Spark-based big data workloads on POWE-R8. However, our methodology could be generalized and extended to other programming software stacks and other architectures. We propose a prediction-based dynamic SMT threading (PBDST) framework to adjust the thread count in SMT cores on POWER8 processors by using versatile machine learning algorithms.Its innovation lies in adopting online SMT configuration predictions derived from micro-architecture level profiling, to regulate the thread counts that could achieve nearly optimal performance. Moreover it is implemented at Spark software stack layer and transparent to user applications. After evaluating a large set of machine learning algorithms, we choose the most efficient ones to perform online predictions. The experimental results demonstrate that our approach can achieve up to 56.3% performance improvement and an average performance gain of 16.2% in comparison with the default configuration---the maximum SMT configuration---SMT8 on our system.


title: Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

abstract: This paper describes our approach towards robust facial expression recognition (FER) for the third Emotion Recognition in the Wild (EmotiW2015) challenge. We train multiple deep convolutional neural networks (deep CNNs) as committee members and combine their decisions. To improve this committee of deep CNNs, we present two strategies: (1) in order to obtain diverse decisions from deep CNNs, we vary network architecture, input normalization, and random weight initialization in training these deep models, and (2) in order to form a better committee in structural and decisional aspects, we construct a hierarchical architecture of the committee with exponentially-weighted decision fusion. In solving a seven-class problem of static FER in the wild for the EmotiW2015, we achieve a test accuracy of 61.6 %. Moreover, on other public FER databases, our hierarchical committee of deep CNNs yields superior performance, outperforming or competing with state-of-the-art results for these databases.


title: Batch Normalization in Convolutional Neural Networks — A comparative study with CIFAR-10 data

abstract: Deep learning is an emerging field of computational science that involves large quantity of data for training a model. In this paper, we have performed a comparative study of various state-of-the-art Convolutional Networks viz. DenseNet, VGG, Inception (v3) Network and Residual Network with different activation function, and demonstrate the importance of Batch Normalization. It is shown that Batch Normalization is not only important in improving the performance of the neural networks, but are essential for being able to train a deep convolutional networks. In this work state-ofthe-art convolutional neural networks viz. DenseNet, VGG, Residual Network and Inception (v3) Network are compared on a standard dataset, CIFAR-10 with batch normalization for 200 epochs. The conventional RELU activation results in accuracy of 82.68%, 88.79%, 81.01%, and 84.92% respectively. With ELU activation Residual and VGG Networks’ performance increases to 84.59% and 89.91%, this is highly significant.


title: Towards Lexicalization of DBpedia Ontology with Unsupervised Learning and Semantic Role Labeling

abstract: Filling the gap between natural language expressions and ontology concepts or properties is the new trend in Semantic Web. Ontology lexicalization introduces a new layer of lexical information for ontology properties and concepts. We propose a method based on unsupervised learning for the extraction of the potential lexical expressions of DBpedia propertiesfrom Wikipedia text corpus. It is a resource-driven approach that comprises three main steps. The first step consists of the extraction of DBpedia triples for the aimed property followed by the extraction of Wikipedia articles describing the resources from these triples. In the second step, sentences mostly related to the property are extracted from the articles and they are analyzed with a Semantic Role Labeler resulting in a set of SRL annotated trees. In the last step, clusters of expressions are built using spectral clustering based on the distances between the SRL trees. The clusters with the least variance are considered to be relevant for the lexical expressions of the property.


title: A traction stress sensor array for use in high-resolution robotic tactile imaging

abstract: A high resolution large-area array capable of resolving the three independent components of a 2D triaxial contact stress profile has been developed. The array, composed of 4096 (64/spl times/64) individual stress sensing elements, was constructed with a fully CMOS-compatible fabrication process, allowing integration of the sensing structures with digital control circuitry. The individual array elements have been shown to demonstrate linear responses to both applied normal stress (1.59 mV/kPa, 0-35 kPa) and applied shear stress (0.32 mV/kPa, 0-60 kPa). A spatial resolution comparable to the spacing of the papillary ridges of the human dermis (/spl ap/300 /spl mu/m) has been achieved within the 1.92/spl times/1.92 cm active sensing area of the array. Descriptions of the sensor structure, the required signal conditioning, and the array architecture are presented in this paper. The results of electrical and mechanical characterization studies are also outlined.


