title: Patient and nurse preferences for nurse handover—using preferences to inform policy: a discrete choice experiment protocol

abstract: Introduction Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting. Methods and analysis We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results. Ethics and dissemination This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.


title: Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio

abstract: Algorithms for the discovery of musical repetition have been developed in audio and symbolic domains more or less independently for over a decade. In this paper we combine algorithms for multiple F0 estimation, beat tracking, quantisation, and pattern discovery, so that for the first time, the note content of motifs, themes, and repeated sections can be discovered directly from polyphonic music audio. Testing on deadpan and expressive piano renditions of pieces, we compared pattern discovery performance against runs on symbolic representations of the same pieces. Comparing deadpan audio with deadpan-symbolic representations, establishment precision and recall fell by ∼ 25%, and by ∼ 50% when comparing expressive audio with deadpan-symbolic representations. The music data and evaluation results establish a benchmark for future work that attempts to bridge the audio-symbolic gap.


title: Beneficial effects of intermittent fasting and caloric restriction on the cardiovascular and cerebrovascular systems.

abstract: Intermittent fasting (IF; reduced meal frequency) and caloric restriction (CR) extend lifespan and increase resistance to age-related diseases in rodents and monkeys and improve the health of overweight humans. Both IF and CR enhance cardiovascular and brain functions and improve several risk factors for coronary artery disease and stroke including a reduction in blood pressure and increased insulin sensitivity. Cardiovascular stress adaptation is improved and heart rate variability is increased in rodents maintained on an IF or a CR diet. Moreover, rodents maintained on an IF regimen exhibit increased resistance of heart and brain cells to ischemic injury in experimental models of myocardial infarction and stroke. The beneficial effects of IF and CR result from at least two mechanisms--reduced oxidative damage and increased cellular stress resistance. Recent findings suggest that some of the beneficial effects of IF on both the cardiovascular system and the brain are mediated by brain-derived neurotrophic factor signaling in the brain. Interestingly, cellular and molecular effects of IF and CR on the cardiovascular system and the brain are similar to those of regular physical exercise, suggesting shared mechanisms. A better understanding of the cellular and molecular mechanisms by which IF and CR affect the blood vessels and heart and brain cells will likely lead to novel preventative and therapeutic strategies for extending health span.


title: Volumetric ray tracing

abstract: This paper presents an eficient ray tracing method for scenes containing volumetric as well as geometric data. A global illumination equation is developed for this method, which is able to capture in a single image both realistic eaects and practical volume rendering. In this method, ray-object intersection calculations result in standard intersection points, as well as intersection segments. For accuracy and eflciency, all objects along a ray are sorted according to distance and intersection classification before any intersection calculations are pelformed. The intersection results are then passed to a shaden which evaluates the intensity equation defined by the illumination model to determine the final pixel value.


title: The language of mental health problems in social media

abstract: Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.


title: Discrete exterior calculus discretization of incompressible Navier-Stokes equations over surface simplicial meshes

abstract: A conservative discretization of incompressible Navier-Stokes equations is developed based on discrete exterior calculus (DEC). A distinguishing feature of our method is the use of an algebraic discretization of the interior product operator and a combinatorial discretization of the wedge product. The governing equations are first rewritten using the exterior calculus notation, replacing vector calculus differential operators by the exterior derivative, Hodge star and wedge product operators. The discretization is then carried out by substituting with the corresponding discrete operators based on the DEC framework. Numerical experiments for flows over surfaces reveal a second order accuracy for the developed scheme when using structured-triangular meshes, and first order accuracy for otherwise unstructured meshes. By construction, the method is conservative in that both mass and vorticity are conserved up to machine precision. The relative error in kinetic energy for inviscid flow test cases converges in a second order fashion with both the mesh size and the time step.


title: Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning

abstract: Social interaction activities embedded in social commerce sites influence the intention to purchase.Positive valence WOM and WOM content significantly affect consumers intention to buy a product.Intention to purchase driven by social interaction facilitates the likelihood of actual buying and sharing information with peers.Observing and learning other consumers behaviors increases the intention to purchase. Social commerce mediated by social media and social network platforms has led to the development of new business models in e-commerce and digitized the consumer decision journey. Social interaction is considered as a prerequisite for successful social commerce since consumers now expect an interactive and social experience while making purchase decisions. Drawing on word of mouth (WOM) and observational learning theories, we conceptualize social interactions in social commerce environments into two forms: WOM communication and observing other consumers purchases, and examine their impact on consumer purchase intention and actual purchase behavior. Analyzing primary data (n=217) collected from surveyed active consumers within social commerce sites at two stages (pre-purchase and post-purchase), we found that positive and negative valence WOM, WOM content, and observing other consumers purchases significantly affect consumers intention to buy a product, thereby increasing the likelihood of actual buying and sharing product information with others on social commerce sites.


title: The role of ecological theory in microbial ecology

abstract: Microbial ecology is currently undergoing a revolution, with repercussions spreading throughout microbiology, ecology and ecosystem science. The rapid accumulation of molecular data is uncovering vast diversity, abundant uncultivated microbial groups and novel microbial functions. This accumulation of data requires the application of theory to provide organization, structure, mechanistic insight and, ultimately, predictive power that is of practical value, but the application of theory in microbial ecology is currently very limited. Here we argue that the full potential of the ongoing revolution will not be realized if research is not directed and driven by theory, and that the generality of established ecological theory must be tested using microbial systems.


title: Centrality estimation in large networks

abstract: Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.


title: Dimensional Modeling: In a Business Intelligence Environment

abstract: In this IBM Redbook we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence. Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model - which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level. There is technical content for designing and maintaining such an environment, but also business content. For example, we use case studies to demonstrate how dimensional modeling can impact the business intelligence requirements for your business initiatives. In addition, we provide a detailed discussion on the query aspects of BI and data modeling. For example, we discuss query optimization and how you can determine performance of the data model prior to implementation. You need a solid base for your data warehousing infrastructure . . . . a solid data model.


title: TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays

abstract: The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.


title: Explainable Neural Networks based on Additive Index Models

abstract: Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples.


title: Bias Discovery in News Articles Using Word Vectors

abstract: Given the ongoing controversy over biased news, it would be useful to have a system that can detect the extent of bias in online news articles and indicate it to the user in real time. Here we measure bias in a given sentence or article as the word vector similarity with a corpus of biased words. We compute the word vector similarity of each of the sentences with the words taken from a Wikipedia Neutral Point of View (NPOV) corpus, measured using the word2vec tool, where our model is trained using Wikipedia articles. We then compute the bias score, which indicates how much that article uses biased words. This is implemented as a web browser extension, which queries an online server running our bias detection algorithm. Finally, we validate the accuracy of our bias detection by comparing bias rankings of a variety of articles from various sources. We get lower bias scores for Wikipedia articles than for news articles, which is lower than that for opinion articles.


title: It All Depends

abstract: Today, we have the ability to utilize scalable, distributed computing environments within the confines of the Internet, a practice known as cloud computing. In this new world of computing, users are universally required to accept the underlying premise of trust. Within the cloud computing world, the virtual environment lets users access computing power that exceeds that contained within their own physical worlds. Typically, users will know neither the exact location of their data nor the other sources of the data collectively stored with theirs. The data you can find in a cloud ranges from public source, which has minimal security concerns, to private data containing highly sensitive information (such as social security numbers, medical records, or shipping manifests for hazardous material). Does using a cloud environment alleviate the business entities of their responsibility to ensure that proper security measures are in place for both their data and applications, or do they share joint responsibility with service providers? The answers to this and other questions lie within the realm of yet-to-be-written law. As with most technological advances, regulators are typically in a "catch-up" mode to identify policy, governance, and law. Cloud computing presents an extension of problems heretofore experienced with the Internet. To ensure that such decisions are informed and appropriate for the cloud computing environment, the industry itself should establish coherent and effective policy and governance to identify and implement proper security methods.


