title: A Novel User Authentication and Key Agreement Protocol for Accessing Multi-Medical Server Usable in TMIS

abstract: Telecare Medical Information System (TMIS) makes an efficient and convenient connection between patient(s)/user(s) at home and doctor(s) at a clinical center. To ensure secure connection between the two entities (patient(s)/user(s), doctor(s)), user authentication is enormously important for the medical server. In this regard, many authentication protocols have been proposed in the literature only for accessing single medical server. In order to fix the drawbacks of the single medical server, we have primarily developed a novel architecture for accessing several medical services of the multi-medical server, where a user can directly communicate with the doctor of the medical server securely. Thereafter, we have developed a smart card based user authentication and key agreement security protocol usable for TMIS system using cryptographic one-way hash function. We have analyzed the security of our proposed authentication scheme through both formal and informal security analysis. Furthermore, we have simulated the proposed scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool and showed that the scheme is secure against the replay and man-in-the-middle attacks. The informal security analysis is also presented which confirms that the protocol has well security protection on the relevant security attacks. The security and performance comparison analysis confirm that the proposed protocol not only provides security protection on the above mentioned attacks, but it also achieves better complexities along with efficient login and password change phase.


title: Women in Management

abstract: Although women account for over one-half of the potential talent base throughout the world, as a group they have been marginalized and their economic, social and environmental contributions go largely unrealized. According to a 2008 report by the International Labour Office (ILO), "Global Employment Trends for Women-March 2008", released on the occasion of International Women's Day, more women are working than ever before, but they are also more likely than men to get low-productivity, low-paid and vulnerable jobs, with no social protection, basic rights nor voice at work. Women's representation in management positions in both the private and public sectors is markedly low and evidences another failure to make full use of available human capital. Barring a handful of companies like ICICI Bank, HSBC and Biocon, an overwhelming majority of Indian boardrooms are still no-go areas for women. Women today comprise only 2 per cent of the total managerial strength in the Indian corporate sector. Very few women hold top positions in corporate houses worldwide as a latest survey shows that only 14 per cent of women in India make it to the senior management level. Though an increasing number of women are proving that when it comes to taking up the corporate challenges, they have much more than just a pretty face to show, there are factors that hamper their growth within the organization. The present paper attempts to throw light on some vital statistics of women in workforce and managerial positions in India as well as over the globe. It further attempts to narrate some crucial factors that are hindering them to take up managerial jobs. Further mote a SWOT analysis of women managers has been carefully crafted which comprises of the Strengths, Weaknesses, Opportunities and Threats of women as managers.


Theory and Application of Digital Signal Processing


title: Collaboration in Software Engineering: A Roadmap

abstract: Software engineering projects are inherently cooperative, requiring many software engineers to coordinate their efforts to produce a large software system. Integral to this effort is developing shared understanding surrounding multiple artifacts, each artifact embodying its own model, over the entire development process. This focus on model- oriented collaboration embedded within a larger process is what distinguishes collaboration research in software engineering from broader collaboration research, which tends to address artifact-neutral coordination technologies and toolkits. This article first presents a list of goals for software engineering collaboration, then surveys existing collaboration support tools in software engineering. The survey covers both tools that focus on a single artifact or stage in the development process (requirements support tools, UML collaboration tools), and tools that support the representation and execution of an entire software process. Important collaboration standards are also described. Several possible future directions for collaboration in software engineering are presented, including tight integration between web and desktop development environments, broader participation by customers and end users in the entire development process, capturing argumentation surrounding design rationale, and use of massively multiplayer online (MMO) game technology as a collaboration medium. The article concludes by noting a problem in performing research on collaborative systems, that of assessing how well certain artifacts, models, and embedded processes work, and whether they are better than other approaches.


title: Convolutional Neural Network for Humor Recognition

abstract: For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The CNN method shows its advantages on both higher recognition accuracies and being able to learn essential features automatically.


Review on Extraction and Isolation of Plant Secondary Metabolites


Deep Learning Approaches to Text Production


title: Supervised hashing for image retrieval via image representation learning

abstract: Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/ dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of handcrafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions. The proposed method has two stages. In the first stage, given the pairwise similarity matrix S over training images, we propose a scalable coordinate descent method to decompose S into a product of HHT where H is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in H and optionally the discrete class labels of the images. Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.


title: An improved LSB based image steganography technique for RGB images

abstract: Steganography is the art and science of invisible communication by hiding secret information into other sources of information like text, video, audio, image etc. In image steganography the digital image is used as cover image in which we hide data and the message implanted image is called stego-image. There are number of steganography techniques proposed to hide data like LSB, DCT, pixel-value differencing, DFT etc. into images with precision level. But these techniques suffering from some problems like less hiding capacity, degrade the quality of image and security of hidden data after hiding more data into it. To overcome these problems this paper proposed an improved LSB technique for color images by embedding the information into three planes of RGB image in a way that enhances the quality of image and achieves high embedding capacity. The PSNR value of the proposed technique is better than previous steganography methods.


title: Epidermal Thickness at Different Body Sites: Relationship to Age, Gender, Pigmentation, Blood Content, Skin Type and Smoking Habits

abstract: Epidermal thickness and its relationship to age, gender, skin type, pigmentation, blood content, smoking habits and body site is important in dermatologic research and was investigated in this study. Biopsies from three different body sites of 71 human volunteers were obtained, and thickness of the stratum corneum and cellular epidermis was measured microscopically using a preparation technique preventing tissue damage. Multiple regressions analysis was used to evaluate the effect of the various factors independently of each other. Mean (SD) thickness of the stratum corneum was 18.3 (4.9) microm at the dorsal aspect of the forearm, 11.0 (2.2) microm at the shoulder and 14.9 (3.4) microm at the buttock. Corresponding values for the cellular epidermis were 56.6 (11.5) microm, 70.3 (13.6) microm and 81.5 (15.7) microm, respectively. Body site largely explains the variation in epidermal thickness, but also a significant individual variation was observed. Thickness of the stratum corneum correlated positively to pigmentation (p = 0.0008) and negatively to the number of years of smoking (p < 0.0001). Thickness of the cellular epidermis correlated positively to blood content (P = 0.028) and was greater in males than in females (P < 0.0001). Epidermal thickness was not correlated to age or skin type.


title: Tehran’s seismic vulnerability classification using granular computing approach

abstract: Tehran, capital of Iran, is located on a number of known and unknown faults which make this mega city exposed to huge earthquakes. Determining locations and intensity of seismic vulnerability of a city is considered as a complicated disaster management problem. As this problem generally depends on various criteria, one of the most important challenges concerned is the existence of uncertainty regarding inconsistency in combining those effective criteria. The emergence of uncertainty in seismic vulnerability map results to some biases in risk management which has multilateral effects in dealing with the consequences of the earthquake. To overcome this problem, this paper proposes a new approach for Tehran’s seismic vulnerability classification based on granular computing. One of the most significant properties of this method is inference of accurate rules having zero entropy from predefined classification undertaken based on training datasets by the expert. Furthermore, not-redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more nonredundant rules. In this paper, Tehran statistical zones (3,173 according to 1996 census) are considered as the study area. Since this city has not experienced a disastrous earthquake since 1830, this work’s results is the relative accurate with respect to the results of previous studies.


title: TernausNetV2: Fully Convolutional Network for Instance Segmentation

abstract: The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks. In this work we present TernausNetV2 - a simple fully convolutional network that allows extracting objects from a high-resolution satellite imagery on an instance level. The network has popular encoder-decoder type of architecture with skip connections but has a few essential modifications that allows using for semantic as well as for instance segmentation tasks. This approach is universal and allows to extend any network that has been successfully applied for semantic segmentation to perform instance segmentation task. In addition, we generalize network encoder that was pre-trained for RGB images to use additional input channels. It makes possible to use transfer learning from visual to a wider spectral range. For DeepGlobe-CVPR 2018 building detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods. The source code corresponding pre-trained weights are publicly available at https://github.com/ternaus/TernausNetV2


title: Designing for social play in co-located mobile games

abstract: In this paper we explore how mobile devices and co-location in mobile contexts contribute social play in game design, addressing the limited understanding of social interactivity in mobile games. Using the Mechanics-Dynamics-Aesthetics (MDA) framework, we code four games illustrating effective use of mobile, social, and co-located elements. Subsequently, we analyse and discuss this data to identify generalisability in these games. In our discussion we identify how these findings address game design problems of designing collaborative games. Furthermore, we contribute to theory of designing for social play from the perspectives of co-located mobile contexts in game design by identifying how mobility: (1) affords co-locating with other players in public spaces, (2) supports physical interactions using spatial context and players bodies, extending games beyond their formal system, (3) supports asymmetric design with information and interaction. Finally we contribute the concept of a pocket magic circle, a particular magic circle, to support a focus on game design leveraging these intersecting properties.


