title: Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks

abstract: Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.


title: Deep Class Aware Denoising

abstract: The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same time, the images captured by these devices can be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. In this paper, we demonstrate how the reconstruction quality improves when a denoiser is aware of the type of content in the image. To this end, we first propose a new fully convolutional deep neural network architecture which is simple yet powerful as it achieves state-of-the-art performance even without being class-aware. We further show that a significant boost in performance of up to $0.4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class. Relying on the hugely successful existing image classifiers, this research advocates for using a class-aware approach in all image enhancement tasks.


Exploratory Data Analysis using Random Forests ∗


title: The Paradox of Music-Evoked Sadness: An Online Survey

abstract: This study explores listeners’ experience of music-evoked sadness. Sadness is typically assumed to be undesirable and is therefore usually avoided in everyday life. Yet the question remains: Why do people seek and appreciate sadness in music? We present findings from an online survey with both Western and Eastern participants (N = 772). The survey investigates the rewarding aspects of music-evoked sadness, as well as the relative contribution of listener characteristics and situational factors to the appreciation of sad music. The survey also examines the different principles through which sadness is evoked by music, and their interaction with personality traits. Results show 4 different rewards of music-evoked sadness: reward of imagination, emotion regulation, empathy, and no “real-life” implications. Moreover, appreciation of sad music follows a mood-congruent fashion and is greater among individuals with high empathy and low emotional stability. Surprisingly, nostalgia rather than sadness is the most frequent emotion evoked by sad music. Correspondingly, memory was rated as the most important principle through which sadness is evoked. Finally, the trait empathy contributes to the evocation of sadness via contagion, appraisal, and by engaging social functions. The present findings indicate that emotional responses to sad music are multifaceted, are modulated by empathy, and are linked with a multidimensional experience of pleasure. These results were corroborated by a follow-up survey on happy music, which indicated differences between the emotional experiences resulting from listening to sad versus happy music. This is the first comprehensive survey of music-evoked sadness, revealing that listening to sad music can lead to beneficial emotional effects such as regulation of negative emotion and mood as well as consolation. Such beneficial emotional effects constitute the prime motivations for engaging with sad music in everyday life.


title: Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports

abstract: Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.


title: Conversion of Lignocellulosic Biomass to Nanocellulose: Structure and Chemical Process

abstract: Lignocellulosic biomass is a complex biopolymer that is primary composed of cellulose, hemicellulose, and lignin. The presence of cellulose in biomass is able to depolymerise into nanodimension biomaterial, with exceptional mechanical properties for biocomposites, pharmaceutical carriers, and electronic substrate's application. However, the entangled biomass ultrastructure consists of inherent properties, such as strong lignin layers, low cellulose accessibility to chemicals, and high cellulose crystallinity, which inhibit the digestibility of the biomass for cellulose extraction. This situation offers both challenges and promises for the biomass biorefinery development to utilize the cellulose from lignocellulosic biomass. Thus, multistep biorefinery processes are necessary to ensure the deconstruction of noncellulosic content in lignocellulosic biomass, while maintaining cellulose product for further hydrolysis into nanocellulose material. In this review, we discuss the molecular structure basis for biomass recalcitrance, reengineering process of lignocellulosic biomass into nanocellulose via chemical, and novel catalytic approaches. Furthermore, review on catalyst design to overcome key barriers regarding the natural resistance of biomass will be presented herein.


title: Applying The CHAID Algorithm to Analyze How Achievement is Influenced by University Students' Demographics, Study Habits, and Technology

abstract: Introduction Because developments in the field of information and communication technology (ICT) have led to a technology-based culture, educators are now educating a new generation of students. Accordingly, one of the important issues to be addressed when designing technology-oriented learning environments is learners' characteristics. With regard to this issue, the education profession faces two challenges. The first issue is that today's learners, who have grown up as digital natives and thus are being defined as the Net generation, are vastly different from the generations of learners that preceded current 21st-century learners (Oblinger & Oblinger, 2005; Prensky, 2001). The second issue is that children are experienced in a new form of play, that is, multiplayer computer and video games, and these experiences have shaped their preferences and capabilities regarding learning (Prensky, 2001). Therefore, to provide today's learners with a better education and a better learning context, educators should consider the characteristics of these new learners. Gender, working habits, motivation, media use, and book reading, as shown in Table 1, may influence Net generation students' academic performance. Motivation has been found to be a significant predictor of achievement among students (Ayub 2010; Chan, Wong, & Lo, 2012). In addition, gender has been correlated with achievement (Sarier, 2010; Veenstra & Kuyper, 2004). However, the use of technology by students has controversial results as well (Turner & Corucher, 2013; Junco, 2012). The research studies conducted on study habits did not always reveal consistent results in that while some found significant correlations between study habits and achievement (Yu, 2011), others did not (Olatoye & Ogunkola, 2008). The majority of the studies in Table 1, which have small sample sizes, focused on the dualities between motivation and achievement, and the use of technology and achievement. However, the current study deals with gender, university level and achievement, study habits and achievement, and the use of technology and achievement in one single study. Review of related literature The literature review of this study is presented beneath three subheadings: gender, university level, and achievement; study habits and achievement; and use of technology and achievement. Gender, university level, and achievement Conger and Long (2010) examined disadvantages that male students experienced with respect to grade-point average, credits earned, and persistence in college, and found that male students have lower GPAs and fewer credits in their first semester of college largely because they came to college with lower high-school grades. Female students' better high-school grades explain some of the gender disparity in performance, but differences in college course-taking and majors also explain gender gaps in credits, grades, persistence, and graduation. A study on persistence and success in an academic program at a community college revealed that students' GPAs, cumulative hours attempted, and cumulative hours completed were significant predictors of persistence and that young male students were a high-risk group (Stewart & Levin, 2001). Veenstra and Kuyper (2004) also revealed that gender was an important variable for academic achievement and female students were found to have higher levels of motivation. Finally, it was indicated that there was a significant difference between the genders in favor of female students regarding academic achievement in secondary education (Sarier, 2010). The literature did not indicate any finding about the effect of both university level and gender on achievement in one study. Study habits and achievement The study habits construct in the current study is twofold: working style (individual or studying in a group) and motivation (intrinsic or extrinsic). …


title: FMCode: A 3D In-the-Air Finger Motion Based User Login Framework for Gesture Interface

abstract: Applications using gesture-based human-computer interface require a new user login method with gestures because it does not have a traditional input method to type a password. However, due to various challenges, existing gesture-based authentication systems are generally considered too weak to be useful in practice. In this paper, we propose a unified user login framework using 3D in-air-handwriting, called FMCode. We define new types of features critical to distinguish legitimate users from attackers and utilize Support Vector Machine (SVM) for user authentication. The features and data-driven models are specially designed to accommodate minor behavior variations that existing gesture authentication methods neglect. In addition, we use deep neural network approaches to efficiently identify the user based on his or her in-air-handwriting, which avoids expansive account database search methods employed by existing work. On a dataset collected by us with over 100 users, our prototype system achieves 0.1% and 0.5% best Equal Error Rate (EER) for user authentication, as well as 96.7% and 94.3% accuracy for user identification, using two types of gesture input devices. Compared to existing behavioral biometric systems using gesture and in-air-handwriting, our framework achieves the state-of-the-art performance. In addition, our experimental results show that FMCode is capable to defend against client-side spoofing attacks, and it performs persistently in the long run. These results and discoveries pave the way to practical usage of gesture-based user login over the gesture interface.