title: Extended subdivision surfaces: Building a bridge between NURBS and Catmull-Clark surfaces

abstract: An extended subdivision surface (ESub) is a generalization of Catmull Clark and NURBS surfaces. Depending on the knot intervals and valences of the vertices and faces, Catmull Clark as well as NURBS patches can be generated using the extended subdivision rules. Moreover, an arbitrary choice of the knot intervals and the topology is possible. Special features like sharp edges and corners are consistently supported by setting selected knot intervals to zero or by applying special rules. Compared to the prior nonuniform rational subdivision surfaces (NURSS), the ESubs offer limit-point rules which are indispensable in many applications, for example, for computer-aided design or in adaptive visualization. The refinement and limit-point rules for our nonuniform, nonstationary scheme are obtained via a new method using local Bezier control points. With our new surface, it is possible to start with existing Catmull Clark as well as NURBS models and to continue the modeling process using the extended subdivision options.


title: Code2Vec: Embedding and Clustering Medical Diagnosis Data

abstract: Identifying disease comorbidities and grouping medical diagnoses into disease incidents are two important problems in health care delivery and assessment. Using vector space embeddings produced using the Global Vectors (GloVe) algorithm, we are able to find useful vector representations of diagnosis codes that can identify related diagnoses and thus improve identification of related disease incidents.


USE OF EDGE DETECTION OPERATORS FOR AGRICULTURE VIDEO SCENE FEATURE EX- TRACTION FROM MANGO FRUITS


title: Corpus Statistics Meet The Noun Compound: Some Empirical Results

abstract: A variety of statistical methods for noun compound analysis are implemented and compared. The results support two main conclusions. First, the use of conceptual association not only enables a broad coverage, but also improves the accuracy. Second, an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents, even though the latter is more prevalent in the literature.


title: Common Sense Knowledge Based Personality Recognition from Text

abstract: Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level, emotive words and other lexical clues such as number of first person or second person words carry major role to identify personality associated with the text. In this work, we propose a new architecture for the same task using common sense knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affective labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined common sense knowledge based features with phycho-linguistic features and frequency based features and later the features were employed in supervised classifiers. We designed five SMO based supervised classifiers for five personality traits. We observe that the use of common sense knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.


title: Predicting MOOC Dropout over Weeks Using Machine Learning Methods

abstract: With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.


title: Enhancing Promotional Strategies Within Social Marketing Programs: Use of Web 2.0 Social Media

abstract: The second generation of Internet-based applications (i.e., Web 2.0), in which users control communication, holds promise to significantly enhance promotional efforts within social marketing campaigns. Web 2.0 applications can directly engage consumers in the creative process by both producing and distributing information through collaborative writing, content sharing, social networking, social bookmarking, and syndication. Web 2.0 can also enhance the power of viral marketing by increasing the speed at which consumers share experiences and opinions with progressively larger audiences. Because of the novelty and potential effectiveness of Web 2.0, social marketers may be enticed to prematurely incorporate related applications into promotional plans. However, as strategic issues such as priority audience preferences, selection of appropriate applications, tracking and evaluation, and related costs are carefully considered, Web 2.0 will expand to allow health promotion practitioners more direct access to consumers with less dependency on traditional communication channels.


title: Tracking groups of people

abstract: A computer vision system for tracking multiple people in relatively unconstrained environments is described. Tracking is performed at three levels of abstraction: regions, people, and groups. A novel, adaptive background subtraction method that combines color and gradient information is used to cope with shadows and unreliable color cues. People are tracked through mutual occlusions as they form groups and separate from one another. Strong use is made of color information to disambiguate occlusion and to provide qualitative estimates of depth ordering and position during occlusion. Simple interactions with objects can also be detected. The system is tested using both indoor and outdoor sequences. It is robust and should provide a useful mechanism for bootstrapping and reinitialization of tracking using more specific but less robust human models.


title: Feedback for Smooth Pursuit Gaze Tracking Based Control

abstract: Smart glasses, like Google Glass or Microsoft HoloLens, can be used as interfaces that expand human perceptual, cognitive, and actuation capabilities in many everyday situations. Conventional manual interaction techniques, however, are not convenient with smart glasses whereas eye trackers can be built into the frames. This makes gaze tracking a natural input technology for smart glasses. Not much is known about interaction techniques for gaze-aware smart glasses. This paper adds to this knowledge, by comparing feedback modalities (visual, auditory, haptic, none) in a continuous adjustment technique for smooth pursuit gaze tracking. Smooth pursuit based gaze tracking has been shown to enable flexible and calibration free method for spontaneous interaction situations. Continuous adjustment, on the other hand, is a technique that is needed in many everyday situations such as adjusting the volume of a sound system or the intensity of a light source. We measured user performance and preference in a task where participants matched the shades of two gray rectangles. The results showed no statistically significant differences in performance, but clear user preference and acceptability for haptic and audio feedback.


title: Subcortical loops through the basal ganglia

abstract: Parallel, largely segregated, closed-loop projections are an important component of cortical–basal ganglia–cortical connectional architecture. Here, we present the hypothesis that such loops involving the neocortex are neither novel nor the first evolutionary example of closed-loop architecture involving the basal ganglia. Specifically, we propose that a phylogenetically older, closed-loop series of subcortical connections exists between the basal ganglia and brainstem sensorimotor structures, a good example of which is the midbrain superior colliculus. Insofar as this organization represents a general feature of brain architecture, cortical and subcortical inputs to the basal ganglia might act independently, co-operatively or competitively to influence the mechanisms of action selection.


title: Machine learned job recommendation

abstract: We address the problem of recommending suitable jobs to people who are seeking a new job. We formulate this recommendation problem as a supervised machine learning problem. Our technique exploits all past job transitions as well as the data associated with employees and institutions to predict an employee's next job transition. We train a machine learning model using a large number of job transitions extracted from the publicly available employee profiles in the Web. Experiments show that job transitions can be accurately predicted, significantly improving over a baseline that always predicts the most frequent institution in the data.


title: A Multilingual Database of Idioms

abstract: This paper presents a possible architecture for a multilingual database of idioms. We discuss the challenges that idioms present to the creation of such a database and propose a possible encoding that maximises the amount of information that can be stored for different languages. Such a resource provides important information for linguistic, computational linguistic and psycholinguistic use, and allows for the comparison of different phenomena in different languages. This can provide the basis for a better understanding of regularities in idioms across languages.