title: Exploration of Virtual and Augmented Reality for Visual Analytics and 3D Volume Rendering of Functional Magnetic Resonance Imaging (fMRI) Data

abstract: Statistical analysis of functional magnetic resonance imaging (fMRI), such as independent components analysis, is providing new scientific and clinical insights into the data with capabilities such as characterising traits of schizophrenia. However, with existing approaches to fMRI analysis, there are a number of challenges that prevent it from being fully utilised, including understanding exactly what a 'significant activity' pattern is, which structures are consistent and different between individuals and across the population, and how to deal with imaging artifacts such as noise. Interactive visual analytics has been presented as a step towards solving these challenges by presenting the data to users in a way that illuminates meaning. This includes using circular layouts that represent network connectivity and volume renderings with 'in situ' network diagrams. These visualisations currently rely on traditional 2D 'flat' displays with mouse-and-keyboard input. Due to the constrained screen space and an implied concept of depth, they are limited in presenting a meaningful, uncluttered abstraction of the data without compromising on preserving anatomic context. In this paper, we present our ongoing research on fMRI visualisation and discuss the potential for virtual reality (VR) and augmented reality (AR), coupled with gesture-based inputs to create an immersive environment for visualising fMRI data. We suggest that VR/AR can potentially overcome the identified challenges by allowing for a reduction in visual clutter and by allowing users to navigate the data abstractions in a 'natural' way that lets them keep their focus on the visualisations. We present exploratory research we have performed in creating immersive VR environments for fMRI data.


title: SELF-ESTEEM AND JOB PERFORMANCE: THE MODERATING ROLE OF SELF-ESTEEM CONTINGENCIES

abstract: Although theoretical perspectives suggest self-esteem level (i.e., high/low) should have main and moderating effects on job performance, empirical and narrative reviews of the literature suggest such effects are either nonexistent or highly variable. To account for these mixed findings, we hypothesized that self-esteem level should only have main and moderating effects on job performance when one's self-esteem is not contingent upon workplace performance. Using multisource ratings across 2 samples of working adults, we found that the importance of performance to self-esteem (IPSE) moderated the effect of self-esteem level on job performance and moderated the buffering interaction between self-esteem level and role conflict in the prediction of job performance. Our results thus support IPSE as an important moderator of both main and moderating effects of self-esteem level.


title: Effect of temperature on removal of antibiotic resistance genes by anaerobic digestion of activated sludge revealed by metagenomic approach

abstract: As antibiotic resistance continues to spread globally, there is growing interest in the potential to limit the spread of antibiotic resistance genes (ARGs) from wastewater sources. In particular, operational conditions during sludge digestion may serve to discourage selection of resistant bacteria, reduce horizontal transfer of ARGs, and aid in hydrolysis of DNA. This study applied metagenomic analysis to examine the removal efficiency of ARGs through thermophilic and mesophilic anaerobic digestion using bench-scale reactors. Although the relative abundance of various ARGs shifted from influent to effluent sludge, there was no measureable change in the abundance of total ARGs or their diversity in either the thermophilic or mesophilic treatment. Among the 35 major ARG subtypes detected in feed sludge, substantial reductions (removal efficiency >90%) of 8 and 13 ARGs were achieved by thermophilic and mesophilic digestion, respectively. However, resistance genes of aadA, macB, and sul1 were enriched during the thermophilic anaerobic digestion, while resistance genes of erythromycin esterase type I, sul1, and tetM were enriched during the mesophilic anaerobic digestion. Efflux pump remained to be the major antibiotic resistance mechanism in sludge samples, but the portion of ARGs encoding resistance via target modification increased in the anaerobically digested sludge relative to the feed. Metagenomic analysis provided insight into the potential for anaerobic digestion to mitigate a broad array of ARGs.


title: DLRS 2017: Second Workshop on Deep Learning for Recommender Systems

abstract: Deep learning methods became widely popular in the recommender systems community in 2016, in part thanks to the previous event of the DLRS workshop series. Now, deep learning has been embedded in the main conference as well and initial research directions have started forming, so the role of DLRS 2017 is to encourage starting new research directions, incentivize the application of very recent techniques from deep learning, and provide a venue for specialized discussion of this topic.


title: Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions.

abstract: Objective: To present nationally representative data on lifetime prevalence and comorbidity of pathological gambling with other psychiatric disorders and to evaluate sex differences in the strength of the comorbid associations. Method: Data were derived from a large national sample of the United States. Some 43,093 household and group quarters residents age 18 years and older participated in the 2001-2002 survey. Prevalence and associations of lifetime pathological gambling and other lifetime psychiatric disorders are presented. The diagnostic interview was the National Institute on Alcohol Abuse and Alcoholism Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version. Fifteen symptom items operationalized the 10 pathological gambling criteria. Results: The lifetime prevalence rate of pathological gambling was 0.42%. Almost three quarters (73.2%) of pathological gamblers had an alcohol use disorder, 38.1% had a drug use disorder, 60.4% had nicotine dependence, 49.6% had a mood disorder, 41.3% had an anxiety disorder, and 60.8% had a personality disorder. A large majority of the associations between pathological gambling and substance use, mood, anxiety, and personality disorders were overwhelmingly positive and significant (p .05). Conclusion: Pathological gambling is highly comorbid with substance use, mood, anxiety, and personality disorders, suggesting that treatment for one condition should involve assessment and possible concomitant treatment for comorbid conditions.


title: Model-based security engineering for cyber-physical systems

abstract: ContextCyber-physical systems (CPSs) have emerged to be the next generation of engineered systems driving the so-called fourth industrial revolution. CPSs are becoming more complex, open and more prone to security threats, which urges security to be engineered systematically into CPSs. Model-Based Security Engineering (MBSE) could be a key means to tackle this challenge via security by design, abstraction, and automation. ObjectiveWe aim at providing an initial assessment of the state of the art in MBSE for CPSs (MBSE4CPS). Specifically, this work focuses on finding out 1) the publication statistics of MBSE4CPS studies; 2) the characteristics of MBSE4CPS studies; and 3) the open issues of MBSE4CPS research. MethodWe conducted a systematic mapping study (SMS) following a rigorous protocol that was developed based on the state-of-the-art SMS and systematic review guidelines. From thousands of relevant publications, we systematically identified 48 primary MBSE4CPS studies for data extraction and synthesis to answer predefined research questions. ResultsSMS results show that for three recent years (2014-2016) the number of primary MBSE4CPS studies has increased significantly. Within the primary studies, the popularity of using Domain-Specific Languages (DSLs) is comparable with the use of the standardised UML modelling notation. Most primary studies do not explicitly address specific security concerns (e.g., confidentiality, integrity) but rather focus on security analyses in general on threats, attacks or vulnerabilities. Few primary studies propose to engineer security solutions for CPSs. Many focus on the early stages of development lifecycle such as security requirement engineering or analysis. ConclusionThe SMS does not only provide the state of the art in MBSE4CPS, but also points out several open issues that would deserve more investigation, e.g., the lack of engineering security solutions for CPSs, limited tool support, too few industrial case studies, and the challenge of bridging DSLs in engineering secure CPSs.


title: Social Media Use and Perceived Social Isolation Among Young Adults in the U.S.