title: Impedance Matching in Photovoltaic Systems Using Cascaded Boost Converters and Sliding-Mode Control

abstract: Switching dc–dc converters are widely used to interface the dc output of renewable energy resources with power distribution systems in order to facilitate the use of energy at the customer side. In the case of residential photovoltaic (PV) applications, high conversion ratio is usually required, in order to adapt the low output voltages of PV modules to a dc bus voltage, while dealing with the appropriate impedance matching. In this paper, a system connected to a PV panel consisting of two cascaded dc–dc boost converters under sliding-mode control and working as loss-free resistors is studied. The modeling, simulation, and design of the system are addressed. First, an ideal reduced-order sliding-mode dynamics model is derived from the full-order switched model taking into account the sliding constraints, the nonlinear characteristic of the PV module, and the dynamics of the MPPT controller. For this model, a design-oriented averaged model is obtained and its dynamic behavior is analyzed showing that the system is asymptotically globally stable. Moreover, the proposed system can achieve a high conversion ratio with an efficiency close to 95 $\%$ for a wide range of working power. Numerical simulations and experimental results corroborate the theoretical analysis and illustrate the advantages of this architecture in PV systems.


title: Occupational therapy, physical therapy and speech-language pathology in the neonatal intensive care unit: Patterns of therapy usage in a level IV NICU.

abstract: OBJECTIVES ::: 1) To describe the use of occupational therapy (OT), physical therapy (PT) and speech-language pathology (SLP) services in a level IV neonatal intensive care unit (NICU), 2) to describe predictors of early therapy usage, and 3) to test the hypothesis that more NICU-based therapy will relate to better neurobehavioral outcomes. ::: ::: ::: METHODS ::: Seventy-nine infants born ≤32 weeks gestation had therapy interventions, as standard of care, tracked across NICU hospitalization. Infants received neurobehavioral testing prior to NICU discharge. ::: ::: ::: RESULTS ::: All (100%) received OT and PT, and 41 (51%) received SLP. The average age at initiation of OT, PT, and SLP was 30.4±1.4, 30.3±1.4, and 35.9±2.3 weeks postmenstrual age, respectively. Infants received therapy an average of 1.8±.4, 1.8±.4 and 1.1±.5 times per week for OT, PT and SLP, respectively. There were 56 different therapeutic interventions performed. There was overlap in the interventions provided by different NICU therapists; however, interventions unique to each discipline were identified. More therapy was not related to better neurobehavioral outcomes, but rather more frequent therapy could be attributed to more complex medical conditions (p<0.05). ::: ::: ::: CONCLUSION ::: Early therapy services in the NICU can start early in gestation and continue routinely until NICU discharge in order to optimize outcomes. These findings can aid our understanding of how neonatal therapy services are implemented in a level IV NICU.


title: Word Embedding based Generalized Language Model for Information Retrieval

abstract: Word2vec, a state-of-the-art word embedding technique has gained a lot of interest in the NLP community. The embedding of the word vectors helps to retrieve a list of words that are used in similar contexts with respect to a given word. In this paper, we focus on using the word embeddings for enhancing retrieval effectiveness. In particular, we construct a generalized language model, where the mutual independence between a pair of words (say t and t') no longer holds. Instead, we make use of the vector embeddings of the words to derive the transformation probabilities between words. Specifically, the event of observing a term t in the query from a document d is modeled by two distinct events, that of generating a different term t', either from the document itself or from the collection, respectively, and then eventually transforming it to the observed query term t. The first event of generating an intermediate term from the document intends to capture how well does a term contextually fit within a document, whereas the second one of generating it from the collection aims to address the vocabulary mismatch problem by taking into account other related terms in the collection. Our experiments, conducted on the standard TREC collection, show that our proposed method yields significant improvements over LM and LDA-smoothed LM baselines.