title: TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

abstract: Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.


title: EPIC: Efficient Private Image Classification (or: Learning from the Masters)

abstract: Outsourcing an image classification task raises privacy concerns, both from the image provider’s perspective, who wishes to keep their images confidential, and from the classification algorithm provider’s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, secure against malicious adversaries. EPIC builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX’18) –the state-of-the-art in private image classification– and it improves the communication cost by 50 times, with a 7% higher accuracy on CIFAR-10 dataset. For the same accuracy as Gazelle achieves on CIFAR-10, EPIC is 700 times faster and the communication cost is reduced by 500 times.


title: NARMA-L2 controller for three-area load frequency control

abstract: This paper investigates the load-frequency control (LFC) based on neural network for improving power system dynamic performance. The objectives of this control action are regulation of the frequency at area and minimization the deviation of interchange power between control areas. The controller is adaptive and is based on a nonlinear autoregressive moving average (NARMA-L2) algorithm. The controller was implemented using MATLAB/Simulink on three-area load frequency power systems. A comparative study between proposed controller and PI controller tuned by real coded genetic algorithm (GA) will be presented and the advantages of this controller on a wide range of operating conditions will be shown.


Internet of Things : Features , Challenges , and Vulnerabilities Authors


title: Cognitive Robotics using the Soar Cognitive Architecture

abstract: Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar’s original symbolic processing, which improves Soar abilities for control of robots. These extensions include mental imagery, episodic and semantic memory, reinforcement learning, and continuous model learning. This paper presents research in mobile robotics, relational and continuous model learning, and learning by situated, interactive instruction.


title: Variational Sequential Monte Carlo

abstract: Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.


title: VHPOP: Versatile Heuristic Partial Order Planner

abstract: VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990's on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.


title: Simulating spiking neural networks on GPU

abstract: Modern graphics cards contain hundreds of cores that can be programmed for intensive calculations. They are beginning to be used for spiking neural network simulations. The goal is to make parallel simulation of spiking neural networks available to a large audience, without the requirements of a cluster. We review the ongoing efforts towards this goal, and we outline the main difficulties.


title: Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

abstract: This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. The Comprehensive experiment is conducted on the ChestX-ray14 dataset. We first report a strong global baseline producing an average AUC of 0.841 with ResNet-50 as backbone. After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community.


Reputation and Corporate Tax Planning : A Moral Licensing View Prepared for the 2017 Lone Star Accounting Research Conference


title: Physical activity and functional limitations in older adults: a systematic review related to Canada's Physical Activity Guidelines

abstract: BackgroundThe purpose was to conduct systematic reviews of the relationship between physical activity of healthy community-dwelling older (>65 years) adults and outcomes of functional limitations, disability, or loss of independence.MethodsProspective cohort studies with an outcome related to functional independence or to cognitive function were searched, as well as exercise training interventions that reported a functional outcome. Electronic database search strategies were used to identify citations which were screened (title and abstract) for inclusion. Included articles were reviewed to complete standardized data extraction tables, and assess study quality. An established system of assessing the level and grade of evidence for recommendations was employed.ResultsSixty-six studies met inclusion criteria for the relationship between physical activity and functional independence, and 34 were included with a cognitive function outcome. Greater physical activity of an aerobic nature (categorized by a variety of methods) was associated with higher functional status (expressed by a host of outcome measures) in older age. For functional independence, moderate (and high) levels of physical activity appeared effective in conferring a reduced risk (odds ratio ~0.5) of functional limitations or disability. Limitation in higher level performance outcomes was reduced (odds ratio ~0.5) with vigorous (or high) activity with an apparent dose-response of moderate through to high activity. Exercise training interventions (including aerobic and resistance) of older adults showed improvement in physiological and functional measures, and suggestion of longer-term reduction in incidence of mobility disability. A relatively high level of physical activity was related to better cognitive function and reduced risk of developing dementia; however, there were mixed results of the effects of exercise interventions on cognitive function indices.ConclusionsThere is a consistency of findings across studies and a range of outcome measures related to functional independence; regular aerobic activity and short-term exercise programmes confer a reduced risk of functional limitations and disability in older age. Although a precise characterization of a minimal or effective physical activity dose to maintain functional independence is difficult, it appears moderate to higher levels of activity are effective and there may be a threshold of at least moderate activity for significant outcomes.


A Case Study: Assumptions and Limitations of a Problem-Solving Method


title: Emotion Regulation in Depression and Anxiety: Examining Diagnostic Specificity and Stability of Strategy Use

abstract: Many psychological disorders are characterized by difficulties in emotion regulation. It is unclear, however, whether different disorders are associated with the use of specific emotion regulation strategies, and whether these difficulties are stable characteristics that are evident even after recovery. It is also unclear whether the use of specific strategies is problematic across all disorders or whether disorders differ in how strongly strategy use is associated with symptom severity. This study investigated (1) the specificity of use of emotion regulation strategies in individuals diagnosed with current major depressive disorder (MDD), with social anxiety disorder (SAD), and in never-disordered controls (CTL); and (2) the stability of strategy use in formerly depressed participants (i.e., remitted; RMD). Path analysis was conducted to examine the relation between strategy use and symptom severity across diagnostic groups. Compared to the CTL group, participants in both clinical groups endorsed more frequent use of rumination and expressive suppression, and less frequent use of reappraisal. Specific to SAD were even higher levels of expressive suppression relative to MDD, as well as a stronger relation between rumination and anxiety levels. In contrast, specific to MDD were even higher levels of rumination and lower levels of reappraisal. Interestingly, elevated rumination, but not decreased reappraisal, was found to be a stable feature characterizing remitted depressed individuals. These results may provide insight into ways in which emotion regulation strategy use maintains psychological disorders.


title: Semantic Document Clustering Using a Similarity Graph

abstract: Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that contains the words "dog" and "cat" multiple times may be placed in the same category as a document that contains the word "pet" even if the two documents share only noise words in common. Our semantic clustering algorithm is based on a similarity graph that stores the degree of semantic relationship between terms (extracted from WordNet), where a term can be a word or a phrase. We experimentally validate our algorithm on the Reuters-21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We show that the second approach produces higher precision and recall, which means that this approach matches closer the results of the human study.