abstract: INTRODUCTION ::: Perceived social isolation (PSI) is associated with substantial morbidity and mortality. Social media platforms, commonly used by young adults, may offer an opportunity to ameliorate social isolation. This study assessed associations between social media use (SMU) and PSI among U.S. young adults. ::: ::: ::: METHODS ::: Participants were a nationally representative sample of 1,787 U.S. adults aged 19-32 years. They were recruited in October-November 2014 for a cross-sectional survey using a sampling frame that represented 97% of the U.S. ::: ::: ::: POPULATION ::: SMU was assessed using both time and frequency associated with use of 11 social media platforms, including Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit. PSI was measured using the Patient-Reported Outcomes Measurement Information System scale. In 2015, ordered logistic regression was used to assess associations between SMU and SI while controlling for eight covariates. ::: ::: ::: RESULTS ::: In fully adjusted multivariable models that included survey weights, compared with those in the lowest quartile for SMU time, participants in the highest quartile had twice the odds of having greater PSI (AOR=2.0, 95% CI=1.4, 2.8). Similarly, compared with those in the lowest quartile, those in the highest quartile of SMU frequency had more than three times the odds of having greater PSI (AOR=3.4, 95% CI=2.3, 5.1). Associations were linear (p<0.001 for all), and results were robust to all sensitivity analyses. ::: ::: ::: CONCLUSIONS ::: Young adults with high SMU seem to feel more socially isolated than their counterparts with lower SMU. Future research should focus on determining directionality and elucidating reasons for these associations.


title: Humanoids and personal robots: Design and experiments

abstract: This paper addresses the field of humanoid and personal robotics—its objectives, motivations, and technical problems. The approach described in the paper is based on the analysis of humanoid and personal robots as an evolution from industrial to advanced and service robotics driven by the need for helpful machines, as well as a synthesis of the dream of replicating humans. ::: ::: ::: ::: The first part of the paper describes the development of anthropomorphic components for humanoid robots, with particular regard to anthropomorphic sensors for vision and touch, an eight-d.o.f. arm, a three-fingered hand with sensorized fingertips, and control schemes for grasping. Then, the authors propose a user-oriented design methodology for personal robots, and describe their experience in the design, development, and validation of a real personal robot composed of a mobile unit integrating some of the anthropomorphic components introduced previously and aimed at operating in a distributed working environment. ::: ::: ::: ::: Based on the analysis of experimental results, the authors conclude that humanoid robotics is a tremendous and attractive technical and scientific challenge for robotics research. The real utility of humanoids has still to be demonstrated, but personal assistance can be envisaged as a promising application domain. Personal robotics also poses difficult technical problems, especially related to the need for achieving adequate safety, proper human–robot interaction, useful performance, and affordable cost. When these problems are solved, personal robots will have an excellent chance for significant application opportunities, especially if integrated into future home automation systems, and if supported by the availability of humanoid robots. © 2001 John Wiley & Sons, Inc.


title: Neuronal correlates of theory of mind and empathy: A functional magnetic resonance imaging study in a nonverbal task

abstract: Theory of Mind (ToM), the ability to attribute mental states to others, and empathy, the ability to infer emotional experiences, are important processes in social cognition. Brain imaging studies in healthy subjects have described a brain system involving medial prefrontal cortex, superior temporal sulcus and temporal pole in ToM processing. Studies investigating networks associated with empathic responding also suggest involvement of temporal and frontal lobe regions. In this fMRI study, we used a cartoon task derived from Sarfati et al. (1997) [Sarfati, Y., Hardy-Bayle, M.C., Besche, C., Widlocher, D. 1997. Attribution of intentions to others in people with schizophrenia: a non-verbal exploration with comic strips. Schizophrenia Research 25, 199-209.]with both ToM and empathy stimuli in order to allow comparison of brain activations in these two processes. Results of 13 right-handed, healthy, male volunteers were included. Functional images were acquired using a 1.5 T Phillips Gyroscan. Our results confirmed that ToM and empathy stimuli are associated with overlapping but distinct neuronal networks. Common areas of activation included the medial prefrontal cortex, temporoparietal junction and temporal poles. Compared to the empathy condition, ToM stimuli revealed increased activations in lateral orbitofrontal cortex, middle frontal gyrus, cuneus and superior temporal gyrus. Empathy, on the other hand, was associated with enhanced activations of paracingulate, anterior and posterior cingulate and amygdala. We therefore suggest that ToM and empathy both rely on networks associated with making inferences about mental states of others. However, empathic responding requires the additional recruitment of networks involved in emotional processing. These results have implications for our understanding of disorders characterized by impairments of social cognition, such as autism and psychopathy.


title: Landmark Matching via Large Deformation

abstract: This paper describes the generation of large deformation diffeomorphisms phi:Omega=[0,1]3<-->Omega for landmark matching generated as solutions to the transport equation dphi(x,t)/dt=nu(phi(x,t),t),epsilon[0,1] and phi(x,0)=x, with the image map defined as phi(.,1) and therefore controlled via the velocity field nu(.,t),epsilon[0,1]. Imagery are assumed characterized via sets of landmarks {xn, yn, n=1, 2, ..., N}. The optimal diffeomorphic match is constructed to minimize a running smoothness cost parallelLnu parallel2 associated with a linear differential operator L on the velocity field generating the diffeomorphism while simultaneously minimizing the matching end point condition of the landmarks. Both inexact and exact landmark matching is studied here. Given noisy landmarks xn matched to yn measured with error covariances Sigman, then the matching problem is solved generating the optimal diffeomorphism phi;(x,1)=integral0(1)nu(phi(x,t),t)dt+x where nu(.)=argmin(nu.)integral1(0) integralOmega parallelLnu(x,t) parallel2dxdt +Sigman=1N[yn-phi(xn,1)] TSigman(-1)[yn-phi(xn,1)]. Conditions for the existence of solutions in the space of diffeomorphisms are established, with a gradient algorithm provided for generating the optimal flow solving the minimum problem. Results on matching two-dimensional (2-D) and three-dimensional (3-D) imagery are presented in the macaque monkey.


title: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

abstract: We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.


title: Graph Clustering using Effective Resistance

abstract: $ \def\vecc#1{\boldsymbol{#1}} $We design a polynomial time algorithm that for any weighted undirected graph $G = (V, E,\vecc w)$ and sufficiently large $\delta>1$, partitions $V$ into subsets $V_1, \ldots, V_h$ for some $h\geq 1$, such that $\bullet$ at most $\delta^{-1}$ fraction of the weights are between clusters, i.e. \[ w(E - \cup_{i = 1}^h E(V_i)) \lesssim \frac{w(E)}{\delta};\] $\bullet$ the effective resistance diameter of each of the induced subgraphs $G[V_i]$ is at most $\delta^3$ times the average weighted degree, i.e. \[ \max_{u, v \in V_i} \mathsf{Reff}_{G[V_i]}(u, v) \lesssim \delta^3 \cdot \frac{|V|}{w(E)} \quad \text{ for all } i=1, \ldots, h.\] In particular, it is possible to remove one percent of weight of edges of any given graph such that each of the resulting connected components has effective resistance diameter at most the inverse of the average weighted degree. Our proof is based on a new connection between effective resistance and low conductance sets. We show that if the effective resistance between two vertices $u$ and $v$ is large, then there must be a low conductance cut separating $u$ from $v$. This implies that very mildly expanding graphs have constant effective resistance diameter. We believe that this connection could be of independent interest in algorithm design.


title: Indigenous Land Rights and Self-Determination in Botswana

abstract: This article argues that government resistance to the recognition of indigenous land tenure and rights in Botswana presents a serious challenge for the future of indigenous Basarwa peoples. While the government of Botswana was a signatory to the United Nations Declaration on the Rights of Indigenous Peoples, it has not been willing to offer policy support for self-determination.


title: Finding motion parameters from spherical motion fields (or the advantages of having eyes in the back of your head)

abstract: A theory is developed for determining the motion of an observer given the motion field over a full 360 degree image sphere. The method is based on the fact that for an observer translating without rotation, the projected circular motion field about any equator can be divided into disjoint semicircles of clockwise and counterclockwise flow, and on the observation that the effects of rotation decouple around the three equators defining the three principal axes of rotation. Since the effect of rotation is geometrical, the three rotational parameters can be determined independently by searching, in each case, for a rotational value for which the derotated equatorial motion field can be partitioned into 180 degree arcs of clockwise and counterclockwise flow. The direction of translation is also obtained from this analysis. This search is two dimensional in the motion parameters, and can be performed relatively efficiently. Because information is correlated over large distances, the method can be considered a pattern recognition rather than a numerical algorithm. The algorithm is shown to be robust and relatively insensitive to noise and to missing data. Both theoretical and empirical studies of the error sensitivity are presented. The theoretical analysis shows that for white noise of bounded magnitude M, the expected errors is at worst linearly proportional to M. Empirical tests demonstrate negligible error for perturbations of up to 20% in the input, and errors of less than 20% for perturbations of up to 200%.


title: An Innovative Nonintrusive Driver Assistance System for Vital Signal Monitoring

abstract: This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.