title: State of the Art in Example-based Texture Synthesis

abstract: Recent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user's needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis.


title: Relationship between Epstein-Barr Virus-Encoded RNA Expression, Apoptosis and Lymphocytic Infiltration in Gastric Carcinoma with Lymphoid-Rich Stroma

abstract: Objective: To investigate the association of apoptosis with apoptotic proteins and Epstein-Barr virus (EBV) expression in gastric carcinoma with lymphoid-rich stroma (GCLRS) in order to clarify the relationship between lymphocytic infiltration, EBV expression and apoptosis in gastric carcinoma. Subjects and Methods: We performed immunohistochemical stains for bcl-2, bax and in situ hybridization for EBV-encoded RNA and the terminal deoxynucleotidyl transferase-mediated dUTP-digoxigenin nick end labeling technique using the 24 cases of GCLRS and 23 cases of gastric carcinoma with lymphoid-poor stroma (GCLPS) out of 1,635 cases of gastric carcinoma. Results: The prevalence of GCLRS was 1.47% (24/1,635). The prevalence of EBV involvement in GCLRS (79%, 19/24) was significantly higher than in GCLPS (8.7%, 2/23). Apoptosis was revealed in all cases and there was no statistical difference between GCLRS and GCLPS. Immunohistochemical expression for bcl-2 was revealed only in the cytoplasm of lymphocytes in all cases, but bax showed distinct expression in the cytoplasm of neoplastic cells in all cases. There was a significant correlation between bax expression and the apoptotic index (p Conclusions: These results support the hypothesis that EBV infection plays an important role in the tumorigenesis of GCLRS, which has a better prognosis than usual gastric adenocarcinoma although there was no significant difference in the apoptotic index between GCLRS and GCLPS.


title: Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal

abstract: The experiments, aimed to compare three methods to create ensemble models implemented in a popular data mining system called WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, linear regression, and support vector machine were used to construct ensemble models. All algorithms were employed to real-world datasets derived from the cadastral system and the registry of real estate transactions. Nonparametric Wilcoxon signed-rank tests to evaluate the differences between ensembles and original models were conducted. The results obtained show there is no single algorithm which produces the best ensembles and it is worth to seek an optimal hybrid multi-model solution.


title: Square Root Unscented Kalman Filter based ceiling vision SLAM

abstract: This paper proposes a new approach of monocular ceiling vision based simultaneous localization and mapping (SLAM) by utilizing an improved Square Root Unscented Kalman Filter (SRUKF). With a monocular camera mounted on the top of a mobile robot and looking upward to the ceiling, the robot only needs to process salient features, which greatly reduce the computational complexity and have a high accuracy. SRUKF is used instead of the standard Extended Kalman Filter (EKF) to improve the linearization problem in both motion and perception models. To address the numerical instability problems in the standard SRUKF, several optimization methods are utilized in this paper. Experiments are performed to illustrate the effectiveness of the proposed approach.


title: Extracting reference text from citation contexts

abstract: Information from the textual context of citations in scientific articles has been studied and used in many applications by the research community. For example, it has been used in topic modeling, sentiment analysis, scientific paper summarization and information retrieval. However, these applications suffer the problem of right identification of citation context window and alternately use the text in a fixed size window around the citation mention. In this way, citation contexts may contain terms or other text that is not used for describing the citation and should not be included in the citation context. Identifying such non-reference text in the citation context is a non-trivial task, yet significant. In this paper, it is attempted to identify and remove the non-reference text from the citation contexts by developing a heuristic algorithm based on pruning the transition-based dependency parse tree. Evaluating the accuracy of our algorithm, results showed 77% macro-precision, 83% macro-recall and 80% F-macro for 88 research articles of testing dataset having varying number of citations. Additionally, we find that for many of the cited articles in our testing dataset, the number of objective citation contexts is more than subjective ones.