A User Story Quality Measurement Model for Reducing Agile Software Development Risk


title: Tuning the composition of conductive thermoplastics for stiffness switching and electrically activated healing

abstract: We introduce a class of stiffness-tuning polymer composites and carefully examine the influence of electrical activation and temperature on stiffness for a wide range of use cases. The composites a...


title: A direct-sequence spread-spectrum super-regenerative receiver

abstract: Current applications of the super-regenerative receiver use narrowband modulations. In this paper a new architecture that allows incoherent detection of spread-spectrum signals is presented. A pseudorandom code generator has been added to the original circuit. It is clocked by the quench oscillator and takes advantage of the characteristic broad reception bandwidth. CDMA can be achieved via ASK and FSK modulated signals with high simplicity in the RF stage as well as low power consumption.


title: Overall Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma

abstract: BACKGROUND Nivolumab combined with ipilimumab resulted in longer progression‐free survival and a higher objective response rate than ipilimumab alone in a phase 3 trial involving patients with advanced melanoma. We now report 3‐year overall survival outcomes in this trial. METHODS We randomly assigned, in a 1:1:1 ratio, patients with previously untreated advanced melanoma to receive nivolumab at a dose of 1 mg per kilogram of body weight plus ipilimumab at a dose of 3 mg per kilogram every 3 weeks for four doses, followed by nivolumab at a dose of 3 mg per kilogram every 2 weeks; nivolumab at a dose of 3 mg per kilogram every 2 weeks plus placebo; or ipilimumab at a dose of 3 mg per kilogram every 3 weeks for four doses plus placebo, until progression, the occurrence of unacceptable toxic effects, or withdrawal of consent. Randomization was stratified according to programmed death ligand 1 (PD‐L1) status, BRAF mutation status, and metastasis stage. The two primary end points were progression‐free survival and overall survival in the nivolumab‐plus‐ipilimumab group and in the nivolumab group versus the ipilimumab group. RESULTS At a minimum follow‐up of 36 months, the median overall survival had not been reached in the nivolumab‐plus‐ipilimumab group and was 37.6 months in the nivolumab group, as compared with 19.9 months in the ipilimumab group (hazard ratio for death with nivolumab plus ipilimumab vs. ipilimumab, 0.55 [P<0.001]; hazard ratio for death with nivolumab vs. ipilimumab, 0.65 [P<0.001]). The overall survival rate at 3 years was 58% in the nivolumab‐plus‐ipilimumab group and 52% in the nivolumab group, as compared with 34% in the ipilimumab group. The safety profile was unchanged from the initial report. Treatment‐related adverse events of grade 3 or 4 occurred in 59% of the patients in the nivolumab‐plus‐ipilimumab group, in 21% of those in the nivolumab group, and in 28% of those in the ipilimumab group. CONCLUSIONS Among patients with advanced melanoma, significantly longer overall survival occurred with combination therapy with nivolumab plus ipilimumab or with nivolumab alone than with ipilimumab alone. (Funded by Bristol‐Myers Squibb and others; CheckMate 067 ClinicalTrials.gov number, NCT01844505.)


title: Detecting signals of new technological opportunities using semantic patent analysis and outlier detection

abstract: In the competitive business environment, early identification of technological opportunities is crucial for technology strategy formulation and research and development planning. There exist previous studies that identify technological directions or areas from a broad view for technological opportunities, while few studies have researched a way to detect distinctive patents that can act as new technological opportunities at the individual patent level. This paper proposes a method of detecting new technological opportunities by using subject---action---object (SAO)-based semantic patent analysis and outlier detection. SAO structures are syntactically ordered sentences that can be automatically extracted by natural language processing of patent text; they explicitly show the structural relationships among technological components in a patent, and thus encode key findings of inventions and the expertise of inventors. Therefore, the proposed method allows quantification of structural dissimilarities among patents. We use outlier detection to identify unusual or distinctive patents in a given technology area; some of these outlier patents may represent new technological opportunities. The proposed method is illustrated using patents related to organic photovoltaic cells. We expect that this method can be incorporated into the research and development process for early identification of technological opportunities.


title: Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

abstract: An efficient algorithm for recurrent neural network training is presented. The approach increases the training speed for tasks where a length of the input sequence may vary significantly. The proposed approach is based on the optimal batch bucketing by input sequence length and data parallelization on multiple graphical processing units. The baseline training performance without sequence bucketing is compared with the proposed solution for a different number of buckets. An example is given for the online handwriting recognition task using an LSTM recurrent neural network. The evaluation is performed in terms of the wall clock time, number of epochs, and validation loss value.


title: Active Flattening of Curved Document Images via Two Structured Beams

abstract: Document images captured by a digital camera often suffer from serious geometric distortions. In this paper, we propose an active method to correct geometric distortions in a camera-captured document image. Unlike many passive rectification methods that rely on text-lines or features extracted from images, our method uses two structured beams illuminating upon the document page to recover two spatial curves. A developable surface is then interpolated to the curves by finding the correspondence between them. The developable surface is finally flattened onto a plane by solving a system of ordinary differential equations. Our method is a content independent approach and can restore a corrected document image of high accuracy with undistorted contents. Experimental results on a variety of real-captured document images demonstrate the effectiveness and efficiency of the proposed method.


title: Multi-Spectral Fusion Based Approach for Arbitrarily Oriented Scene Text Detection in Video Images

abstract: Scene text detection from video as well as natural scene images is challenging due to the variations in background, contrast, text type, font type, font size, and so on. Besides, arbitrary orientations of texts with multi-scripts add more complexity to the problem. The proposed approach introduces a new idea of convolving Laplacian with wavelet sub-bands at different levels in the frequency domain for enhancing low resolution text pixels. Then, the results obtained from different sub-bands (spectral) are fused for detecting candidate text pixels. We explore maxima stable extreme regions along with stroke width transform for detecting candidate text regions. Text alignment is done based on the distance between the nearest neighbor clusters of candidate text regions. In addition, the approach presents a new symmetry driven nearest neighbor for restoring full text lines. We conduct experiments on our collected video data as well as several benchmark data sets, such as ICDAR 2011, ICDAR 2013, and MSRA-TD500 to evaluate the proposed method. The proposed approach is compared with the state-of-the-art methods to show its superiority to the existing methods.


title: Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

abstract: This paper discusses faults in rotating electrical machines in general and describes a fault detection technique using artificial neural network (ANN) which is an expert system to detect short-circuit fault currents in the stator windings of a permanent-magnet synchronous machine (PMSM). The experimental setup consists of PMSM coupled mechanically to a dc motor configured to run in torque mode. Particle swarm optimization is used to adjust the weights of the ANN. All simulations are carried out in MATLAB/SIMULINK environment. The technique is shown to be effective and can be applied to real-time fault detection.


title: Federated Optimization for Heterogeneous Networks

abstract: Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While FedProx makes only minor algorithmic modifications to FedAvg, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of federated datasets. In particular, in highly heterogeneous settings, FedProx demonstrates significantly more stable and accurate convergence behavior relative to FedAvg---improving absolute test accuracy by 22% on average.


Tetris-: Exploring Human Performance via Cross Entropy Reinforcement Learning Models.


title: A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication

abstract: This paper explores the possible application of a single SVM classifier and its variants to churner identification problem in mobile telecommunication industry in which the role of customer retention program becomes more important than ever due to its very competitive business environment. In particular, this study introduces a uniformly sub sampled ensemble model of SVM classifiers combined with principal component analysis (PCA) not only to reduce the high dimensionality of data but also to boost the reliability and accuracy of calibrated models on data sets with highly skewed class distributions. According to our experiments, the performance of USE SVM model with PCA is superior to all compared models and the number of principal components (PCs) affect the accuracy of ensemble models.


title: Exploring the Use of Leaf Shape Frequencies for Plant Classification

abstract: Plant identification and classification play an important role in ecology, but the manual process is cumbersome even for experimented taxonomists. Technological advances allows the development of strategies to make these tasks easily and faster. In this context, this paper describes a methodology for plant identification and classification based on leaf shapes, that explores the discriminative power of the contour-centroid distance in the Fourier frequency domain in which some invariance (e.g. Rotation and scale) are guaranteed. In addition, it is also investigated the influence of feature selection techniques regarding classification accuracy. Our results show that by combining a set of features vectors - in the principal components space - and a feed forward neural network, an accuracy of 97.45% was achieved.


title: Combining One-Class Classifiers

abstract: In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer performance might waste valuable information. To improve performance the results of different classifiers (which may differ in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, combining one-class classifiers is more difficult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.


title: Waiting for the ultimate display: can decreased fidelity positively influence perceived realism?