title: Perception on the Usage of Mobile Assisted Language Learning (MALL) in English as a Second Language (ESL) Learning among Vocational College Students

abstract: Mobile phones are viewed as significant aids to English language learning. Numerous studies have been conducted in different contexts on mobile assisted language learning (MALL) that indicate the benefits of mobile devices for English language learning. Nevertheless, few studies on MALL had been conducted on among vocational college students in Malaysia. Therefore, this study was aimed to investigate the perception on the usage of MALL in English as a Second Language (ESL) among private vocational college students. Data were collected from a survey questionnaire adapted from Technology Acceptance Model (TAM). The results show the respondents have positive perception on the usage of MALL. Majority of the respondents showed overall agreement on both constructs perceived usefulness (PU) and perceived ease of use (PEoU) of MALL. They believed the usage of MALL will enhance the teaching and learning process. This evidence of acceptance has implication for educators and curriculum designers to exploit the mobile phone for autonomous and interactive ESL learning beyond the classroom context. It is hoped that MALL will be used as one of the teaching aids that could assist educators to teach English as a Second Language (ESL) more effectively.


title: Viscoelastic properties of polyamic acid solutions—precursors of polyimides

abstract: The rheology of polyamic acid (PAA) solutions, precursors of polyimides used in microelectronic device applications, has been investigated by dynamic (oscillatory) shear flow measurements. Frequency dependent storage and loss moduli and dynamic viscosity were measured in the frequency range 10−1 to 103 rad/s at 23°C. The storage modulus G′ (ω) and loss modulus G″ (ω) exhibited quadratic and linear dependence in frequency at low frequencies respectively, the viscoelastic fluid behavior commonly predicted for polymer solutions from many molecular theories. At high frequencies both dynamic moduli become proportional to ω2/3. The results show that PAA solutions are very high loss viscoelastic fluids, judging from the loss tangent values which far exceed unity. It is suggested that dynamic viscoelastic properties could be used to monitor the degree of imidization since there is a gradual change from viscoelastic fluids to soft viscoelastic solids to hard viscoelastic solids as PAA is converted to polyimides. Onset of non-Newtonian flow as shown on the frequency dependent dynamic viscosity was in the range 30 to 200 rad/s. The viscoelastic constants, zero-shear rate viscosity ηo and steady-state compliance Je0, where also determined from the dynamic data and compared to previous steady shear flow results.


title: An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews

abstract: Opinion mining is a challenging task to identify the opinions or sentiments underlying user generated contents, such as online product reviews, blogs, discussion forums, etc. Previous studies that adopt machine learning algorithms mainly focus on designing effective features for this complex task. This paper presents our approach based on tree kernels for opinion mining of online product reviews. Tree kernels alleviate the complexity of feature selection and generate effective features to satisfy the special requirements in opinion mining. In this paper, we define several tree kernels for sentiment expression extraction and sentiment classification, which are subtasks of opinion mining. Our proposed tree kernels encode not only syntactic structure information, but also sentiment related information, such as sentiment boundary and sentiment polarity, which are important features to opinion mining. Experimental results on a benchmark data set indicate that tree kernels can significantly improve the performance of both sentiment expression extraction and sentiment classification. Besides, a linear combination of our proposed tree kernels and traditional feature vector kernel achieves the best performances using the benchmark data set.


title: CUSUM and chi-squared attack detection of compromised sensors

abstract: A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying falsified sensor measurements. To fulfill a desired detection performance, given the system dynamics, we derive tools for tuning the CUSUM procedure. We characterize the state degradation that a stealthy attacker can induce to the system while remaining undetected by the detection procedure. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulation experiments are presented to illustrate the performance of the detection scheme.


Molecular cloning and sequence analysis of the catalytic subunit of bovine type 2


title: PRADA: Protecting Against DNN Model Stealing Attacks

abstract: Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives.


title: Food, Toys, and Love: Pediatric Palliative Care

abstract: P ediatric palliative care is a new interdisciplinary frontier in the comprehensive care of children. While children with life-threatening and life-limiting conditions have always been part of the health care system, it is only now that an integrated vision toward their care is emerging. The underlying principles and ethics of palliative care are universal across the life span. However, as in all specialties, children bring with them unique issues and dilemmas. A child or adolescent with a life-threatening or lifelimiting condition throws an assumed sequence out of order. A time of role reversal is expected, when children will care for dying parents. When parents instead find themselves watching their child face the threat of death, imminent or not, any sense of order is shattered. Even before the child has become a differentiated individual through a natural developmental sequence, that child is wrested away. There is little preparation for separation by death when a psychological separation has not yet been effected. The adolescent who is beginning to negotiate an independent existence often seems especially hard to face when that “moving forward” is irreversibly halted, or at least disrupted. The critical importance of this new field was recently highlighted in the Institute of Medicine Report: When Children Die: Improving Palliative and End-of-Life Care for Children and their Families. Previously, several powerful publications had already set the stage for this seminal report: a thematic issue of the Journal of Palliative Care (When Children Have to Die: Pediatric Palliative Care), a powerful statement by the American Academy of Pediatrics, the findings of two comprehensive British reports on children and young people with life-threatening and terminal conditions, and two earlier Institute of Medicine studies. The Institute of Medicine Report in 2003 summarized the current evidence base of the field as follows:


title: Telecom Inventory Management via Object Recognition and Localisation on Google Street View Images

abstract: We present a novel method to update assets for telecommunication infrastructure using google street view (GSV) images. The problem is formulated as a object recognition task, followed by use of triangulation to estimate the object coordinates from sensor plane coordinates, To this end, we have explored different state-of-the-art object recognition techniques both from feature engineering and using deep learning namely HOG descriptors with SVM, Deformable parts model (DPM), and Deep learning (DL) using faster RCNNs. While HOG+SVM has proved to be robust human detector, DPM which is based on probabilistic graphical models and DL which is a non-linear classifier have proved their versatility in different types of object recognition problems. Asset recognition from the street view images however pose unique challenge as they could be installed on the ground in various poses, orientations and with occlusions, objects camouflaged in the background and in some cases inter class variation is small. We present comparative performance of these techniques for specific use-case involving telecom equipment for highest precision and recall. The blocks of proposed pipeline are detailed and compared to traditional inventory management methods.


title: Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

abstract: The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.


title: Endoscopic submucosal dissection (ESD) compared with gastrectomy for treatment of early gastric neoplasia: a retrospective cohort study

abstract: INTRODUCTION ::: This study aims to compare perioperative outcomes and oncological clearance of endoscopic submucosal dissection (ESD) versus gastrectomy for treatment of early gastric cancer (EGC). ::: ::: ::: METHODS ::: This is a retrospective cohort study including all cases of EGC or severe dysplasia treated at a university-affiliated hospital from 1993 to 2010. Preoperative endoscopic ultrasound and image-enhanced endoscopy were employed to determine depth of invasion. Clinical outcomes including baseline demographics, pathology, postoperative complication, and hospital stay, as well as 3-year survival were compared. ::: ::: ::: RESULTS ::: From 1993 to 2010, 114 patients with severe dysplasia or EGC were treated: 40 of them received gastrectomy, while 74 received ESD. There was no difference in age, gender, comorbidity or American Society of Anesthesiologists grade between the two groups. Of patients in the gastrectomy group, 92.5% presented with symptoms as compared with 27.0% of those treated by ESD (p < 0.001). More patients in the ESD group had atrophic gastritis (31.1 vs 10%; p = 0.009) and intestinal metaplasia (68.9 vs 55.0%; p = 0.04). Patients treated by gastrectomy sustained longer operative time [265 (150-360) min] when compared with ESD [89.6 (45-360) min; p < 0.001]. They also had longer median hospital stay [9.9 (6-26) days vs 3.0 (2-10) days; p < 0.001]. There was no perioperative mortality, but the overall complication rate was significantly higher in the gastrectomy group. The 3-year survival rate was 94.6% for ESD and 89.7% for gastrectomy group (log-rank test, p = 0.44). ::: ::: ::: CONCLUSIONS ::: ESD achieved similar oncological outcomes when compared with radical gastrectomy for treatment of EGC. Patients receiving ESD had better perioperative outcomes in terms of operative time, complication rate, and hospital stay.


title: Love You to Bits and Pieces: Life with David Helfgott

abstract: The biography of David Helfott, a gifted young Australian pianist who suffered a severe mental breakdown and was institutionalized. Although brilliant and charismatic, he was insecure and reliant on anti-psychotic medication, but eventually overcame his illness to achieve artistic greatness. This story was written by his wife Gillian, who tells of the powerful bond between them and of sharing her life with a man who came through the darkness of his past.