DeepInf : Modeling Influence Locality in Large Social Networks


title: Using Pivots to Speed-Up k-Medoids Clustering

abstract: Clustering is a key technique within the KDD process, with k-means, and the more general k-medoids, being well-known incremental partition-based clustering algorithms. A fundamental issue within this class of algorithms is to find an initial set of medians (or medoids) that improves the efficiency of the algorithms (e.g., accelerating its convergence to a solution), at the same time that it improves its effectiveness (e.g., finding more meaningful clusters). Thus, in this article we aim at providing a technique that, given a set of elements, quickly finds a very small number of elements as medoid candidates for this set, allowing to improve both the efficiency and effectiveness of existing clustering algorithms. We target the class of k-medoids algorithms in general, and propose a technique that selects a well-positioned subset of central elements to serve as the initial set of medoids for the clustering process. Our technique leads to a substantially smaller amount of distance calculations, thus improving the algorithm's efficiency when compared to existing methods, without sacrificing effectiveness. A salient feature of our proposed technique is that it is not a new k-medoid clustering algorithm per se, rather, it can be used in conjunction with any existing clustering algorithm that is based on the k-medoid paradigm. Experimental results, using both synthetic and real datasets, confirm the efficiency, effectiveness and scalability of the proposed technique.


Enterprise Applications: A Conceptual Look at ERP, CRM, and SCM


title: AML results for OAEI 2015

abstract: AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2015 competition and discusses its results. For this OAEI edition, we focused mainly on the Interactive Matching track due to its expansion, as handling user interactions on large-scale tasks is a critical challenge in ontology matching. AML's participation in the OAEI 2015 was successful, as it obtained the highest F-measure in 6 of the 7 ontology matching tracks. Notably, it obtained the highest F-measure in all tasks of the Interactive Matching track while posing less queries to the user than comparable participating systems.


title: Using ‘Big Data’ for analytics and decision support

abstract: People and the computers they use are generating large amounts of varied data. The phenomenon of capturing and trying to use all of the semi-structured and unstructured data has been called by vendors and bloggers ‘Big Data’. Organisations can capture and store data of many types from almost any source, but capturing and storing data only adds value when it has a useful purpose. Big Data must be used to provide input to analytics and decision support capabilities if it is to create real value for organisations. Some bloggers, industry leaders and academics have become disillusioned by the term Big Data. It is a marketing term and not a technical term. More descriptive terms like unstructured data, process data and machine data are more useful for information technology (IT) professionals. Researchers need to study and document use cases that explain how specific, novel data, so-called Big Data, can be used to support decision-making.


title: PersonaBank: A Corpus of Personal Narratives and Their Story Intention Graphs

abstract: We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their Story Intention Graphs, a deep representation of the fabula of a story. We describe the topics of the stories and the basis of the Story Intention Graph representation, as well as the process of annotating the stories to produce the Story Intention Graphs and the challenges of adapting the tool to this new personal narrative domain We also discuss how the corpus can be used in applications that retell the story using different styles of tellings, co-tellings, or as a content planner.


title: EBG-Embedded Multiple-Mode Resonator for UWB Bandpass Filter With Improved Upper-Stopband Performance

abstract: An electromagnetic bandgap (EBG) embedded multiple-mode resonator (MMR) is proposed to constitute an upper-stopband-improved and size-miniaturized ultra-wideband (UWB) bandpass filter (BPF). This EBG-embedded MMR is studied to relocate its first three resonant modes within the 3.1-10.6GHz passband, whereas placing its 4th resonant mode at the coupling transmission zero of interdigital coupled-lines that drive this MMR at two sides. Meanwhile, the fifth resonant mode is rejected by virtue of the bandstop behavior of the EBG itself. Thus, a modified UWB BPF with widened upper-stopband, sharpened upper rejection skirt and lowered loss in the passband is finally constituted, designed and fabricated. The measured results demonstrate that the insertion loss is lower than 1.0 dB in the passband (4.0-10.6GHz) and higher than 15.0dB in the upper-stopband (12.0 to 20.0GHz) while the group delay variation in the passband is less than 0.2ns


title: Cognitive gains in 7-month-old bilingual infants.