title: The swrc ontology - semantic web for research communities

abstract: Representing knowledge about researchers and research communities is a prime use case for distributed, locally maintained, interlinked and highly structured information in the spirit of the Semantic Web. In this paper we describe the publicly available ‘Semantic Web for Research Communities’ (SWRC) ontology, in which research communities and relevant related concepts are modelled. We describe the design decisions that underlie the ontology and report on both experiences with and known usages of the SWRC Ontology. We believe that for making the Semantic Web reality the re-usage of ontologies and their continuous improvement by user communities is crucial. Our contribution aims to provide a description and usage guidelines to make the value of the SWRC explicit and to facilitate its re-use.


title: Graphene-on-Paper Sound Source Devices

abstract: We demonstrate an interesting phenomenon that graphene can emit sound. The application of graphene can be expanded in the acoustic field. Graphene-on-paper sound source devices are made by patterning graphene on paper substrates. Three graphene sheet samples with the thickness of 100, 60, and 20 nm were fabricated. Sound emission from graphene is measured as a function of power, distance, angle, and frequency in the far-field. The theoretical model of air/graphene/paper/PCB board multilayer structure is established to analyze the sound directivity, frequency response, and efficiency. Measured sound pressure level (SPL) and efficiency are in good agreement with theoretical results. It is found that graphene has a significant flat frequency response in the wide ultrasound range 20–50 kHz. In addition, the thinner graphene sheets can produce higher SPL due to its lower heat capacity per unit area (HCPUA). The infrared thermal images reveal that a thermoacoustic effect is the working principle. We find that t...


title: SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

abstract: We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence.


title: Earnings management and the auditing value in China

abstract: Auditing has been identified as an area of immense importance for the development of China's stock market. Auditing not only provides reliable information for users, but, more importantly, it must coordinate the potential interests among the public, central and local state, and the nation. This study investigates the association between the auditing value and earnings management risk (EMR) induced by security regulation. It is hypothesized that, from the perspective of risk premiums, the auditing value increases as the EMR increase. Using a sample of 4,594 Chinese listed firms during 2001 to 2006, we find that auditing value is positively associated with EMR. We also find that different types of security regulation affect the auditing value. The results are robust to different model specifications and alternatives sample. Apart from providing some new evidence in the important area of auditing value, we also reveal interesting implications for auditing profession, regulators, entrepreneurs, and investors. ::: ::: ::: ::: Key words: Earnings management, auditing value, security regulation, risk premium.


title: SEMANTIC SEGMENTATION USING GRABCUT

abstract: This work analyzes how to utilize the power of the popular GrabCut algorithm for the task of pixel-wise labeling of images, which is also known as semantic segmentation and an important step for scene understanding in various application domains. In contrast to the original GrabCut, the aim of the presented methods is to segment objects in images in a completely automatic manner and label them as one of the previously learned object categories. In this paper, we introduce and analyze two different approaches that extend GrabCut to make use of training images. C-GrabCut generates multiple class-specific segmentations and classifies them by using shape and color information. L-GrabCut uses as a first step an object localization algorithm, which returns a classified bounding box as a hypothesis of an object in the image. Afterwards, this hypothesis is used as an initialization for the GrabCut algorithm. In our experiments, we show that both methods lead to similar results and demonstrate their benefits compared to semantic segmentation methods only based on local features.


title: Possible and Certain SQL Key

abstract: Driven by the dominance of the relational model, the requirements of modern applications, and the veracity of data, we revisit the fundamental notion of a key in relational databases with NULLs. In SQL database systems primary key columns are NOT NULL by default. NULL columns may occur in unique constraints which only guarantee uniqueness for tuples which do not feature null markers in any of the columns involved, and therefore serve a different function than primary keys. We investigate the notions of possible and certain keys, which are keys that hold in some or all possible worlds that can originate from an SQL table, respectively. Possible keys coincide with the unique constraint of SQL, and thus provide a semantics for their syntactic definition in the SQL standard. Certain keys extend primary keys to include NULL columns, and thus form a sufficient and necessary condition to identify tuples uniquely, while primary keys are only sufficient for that purpose. In addition to basic characterization, axiomatization, and simple discovery approaches for possible and certain keys, we investigate the existence and construction of Armstrong tables, and describe an indexing scheme for enforcing certain keys. Our experiments show that certain keys with NULLs do occur in real-world databases, and that related computational problems can be solved efficiently. Certain keys are therefore semantically well-founded and able to maintain data quality in the form of Codd's entity integrity rule while handling the requirements of modern applications, that is, higher volumes of incomplete data from different formats.


title: Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning

abstract: Melanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.


title: Lung Cancer Detection Using Image Processing Techniques

abstract: Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage where low pre-processing techniques is used based on Gabor filter within Gaussian rules. Following the segmentation principles, an enhanced region of the object of interest that is used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. In this research, the main detected features for accurate images comparison are pixels percentage and mask-labelling.


Vanadate oxidative and apoptotic effects are mediated by the MAPK-Nrf2 pathway in layer oviduct magnum epithelial cells


title: Leadership and Neuroscience: Can We Revolutionize the Way That Inspirational Leaders Are Identified and Developed?

abstract: Executive Overview Recent advances in the field of neuroscience can significantly add to our understanding of leadership and its development. Specifically, we are interested in what neuroscience can tell us about inspirational leadership. Based on our findings, we discuss how future research in leadership can be combined with neuroscience, as well as potential neurofeedback interventions for the purpose of leadership development. We also consider ethical implications and applications to management-related areas beyond leadership.


title: Optimal booking and scheduling in outpatient procedure centers

abstract: Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.


title: Location prediction in social media based on tie strength

abstract: We propose a novel network-based approach for location estimation in social media that integrates evidence of the social tie strength between users for improved location estimation. Concretely, we propose a location estimator -- FriendlyLocation -- that leverages the relationship between the strength of the tie between a pair of users, and the distance between the pair. Based on an examination of over 100 million geo-encoded tweets and 73 million Twitter user profiles, we identify several factors such as the number of followers and how the users interact that can strongly reveal the distance between a pair of users. We use these factors to train a decision tree to distinguish between pairs of users who are likely to live nearby and pairs of users who are likely to live in different areas. We use the results of this decision tree as the input to a maximum likelihood estimator to predict a user's location. We find that this proposed method significantly improves the results of location estimation relative to a state-of-the-art technique. Our system reduces the average error distance for 80% of Twitter users from 40 miles to 21 miles using only information from the user's friends and friends-of-friends, which has great significance for augmenting traditional social media and enriching location-based services with more refined and accurate location estimates.


title: Combining SAO semantic analysis and morphology analysis to identify technology opportunities

abstract: Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject---action---object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.