abstract: The first virtual reality (VR) systems have hit the shelves, and 2017 may become the year where VR finally enters the homes of consumers in a big way. By allowing users to perceive and interact in a natural manner, VR offers the promise of realistic experiences of familiar, foreign, and fantastic virtual places and events. However, should we always opt for the highest degree of fidelity when striving to provide users with realistic experiences? In this position paper, we argue that when certain components of fidelity are limited, as they will be in relation to consumer VR, then maximizing the fidelity of other components may be detrimental to the perceived realism of the user. We present three cases supporting this hypothesis, and discuss the potential implications for researchers and developers relying on commercially available VR systems.


title: Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

abstract: In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.


title: ICDAR 2015 competition on Robust Reading

abstract: Results of the ICDAR 2015 Robust Reading Competition are presented. A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.


title: Adaptive event detection with time-varying poisson processes

abstract: Time-series of count data are generated in many different contexts, such as web access logging, freeway traffic monitoring, and security logs associated with buildings. Since this data measures the aggregated behavior of individual human beings, it typically exhibits a periodicity in time on a number of scales (daily, weekly,etc.) that reflects the rhythms of the underlying human activity and makes the data appear non-homogeneous. At the same time, the data is often corrupted by a number of bursty periods of unusual behavior such as building events, traffic accidents, and so forth. The data mining problem of finding and extracting these anomalous events is made difficult by both of these elements. In this paper we describe a framework for unsupervised learning in this context, based on a time-varying Poisson process model that can also account for anomalous events. We show how the parameters of this model can be learned from count time series using statistical estimation techniques. We demonstrate the utility of this model on two datasets for which we have partial ground truth in the form of known events, one from freeway traffic data and another from building access data, and show that the model performs significantly better than a non-probabilistic, threshold-based technique. We also describe how the model can be used to investigate different degrees of periodicity in the data, including systematic day-of-week and time-of-day effects, and make inferences about the detected events (e.g., popularity or level of attendance). Our experimental results indicate that the proposed time-varying Poisson model provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.


Corporate Yield Spreads and Bond Liquidity


title: Modeling Topics and Behavior of Microbloggers: An Integrated Approach

abstract: Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users’ topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user’s dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behavior with the latent background and personal topics, GBT helps to model user behavior by the two types of topics. GBT also distinguishes itself from other earlier works by modeling multiple types of behavior together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.


title: Multimodal Similarity-Preserving Hashing

abstract: We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.


title: Evaluating Display Fidelity and Interaction Fidelity in a Virtual Reality Game

abstract: In recent years, consumers have witnessed a technological revolution that has delivered more-realistic experiences in their own homes through high-definition, stereoscopic televisions and natural, gesture-based video game consoles. Although these experiences are more realistic, offering higher levels of fidelity, it is not clear how the increased display and interaction aspects of fidelity impact the user experience. Since immersive virtual reality (VR) allows us to achieve very high levels of fidelity, we designed and conducted a study that used a six-sided CAVE to evaluate display fidelity and interaction fidelity independently, at extremely high and low levels, for a VR first-person shooter (FPS) game. Our goal was to gain a better understanding of the effects of fidelity on the user in a complex, performance-intensive context. The results of our study indicate that both display and interaction fidelity significantly affect strategy and performance, as well as subjective judgments of presence, engagement, and usability. In particular, performance results were strongly in favor of two conditions: low-display, low-interaction fidelity (representative of traditional FPS games) and high-display, high-interaction fidelity (similar to the real world).


title: Classified patch learning for spatially scalable video coding

abstract: This paper proposes an advanced spatially scalable video coding approach that exploits the inter layer correlation between different resolution layers by classified patch learning. The novelty of our proposed scheme is twofold. First, the correlation between low and high resolution frames is explored at patch level with regard to image features. Patches extracted from the previous coded frame are classified into structural and textural sets according to the gradient information. Then the inter layer correlation is separately studied for the two sets, resulting in two databases containing pairs of patches at different resolutions. Second, our proposed patch-based compensation manages to simultaneously exploit the spatial and temporal redundancies without overhead bit for motion. Based on the two databases, a high resolution prediction is derived from the current low resolution reconstruction at structural and textural regions, respectively. Experimental results show that our proposed approach improves the performance of H.264/MPEG spatially scalable coding up to 1.9dB and significantly enhances the subjective quality, especially at low bit rates.


title: Improving CNN Performance Accuracies With Min–Max Objective

abstract: We propose a novel method for improving performance accuracies of convolutional neural network (CNN) without the need to increase the network complexity. We accomplish the goal by applying the proposed Min–Max objective to a layer below the output layer of a CNN model in the course of training. The Min–Max objective explicitly ensures that the feature maps learned by a CNN model have the minimum within-manifold distance for each object manifold and the maximum between-manifold distances among different object manifolds. The Min–Max objective is general and able to be applied to different CNNs with insignificant increases in computation cost. Moreover, an incremental minibatch training procedure is also proposed in conjunction with the Min–Max objective to enable the handling of large-scale training data. Comprehensive experimental evaluations on several benchmark data sets with both the image classification and face verification tasks reveal that employing the proposed Min–Max objective in the training process can remarkably improve performance accuracies of a CNN model in comparison with the same model trained without using this objective.


title: THE CORE SELF-EVALUATIONS SCALE: DEVELOPMENT OF A MEASURE

abstract: Despite an emerging body of research on a personality trait termed core self-evaluations, the trait continues to be measured indirectly. The present study reported the results of a series of studies that developed and tested the validity of the Core Self-Evaluations Scale (CSES), a direct and relatively brief measure of the trait. Results indicated that the 12-item CSES was reliable, displayed a unitary factor structure, correlated significantly with job satisfaction, job performance, and life satisfaction, and had validity equal to that of an optimal weighting of the 4 specific core traits (self-esteem, generalized self-efficacy, neuroti-cism, and locus of control), and incremental validity over the 5-factor model. Overall, results suggest that the CSES is a valid measure that should prove useful in applied psychology research.


title: Memory forensics tools: Comparing processing time and left artifacts on volatile memory

abstract: Digital investigation is becoming an increasing concern. Many digital forensic tools are being developed to deal with the challenge of investigating digital crimes. Acquisition of volatile memory is one of the vital steps of digital forensics process. Passwords data, indications of digital forensics methods, memory malware may be contained in volatile data which may overlooked by the investigator. The Success of memory acquisition mainly depends on the effectiveness of the memory acquisition tool. This paper compares memory forensics tools based on processing time and left artifacts on volatile memory. Furthermore, we examined how the processing time of the tools varies in terms of different volatile memory size. In order to conduct this work, we use the following tools: FTK Imager, Pro Discover, Nigilant32, Helix3(dd), OSForensics and Belkasoft RAM Capturer. The results show that Belkasoft RAM Capturer has the least amount of left artifacts, and it has also the lowest processing time. Moreover, this work concludes that tested tools are significantly different based on left artifacts on the volatile memory with 95% confidence level. Also, statistically, increasing the memory size × times does not increase the processing time × times of the tools.