Towards a Taxonomy of Bad Smells Detection Approaches


title: Visible Light Communication Channel Modeling for Underwater Environments With Blocking and Shadowing

abstract: In this paper, we present a comprehensive channel modeling and characterization study for underwater visible light communications. Our study is based on the advanced ray tracing, which allows for an accurate description of the interaction of rays emitted from the lighting source within an underwater environment. Contrary to existing works, which are mainly limited to simplified underwater scenarios, i.e., empty sea, we take into account the presence of human and man-made objects to investigate the effects of shadowing and blockage. The reflection characteristics of the sea surface and sea bottom as well as the water characteristics, i.e., extinction coefficient and scattering phase function of particles, are precisely considered. As case studies, we consider various underwater scenarios with different transmitter/receiver specifications (i.e., viewing angle, aperture size) and different depths from the sea surface. For each environment, we obtain channel impulse responses and present a characterization study where channel parameters, such as channel DC gain, path loss, and delay spread, are obtained.


title: PKDE4J: Entity and relation extraction for public knowledge discovery

abstract: Abstract Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.


title: A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces

abstract: Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.


title: Executive Functions after Age 5: Changes and Correlates.

abstract: Research and theorizing on executive function (EF) in childhood has been disproportionately focused on preschool age children. This review paper outlines the importance of examining EF throughout childhood, and even across the lifespan. First, examining EF in older children can address the question of whether EF is a unitary construct. The relations among the EF components, particularly as they are recruited for complex tasks, appear to change over the course of development. Second, much of the development of EF, especially working memory, shifting, and planning, occurs after age 5. Third, important applications of EF research concern the role of school-age children’s EF in various aspects of school performance, as well as social functioning and emotional control. Future research needs to examine a more complete developmental span, from early childhood through late adulthood, in order to address developmental issues adequately.


title: Scalable Diversified Ranking on Large Graphs

abstract: Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm to find the top-K diversified ranking list on graphs. The key idea of our algorithm is that we first compute the Page rank of the nodes of the graph, and then perform a carefully designed vertex selection algorithm to find the top-K diversified ranking list. Specifically, we firstly present a new diversified ranking measure, which can capture both \emph{relevance} and \emph{diversity}. Secondly, we prove the sub modularity of the proposed measure. And then we propose an efficient greedy algorithm with linear time and space complexity with respect to the size of the graph to achieve near-optimal diversified ranking. Finally, we evaluate the proposed method through extensive experiments on four real networks. The experimental results indicate that the proposed method outperforms existing diversified ranking algorithms both on improving diversity in ranking and the efficiency of the algorithms.


title: Robust saliency detection via regularized random walks ranking

abstract: In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. In this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. To provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. By taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. Experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches.


title: Diabetic Retin Reg

abstract: Diabetic Retinopathy (DR) is one of the disease due to complication of diabetes. Late in treatment of this disease can cause blindness. Therefore early eye screening to detect the existing of the abnormality in retina is very crucial for further treatment. There are several abnormalities or lesions as sign of DR disease such as microaneurysm, hemorrhage, soft exudates and hard exudates. In this article automated method using region based approach was proposed to identify abnormal region on fundus image. The approach involves region feature extraction using haralick texture features and two different classifier; support vector machine (SVM) and multi layer perceptron (MLP) with different experiment. The advantageous of this approach are not involving normal structure segmentation process as standard procedure applied by many researchers. The proposed approach was tested on several types dataset and the result show promising result.


title: Randomized dynamic graph algorithms with polylogarithmic time per operation

abstract: This paper solves a longstanding open problem in fully dynamic algorithms: We present the first fully dynamic algorithms that maintain connectivity, bipartiteness, and approximate minimum spanning trees in polylogarithmic time per edge insertion or deletion. The algorithms are designed using a new dynamic technique that combines a novel graph decomposition with randomization. They are Las-Vegas type randomized algorithms which use simple data structures and have a small constant factor. Let n denote the number of nodes in the graph. For a sequence of V(m0) operations, where m0 is the number of edges in the initial graph, the expected time for p updates is O( p log 3 n) (Throughout the paper the logarithms are base 2.) for connectivity and bipartiteness. The worst-case time for one query is O(log n/log log n). For the k-edge witness problem ("Does the removal of k given edges disconnect the graph?") the expected time for p updates is O( p log 3 n) and the expected time for q queries is O(qk log 3 n). Given a graph with k different weights, the minimum spanning tree can be maintained during a sequence of p updates in expected time O( pk log 3 n). This implies an algorithm to maintain a 1 1 e-approximation of the minimum spanning tree in expected time O((p log 3 n log U)/e) for p updates, where the weights of the edges are between 1 and U.


Experience of surgical treatment of the carotid body tumor and tumors with invasion at the carotid vessels


title: Multi-core data analytics SoC with a flexible 1.76 Gbit/s AES-XTS cryptographic accelerator in 65 nm CMOS

abstract: Embedded systems for Internet-of-Things applications present new challenges to system design. From a hardware design perspective, energy efficiency is paramount, as most of devices have a limited power supply due to size considerations. Transmitting data away from the node remains a very power hungry operation, and the only viable solution to this problem is to reduce the amount of data by performing pre-processing which again requires additional computational power. Hence modern embedded devices need to strike a fine balance between the power needed for acquisition/processing and communication. In many scenarios, small IoT devices will be deployed widely making them vulnerable to malicious attacks. Thus, for practical applications, these devices also need to fit the necessary resources to provide adequate security services. We present a cryptographic hardware accelerator capable of supporting multiple encryption and decryption modes for different cryptographic algorithms (AES, Keccak) in an energy efficient multi-core cluster optimized for embedded digital signal processing applications implemented in 65 nm CMOS technology. We show that it is possible to have the necessary computation power to perform cryptographic services in addition to state of the art processing in a power budget that is compatible with IoT devices in a mature 65 nm CMOS technology. When running at 0.8 V the SoC with the cryptographic accelerator can be clocked at 84 MHz running AES-XTS at more than 250 Mbits/s consuming a total of 27 mW, which is a 100 × gain in energy and 496 × gain in operation speed over an optimized software implementation running on a single 32 bit OpenRISC core.


title: Combining a modified vector field histogram algorithm and real-time image processing for unknown environment navigation

abstract: Q is an unmanned ground vehicle designed to compete in the Autonomous and Navigation Challenges of the AUVSI ::: Intelligent Ground Vehicle Competition (IGVC). Built on a base platform of a modified PerMobil Trax off-road wheel ::: chair frame, and running off a Dell Inspiron D820 laptop with an Intel t7400 Core 2 Duo Processor, Q gathers ::: information from a SICK laser range finder (LRF), video cameras, differential GPS, and digital compass to localize its ::: behavior and map out its navigational path. This behavior is handled by intelligent closed loop speed control and robust ::: sensor data processing algorithms. In the Autonomous challenge, data taken from two IEEE 1394 cameras and the LRF ::: are integrated and plotted on a custom-defined occupancy grid and converted into a histogram which is analyzed for ::: openings between obstacles. The image processing algorithm consists of a series of steps involving plane extraction, ::: normalizing of the image histogram for an effective dynamic thresholding, texture and morphological analysis and ::: particle filtering to allow optimum operation at varying ambient conditions. In the Navigation Challenge, a modified ::: Vector Field Histogram (VFH) algorithm is combined with an auto-regressive path planning model for obstacle ::: avoidance and better localization. Also, Q features the Joint Architecture for Unmanned Systems (JAUS) Level 3 ::: compliance. All algorithms are developed and implemented using National Instruments (NI) hardware and LabVIEW ::: software. The paper will focus on explaining the various algorithms that make up Q's intelligence and the different ways ::: and modes of their implementation.