title: Phytostabilisation of arsenical gold mine tailings using four Eucalyptus species: Growth, arsenic uptake and availability after five years

abstract: Abstract Arsenic (As) contamination is a worldwide problem. Where arsenic is highly concentrated and confined within a limited area, such as in many mine tailings facilities, phytostabilisation is an attractive technology for long-term remediation. Important characteristics of a plant to be useful for phytostabilisation include As tolerance and low levels of As accumulation, as well as the ability to limit As availability. Performance needs to be monitored over the long term to ensure an ongoing vegetation community, though this is rarely done. In this study, the suitability of four Eucalyptus species ( E. cladocalyx , E. melliodora , E. polybractea , E. viridis ) for the phytostabilisation of arsenical, sulphidic gold mine tailings was assessed after five years. All four species accumulated low As concentrations, the highest being recorded in mature leaves, ranging from 0.29 to 5.14 μg g − 1 As. E. polybractea had significantly higher foliar As than the other three species but there was also great variation within the species. Between 5–10 times lower concentrations were recorded in stem samples and no As was detected in young leaf tips. There was also significant variation in the growth of trees upon the site. Eucalyptus cladocalyx grew significantly taller than other species although greater variation was detected within the species than between. The variation in tree heights was not correlated with As concentrations in either stems or leaves. Arsenic availability was determined to depths of 2.2 m and found to be low when compared to total As in the tailings. Importantly, no effect of trees on As availability or soil pH was detected. We conclude that E. cladocalyx, in particular is an ideal candidate for the long-term phytostabilisation of As-contaminated land and mine tailings. The variation detected in both As accumulation and growth is also promising for the selection of desirable traits.


title: Engaging Computing Students with AI and Robotics.

abstract: In this paper we describe a new curriculum for a CS1 course that uses personal robots as a context for learning introductory computer science. Students learn several computing and AI-related concepts in the process of exploring and designing robot behaviors. We believe that the use of personal robots and engaging examples can provide a good foundation for learning computing and hence serve to attract a more diverse body of students into the computing disciplines. In this paper, we describe how we have embedded numerous AI concepts in the design of our curriculum.


title: Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

abstract: Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches.


title: How information technology governance mechanisms and strategic alignment influence organizational performance: insights from a matched survey of business and it managers

abstract: Previous research has proposed different types for and contingency factors affecting information technology governance. Yet, in spite of this valuable work, it is still unclear through what mechanisms IT governance affects organizational performance. We make a detailed argument for the mediation of strategic alignment in this process. Strategic alignment remains a top priority for business and IT executives, but theory-based empirical research on the relative importance of the factors affecting strategic alignment is still lagging. By consolidating strategic alignment and IT governance models, this research proposes a nomological model showing how organizational value is created through IT governance mechanisms. Our research model draws upon the resource-based view of the firm and provides guidance on how strategic alignment can mediate the effectiveness of IT governance on organizational performance. As such, it contributes to the knowledge bases of both alignment and IT governance literatures. Using dyadic data collected from 131 Taiwanese companies (cross-validated with archival data from 72 firms), we uncover a positive, significant, and impactful linkage between IT governance mechanisms and strategic alignment and, further, between strategic alignment and organizational performance. We also show that the effect of IT governance mechanisms on organizational performance is fully mediated by strategic alignment. Besides making contributions to construct and measure items in this domain, this research contributes to the theory base by integrating and extending the literature on IT governance and strategic alignment, both of which have long been recognized as critical for achieving organizational goals.


title: Similarities and differences between TQM, six sigma and lean

abstract: Purpose – During the last decades, different quality management concepts, including total quality management (TQM), six sigma and lean, have been applied by many different organisations. Although much important work has been documented regarding TQM, six sigma and lean, a number of questions remain concerning the applicability of these concepts in various organisations and contexts. Hence, the purpose of this paper is to describe the similarities and differences between the concepts, including an evaluation and criticism of each concept. ::: ::: Design/methodology/approach – Within a case study, a literature review and face-to-face interviews in typical TQM, six sigma and lean organisations have been carried out. ::: ::: Findings – While TQM, six sigma and lean have many similarities, especially concerning origin, methodologies, tools and effects, they differ in some areas, in particular concerning the main theory, approach and the main criticism. The lean concept is slightly different from TQM and six sigma. However, there is a lot to gain if organisations are able to combine these three concepts, as they are complementary. Six sigma and lean are excellent road-maps, which could be used one by one or combined, together with the values in TQM. ::: ::: Originality/value – The paper provides guidance to organisations regarding the applicability and properties of quality concepts. Organisations need to work continuously with customer-orientated activities in order to survive; irrespective of how these activities are labelled. The paper will also serve as a basis for further research in this area, focusing on practical experience of these concepts.


title: Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship

abstract: Abstract The main objective of this paper is to non-parametrically estimate the quantiles of a conditional distribution in the censorship model when the sample is considered as an -mixing sequence. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Afterwards, we estimate the quantiles by inverting this estimated cond-cdf and state the asymptotic properties when the observations are linked with a single-index structure. The pointwise almost complete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. This approach can be applied in time series analysis.


title: PredicT-ML: a tool for automating machine learning model building with big clinical data

abstract: Background ::: Predictive modeling is fundamental to transforming large clinical data sets, or “big clinical data,” into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs.


title: Article Remote Sensing of Vegetation Structure Using Computer Vision

abstract: High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft. Here we demonstrate that high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using inexpensive hobbyist aerial platforms. Digital photographs were acquired using a kite aerial platform across two 2.25 ha test sites in Baltimore, MD, USA. An open-source computer vision algorithm generated 3D point cloud datasets with RGB spectral attributes from the photographs and these were geocorrected to a horizontal precision of 0.82) than computer vision (R2 > 0.64), primarily because of difficulties observing terrain under closed canopy forest. Results confirm that computer vision can support ultra-low-cost, user-deployed high spatial resolution 3D remote sensing of vegetation structure.


title: High-voltage power delivery through charge recycling

abstract: In this paper, we describe a technique for delivering power to a digital integrated circuit at high voltages, reducing current demands and easing requirements on power-ground network impedances. The design approach consists of stacking CMOS logic domains to operate from a voltage supply that is a multiple of the nominal supply voltage. DC-DC downconversion is performed using charge recycling without the need for explicit downconverters. Experimental results are presented for the prototype system in a 0.18-/spl mu/m CMOS technology operating at both 3.6 V and 5.4 V. Peak energy efficiencies as high as 93% are demonstrated at 3.6 V.


title: Thirty years of research on race differences in cognitive ability

abstract: The culture-only (0% genetic–100% environmental) and the hereditarian (50% genetic–50% environmental) models of the causes of mean Black–White differences in cognitive ability are compared and contrasted across 10 categories of evidence: the worldwide distribution of test scores, g factor of mental ability, heritability, brain size and cognitive ability, transracial adoption, racial admixture, regression, related life-history traits, human origins research, and hypothesized environmental variables. The new evidence reviewed here points to some genetic component in Black–White differences in mean IQ. The implication for public policy is that the discrimination model (i.e., Black–White differences in socially valued outcomes will be equal barring discrimination) must be tempered by a distributional model (i.e., Black–White outcomes reflect underlying group characteristics).