abstract: Children exposed to bilingual input typically learn 2 languages without obvious difficulties. However, it is unclear how preverbal infants cope with the inconsistent input and how bilingualism affects early development. In 3 eye-tracking studies we show that 7-month-old infants, raised with 2 languages from birth, display improved cognitive control abilities compared with matched monolinguals. Whereas both monolinguals and bilinguals learned to respond to a speech or visual cue to anticipate a reward on one side of a screen, only bilinguals succeeded in redirecting their anticipatory looks when the cue began signaling the reward on the opposite side. Bilingual infants rapidly suppressed their looks to the first location and learned the new response. These findings show that processing representations from 2 languages leads to a domain-general enhancement of the cognitive control system well before the onset of speech.


title: Self-Previewing Gestures and the Gesture-and-Effect Model: Experimentation with Responsive Visual Feedback for New and Unlearned Interactions

abstract: Multi-touch gestures embedded in touch-based interfaces and devices present challenges. It can be difficult for users to discover different gestures and understand their effects. The study reported in this paper hypothesises that presenting automatic visual prompts, termed self-previewing gestures (SPGs) in this research, that depict touch and preview gesture execution will mitigate the problems that users encounter with unfamiliar gestural interfaces. A within-subjects experiment (n=45) is reported in which an iPad application with two alternative gestural designs, and five alternative user interface versions (one industry, two research baselines, and two SPGs) was created with the purpose of making the available gestures evident. A rating system that adapts Norman's Theory of Action to touch-based interactions by making use of known principles within interaction design (perceptible affordances, feedforward and feedback) is proposed. The system was used to assess participants' perceptions of and interactions with the SPGs, and the results revealed positive and negative aspects of designs and UI versions.


title: Multi-label Fashion Image Classification with Minimal Human Supervision

abstract: We tackle the problem of multi-label classification of fashion images, learning from noisy data with minimal human supervision. We present a new dataset of full body poses, each with a set of 66 binary labels corresponding to the information about the garments worn in the image obtained in an automatic manner. As the automatically-collected labels contain significant noise, we manually correct the labels for a small subset of the data, and use these correct labels for further training and evaluation. We build upon a recent approach that both cleans the noisy labels and learns to classify, and introduce simple changes that can significantly improve the performance.


title: Ensemble learning methods for pay-per-click campaign management

abstract: We propose using ensemble techniques to improve pay-per-click campaign management.Actual data containing pay-per-click campaign results is used for our analysis.Four base classifiers are analyzed as inputs to four ensemble modeling techniques.A MetaCost ensemble predicted the highest campaign portfolio profit. Sponsored search advertising has become a successful channel for advertisers as well as a profitable business model for the leading commercial search engines. There is an extensive sponsored search research stream regarding the classification and prediction of performance metrics such as clickthrough rate, impression rate, average results page position and conversion rate. However, there is limited research on the application of advanced data mining techniques, such as ensemble learning, to pay per click campaign classification. This research presents an in-depth analysis of sponsored search advertising campaigns by comparing the classification results from four base classification models (Naive Bayes, logistic regression, decision trees, and Support Vector Machines) with four popular ensemble learning techniques (Voting, Boot Strap Aggregation, Stacked Generalization, and MetaCost). The goal of our research is to determine whether ensemble learning techniques can predict profitable pay-per-click campaigns and hence increase the profitability of the overall portfolio of campaigns when compared to standard classifiers. We found that the ensemble learning methods were superior classifiers based on a profit per campaign evaluation criterion. This paper extends the research on applied ensemble methods with respect to sponsored search advertising.


title: Single-Energy Partial Wave Analysis for $\pi^0$ Photoproduction on Proton with Fixed-$t$ Analyticity Imposed

abstract: High precision data of the $\gamma p \to \pi^0 p$ reaction from its threshold up to $W=2$~GeV have been used in order to perform a single-energy partial wave analysis with minimal model dependence. Continuity in energy was achieved by imposing constraints from fixed-$t$ analyticity in an iterative procedure. Reaction models were only used as starting point in the very first iteration. We demonstrate that with this procedure partial wave amplitudes can be obtained which show only a minimal dependence on the initial model assumptions.