Measuring Semantic Similarity of Words Using Concept Networks


title: Uncertainty, Action, and Interaction: In Pursuit of Mixed-Initiative Computing

abstract: 1One group of investigators has expressed optimism about the potential for refining intelligent-interface agents, suggesting that research should focus on developing more powerful representations and inferential machinery for sensing a user’s activity and taking automated actions. 2–4 Other researchers have voiced concerns that efforts focused on automation might be better expended on tools and metaphors that enhance the abilities of users to directly manipulate and inspect objects and information. 5 Rather than advocating one approach over the other, a creative integration of direct manipulation and automated services could provide fundamentally new kinds of user experiences, characterized by deeper, more natural collaborations between users and computers. In particular, there are rich opportunities for interweaving direct control and automation to create mixed-initiative systems and interfaces. Computer scientists have used the term mixed-initiative in various ways. These include references to the automated control of turn taking in human-computer conversation, 6 and the


The Robo-havioral Methodology : Developing Neuroscience Theories with FOES


title: Metric Rectification of Curved Document Images

abstract: In this paper, we propose a metric rectification method to restore an image from a single camera-captured document image. The core idea is to construct an isometric image mesh by exploiting the geometry of page surface and camera. Our method uses a general cylindrical surface (GCS) to model the curved page shape. Under a few proper assumptions, the printed horizontal text lines are shown to be line convergent symmetric. This property is then used to constrain the estimation of various model parameters under perspective projection. We also introduce a paraperspective projection to approximate the nonlinear perspective projection. A set of close-form formulas is thus derived for the estimate of GCS directrix and document aspect ratio. Our method provides a straightforward framework for image metric rectification. It is insensitive to camera positions, viewing angles, and the shapes of document pages. To evaluate the proposed method, we implemented comprehensive experiments on both synthetic and real-captured images. The results demonstrate the efficiency of our method. We also carried out a comparative experiment on the public CBDAR2007 data set. The experimental results show that our method outperforms the state-of-the-art methods in terms of OCR accuracy and rectification errors.


title: Congestion Adaptive Routing in Mobile Ad Hoc Networks

abstract: Mobility, channel error, and congestion are the main causes for packet loss in mobile ad hoc networks. Reducing packet loss typically involves congestion control operating on top of a mobility and failure adaptive routing protocol at the network layer. In the current designs, routing is not congestion-adaptive. Routing may let a congestion happen which is detected by congestion control, but dealing with congestion in this reactive manner results in longer delay and unnecessary packet loss and requires significant overhead if a new route is needed. This problem becomes more visible especially in large-scale transmission of heavy traffic such as multimedia data, where congestion is more probable and the negative impact of packet loss on the service quality is of more significance. We argue that routing should not only be aware of, but also be adaptive to, network congestion. Hence, we propose a routing protocol (CRP) with such properties. Our ns-2 simulation results confirm that CRP improves the packet loss rate and end-to-end delay while enjoying significantly smaller protocol overhead and higher energy efficiency as compared to AODV and DSR


title: The impact of glucuronidation on the bioactivation and DNA adduction of the cooked-food carcinogen 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine in vivo

abstract: UDP-glucuronosyltransferases (UGTs) catalyze the glucuronidation of many different chemicals. Glucuronidation is especially important for detoxifying reactive intermediates from metabolic reactions, which otherwise can be biotransformed into highly reactive cytotoxic or carcinogenic species. Detoxification of certain food-borne-carcinogenic heterocyclic amines (HAs) is highly dependent on UGT1A-mediated glucuronidation. 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), the most mass abundant carcinogenic HA found in well-done cooked meat, is extensively glucuronidated by UGT1A proteins. In humans, CYP1A2 catalyzed N-hydroxylation and subsequent UGT1A-mediated glucuronidation is a dominant pathway in the metabolism of PhIP. Therefore, changes in glucuronidation rates could significantly alter PhIP metabolism. To determine the importance of UGT1A-mediated glucuronidation in the biotransformation of PhIP, hepatic UGT1A deficient Gunn and UGT1A proficient Wistar rats were exposed to a 100 microg/kg oral dose of [(14)C]PhIP. Urine was collected over 24 h and the PhIP urinary metabolite profiles were compared between the two strains. After the 24 h exposure, livers and colons were removed and analyzed for DNA adduct formation by accelerator mass spectrometry. Wistar rats produced several PhIP and N-hydroxy-PhIP glucuronides that accounted for approximately 25% of the total amount of recovered urinary metabolites. In the Gunn rats, PhIP and N-hydroxy-PhIP glucuronides were reduced by 68-92%, compared with the Wistar rats. PhIP-DNA adduct analysis from the Gunn rats revealed a correlation between reduced urinary PhIP and N-hydroxy-PhIP glucuronide levels and increased hepatic DNA adducts, compared with the Wistar rats. In the colon, DNA adduct levels were lower in the Gunn rats compared with the Wistar rats, suggesting deficient hepatic UGT1A activity provides protection against DNA adduct formation in peripheral tissue. Due to differences in PhIP metabolism between humans and rodents, extrapolation of these results to the human situation must be done with caution. These results indicate that UGT1A-mediated glucuronidation of PhIP and N-hydroxy-PhIP is an important pathway for PhIP detoxification, and demonstrate the importance of tissue-specific metabolism. Tissues with reduced UGT1A activity can have a higher rate of PhIP activation and be more inclined to form DNA adducts compared with tissues with normal UGT1A activity.


title: Power Assist Wear Driven with Pneumatic Rubber Artificial Muscles

abstract: In the coming advanced age society, an innovative technology to assist the activities of daily living of elderly and disabled people and the heavy work in nursing is desired. To develop such a technology, an actuator safe and friendly for human is required. It should be small, lightweight and has to provide a proper softness. A pneumatic rubber artificial muscle is available as such actuators. We have developed some types of pneumatic rubber artificial muscles and applied them to wearable power assist devices. A wearable power assist device is equipped to the human body to assist the muscular force, which supports activities of daily living, rehabilitation, heavy working, training and so on. In this paper, some types of pneumatic rubber artificial muscles developed in our laboratory are introduced. Further, two kinds of wearable power assist devices driven with the rubber artificial muscles are described. Some evaluations can clarify the effectiveness of pneumatic rubber artificial muscle for such an innovative human assist technology.


title: Concepts and Concept Formation

abstract: categories. That is, since cue validity is the probability of being in some category given some property, this probability will increase (or at worst not decrease) as the size of the category increases (e.g. the probability of being an animal given the property of flying is greater than the probability of bird given flying, since there must be more animals that fly than birds that fly).6 The idea that cohesive categories maximize the probability of particular properties given the category fares no better. In this case, the most specific categories will always be picked out. Medin (1982) has analyzed a variety of formal measures of category cohe siveness and pointed out problems with all of them. For example, one possible principle is to have concepts such that they minimize the similarity between contrasting categories; but minimizing between-category similarity will always lead one to sort a set of n objects into exactly two categories. Similarly, functions based on maximizing within-category similarity while minimizing between-category similarity lead to a variety of problems and counterintuitive expectations about when to accept new members into existent categories versus when to set up new categories. At a less formal but still abstract level, Sternberg (1982) has tried to translate some of Goodman's (e.g. 1983) ideas about induction into possible constraints on natural concepts. Sternberg suggests that the apparent naturalness of a concept increases with the familiarity of the concept (where familiarity is related to Goodman's notion of entrenchment), and decreases with the number of transformations specified in the concept (e.g. aging specifies certain trans


title: Gender and ethnicity classification using deep learning in heterogeneous face recognition

abstract: Although automated classification of soft biometric traits in terms of gender, ethnicity and age is a well-studied problem with a history of more than three decades, it is still far from being considered a solved problem for the case of difficult exposure conditions, such as during night-time, in environments with unconstrained lighting, or at large distances from the camera. In this paper, we investigate the advantages and limitations of the automated classification of soft biometric traits in terms of gender and ethnicity in Near-Infrared (NIR) long-range, night-time face images. The impact of soft biometric traits in terms of gender and ethnicity is explored for the purpose of improving cross-spectral face recognition (FR) performance. The main contributions are, (i) a dual database collected in NIR band at night time and at four different distances of 30, 60, 90 and 120 meters is used, (ii) a deep convolution neural network to perform the classification in terms of gender and ethnicity is proposed, (iii) a set of experiments is performed indicating that, the usage of soft biometric traits to perform face matching, resulted in a significantly improved rank-1 identification rate when compared to the original biometric system (scenario dependent).