title: High-efficiency focusing grating coupler for large-scale silicon photonic integration

abstract: A novel high-efficiency compact optical coupler employing curved gratings with non-uniform pitches is proposed to coupling light off or into silicon photonic chips for large-scale integrated functions. The optimized non-uniform pitches improve the coupling efficiency of single-mode fiber and waveguide by reducing the mode matching loss between the fiber and grating coupler. This kind of focusing grating coupler has a decreased transition length between the grating and single-mode waveguide.


title: Enabling Adaptive Cloud Gaming in an Open-Source Cloud Gaming Platform

abstract: We study the problem of optimally adapting ongoing cloud gaming sessions to maximize the gamer experience in dynamic environments. The considered problem is quite challenging because: 1) gamer experience is subjective and hard to quantify; 2) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs; and 3) the resource allocation among concurrent gamers leaves a huge room to optimize. We rigorously address these three challenges by: 1) conducting a crowdsourced user study over the live Internet for an empirical gaming experience model; 2) enhancing the cloud gaming platform to support frame rate and bitrate adaptation on-the-fly; and 3) proposing optimal yet efficient algorithms to maximize the overall gaming experience or ensure the fairness among gamers. We conduct extensive trace-driven simulations to demonstrate the merits of our algorithms and implementation. Our simulation results show that the proposed efficient algorithms: 1) outperform the baseline algorithms by up to 46% and 30%; 2) run fast and scale to large (≤8000 gamers) problems; and 3) achieve the user-specified optimization criteria, such as maximizing average gamer experience or maximizing the minimum gamer experience. The resulting cloud gaming platform can be leveraged by many researchers, developers, and gamers.


title: Combining Argument Mining Techniques

abstract: In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human reasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.


title: Large-scale functional connectivity networks in the rodent brain

abstract: Resting-state functional Magnetic Resonance Imaging (rsfMRI) of the human brain has revealed multiple large-scale neural networks within a hierarchical and complex structure of coordinated functional activity. These distributed neuroanatomical systems provide a sensitive window on brain function and its disruption in a variety of neuropathological conditions. The study of macroscale intrinsic connectivity networks in preclinical species, where genetic and environmental conditions can be controlled and manipulated with high specificity, offers the opportunity to elucidate the biological determinants of these alterations. While rsfMRI methods are now widely used in human connectivity research, these approaches have only relatively recently been back-translated into laboratory animals. Here we review recent progress in the study of functional connectivity in rodent species, emphasising the ability of this approach to resolve large-scale brain networks that recapitulate neuroanatomical features of known functional systems in the human brain. These include, but are not limited to, a distributed set of regions identified in rats and mice that may represent a putative evolutionary precursor of the human default mode network (DMN). The impact and control of potential experimental and methodological confounds are also critically discussed. Finally, we highlight the enormous potential and some initial application of connectivity mapping in transgenic models as a tool to investigate the neuropathological underpinnings of the large-scale connectional alterations associated with human neuropsychiatric and neurological conditions. We conclude by discussing the translational potential of these methods in basic and applied neuroscience.


title: A systematic derivation of the STG machine verified in Coq

abstract: Shared Term Graph (STG) is a lazy functional language used as an intermediate language in the Glasgow Haskell Compiler (GHC). In this article, we present a natural operational semantics for STG and we mechanically derive a lazy abstract machine from this semantics, which turns out to coincide with Peyton-Jones and Salkild's Spineless Tagless G-machine (STG machine) used in GHC. Unlike other constructions of STG-like machines present in the literature, ours is based on a systematic and scalable derivation method (inspired by Danvy et al.'s functional correspondence between evaluators and abstract machines) and it leads to an abstract machine that differs from the original STG machine only in inessential details. In particular, it handles non-trivial update scenarios and partial applications identically as the STG machine. ::: The entire derivation has been formalized in the Coq proof assistant. Thus, in effect, we provide a machine checkable proof of the correctness of the STG machine with respect to the natural semantics.


title: Scalp dermoscopy of androgenetic alopecia in Asian people

abstract: Although dermoscopy is used mainly for diagnosing pigmented skin lesions, this device has been reported to be useful in observing alopecia areata and frontal fibrosing alopecia. Herein, we investigated the dermoscopic features and their incidence of androgenetic alopecia (AGA; n = 50 men) and female AGA (FAGA; n = 10 women) in Asian people. More than 20% hair diameter diversity (HDD), which reportedly is an early sign of AGA and corresponds to hair follicle miniaturization, was observed in the affected area of all AGA and FAGA cases, suggesting that HDD is an essential feature to diagnose AGA and FAGA. Peripilar signs, corresponding to perifollicular pigmentation, were seen in 66% (33/50) of AGA and 20% (2/10) of FAGA women. This incidence in the present study was lower than previously reported in white subjects possibly because the Asian skin color conceals slight peripilar pigmentation. Yellow dots were observed in 26% (13/50) of AGA and 10% (1/10) of FAGA cases and the number of yellow dots in AGA and FAGA was limited to 10 on the overall hair loss area. Yellow dots possibly indicate the coincidence of AGA and enlargement of the sebaceous glands caused by common end-organ hypersensitivity to androgen. In conclusion, dermoscopy is useful to diagnose AGA and FAGA and provides insights into the pathogenesis of AGA.


title: On Persistence in Mutual Fund Performance

abstract: Using a sample free of survivor bias, I demonstrate that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds' mean and risk-adjusted returns. Hendricks, Patel and Zeckhauser's (1993) "hot hands" result is mostly driven by the one-year momentum effect of Jegadeesh and Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks. The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds. The results do not support the existence of skilled or informed mutual fund portfolio managers. PERSISTENCE IN MUTUAL FUND performance does not reflect superior stock-picking skill. Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predictability in mutual fund returns. Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous. Mutual fund persistence is well documented in the finance literature, but not well explained. Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers (1996) find evidence of persistence in mutual fund performance over short-term horizons of one to three years, and attribute the persistence to "hot hands" or common investment strategies. Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), and Elton, Gruber, Das, and Blake (1996) document mutual fund return predictability over longer horizons of five to ten years, and attribute this to manager differential information or stock-picking talent. Contrary evidence comes from Jensen (1969), who does not find that good subsequent performance follows good past performance. Carhart (1992) shows that persistence in expense ratios drives much of the long-term persistence in mutual fund performance. My analysis indicates that Jegadeesh and Titman's (1993) one-year momentum in stock returns accounts for Hendricks, Patel, and Zeckhauser's (1993) hot hands effect in mutual fund performance. However, funds that earn higher


title: Query-Reduction Networks for Question Answering

abstract: In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN's time axis, saving an order of magnitude in time complexity for training and inference.