title: Changes in appetite, food preference, and eating habits in frontotemporal dementia and Alzheimer's disease

abstract: Background: Despite numerous reports of changes in satiety, food preference, and eating habits in patients with frontotemporal dementia, there have been few systematic studies. Objectives: To investigate the frequency of changes in eating behaviours and the sequence of development of eating behaviours in frontotemporal dementia and Alzheimer’s disease, using a caregiver questionnaire. Methods: Three groups of patients were studied: frontal variant frontotemporal dementia (fv-FTD) (n = 23), semantic dementia (n = 25), and Alzheimer’s disease (n = 43). Level of education and dementia severity was similar in the three groups. The questionnaire consisted of 36 questions investigating five domains: swallowing problems, appetite change, food preference, eating habits, and other oral behaviours. Results: The frequencies of symptoms in all five domains, except swallowing problems, were higher in fv-FTD than in Alzheimer’s disease, and changes in food preference and eating habits were greater in semantic dementia than in Alzheimer’s disease. In semantic dementia, the developmental pattern was very clear: a change in food preference developed initially, followed by appetite increase and altered eating habits, other oral behaviours, and finally swallowing problems. In fv-FTD, the first symptom was altered eating habits or appetite increase. In Alzheimer’s disease, the pattern was not clear although swallowing problems developed in relatively early stages. Conclusions: Change in eating behaviour was significantly more common in both of the frontotemporal dementia groups than in Alzheimer’s disease. It is likely that the changing in eating behaviours reflects the involvement of a common network in both variants of frontotemporal dementia—namely, the ventral (orbitobasal) frontal lobe, temporal pole, and amygdala.


title: Growth of the flickr social network

abstract: Online social networking sites like MySpace, Orkut, and Flickr are among the most popular sites on the Web and continue to experience dramatic growth in their user population. The popularity of these sites offers a unique opportunity to study the dynamics of social networks at scale. Having a proper understanding of how online social networks grow can provide insights into the network structure, allow predictions of future growth, and enable simulation of systems on networks of arbitrary size. However, to date, most empirical studies have focused on static network snapshots rather than growth dynamics. In this paper, we collect and examine detailed growth data from the Flickr online social network, focusing on the ways in which new links are formed. Our study makes two contributions. First, we collect detailed data covering three months of growth, encompassing 950,143 new users and over 9.7 million new links, and we make this data available to the research community. Second, we use a first-principles approach to investigate the link formation process. In short, we find that links tend to be created by users who already have many links, that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close in the network.


title: Do Unbalanced Data Have a Negative Effect on LDA?

abstract: For two-class discrimination, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557-562] claimed that, when covariance matrices of the two classes were unequal, a (class) unbalanced data set had a negative effect on the performance of linear discriminant analysis (LDA). Through re-balancing 10 real-world data sets, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557-562] provided empirical evidence to support the claim using AUC (Area Under the receiver operating characteristic Curve) as the performance metric. We suggest that such a claim is vague if not misleading, there is no solid theoretical analysis presented in Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557-562], and AUC can lead to a quite different conclusion from that led to by misclassification error rate (ER) on the discrimination performance of LDA for unbalanced data sets. Our empirical and simulation studies suggest that, for LDA, the increase of the median of AUC (and thus the improvement of performance of LDA) from re-balancing is relatively small, while, in contrast, the increase of the median of ER (and thus the decline in performance of LDA) from re-balancing is relatively large. Therefore, from our study, there is no reliable empirical evidence to support the claim that a (class) unbalanced data set has a negative effect on the performance of LDA. In addition, re-balancing affects the performance of LDA for data sets with either equal or unequal covariance matrices, indicating that having unequal covariance matrices is not a key reason for the difference in performance between original and re-balanced data.


title: Uncovering the Honeypot Effect: How Audiences Engage with Public Interactive Systems

abstract: In HCI, the honeypot effect describes how people interacting with a system passively stimulate passers-by to observe, approach and engage in an interaction. Previous research has revealed the successive engagement phases and zones of the honeypot effect. However, there is little insight into: 1) how people are stimulated to transition between phases; 2) what aspects drive the honeypot effect apart from watching others; and 3) what constraints affect its self-reinforcing performance. In this paper, we discuss the honeypot effect as a spatiotemporal model of trajectories and influences. We introduce the Honeypot Model based on the analysis of observations and interaction logs from Encounters, a public installation that interactively translated bodily movements into a dynamic visual and sonic output. In providing a model that describes trajectories and influences of audience engagement in public interactive systems, our paper seeks to inform researchers and designers to consider contextual, spatial and social factors that influence audience engagement.


title: A PageRank based detection technique for phishing web sites

abstract: Phishing is an attempt to acquire one's information without user's knowledge by tricking him by making similar kind of website or sending emails to user which looks like legitimate site or email. Phishing is a social cyber threat attack, which is causing severe loss of economy to the user, due to phishing attacks online transaction users are declining. This paper aims to design and implement a new technique to detect phishing web sites using Google's PageRank. Google gives a PageRank value to each site in the web. This work uses the PageRank value and other features to classify phishing sites from normal sites. We have collected a dataset of 100 phishing sites and 100 legitimate sites for our use. By using this Google PageRank technique 98% of the sites are correctly classified, showing only 0.02 false positive rate and 0.02 false negative rate.


title: 24-GHz Frequency-Modulation Continuous-Wave Radar Front-End System-on-Substrate

abstract: Design and implementation of a 24-GHz frequency-modulation continuous-wave radar front-end system is presented and discussed, and its hybrid planar and waveguide building blocks are fully integrated on one single substrate. A flexible and compact integration methodology on the basis of the substrate-integrated-circuits concept is deployed to design such a microwave front-end system-on-substrate. In this study, it is found that this surface-volume hybrid integration scheme not only enables the complete system integration of planar and nonplanar microwave circuits, but also combines respective advantages of such structures in connection with microstrip lines (planar) and waveguides (nonplanar). Design strategies of the system building blocks including mixers, power dividers, and antenna arrays are discussed together along with the measured results. To verify the developed radar prototype, laboratory-based target-range measurements are conducted.


title: A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor

abstract: Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA’s) shine as popular evolutionary algorithms (EA’s) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.


A charm for staunching blood


title: Midline Mandibular Osteotomy in an Asymmetric Patient

abstract: Abstract This case report shows the possibility of the application of a mandibular osteotomy to resolve mandibular asymmetry with independent and discordant movements of both bony segments. The authors report the case of a 25-year-old woman referred for mandibular asymmetry, with a transverse excess of the right hemi mandible and vertical defect of the left one. The patient underwent a bilateral sagittal split osteotomy, midline osteotomy, and genioplasty, which corrected the mandibular asymmetry with contraction of the entire right hemi mandible. A slight left vertical increase was also obtained through the surgically created lateral open bite. In the follow-up assessment, the patient's face appeared symmetrical with normalization of the bizygomatic-bigonial relationships, and the facial shape corresponded to ideal anthropometric features. This technique resulted in resolution of mandibular asymmetry. In addition, mandibular osteotomy permits the esthetic management of the shape of the entire mandibular ...


title: What Exactly Is an Unusual Sexual Fantasy

abstract: Abstract Introduction Although several theories and treatment plans use unusual sexual fantasies (SF) as a way to identify deviancy, they seldom describe how the fantasies referred to were determined to be unusual. Aim The main goal of this study was to determine which SF are rare, unusual, common, or typical from a statistical point of view among a relatively large sample of adults recruited from the general population. A secondary goal was to provide a statistical comparison of the nature and intensity of sexual fantasies for men and women. This study also aims at demonstrating with both quantitative and qualitative analyses that certain fantasies often considered to be unusual are common. Methods An Internet survey was conducted with 1,516 adults (799 ♀; 717 ♂) who ranked 55 different SF and wrote their own favorite SF. Each SF was rated as statistically rare (2.3% or less), unusual (15.9% or less), common (more than 50%), or typical (more than 84.1% of the sample). Main Outcome Measures An extended version of the Wilson's Sex Fantasy Questionnaire with an open question. Results Only two sexual fantasies were found to be rare for women or men, while nine others were unusual. Thirty sexual fantasies were common for one or both genders, and only five were typical. These results were confirmed with qualitative analyses. Submission and domination themes were not only common for both men and women, but they were also significantly related to each other. Moreover, the presence of a single submissive fantasy was a significant predictor of overall scores for all SF in both genders. Conclusion Care should be taken before labeling an SF as unusual, let alone deviant. It suggested that the focus should be on the effect of a sexual fantasy rather than its content. Joyal CC, Cossette A, and Lapierre V. What exactly is an unusual sexual fantasy? J Sex Med 2015;12:328–340.


Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders


title: Probing the Spatial Dependence of the Emission Spectrum of Single Human Retinal Lipofuscin Granules Using Near-field Scanning Optical Microscopy¶

abstract: Abstract The emission spectra of single lipofuscin granules are examined using spectrally resolved confocal microscopy and near-field scanning optical microscopy (NSOM). The emission spectrum varies among the granules examined revealing that individual granules are characterized by different distributions of fluorophores. The range of spectra observed is consistent with in vivo spectra of human retinal pigment epithelium cells. NSOM measurements reveal that the shape of the spectrum does not vary with position within the emissive regions of single lipofuscin granules. These results suggest that the relative distribution of fluorophores within the emissive regions of an individual granule is homogeneous on the spatial scale ∼150 nm.


title: Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes]

abstract: This article provides a simple and intuitive derivation of the Kalman filter, with the aim of teaching this useful tool to students from disciplines that do not require a strong mathematical background. The most complicated level of mathematics required to understand this derivation is the ability to multiply two Gaussian functions together and reduce the result to a compact form.


title: Emotional contagion and burnout among nurses and doctors: Do joy and anger from different sources of stakeholders matter?

abstract: The present study adds novel knowledge to the literature on emotional contagion (EC), discrete emotions, job burnout, and the management of healthcare professionals by simultaneously considering EC as both a job demand and a job resource with multiple social pathways. Integrating EC into the job demands‐resource model, we develop and test a conceptual model wherein multiple stakeholder sources of emotional exchanges (i.e., leaders, colleagues, patients) play a differential role in predicting caregivers’ absorption of positive (i.e., joy) and negative (i.e., anger) emotions, and in turn, burnout. We tested this nomological network using structural equation modeling and invariance analyses on a sample of 252 nurses and 102 doctors from diverse healthcare wards in three Italian hospitals. Our findings show that not all emotional exchange sources contribute to the EC experience or likelihood of burnout. Specifically, we found that doctors absorbed joy and anger from their colleagues but not from their leaders or patients. In contrast, nurses absorbed joy and anger from leaders, colleagues, and patients. Surprisingly, we found that joy‐absorbed and anger‐absorbed were related to doctors’ exhaustion and cynicism, but only to nurses’ cynicism. We conclude with suggestions for advancing research and practice in the management of emotions for preventing burnout.


Mondor disease: Thrombosis of the dorsal vein of the penis


Robust MIL-Based Feature Template Learning for Object Tracking.


title: A New Approach of Digital Forensic Model for Digital Forensic Investigation

abstract: The research introduces a structured and consistent approach for digital forensic investigation. Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court. This research focuses on a structured and consistent approach to digital forensic investigation. This research aims at identifying activities that facilitate and improves digital forensic investigation process. Existing digital forensic framework will be reviewed and then the analysis will be compiled. The result from the evaluation will produce a new model to improve the whole investigation process.


title: Addition of EEG improves accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI

abstract: Abstract To investigate whether addition of EEG would improve accuracy of a logistic model that uses neuropsychological assessment and cardiovascular history to identify dementia and mild cognitive impairment (MCI) as a single group, we collected data and constructed logistic models from a sample of 78 normal adults and 33 patients (aged 50–85 years). To determine accuracy, we compared logistic regression results to a geriatrician's diagnosis of MCI or dementia that included Alzheimer's disease, vascular dementia or mixed dementia. We found that the addition of EEG (non-linear complexity) to a logistic model that included both neuropsychological assessment (ADAS-Cog) and cardiovascular history increased overall accuracy from 80% to 92%. The logistic model identified dementia and MCI as a single group comprised of the following subgroups (with accuracies): Alzheimer's disease (92%; 12/13), vascular dementia (73%; 8/11), mixed dementia (100%; 4/4), and mild cognitive impairment (80%; 4/5). Whereas the analysis is limited by small sample sizes and mixing of diverse pathologies, the findings do provide support that the subgroups may share changes in neuropsychological, cardiovascular, and electroencephalographic factors (specifically ADAS-Cog total score, cardiovascular history, and EEG complexity). Taken together, the study results provide support that EEG might complement the clinician's evaluation of dementia and MCI.


Vehicle Detection Method Based on Edge Information and Local Transform Histogram


title: Person Identification Technique Using Human Iris Recognition

abstract: The biometric person authentication technique based on the pattern of the human iris is well suited to be applied to any access control system requiring a high level of security. This paper examines a new iris recognition system that implements (i) gradient decomposed Hough transform / integro-differential operators combination for iris localization and (ii) the "analytic image" concept (2D Hilbert transform) to extract pertinent information from iris texture. All these image-processing algorithms have been validated on noised real iris images database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.


title: Measuring Energy Consumption in Wireless Sensor Networks Using GSP

abstract: The energy consumption rate for sensors in a wireless sensor network vary greatly depending on the protocols the sensors use for communication. The gossip-based sleep protocol (GSP) (X. Hou et al., 2004) is an example of a protocol that implements routing and some MAC functions in an effort to conserve energy. Simulations show that GSP can conserve energy. We expand on this effort by building a prototype system and measuring energy consumption rates. GSP was implemented on the Mica2 platform and measurements were conducted to determine energy consumption. The measurements were then used to build an energy consumption model for GSP


title: LAe-R: A new learning analytics tool in Moodle for assessing students' performance

abstract: A challenging and demanding task for the teachers in e-learning environments is the assessment of students' performance. Several learning management systems (LMS) like Moodle offer several assessment tools such as quizzes, scales, "classic" rubrics, etc. In this paper, we present a new cloud based assessment tool, called Learning Analytics Enriched Rubric (LAe-R) which is seamlessly integrated into Moodle. LAe-R is based on the concept of the "enriched rubrics", which is becoming a popular assessment technique in education. This technique is a blend of marking criteria and grading levels of a "classic/traditional" rubric and performance indicators stemmed from analysis of learners' interaction and learning behavior in a LMS based e-learning environment. Finally, in this paper we present the findings of a case study scenario showed that LAe-R is a very usable tool and is highly appreciated by teachers and students.


title: Single-Site Colectomy With Miniature In Vivo Robotic Platform

abstract: There has been a continuing push to reduce the invasiveness of surgery by accessing the abdominal cavity through a single incision, such as with laparoendoscopic single-site (LESS) surgery. Although LESS procedures offer significant benefits, added complexities still inhibit the procedures. Robotic surgery is proving to be an excellent option to overcome these limitations. This paper presents the experimental results of the single-incision in vivo surgical robot (SISR), a multifunctional, dexterous, two-armed robot capable of performing surgical tasks while overcoming the issues associated with manual LESS operations. In vivo surgical procedures have been used to demonstrate the efficacy of using a robotic platform over traditional laparoscopic tools. The most recent experimental test resulted in the first successful in vivo robotic LESS colectomy utilizing a robot completely contained within the abdominal cavity. In this test, SISR showed significant benefits including access to all quadrants in the peritoneal cavity and improved dexterity.