title: Optimal Sensor Placement for Monitoring of Spatial Networks

abstract: The problem of optimal placement of sensors for monitoring a spatial network (e.g., a road network with moving ground targets or intruders) is considered in this paper. In particular, the optimization of locations of a set of sensors (that can each obtain measurements in a local region around the sensor location) is considered so as to maximize an overall sensor coverage metric defined over the spatial network. The sensor coverage optimality metric for spatial network coverage is based on a novel formulation of a sensor influence wave based on a spatiotemporal model of the measurement reach of a set of sensors given a spatial network topology, a probabilistic model of target movements on the network, and spatial weight maps that model the relative importance/utility of different locations in the spatial region. The sensor placement optimization is based on an iterative genetic algorithm for the optimization of a scalar metric computed from the spatial integration of the sensor influence wave. The efficacy of the proposed approach is demonstrated through simulation studies for several road network geometries. Note to Practitioners —This paper considers the problem of finding optimal locations for sensors for monitoring a spatial network for moving targets (e.g., a road network with moving ground targets or intruders). Sensor-based monitoring of a spatial region is relevant in a variety of applications (including, in general, such diverse application areas as traffic monitoring in transportation applications, intruder monitoring, surveillance, power systems monitoring, structural health monitoring, etc). This paper offers two primary novel aspects: an optimality metric formulation for target monitoring effectiveness on spatial networks and an iterative genetic algorithm-based method for optimization of the sensor placement configuration. The proposed approach enables addressing of multiple spatial criteria within a unified framework, including the probabilistic characterizations of spatial target movements over the network and the models of relative importance/utility of different locations/areas in the spatial region.


title: A highly area-efficient controller for capacitive touch screen panel systems

abstract: In this paper, a highly area-efficient controller for capacitive touch screen panels (TSPs) is proposed. The proposed controller uses a 10-bit successive approximation register analog-to-digital converter (SAR ADC) with an adder to compensate for the capacitance variation in the TSP and for the offset voltage variation in the charge amplifier of the sensing circuit. By using the proposed compensation method, the area of the controller can be reduced by 90.3% of the area of the conventional controllers. The measurement results showed that the signal-to-noise ratio (SNR) of the controller increases from 12.5 to 21.3 dB after compensation. Also, its spatial jitter decreases from ±1.5 to ±0.46 mm, which is 7% of the sensor pitch of 8 mm.


title: Disease diagnosis in smart healthcare: Innovation, technologies and applications

abstract: To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.


title: LoDPD: A Location Difference-Based Proximity Detection Protocol for Fog Computing

abstract: Proximity detection is one of the most common location-based applications in daily life when users intent to find their friends who get into their proximity. Studies on protecting user privacy information during the detection process have been widely concerned. In this paper, we first analyze a theoretical and experimental analysis of existing solutions for proximity detection, and then demonstrate that these solutions either provide a weak privacy preserving or result in a high communication and computational complexity. Accordingly, a location difference-based proximity detection protocol is proposed based on the Paillier cryptosystem for the purpose of dealing with the above shortcomings. The analysis results through an extensive simulation illustrate that our protocol outperforms traditional protocols in terms of communication and computation cost.


title: Ultralow-Power Electronics for Biomedical Applications

abstract: The electronics of a general biomedical device consist of energy delivery, analog-to-digital conversion, signal processing, and communication subsystems. Each of these blocks must be designed for minimum energy consumption. Specific design techniques, such as aggressive voltage scaling, dynamic power-performance management, and energy-efficient signaling, must be employed to adhere to the stringent energy constraint. The constraint itself is set by the energy source, so energy harvesting holds tremendous promise toward enabling sophisticated systems without straining user lifestyle. Further, once harvested, efficient delivery of the low-energy levels, as well as robust operation in the aggressive low-power modes, requires careful understanding and treatment of the specific design limitations that dominate this realm. We outline the performance and power constraints of biomedical devices, and present circuit techniques to achieve complete systems operating down to power levels of microwatts. In all cases, approaches that leverage advanced technology trends are emphasized.