title: Binocular Rivalry: Frontal Activity Relates to Introspection and Action But Not to Perception

abstract: When two dissimilar stimuli are presented to the eyes, perception alternates between multiple interpretations, a phenomenon dubbed binocular rivalry. Numerous recent imaging studies have attempted to unveil neural substrates underlying multistable perception. However, these studies had a conceptual constraint: access to observers9 perceptual state relied on their introspection and active report. Here, we investigated to what extent neural correlates of binocular rivalry in healthy humans are confounded by this subjective measure and by action. We used the optokinetic nystagmus and pupil size to objectively and continuously map perceptual alternations for binocular-rivalry stimuli. Combining these two measures with fMRI allowed us to assess the neural correlates of binocular rivalry time locked to the perceptual alternations in the absence of active report. When observers were asked to actively report their percept, our objective measures matched the report. In this active condition, objective measures and subjective reporting revealed that occipital, parietal, and frontal areas underlie the processing of binocular rivalry, replicating earlier findings. Furthermore, objective measures provided additional statistical power due to their continuous nature. Importantly, when observers passively experienced rivalry without reporting perceptual alternations, a different picture emerged: differential neural activity in frontal areas was absent, whereas activation in occipital and parietal regions persisted. Our results question the popular view of a driving role of frontal areas in the initiation of perceptual alternations during binocular rivalry. Instead, we conclude that frontal areas are associated with active report and introspection rather than with rivalry per se.


title: Improved ensemble learning for classification techniques based on majority voting

abstract: This paper proposes the methodology for improving the performance of the classification model, over several methods. The accuracy values obtained through experiments permit the evaluation of each method's performance. We propose a concept that brings Ensemble learning to model classification, in order to improve performance through majority voting, called M-Ensemble learning. The improved Ensemble learning approach is divided into two main formats of combined methods, namely the 3-Ensemble model (combining odd number methods, such as Naive Bayes, Decision Tree, and Multilayer Perceptron); and the 4-Ensemble model (combining even number methods, such as Naive Bayes, Decision Tree, Multilayer Perceptron, and K-Nearest Neighbor). The most improved classification model resulted from the improved 3-Ensemble method, with an accuracy value of 83.13%, compared with the Multilayer Perceptron based model classification and the 4-Ensemble model, which yielded accuracy values of 80.67% and 81.86%, respectively.


title: Multimedia traffic security architecture for the internet of things

abstract: An important challenge for supporting multimedia applications in the Internet of Things is the security heterogeneity of wired and wireless sensor and actuator networks. In this work, we design a new and efficient media-aware security framework for facilitating various multimedia applications in the Internet of Things. First, we present a novel multimedia traffic classification and analysis method for handling the heterogeneity of diverse applications. Then a media-aware traffic security architecture is proposed based on the given traffic classification to enable various multimedia services being available anywhere and anytime. Furthermore, we provide a design rule and strategy to achieve a good trade-off between a system?s flexibility and efficiency. To the best of our knowledge, this study is the first to provide general media-aware security architecture by jointly considering the characteristics of multimedia traffic, security service, and the Internet of Things.


title: A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy

abstract: Nowadays, a prosperity of electronic commerce (E-commerce) not only gives more convenience to consumers but brings more new opportunities in online advertising and marketing. Online advertisers can understand more about consumer preferences based on their daily online shopping and browsing behaviors. The development of big data and cloud computing techniques further empower advertisers and marketers to have a data-driven and consumer-specific preference recommendation based on the online browsing histories. In this research, a decision support system is proposed to predict a consumer purchase intention during browsing sessions. The proposed decision support system categorizes online browsing activities into purchase-oriented and general sessions using extreme boosting machines. With the browsing content entropy features, the proposed method achieves 41.81% recall and 34.35% F score. It further shows its strong predictive capability compared to other benchmark algorithms including logistic regression and traditional ensemble models. The proposed method can be implemented in real-time bidding algorithms for online advertising strategies. Ad deliveries on browsing session with potential purchase intention not only improve the effectiveness of advertisements but significantly increase last-touch attributions for campaign performance.


title: Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation

abstract: Learning visual representations from web data has recently attracted attention for object recognition. Previous studies have mainly focused on overcoming label noise and data bias and have shown promising results by learning directly from web data. However, we argue that it might be better to transfer knowledge from existing human labeling resources to improve performance at nearly no additional cost. In this paper, we propose a new semi-supervised method for learning via web data. Our method has the unique design of exploiting strong supervision, i.e., in addition to standard image-level labels, our method also utilizes detailed annotations including object bounding boxes and part landmarks. By transferring as much knowledge as possible from existing strongly supervised datasets to weakly supervised web images, our method can benefit from sophisticated object recognition algorithms and overcome several typical problems found in webly-supervised learning. We consider the problem of fine-grained visual categorization, in which existing training resources are scarce, as our main research objective. Comprehensive experimentation and extensive analysis demonstrate encouraging performance of the proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is likely to be highly effective for real-world applications.


Experiential mixed reality learning environments for design education


title: Smart grid data integrity attacks: characterizations and countermeasuresπ

abstract: Coordinated cyberattacks of power meter readings can be arranged to be undetectable by any bad data detection algorithm in the power system state estimation process. These unobservable attacks present a potentially serious threat to grid operations. Of particular interest are sparse attacks that involve the compromise of a modest number of meter readings. An efficient algorithm to find all unobservable attacks [under standard DC load flow approximations] involving the compromise of exactly two power injection meters and an arbitrary number of line power meters is presented. This requires O(n2m) flops for a power system with n buses and m line meters. If all lines are metered, there exist canonical forms that characterize all 3, 4, and 5-sparse unobservable attacks. These can be quickly detected in power systems using standard graph algorithms. Known-secure phasor measurement units [PMUs] can be used as countermeasures against an arbitrary collection of cyberattacks. Finding the minimum number of necessary PMUs is NP-hard. It is shown that p + 1 PMUs at carefully chosen buses are sufficient to neutralize a collection of p cyberattacks.


title: Comparison of risk and protective factors associated with smartphone addiction and Internet addiction

abstract: Background and AimsSmartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. This study assessed the risk and protective factors associated with smartphone addiction in college students and compared these factors to those linked to Internet addiction.MethodsCollege students (N = 448) in South Korea completed the Smartphone Addiction Scale, the Young’s Internet Addiction Test, the Alcohol Use Disorders Identification Test, the Beck Depression Inventory I, the State–Trait Anxiety Inventory (Trait Version), the Character Strengths Test, and the Connor–Davidson Resilience Scale. The data were analyzed using multiple linear regression analyses.ResultsThe risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance. In contrast, the risk factors for Internet addiction were male gender, smartphone use, anxiety, and wisdom/knowledge, while the protective factor ...