Route Selection Problem Based on Hopfield Neural Network


title: Text Classification Using the N-Gram Graph Representation Model Over High Frequency Data Streams

abstract: A prominent challenge in our information age is the classification over high frequency data streams. In this research, we propose an innovative and high-accurate text stream classification model that is designed in an elastic distributed way and is capable to service text load with fluctuated frequency. In this classification model, text is represented as N-Gram Graphs and the classification process takes place using text preprocessing, graph similarity and feature classification techniques following the supervised machine learning approach. The work involves the analysis of many variations of the proposed model and its parameters, such as various representations of text as N-Gram Graphs, graph comparisons metrics and classification methods in order to conclude to the most accurate setup. To deal with the scalability, the availability and the timely response in case of high frequency text we employ the Beam programming model. Using the Beam programming model the classification process occurs as a sequence of distinct tasks and facilitates the distributed implementation of the most computational demanding tasks of the inference stage. The proposed model and the various parameters that constitute it are evaluated experimentally and the high frequency stream emulated using two public datasets (20NewsGroup and Reuters-21578) that are commonly used in the literature for text classification


title: Flooding region growing: a new parallel image segmentation model based on membrane computing

abstract: Region-growing (RG) algorithm is one of the most common image segmentation methods used for different image processing and machine vision applications. However, this algorithm has two main problems: (1) high computational complexity and the difficulty of its parallel implementation caused by sequential process of adding pixels to regions; (2) low performance of RG in region with weak edges, due to the use of location and the number of seed points. In this paper, a new model of RG algorithm based on tissue-like P system is proposed to resolve these limitations. In this model, each pixel is modeled by a membrane, and in one step, the similarity of each membrane with its neighbors is computed. Then, all membranes are used as seed points to grow simultaneously in a parallel and flood-like manner. To realize the parallel implementation of the proposed model, Graphic Processing Unit (GPU) and CUDA programming language are used. The evaluation of execution time indicates that the proposed model has better performance than the conventional RG algorithm, its speed-up is about 12.5×. Qualitative and quantitative evaluations of segmentation performance also demonstrate that the proposed method not only does not damage the overall segmentation accuracy, but also it has better results on images with complicated background compared to the state-of-the-art methods.


title: Neural networks for financial market prediction

abstract: The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In this paper, the author examines several neural networks to evaluate their capability in prediction and in trend estimation which is treated as a classification problem. The networks considered are the backpropagation trained network (BPN), the general regression neural network (GRNN), the class-sensitive neural network (CSNN), and the conjugate gradient trained network (CGNN). It is concluded that CSNN is among the best performing networks in both prediction and trend estimation. All major indicators are evaluated by the neural networks. It is found that the use of good indicators like the rate of change, momentum, moving average, etc. can lead to about 5% improvement over the case that no indicator is used. Momentum computed from the previous 14 day data is the best single indicator. The complexity of the financial market probably explains why the large number of indicators cannot provide any significant improvement in network classification. >


Aesthetic porcelain laminate veneer restoration following orthodontic treatment: sequential technique.


title: Automatic Extraction of Figures from Scholarly Documents

abstract: Scholarly papers (journal and conference papers, technical reports, etc.) usually contain multiple ``figures'' such as plots, flow charts and other images which are generated manually to symbolically represent and illustrate visually important concepts, findings and results. These figures can be analyzed for automated data extraction or semantic analysis. Surprisingly, large scale automated extraction of such figures from PDF documents has received little attention. Here we discuss the challenges of how to build a heuristic independent trainable model for such an extraction task and how to extract figures at scale. Motivated by recent developments in table extraction, we define three new evaluation metrics: figure-precision, figure-recall, and figure-F1-score. Our dataset consists of a sample of 200 PDFs, randomly collected from five million scholarly PDFs and manually tagged for 180 figure locations. Initial results from our work demonstrate an accuracy greater than 80%.


title: Design and fabrication by example

abstract: We propose a data-driven method for designing 3D models that can be fabricated. First, our approach converts a collection of expert-created designs to a dataset of parameterized design templates that includes all information necessary for fabrication. The templates are then used in an interactive design system to create new fabri-cable models in a design-by-example manner. A simple interface allows novice users to choose template parts from the database, change their parameters, and combine them to create new models. Using the information in the template database, the system can automatically position, align, and connect parts: the system accomplishes this by adjusting parameters, adding appropriate constraints, and assigning connectors. This process ensures that the created models can be fabricated, saves the user from many tedious but necessary tasks, and makes it possible for non-experts to design and create actual physical objects. To demonstrate our data-driven method, we present several examples of complex functional objects that we designed and manufactured using our system.


title: Cross-Domain Recommender Systems

abstract: Most recommender systems work on single domains, i.e., they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, users that have watched live TV programs could like to be recommended with on-demand movies, music, mobile applications, friends to connect to, etc. This paper focuses on cross-domain collaborative recommender systems, whose aim is to suggest items related to multiple domains. We first formalize the cross-domain problem in order to provide a common framework for the classification and the evaluation of state-of-the-art algorithms. We later define a new class of cross-domain algorithms based on neighborhood collaborative filtering, either item-based or user-based. The main idea is to first model the classical similarity relationships (e.g., Pearson, cosine) as a direct graph and to later explore all possible paths connecting users or items in order to find new, cross-domain, relationships. The algorithms have been tested on three cross-domain scenarios artificially reproduced by partitioning the Netflix dataset.


title: A Simplified Model for Resistive Switching of Oxide-Based Resistive Random Access Memory Devices

abstract: A physical-based simplified model is presented to quantify the resistive switching behavior of oxide-based resistive random access memory (RRAM). In this model, the analytical expressions of the RESET time of RRAM devices and the correlated resistance in high-resistance states are presented and experimentally verified. Based on the extracted physical-based model parameters from measured data, the resistive switching characteristics of oxide-based RRAM devices can be evaluated.


title: Modeling Dominance in Group Conversations Using Nonverbal Activity Cues

abstract: Dominance - a behavioral expression of power - is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audiovisual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audiovisual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date.


title: The light field stereoscope: immersive computer graphics via factored near-eye light field displays with focus cues

abstract: Over the last few years, virtual reality (VR) has re-emerged as a technology that is now feasible at low cost via inexpensive cellphone components. In particular, advances of high-resolution micro displays, low-latency orientation trackers, and modern GPUs facilitate immersive experiences at low cost. One of the remaining challenges to further improve visual comfort in VR experiences is the vergence-accommodation conflict inherent to all stereoscopic displays. Accurate reproduction of all depth cues is crucial for visual comfort. By combining well-known stereoscopic display principles with emerging factored light field technology, we present the first wearable VR display supporting high image resolution as well as focus cues. A light field is presented to each eye, which provides more natural viewing experiences than conventional near-eye displays. Since the eye box is just slightly larger than the pupil size, rank-1 light field factorizations are sufficient to produce correct or nearly-correct focus cues; no time-multiplexed image display or gaze tracking is required. We analyze lens distortions in 4D light field space and correct them using the afforded high-dimensional image formation. We also demonstrate significant improvements in resolution and retinal blur quality over related near-eye displays. Finally, we analyze diffraction limits of these types of displays.


title: Improving pattern tracking with a language-aware tree differencing algorithm

abstract: Tracking code fragments of interest is important in monitoring a software project over multiple versions. Various approaches, including our previous work on Herodotos, exploit the notion of Longest Common Subsequence, as computed by readily available tools such as GNU Diff, to map corresponding code fragments. Nevertheless, the efficient code differencing algorithms are typically line-based or word-based, and thus do not report changes at the level of language constructs. Furthermore, they identify only additions and removals, but not the moving of a block of code from one part of a file to another. Code fragments of interest that fall within the added and removed regions of code have to be manually correlated across versions, which is tedious and error-prone. When studying a very large code base over a long time, the number of manual correlations can become an obstacle to the success of a study. In this paper, we investigate the effect of replacing the current line-based algorithm used by Herodotos by tree-matching, as provided by the algorithm of the differencing tool GumTree. In contrast to the line-based approach, the tree-based approach does not generate any manual correlations, but it incurs a high execution time. To address the problem, we propose a hybrid strategy that gives the best of both approaches.