title: HEMnet: Integration of Electronic Medical Records with Molecular Interaction Networks and Domain Knowledge for Survival Analysis

abstract: The continual growth of electronic medical record (EMR) databases has paved the way for many data mining applications, including the discovery of novel disease-drug associations and the prediction of patient survival rates. However, these tasks are hindered because EMRs are usually segmented or incomplete. EMR analysis is further limited by the overabundance of medical term synonyms and morphologies, which causes existing techniques to mismatch records containing semantically similar but lexically distinct terms. Current solutions fill in missing values with techniques that tend to introduce noise rather than reduce it. In this paper, we propose to simultaneously infer missing data and solve semantic mismatching in EMRs by first integrating EMR data with molecular interaction networks and domain knowledge to build the HEMnet, a heterogeneous medical information network. We then project this network onto a low-dimensional space, and group entities in the network according to their relative distances. Lastly, we use this entity distance information to enrich the original EMRs. We evaluate the effectiveness of this method according to its ability to separate patients with dissimilar survival functions. We show that our method can obtain significant (p-value


title: Through glass vias (TGV) and aspects of reliability

abstract: Glass as a substrate for electronics packaging has many potential benefits, including the ability to tailor the thermomechanical and electrical properties of the glass to meet the demands of a given application. Because this process may entail modifications to the chemical composition of the glass, the impact of the composition changes to the long term reliability of the electronic package should be carefully considered. Here, two different compositions of glass are examined with regards to their use as a glass interposer, and specific aspects of their reliability are tested. A glass interposer design was fabricated on glass substrates of two different coefficients of thermal expansion (CTEs), 3 and 8 ppm/°C. This design has 35 × 120 µm through glass vias (TGVs) and front and backside Cu routing metal. The routing metal and TGVs form electrically testable daisy chains. Samples of these glass interposers were subjected to 1000 thermal cycles from - 40 °C to 125 °C to compare the two different glass compositions with regard to their long term reliability. Electrical testing on daisy-chained arrays of 400 TGVs each before and after thermal cycling showed no failures of the structures. In a separate test, Cu interdigitated test structures with 10 µm lines/space were fabricated on glass substrates of the same two types, on silicon with a thermal oxide layer, and also on fused silica. The leakage current between isolated structures was tested before and after 96 hrs of biased highly accelerated stress testing (HAST). The results indicate that for the glass substrate with a CTE of 8 ppm/°C, a barrier layer is necessary between the substrate and Cu metallization to prevent Cu migration.


title: A guide to selecting a network similarity method

abstract: We consider the problem of determining how similar two networks (without known node-correspondences) are. This problem occurs frequently in real-world applications such as transfer learning and change detection. Many networksimilarity methods exist; and it is unclear how one should select from amongst them. We provide the first empirical study on the relationships between different networksimilarity methods. Specifically, we present (1) an approach for identifying groups of comparable network-similarity methods and (2) an approach for computing the consensus among a given set of network-similarity methods. We compare and contrast twenty network-similarity methods by applying our approaches to a variety of real datasets spanning multiple domains. Our experiments demonstrate that (1) different network-similarity methods are surprisingly well correlated, (2) some complex network-similarity methods can be closely approximated by a much simpler method, and (3) a few network similarity methods produce rankings that are very close to the consensus ranking.


title: Bricolage versus breakthrough: distributed and embedded agency in technology entrepreneurship

abstract: We develop a perspective on technology entrepreneurship as involving agency that is distributed across different kinds of actors. Each actor becomes involved with a technology, and, in the process, generates inputs that result in the transformation of an emerging technological path. The steady accumulation of inputs to a technological path generates a momentum that enables and constrains the activities of distributed actors. In other words, agency is not only distributed, but it is embedded as well. We explicate this perspective through a comparative study of processes underlying the emergence of wind turbines in Denmark and in United States. Through our comparative study, we flesh out “bricolage” and “breakthrough” as contrasting approaches to the engagement of actors in shaping technological paths. © 2002 Elsevier Science B.V. All rights reserved.


title: An Unsupervised Approach to Product Attribute Extraction

abstract: Product Attribute Extraction is the task of automatically discovering attributes of products from text descriptions. In this paper, we propose a new approach which is both unsupervised and domain independent to extract the attributes. With our approach, we are able to achieve 92% precision and 62% recall in our experiments. Our experiments with varying dataset sizes show the robustness of our algorithm. We also show that even a minimum of 5 descriptions provide enough information to identify attributes.


title: Automatic vs. Explicit Memory Management: Settling the Performance Debate

abstract: While garbage collection’s software engineering benefits are indisputable, its performance impact remains controversial. Garbage collection proponents argue that its benefits outweigh its costs, but it is widely believed that garbage collection imposes an unacceptably high runtime and space performance penalty. This paper aims to settle this debate. We present the first empirical comparison of the performance costs of automatic versus explicit memory management in a garbage-collected language. Using a tracing and simulation based oracular memory manager, we execute unaltered Java programs as if they used explicit memory management. We examine the runtime, space consumption and virtual memory footprint of Java benchmarks across a range of general-purpose allocators and both copying and non-copying garbage collectors. We show that, at large heap sizes and under no memory pressure, the runtime performance of some garbage collection algorithms is competitive with the Lea memory allocator and occasionally outperforms it by up to 4%. However, our results confirm that garbage collection requires six times the physical memory to achieve this performance and suffers order-of-magnitude performance penalties when paging occurs.


title: Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control

abstract: Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori they offer convergence guarantees as well as bounds around the predictions. Considering a more general setting that builds on Hoelder continuity relative to pseudo-metrics, we propose an online method for estimating the Hoelder constant online from function value observations that possibly are corrupted by bounded observational errors. Utilising this to compute adaptive parameters within a kinky inference rule gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function in the limit of increasingly dense data to within a worst-case error bound that depends on the level of observational uncertainty. We apply our method in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks. For discrete-time systems, we provide stability guarantees for our learning-based controllers both for the batch and the online learning setting.


title: Sitting spinal posture in adolescents differs between genders, but is not clearly related to neck/shoulder pain: an observational study

abstract: Question Is neck/shoulder pain in adolescents related to their sitting spinal posture, taking account of gender? Design Cross-sectional survey and direct observation. Participants 1597 adolescents from the ‘Raine' birth cohort study (781 females, 816 males) with a mean age of 14.1 years (SD 0.2). Outcome measures Neck/shoulder pain prevalence and gender was measured by survey. Spinal posture (7 angles) during sitting was measured from photographs. Results Life, month, and point prevalence for neck/shoulder pain among adolescents were 47%, 29%, and 5% respectively. Life prevalence was 10% higher in females than in males and month prevalence was 12% higher. When looking straight ahead, females sat with 2 degrees (95% CI 1 to 3) less neck flexion, 2 degrees (95% CI 0 to 3) less craniocervical angle, 7 degrees (95% CI 6 to 8) less cervicothoracic angle, 13 degrees (95% CI 12 to 14) less trunk angle, 10 degrees (95% CI 8 to 12) less lumbar angle, and 9 degrees (95% CI 7 to 11) more anterior pelvic tilt than males. Adolescents with neck/shoulder pain sat with 2 degrees (95% CI 1 to 3) less trunk angle, and 1 degree (95% CI 0 to 2) less cervicothoracic angle than those without pain. After controlling for gender, OR for neck/shoulder pain ever predicted by any angle ranged from 0.99 to 1.00 (range of 95% CI 0.98 to 1.01). Conclusion Neck/shoulder pain is highly prevalent in Australian adolescents. Sitting spinal posture differs between males and females and differs slightly between those with and without neck/shoulder pain. However, posture was not predictive of neck/shoulder pain ever after controlling for gender.


title: Peripheral venepuncture in infants and children.

abstract: Venepuncture can be a painful and frightening experience for children. Nurses play an active role in helping them to cope with this experience and in reducing the adverse effects of venepuncture. This article explains how infants, children and their families can be supported and cared for before, during and after venepuncture.


title: Productivity Catching Up: Evidence from Spanish manufacturing flrms ⁄

abstract: We study the flrms’ productivity dynamics and its relationship with the business cycle. The Spanish economy during the 1990s provides the adequate conditions to to perform this study because it shows two clear sub-periods in terms of growth: the growth rate of GDP in the second half of the decade (expansion) more than doubled the growth rate in the flrst half (recession). We use the methodology developed by the growth literature to test for productivity catch up (less productive flrms experience higher productivity growth than more productive flrms) and convergence (the variance of the productivity distribution decreases). We also develop a novel methodology to test whether there is higher catch up in recessions than in expansions. We flnd evidence of catch up in the whole analyzed period, but convergence only in the flrst half. Moreover, we also flnd evidence of higher catch up during the recessive period. These flndings suggest a cyclical process of innovation and imitation. Leaders have more incentives to innovate during expansions, but imitation takes place at a steady rate.