title: Reinforcement Learning to Rank with Markov Decision Process

abstract: One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures such as normalized discounted cumulative gain~(ND CG). Existing methods usually focus on optimizing a specific evaluation measure calculated at a fixed position, e.g., NDCG calculated at a fixed position K. In information retrieval the evaluation measures, including the widely used NDCG and P@K, are usually designed to evaluate the document ranking at all of the ranking positions, which provide much richer information than only measuring the document ranking at a single position. Thus, it is interesting to ask if we can devise an algorithm that has the ability of leveraging the measures calculated at all of the ranking postilions, for learning a better ranking model. In this paper, we propose a novel learning to rank model on the basis of Markov decision process (MDP), referred to as MDPRank. In the learning phase of MDPRank, the construction of a document ranking is considered as a sequential decision making, each corresponds to an action of selecting a document for the corresponding position. The policy gradient algorithm of REINFORCE is adopted to train the model parameters. The evaluation measures calculated at every ranking positions are utilized as the immediate rewards to the corresponding actions, which guide the learning algorithm to adjust the model parameters so that the measure is optimized. Experimental results on LETOR benchmark datasets showed that MDPRank can outperform the state-of-the-art baselines.


title: Moduli spaces of metric graphs of genus 1 with marks on vertices

abstract: Abstract In this paper we study homotopy type of certain moduli spaces of metric graphs. More precisely, we show that the spaces M G 1 , n v , which parametrize the isometry classes of metric graphs of genus 1 with n marks on vertices are homotopy equivalent to the spaces T M 1 , n , which are the moduli spaces of tropical curves of genus 1 with n marked points. Our proof proceeds by providing a sequence of explicit homotopies, with key role played by the so-called scanning homotopy. We conjecture that our result generalizes to the case of arbitrary genus.


title: Content delivery over TLS: a cryptographic analysis of keyless SSL

abstract: The Transport Layer Security (TLS) protocol is designed to allow two parties, a client and a server, to communicate securely over an insecure network. However, when TLS connections are proxied through an intermediate middlebox, like a Content Delivery Network (CDN), the standard endto- end security guarantees of the protocol no longer apply. In this paper, we investigate the security guarantees provided by Keyless SSL, a CDN architecture currently deployed by CloudFlare that composes two TLS 1.2 handshakes to obtain a proxied TLS connection. We demonstrate new attacks that show that Keyless SSL does not meet its intended security goals. These attacks have been reported to CloudFlare and we are in the process of discussing fixes. We argue that proxied TLS handshakes require a new, stronger, 3-party security definition. We present 3(S)ACCEsecurity, a generalization of the 2-party ACCE security definition that has been used in several previous proofs for TLS. We modify Keyless SSL and prove that our modifications guarantee 3(S)ACCE-security, assuming ACCE-security for the individual TLS 1.2 connections. We also propose a new design for Keyless TLS 1.3 and prove that it achieves 3(S)ACCEsecurity, assuming that the TLS 1.3 handshake implements an authenticated 2-party key exchange. Notably, we show that secure proxying in Keyless TLS 1.3 is computationally lighter and requires simpler assumptions on the certificate infrastructure than our proposed fix for Keyless SSL. Our results indicate that proxied TLS architectures, as currently used by a number of CDNs, may be vulnerable to subtle attacks and deserve close attention.


title: Simultaneous determination of misonidazole and desmethylmisonidazole in plasma by high-performance liquid chromatography.

abstract: A high-performance liquid chromatographic method for the simultaneous determination of misonidazole and desmethylmisonidazole in plasma is described. After plasma is deproteinized with methanol and the diluted supernatant is chromatographed on a C18 reversed-phase column, both compounds are quantitated by means of an internal standard. The coefficients of variation of within-day and day-to-day precision are below 5.0% for misonidazole in the concentration range of 25-250 mg/l and below 6.1% for desmethylmisonidazole in the concentration range of 2.5-25.0 mg/l. Calibration curves are linear and an analytical recovery varying from 97.6 to 99.8% is obtained. The detection limits for misonidazole and desmethylmisonidazole in plasma are 1.4 mg/l and 0.7 mg/l, respectively.


title: Importance of Brand Awareness and Brand Loyalty in assessing Purchase Intentions of Consumer

abstract: The aim of this endeavor is to identify the effect of brand awareness and brand loyalty on purchase intention. Questionnaires were distributed to collect the responses from the employees in services sectors and conveniently available general public while descriptive statistics and regression analysis were used to analyze the data and draw the conclusions. Brand Awareness and brand loyalty have strong positive association with purchase intention. Managers all over the world should strive to promote the brand awareness along with brand loyalty as both of them contribute towards positive purchase intentions.


title: Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation

abstract: Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities $\sim 100\%$ of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.


title: Fast parallel algorithm for unfolding of communities in large graphs

abstract: Detecting community structures in graphs is a well studied problem in graph data analytics. Unprecedented growth in graph structured data due to the development of the world wide web and social networks in the past decade emphasizes the need for fast graph data analytics techniques. In this paper we present a simple yet efficient approach to detect communities in large scale graphs by modifying the sequential Louvain algorithm for community detection. The proposed distributed memory parallel algorithm targets the costly first iteration of the initial method by parallelizing it. Experimental results on a MPI setup with 128 parallel processes shows that up to ≈5× performance improvement is achieved as compared to the sequential version while not compromising the correctness of the final result.


Unsupervised Learning for Object Recognition


title: Relationship Quality and Stability in Couples When One Partner Suffers From Borderline Personality Disorder

abstract: The main goal of the present study was to examine and compare the psychosocial functioning of 35 couples including a woman diagnosed with borderline personality disorder (BPD) to that of a nonclinical control sample of 35 couples. The BPD status of women from the clinical group and the prevalence of personality disorder in their partner were ascertained through the SCID-II. Participants completed self-report measures of couple functioning. A majority of couples in which the woman suffered from BPD (68.7%) evidenced frequent episodes of breakups and reconciliations and, over an 18-month period, nearly 30% of these couples dissolved their relationship. Nearly half of the men involved in a romantic relationship with a woman suffering from BPD met criteria for one personality disorder or more. As compared with nonclinical couples, clinical couples showed lower marital satisfaction, higher attachment insecurity, more demand/withdraw communication problems, and higher levels of violence.


title: New features and insights for pedestrian detection

abstract: Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly — even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce a new feature, self-similarity on color channels, which consistently improves detection performance both for static images and for video sequences, across different datasets. In combination with HOG, these two features outperform the state-of-the-art by up to 20%. Finally, we report two insights concerning detector evaluations, which apply to classifier-based object detection in general. First, we show that a commonly under-estimated detail of training, the number of bootstrapping rounds, has a drastic influence on the relative (and absolute) performance of different feature/classifier combinations. Second, we discuss important intricacies of detector evaluation and show that current benchmarking protocols lack crucial details, which can distort evaluations.