title: Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice

abstract: Summary We report a technique for two-photon fluorescence imaging with cellular resolution in awake, behaving mice with minimal motion artifact. The apparatus combines an upright, table-mounted two-photon microscope with a spherical treadmill consisting of a large, air-supported Styrofoam ball. Mice, with implanted cranial windows, are head restrained under the objective while their limbs rest on the ball's upper surface. Following adaptation to head restraint, mice maneuver on the spherical treadmill as their heads remain motionless. Image sequences demonstrate that running-associated brain motion is limited to ∼2-5 μm. In addition, motion is predominantly in the focal plane, with little out-of-plane motion, making the application of a custom-designed Hidden-Markov-Model-based motion correction algorithm useful for postprocessing. Behaviorally correlated calcium transients from large neuronal and astrocytic populations were routinely measured, with an estimated motion-induced false positive error rate of


title: Accurate and Efficient Approximation of Clothoids Using Bézier Curves for Path Planning

abstract: An accurate and efficient clothoid approximation approach is presented in this paper using Bezier curves based on the minimization of curvature profile difference. Compared with existing methods, the proposed approach is able to guarantee higher order geometric continuity with smaller approximation error in terms of position, orientation, and curvature. The approximation scheme takes place in three stages. First, a subset of clothoids with specific winding angle constraints referred to as elementary clothoids is approximated using quintic Bezier curves. Then, a basic clothoid defined in the first quadrant is formulated, which is composed of a series of transformed elementary clothoids. An adaptive sampling stra-tegy is applied to ensure that the resulting Bezier segments are computed within a specified accuracy and all the required information can be obtained offline and stored in a lookup table. Finally, a general clothoid with arbitrary parameters can be conveniently approximated based on the lookup table through appropriate geometric transformations. A comparison with the recent circular interpolation and rational Bezier curve based approximation shows that the proposed approach is able to achieve equivalent or greater computational efficiency in most scenarios.


title: Impact of Parent’s Occupation and Family Income on Children’s Performance

abstract: The main purpose of the study is to examine the impact of parent’s occupation and family income on children performance.


title: Investigation of the Impacts of Large-Scale Wind Power Penetration on the Angle and Voltage Stability of Power Systems

abstract: The complexity of power systems has increased in recent years due to the operation of existing transmission lines closer to their limits, using flexible AC transmission system (FACTS) devices, and also due to the increased penetration of new types of generators that have more intermittent characteristics and lower inertial response, such as wind generators. This changing nature of a power system has considerable effect on its dynamic behaviors resulting in power swings, dynamic interactions between different power system devices, and less synchronized coupling. This paper presents some analyses of this changing nature of power systems and their dynamic behaviors to identify critical issues that limit the large-scale integration of wind generators and FACTS devices. In addition, this paper addresses some general concerns toward high compensations in different grid topologies. The studies in this paper are conducted on the New England and New York power system model under both small and large disturbances. From the analyses, it can be concluded that high compensation can reduce the security limits under certain operating conditions, and the modes related to operating slip and shaft stiffness are critical as they may limit the large-scale integration of wind generation.


title: Original Contribution: Recognition and segmentation of connected characters with selective attention

abstract: Abstract We have modified the original model of selective attention, which was previously proposed by Fukushima, and extended its ability to recognize and segment connected characters in cursive handwriting. Although the original model of selective attention already had the ability to recognize and segment patterns, it did not always work well when too many patterns were presented simultaneously. In order to restrict the number of patterns to be processed simultaneously, a search controller has been added to the original model. The new model mainly processes the patterns contained in a small “search area,” which is moved by the search controller. A preliminary experiment with computer simulation has shown that this approach is promising. The recognition and segmentation of characters can be successful even though each character in a handwritten word changes its shape by the effect of the characters before and behind.


title: Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method

abstract: Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas. In this paper, we propose a TCM prescription generation task that aims to automatically generate a herbal medicine prescription based on textual symptom descriptions. Sequence-to-sequence (seq2seq) model has been successful in dealing with conditional sequence generation tasks like dialogue generation. We explore a potential end-to-end solution to the TCM prescription generation task using seq2seq models. However, experiments show that directly applying seq2seq model leads to unfruitful results due to the severe repetition problem. To solve the problem, we propose a novel architecture for the decoder with masking and coverage mechanism. The experimental results demonstrate that the proposed method is effective in diversifying the outputs, which significantly improves the F1 score by nearly 10 points (8.34 on test set 1 and 10.23 on test set 2).