title: Bach in 2014: Music Composition with Recurrent Neural Network

abstract: We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation through time (BPTT).


title: Dynamic malware analysis using machine learning algorithm

abstract: Malware detection is a vital think about the protection of the Personal computer systems. However, presently using signature-based strategies cannot offer correct detection of zero-day attacks and polymorphic viruses. That's why the requirement for machine learning-based detection arises. The purpose of this work was to work out the most effective feature extraction, feature illustration, and classification ways that end in the most effective accuracy. This work presents suggested ways for machine learning based malware classification and detection, also as the tips for its implementation. Moreover, the study performed is often helpful as a base for any analysis within the field of malware analysis with machine learning strategies.


title: FogStore: A Geo-Distributed Key-Value Store Guaranteeing Low Latency for Strongly Consistent Access

abstract: We design Fogstore, a key-value store for event-based systems, that exploits the concept of relevance to guarantee low-latency access to relevant data with strong consistency guarantees, while providing tolerance from geographically correlated failures. Distributed event-based processing pipelines are envisioned to utilize the resources of densely geo-distributed infrastructures for low-latency responses - enabling real-time applications. Increasing complexity of such applications results in higher dependence on state, which has driven the incorporation of state-management as a core functionality of contemporary stream processing engines a la Apache Flink and Samza. Processing components executing under the same context (like location) often produce information that may be relevant to others, thereby necessitating shared state and an out-of-band globally-accessible data-store. Efficient access to application state is critical for overall performance, thus centralized data-stores are not a viable option due to the high-latency of network traversals. On the other hand, a highly geo-distributed datastore with low-latency implemented with current key-value stores would necessitate degrading client expectation of consistency as per the PACELC theorem. In this paper we exploit the notion of contextual relevance of events (data) in situation-awareness applications - and offer differential consistency guarantees for clients based on their context. We highlight important systems concerns that may arise with a highly geo-distributed system and show how Fogstore's design tackles them. We present, in detail, a prototype implementation of Fogstore's mechanisms on Apache Cassandra and a performance evaluation. Our evaluations show that Fogstore is able to achieve the throughput of eventually consistent configurations while serving data with strong consistency to the contextually relevant clients.


title: Debloating Software through Piece-Wise Compilation and Loading

abstract: Programs are bloated. Our study shows that only 5% of libc is used on average across Ubuntu Desktop environment (>2200 programs); the heaviest user, vlc media player, only used 18%. This is striking because bloating presents a vulnerable attack surface for software exploitation and imposes undue burden on defenses (e.g., CFI defenses). ::: In this paper: (1) We present a debloating framework built on a compiler toolchain that can successfully debloat software (shared/static libraries and executables). Our solution can successfully compile and load most libraries on Ubuntu Desktop 16.04. (2) We demonstrate an elimination of over 84% code from coreutils and 85% code from SPEC CPU 2006 benchmark programs without affecting functionality. We show that even complex COTS programs (e.g., FireFox, Curl) can be debloated {without a need to recompile}. (3) We demonstrate the security impact of our system by eliminating over 70% of reusable code gadgets from coreutils suite, and show that unused code that contain {real-world vulnerabilities} can be successfully eliminated without adverse effects on the program. (4) Our solution imposes a low load time overhead.


title: Color Parameters of the Chromascop Shade Guide

abstract: The purposes of this study are: (1) determine the color of the twenty shades in the Ivoclar’s Chromascop shade guide, (2) determine the color representation of the shade guide described as coverage error (CE), and (3) compare this shade guide with the Vita Classical and Bioform shade guides. The spectral data was collected using Beckman model DU reflectance spectrophotometer equipped with an integrating sphere. Commission International de l’Eclairage (CIE) chromaticity coordinates were calculated using CIE illuminant C and 1931 observer data, then converted to CIE L*a*b* and Munsell notation. Each shade was spectrophotometrically compared to the published colors of 335 human teeth. The minimum CIE L*a*b* color difference was calculated for each tooth and the average of these color differences was defined as the CE. The measured colors of the Chromascop guide had a CIE L* range of 79.67 to 65.61, an a* range of -0.71 to 3.85, and a b* range of 14.58 to 27.69. The average CE of the Chromascop shade guide was 3.38. The Chromascop shade guide has similar colors and a CE compared with the Bioform and Vita Classical shade guides, but with some shades of higher red and yellow components.


title: Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning

abstract: For real-world applications, virtual agents must be able to learn new behaviors from non-technical users. Positive and negative feedback are an intuitive way to train new behaviors, and existing work has presented algorithms for learning from such feedback. That work, however, treats feedback as numeric reward to be maximized, and assumes that all trainers provide feedback in the same way. In this work, we show that users can provide feedback in many different ways, which we describe as "training strategies." Specifically, users may not always give explicit feedback in response to an action, and may be more likely to provide explicit reward than explicit punishment, or vice versa, such that the lack of feedback itself conveys information about the behavior. We present a probabilistic model of trainer feedback that describes how a trainer chooses to provide explicit reward and/or explicit punishment and, based on this model, develop two novel learning algorithms (SABL and I-SABL) which take trainer strategy into account, and can therefore learn from cases where no feedback is provided. Through online user studies we demonstrate that these algorithms can learn with less feedback than algorithms based on a numerical interpretation of feedback. Furthermore, we conduct an empirical analysis of the training strategies employed by users, and of factors that can affect their choice of strategy.


title: A 100 MHz 82.4% Efficiency Package-Bondwire Based Four-Phase Fully-Integrated Buck Converter With Flying Capacitor for Area Reduction

abstract: In today's fully-integrated converters, the integrated LC components dominate the chip-area and have become the major limitation of reducing the cost and increasing the current density. This paper presents a 100 MHz four-phase fully-integrated buck converter with standard package bondwire inductors and a flying capacitor (CFLY) topology for chip-area reduction, occupying 1.25 mm2 effective area in 0.13-μm CMOS technology. A four-phase operation is introduced for chip-area reduction with the cost penalty minimized by utilizing standard package bondwire inductance as power inductors. Meanwhile, an extra more than 40% chip-area saving is achieved by the simple but effective CFLY topology to take advantage of the parasitic bondwire inductance at the input for ripple attenuation. A maximum output current of 1.2 A is obtained by the four-phase operation, while only 3.73 nF overall integrated capacitors are required. Also, with the chip-area hungry integrated spiral metal inductors eliminated, the current density is significantly increased. 0.96 A/mm2 current density and 82.4% efficiency is obtained with 1.2 V to 0.9 V voltage conversion without using any off-chip inductors or advanced processes. The reliability is also verified by measurement with various bondwire inductances and configurations.


title: Wound image evaluation with machine learning

abstract: A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to succeed on the treatment decision and, in some cases, to save the patient?s life. However, current clinical evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detect and classify wound tissue types that play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and compares three different machine learning approaches-neural networks, support vector machines and random forest decision trees-to classify effectively each segmented region as the appropriate tissue type. Feature selection based on a wrapper approach with recursive feature elimination is shown to be effective in keeping the efficacy of the classifiers up and significantly reducing the number of necessary predictors. Results obtained show high performance rates from classifiers based on fitted neural networks, random forest models and support vector machines (overall accuracy on a testing set 95% CI], respectively: 81.87% 80.03%, 83.61%]; 87.37% 85.76%, 88.86%]; 88.08% 86.51%, 89.53%]), with significant differences found between the three machine learning approaches. This study seeks to provide, using standard classification algorithms, a consistent and robust methodological framework as a basis for the development of reliable computational systems to support ulcer diagnosis.


A Possibility for Implementing Curiosity and Boredom in Model-Building Neural Controllers