title: Highlight Detection with Pairwise Deep Ranking for First-Person Video Summarization

abstract: The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos. Browsing such long unstructured videos is time-consuming and tedious. This paper studies the discovery of moments of user's major or special interest (i.e., highlights) in a video, for generating the summarization of first-person videos. Specifically, we propose a novel pairwise deep ranking model that employs deep learning techniques to learn the relationship between high-light and non-highlight video segments. A two-stream network structure by representing video segments from complementary information on appearance of video frames and temporal dynamics across frames is developed for video highlight detection. Given a long personal video, equipped with the highlight detection model, a highlight score is assigned to each segment. The obtained highlight segments are applied for summarization in two ways: video time-lapse and video skimming. The former plays the highlight (non-highlight) segments at low (high) speed rates, while the latter assembles the sequence of segments with the highest scores. On 100 hours of first-person videos for 15 unique sports categories, our highlight detection achieves the improvement over the state-of-the-art RankSVM method by 10.5% in terms of accuracy. Moreover, our approaches produce video summary with better quality by a user study from 35 human subjects.


title: CNN-RNN: A Unified Framework for Multi-label Image Classification

abstract: While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification model


title: LMVP: Video Predictor with Leaked Motion Information

abstract: We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly proposed component, motion guider, which plays the role of both learner and teacher. Specifically, it {\em learns} the temporal features from real data and {\em guides} the generator to predict future frames. The spatial consistency in video is modeled by an adaptive filtering network. To further ensure the spatio-temporal consistency of the prediction, a discriminator is also adopted to distinguish the real and generated frames. Further, the discriminator leaks information to the motion guider and the generator to help the learning of motion. The proposed LMVP can effectively learn the static and temporal features in videos without the need for human labeling. Experiments on synthetic and real data demonstrate that LMVP can yield state-of-the-art results.


title: Study of Design and Analysis of Low-Power 10-Transistor Full Adders Using Novel XOR–XNOR Gates

abstract: Full adders are vital components in applications such as digital signal processors (DSP) architectures and microprocessors. In this paper, we propose a technique to build a total of 41 new 10-transistor FA using novel XOR and XNOR gates in combination with existing ones.We have done over ten thousand HSPICE simulation runs of the entire the different adders in different input patterns, frequencies, and load capacitances. Almost all those new adders consume less power in high frequencies, while 3 new adders consistently use on average 10% less power and have higher speed compared with the previous Ten-transistor full adder and the conventional 28-T CMOS adder. One draw back of the novel adders is the threshold-voltage loss of the pass transistors.


title: Exploring and modeling unimanual object manipulation on multi-touch displays

abstract: Touch-sensitive devices are becoming increasingly wide-spread, and consequently gestural interfaces have become familiar to the public. Despite the fact that many gestures require frequently dragging, pinching, spreading, and rotating the finger-tips, there currently does not exist a human performance model describing this interaction. In this paper, a novel user performance model is derived for virtual object manipulation on touch-sensitive displays, which involves simultaneous translation, rotation, and scaling of the object. Two controlled experiments with dual-finger unimanual manipulations were conducted to validate the new model. The results indicate that the model fits the experimental data well (with R2 and R values above 0.9), and performs the best among several alternative models. Moreover, based on the analysis of the empirical data, the simultaneity nature of manipulation in the task is explored and several design implications are provided. HighlightsWe propose a quantitative model for unimanual dual-finger multi-touch manipulations.We conduct two formal experiments to explore the task nature and validate the model.Experimental results and comparisons indicate the excellent performance of our model.In-depth analysis and discussion provide insights of the task and implications for design.


AUTOMATED SPEECH RECOGNITION (ASR) PROCESSES AND ALGORITHMS-A REVIEW


Recognition of Volcanoes on Venus using Correlation Methods


title: A Survey of Face Recognition Techniques

abstract: Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.


title: Autonomous Reinforcement Learning with Experience Replay for Humanoid Gait Optimization

abstract: Abstract This paper demonstrates application of Reinforcement Learning to optimization of control of a complex system in realistic setting that requires efficiency and autonomy of the learning algorithm. Namely, Actor-Critic with experience replay (which addresses efficiency), and the Fixed Point method for step-size estimation (which addresses autonomy) is applied here to approximately optimize humanoid robot gait. With complex dynamics and tens of continuous state and action variables, humanoid gait optimization represents a challenge for analytical synthesis of control. The presented algorithm learns a nimble gait within 80 minutes of training.


title: How Social Media Will Change Public Health

abstract: Recent work in machine learning and natural language processing has studied the health content of tweets and demonstrated the potential for extracting useful public health information from their aggregation. This article examines the types of health topics discussed on Twitter, and how tweets can both augment existing public health capabilities and enable new ones. The author also discusses key challenges that researchers must address to deliver high-quality tools to the public health community.


title: TUCH:Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets

abstract: Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Cross-view hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multi-view embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Cross-view Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on image-sentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO.


title: Improving User Topic Interest Profiles by Behavior Factorization

abstract: Many recommenders aim to provide relevant recommendations to users by building personal topic interest profiles and then using these profiles to find interesting contents for the user. In social media, recommender systems build user profiles by directly combining users' topic interest signals from a wide variety of consumption and publishing behaviors, such as social media posts they authored, commented on, +1'd or liked. Here we propose to separately model users' topical interests that come from these various behavioral signals in order to construct better user profiles. Intuitively, since publishing a post requires more effort, the topic interests coming from publishing signals should be more accurate of a user's central interest than, say, a simple gesture such as a +1. By separating a single user's interest profile into several behavioral profiles, we obtain better and cleaner topic interest signals, as well as enabling topic prediction for different types of behavior, such as topics that the user might +1 or comment on, but might never write a post on that topic. To do this at large scales in Google+, we employed matrix factorization techniques to model each user's behaviors as a separate example entry in the input user-by-topic matrix. Using this technique, which we call "behavioral factorization", we implemented and built a topic recommender predicting user's topical interests using their actions within Google+. We experimentally showed that we obtained better and cleaner signals than baseline methods, and are able to more accurately predict topic interests as well as achieve better coverage.


title: Unveiling the properties of structured grammatical evolution

abstract: Structured grammatical evolution (SGE) is a new genotypic representation for grammatical evolution (GE). It comprises a hierarchical organization of the genes, where each locus is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals. We present a comprehensive set of optimization results obtained with problems from three different categories: symbolic regression, path finding, and predictive modeling. In most of the situations SGE outperforms standard GE, confirming the effectiveness of the new representation. To understand the reasons for SGE enhanced performance, we scrutinize its main features. We rely on a set of static measures to model the interactions between the representation and variation operators and assess how they influence the interplay between the genotype-phenotype spaces. The study reveals that the structured organization of SGE promotes an increased locality and is less redundant than standard GE, thus fostering an effective exploration of the search space.