title: Deep learning for time series classification: a review

abstract: Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.


title: Overview of the DART project

abstract: DART (DSP Arrary for Reconfigurable Tasks) is a parallel architecture of two high-performance SDP (digital signal processing) chips with the flexibility to handle a wide range of real-time applications. Each of the 32-bit floating-point DSP processes in DART is programmable in a high-level languate (``C`` or Ada). We have added extensions to the real-time operating system used by DART in order to support parallel processor. The combination of high-level language programmability, a real-time operating system, and parallel processing support significantly reduces the development cost of application software for signal processing and control applications. We have demonstrated this capability by using DART to reconstruct images in the prototype VIP (Video Imaging Projectile) groundstation.


title: Designing of compact micro strip patch antenna with diamond structure and modified ground for isolation improvement

abstract: The wireless technology and broadband applications nowadays are very advanced from their early stages, but still an antenna is the indispensable item in these technologies. Hence this paper presented a compact micro strip 2X1 patch antenna for applications in WLAN band operating in the 5GHz–6GHz frequency range. Two element compact patch antenna with a combination of simple diamond decoupling structure is presented here. This antenna is constructed using RO 4350 substrate used for high frequency applications. After simulation with CST software the performance of the proposed antennas is investigated and compared to both the cases. An improvement of 9.2dB in coupling loss (S21) and 3.2dB in return loss (S11) are achieved.


title: Fuzzy Hidden Markov Models for Indonesian Speech Classification

abstract: Indonesia has a lot of tribe, so that there are a lot of dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. This study designs the solution of the different characteristics for Indonesian speech classification. The solution combines Fuzzy on Hidden Markov Models. The new design of fuzzy Hidden Markov Models will be proposed in this study. The models will consist of Fuzzy C-Means Clustering which will be designed to substitute the vector quantization process and a new forward and backward method to handle the membership degree of data. The result shows FHMM is better than HMM and the improvement was 3.33 %.


title: Full length article: Terahertz band: Next frontier for wireless communications

abstract: This paper provides an in-depth view of Terahertz Band (0.1-10 THz) communication, which is envisioned as a key technology to satisfy the increasing demand for higher speed wireless communication. THz Band communication will alleviate the spectrum scarcity and capacity limitations of current wireless systems, and enable new applications both in classical networking domains as well as in novel nanoscale communication paradigms. In this paper, the device design and development challenges for THz Band are surveyed first. The limitations and possible solutions for high-speed transceiver architectures are highlighted. The challenges for the development of new ultra-broadband antennas and very large antenna arrays are explained. When the devices are finally developed, then they need to communicate in the THz band. There exist many novel communication challenges such as propagation modeling, capacity analysis, modulation schemes, and other physical and link layer solutions, in the THz band which can be seen as a new frontier in the communication research. These challenges are treated in depth in this paper explaining the existing plethora of work and what still needs to be tackled.


A multimodal data-set of a unidirectional glass fibre reinforced polymer composite


title: Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

abstract: This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.


title: Fractal analysis of ice crystals in frozen food

abstract: Fractal analysis of the morphology of ice crystal particles in frozen food was attempted for frozen soybean curd (tofu). From a microscopic image analysis of the ice crystal particles, it was found that the perimeter of the ice crystal particles could be recognized as a fractal. Effects of the storage time and storage temperature on the fractal dimension (dp) of the perimeter of the ice crystal particles were also investigated. As storage time was increased, the dp value tended to decrease. When the storage temperature was increased, the value of dp decreased more rapidly. The changes corresponded to the visual observation of the shape change for the ice crystal particles during storage reported by many researchers, suggesting that the fractal dimension dp could be used as a quantitative indicator reflecting the surface roughness of ice crystal particles. Keywords: Fractal; ice crystal; image analysis; recrystallization