title: Running head: Do-gooder derogation Do-Gooder Derogation: Disparaging Morally-Motivated Minorities To Defuse Anticipated Reproach

abstract: Two studies document do-gooder derogation (the putting down of morally motivated others), by studying the reactions of meat eaters to vegetarians. In Study 1, 47% of participants freely associated ...


title: Adaptive Caustic Maps Using Deferred Shading

abstract: Caustic maps provide an interactive image-space method to render caustics, the focusing of light via reflection and refraction. Unfortunately, caustic mapping suffers problems similar to shadow mapping: aliasing from poor sampling and map projection as well as temporal incoherency from frame-to-frame sampling variations. To reduce these problems, researchers have suggested methods ranging from caustic blurring to building a multiresolution caustic map. Yet these all require a fixed photon sampling, precluding the use of importance-based photon densities. This paper introduces adaptive caustic maps. Instead of densely sampling photons via a rasterization pass, we adaptively emit photons using a deferred shading pass. We describe deferred rendering for refractive surfaces, which speeds rendering of refractive geometry up to 25% and with adaptive sampling speeds caustic rendering up to 200%. These benefits are particularly noticable for complex geometry or using millions of photons. While developed for a GPU rasterizer, adaptive caustic map creation can be performed by any renderer that individually traces photons, e.g., a GPU ray tracer.


title: Evaluating the Effectiveness of Serious Games for Cultural Awareness: The Icura User Study

abstract: There is an increasing awareness about the potential of serious games for education and training in many disciplines. However, research still witnesses a lack of methodologies, guidelines and best practices on how to develop effective serious games and how to integrate them in the actual learning and training processes. This process of integration heavily depends on providing and spreading evidence of the effectiveness of serious games This paper reports a user study to evaluate the effectiveness of Icura, a serious game about Japanese culture and etiquette. The evaluation methodology extends the set of instruments used in previous studies by evaluating the effects of the game on raising awareness, by avoiding the selective attention bias and by assessing the medium-term retention. With this research we aim to provide a handy toolkit for evaluating the effectiveness a serious games for cultural awareness and heritage.


title: Adaptive extraction of highlights from a sport video based on excitement modeling

abstract: This paper addresses the challenge of automatically extracting the highlights from sports TV broadcasts. In particular, we are interested in finding a generic method of highlights extraction, which does not require the development of models for the events that are thought to be interpreted by the users as highlights. Instead, we search for highlights in those video segments that are expected to excite the users most. It is namely realistic to assume that a highlighting event induces a steady increase in a user's excitement, as compared to other, less interesting events. We mimic the expected variations in a user's excitement by observing the temporal behavior of selected audiovisual low-level features and the editing scheme of a video. Relations between this noncontent information and the evoked excitement are drawn partly from psychophysiological research and partly from analyzing the live-video directing practice. The expected variations in a user's excitement are represented by the excitement time curve, which is, subsequently, filtered in an adaptive way to extract the highlights in the prespecified total length and in view of the preferences regarding the highlights strength: extraction can namely be performed with variable sensitivity to capture few "strong" highlights or more "less strong" ones. We evaluate and discuss the performance of our method on the case study of soccer TV broadcasts.


title: Human-computer interaction using eye-gaze input

abstract: A description is given of Erica, a computer workstation with a unique user interface. The workstation is equipped with imaging hardware and software, which automatically record a digital portrait of the user's eye. From the features of the current portrait, the interface calculates the approximate location of the user's eye-gaze on the computer screen. The computer then executes commands associated with the menu option currently displayed at this screen location. In this way, the user can interact with the computer, run applications software, and manage peripheral devices-all simply by looking at an appropriate sequence of menu options displayed on the screen. The eye-gaze interface technology, its implementation in Erica, and its application as a prosthetic device are described. >


title: Recovery of metals and nonmetals from electronic waste by physical and chemical recycling processes.

abstract: Abstract This paper reviews the existing and state of art knowledge for electronic waste (e-waste) recycling. Electrical and/or electronic devices which are unwanted, broken or discarded by their original users are known as e-waste. The main purpose of this article is to provide a comprehensive review of e-waste problem, strategies of e-waste management and various physical, chemical and metallurgical e-waste recycling processes, their advantages and disadvantages towards achieving a cleaner process of waste utilization, with special attention towards extraction of both metallic values and nonmetallic substances. The hazards arise from the presence of heavy metals Hg, Cd, Pb, etc., brominated flame retardants (BFRs) and other potentially harmful substances in e-waste. Due to the presence of these substances, e-waste is generally considered as hazardous waste and, if improperly managed, may pose significant human and environmental health risks. This review describes the potential hazards and economic opportunities of e-waste. Firstly, an overview of e-waste/printed circuit board (PCB) components is given. Current status and future perspectives of e-waste/PCB recycling are described. E-waste characterization, dismantling methods, liberation and classification processes are also covered. Manual selective dismantling after desoldering and metal-nonmetal liberation at −150 μm with two step crushing are seen to be the best techniques. After size reduction, mainly physical separation processes employing gravity, electrostatic, magnetic separators, froth floatation, etc. have been critically reviewed here for separation of metals and nonmetals, along with useful utilizations of the nonmetallic materials. The recovery of metals from e-waste material after physical separation through pyrometallurgical, hydrometallurgical or biohydrometallurgical routes is also discussed along with purification and refining. Suitable PCB recycling flowsheets for industrial applications are also given. It seems that hydrometallurgical route will be a key player in the base and precious metals recoveries from e-waste. E-waste recycling will be a very important sector in the near future from economic and environmental perspectives. Recycling technology aims to take today’s waste and turn it into conflict-free, sustainable polymetallic secondary resources (i.e. Urban Mining) for tomorrow. Recycling technology must ensure that e-waste is processed in an environmentally friendly manner, with high efficiency and lowered carbon footprint, at a fraction of the costs involved with setting multibillion dollar smelting facilities. Taking into consideration our depleting natural resources, this Urban Mining approach offers quite a few benefits. This results in increased energy efficiency and lowers demand for mining of new raw materials.


title: Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model

abstract: Much previous research has established that perceived ease of use is an important factor influencing user acceptance and usage behavior of information technologies. However, very little research has been conducted to understand how that perception forms and changes over time. The current work presents and tests an anchoring and adjustment-based theoretical model of the determinants of system-specific perceived ease of use. The model proposes control (internal and external--conceptualized as computer self-efficacy and facilitating conditions, respectively), intrinsic motivation (conceptualized as computer playfulness), and emotion (conceptualized as computer anxiety) as anchors that determine early perceptions about the ease of use of a new system. With increasing experience, it is expected that system-specific perceived ease of use, while still anchored to the general beliefs regarding computers and computer use, will adjust to reflect objective usability, perceptions of external control specific to the new system environment, and system-specific perceived enjoyment. The proposed model was tested in three different organizations among 246 employees using three measurements taken over a three-month period. The proposed model was strongly supported at all points of measurement, and explained up to 60% of the variance in system-specific perceived ease of use, which is twice as much as our current understanding. Important theoretical and practical implications of these findings are discussed.


title: Forensic investigations in Cloud environments

abstract: Cloud computing presents many promising technological and economical opportunities, however, it can pose a challenge for cybercrime investigation and digital forensics. Therefore, there is an urgent need to extend the applications of investigation processes in the Cloud. In this paper, we present the definition of cloud computing and its various models. Further we compare traditional forensic investigations and forensic investigations in cloud and analyze the challenges of forensic investigations in cloud environments.


title: A cyclical evaluation model of information security maturity

abstract: Purpose – This paper aims at presenting a cyclical evaluation model of information security (IS) maturity. The lack of a security evaluation method might expose organizations to several risky situations. Design/methodology/approach – This model was developed through the definition of a set of steps to be followed to obtain periodical evaluation of maturity and continuous improvement of controls. Findings – This model, based on controls present in ISO/IEC 27002, provides a means to measure the current situation of IS management through the use of a maturity model and provides a subsidy to take appropriate and feasible improvement actions, based on risks. A case study is performed, and the results indicate that the method is efficient for evaluating the current state of IS, to support IS management, risks identification and business and internal control processes. Research limitations/implications – It is possible that modifications to the process may be needed where there is less understanding of security ...