title: Supervised and Unsupervised Tumor Characterization in the Deep Learning Era

abstract: Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images. Any improvement in robust and accurate image-based tumor characterization can assist in determining non-invasive cancer stage, prognosis, and personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D approach and transfer learning to address the requirements of volumetric and large amount of training data, respectively. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL) framework. ::: In the second approach, we explore an unsupervised scheme in order to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. In this second approach, we also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. Finally, we study the effect of unsupervised representation learning using Generative Adversarial Networks (GAN) on classification performance. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively.


title: Principles of professional studies in nursing

abstract: Principles of Professional Studies in Nursing supports readers as they journey towards becoming professional nurses, aware of their unique professional identity and with a sound knowledge base to practice within a multi-disciplinary context. Highly interactive and student-centred with case studies and learning activities/exercises throughout, this text enables nursing students to discuss, debate and develop their understanding and practice for the delivery of high quality, effective client-centred care.


title: Deep generative-contrastive networks for facial expression recognition

abstract: As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image that is estimated based on the given image. Consequently, we deploy deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner. In our proposed networks, we attempt to disentangle a facial expressive factor in two steps including learning of a generator network and a contrastive encoder network. We conducted extensive experiments on publicly available face expression databases (CK+, MMI, Oulu-CASIA, and in-the-wild databases) that have been widely adopted in the recent literatures. The proposed method outperforms the known state-of-the art methods in terms of the recognition accuracy.


Shaping particle simulations with interaction forces


Naïve and Accidental Behaviours that Compromise Information Security: What the Experts Think.


title: DETECTING LASER SPOT IN SHOOTING SIMULATOR USING AN EMBEDDED CAMERA

abstract: This paper presents the application of an embedded camera system for detecting laser spot in ::: the shooting simulator. The proposed shooting simulator uses a specific target box, where the circular ::: pattern target is mounted. The embedded camera is installed inside the box to capture the circular ::: pattern target and laser spot image. To localize the circular pattern automatically, two colored solid ::: circles are painted on the target. This technique allows the simple and fast color tracking to track the ::: colored objects for localizing the circular pattern. The CMUCam4 is employed as the embedded ::: camera. It is able to localize the target and detect the laser spot in real-time at 30 fps. From the ::: experimental results, the errors in calculating shooting score and detecting laser spot are 3.82% and ::: 0.68% respectively. Further the proposed system provides the more accurate scoring system in real ::: number compared to the conventional integer number.


title: A Probabilistic Analysis of Link Duration in Vehicular Ad Hoc Networks

abstract: The past decade has witnessed a phenomenal market penetration of wireless communications and a steady increase in the number of mobile users. Unlike wired networks, where communication links are inherently stable, in wireless networks, the lifetime of a link is a random variable whose probability distribution depends on mobility, transmission range, and various impairments of radio communications. Because of the very dynamic nature of Vehicular Ad hoc NETworks (VANETs) and the short transmission range mandated by the Federal Communications Commission (FCC), individual communication links come into existence and vanish unpredictably, making the task of establishing and maintaining routing paths between fast-moving vehicles very challenging. The main contribution of this work is to investigate the probability distribution of the lifetime of individual links in a VANET under the combined assumptions of a realistic radio transmission model and a realistic probability distribution model of intervehicle headway distance. Our analytical results were validated and confirmed by extensive simulation.


title: Brevia Next-Generation Digital Information Storage in DNA

abstract: Digital information is accumulating at an astounding rate, straining our ability to store and archive it. DNA is among the most dense and stable information media known. The development of new technologies in both DNA synthesis and sequencing make DNA an increasingly feasible digital storage medium. We developed a strategy to encode arbitrary digital information in DNA, wrote a 5.27-megabit book using DNA microchips, and read the book by using next-generation DNA sequencing.


title: Top-down control of visual attention

abstract: Top-down visual attention improves perception of selected stimuli and that improvement is reflected in the neural activity at many stages throughout the visual system. Recent studies of top-down attention have elaborated on the signatures of its effects within visual cortex and have begun identifying its causal basis. Evidence from these studies suggests that the correlates of spatial attention exhibited by neurons within the visual system originate from a distributed network of structures involved in the programming of saccadic eye movements. We summarize this evidence and discuss its relationship to the neural mechanisms of spatial working memory.


title: Measure Transformer Semantics for Bayesian Machine Learning

abstract: The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define measure-transformer combinators inspired by theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that is processed by an existing inference engine for factor graphs, which are data structures that enable many efficient inference algorithms. This allows efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.


title: ProfileDroid: multi-layer profiling of android applications

abstract: The Android platform lacks tools for assessing and monitoring apps in a systematic way. This lack of tools is particularly problematic when combined with the open nature of Google Play, the main app distribution channel. As our key contribution, we design and implement ProfileDroid, a comprehensive, multi-layer system for monitoring and profiling apps. Our approach is arguably the first to profile apps at four layers: (a) static, or app specification, (b) user interaction, (c) operating system, and (d) network. We evaluate 27 free and paid Android apps and make several observations: (a) we identify discrepancies between the app specification and app execution, (b) free versions of apps could end up costing more than their paid counterparts, due to an order of magnitude increase in traffic, (c) most network traffic is not encrypted, (d) apps communicate with many more sources than users might expect---as many as 13, and (e) we find that 22 out of 27 apps communicate with Google during execution. ProfileDroid is the first step towards a systematic approach for (a) generating cost-effective but comprehensive app profiles, and (b) identifying inconsistencies and surprising behaviors.


title: Software-defined network virtualization: an architectural framework for integrating SDN and NFV for service provisioning in future networks

abstract: SDN and NFV are two significant innovations in networking. The evolution of both SDN and NFV has shown strong synergy between these two paradigms. Recent research efforts have been made toward combining SDN and NFV to fully exploit the advantages of both technologies. However, integrating SDN and NFV is challenging due to the variety of intertwined network elements involved and the complex interaction among them. In this article, we attempt to tackle this challenging problem by presenting an architectural framework called SDNV. This framework offers a clear holistic vision of integrating key principles of both SDN and NFV into unified network architecture, and provides guidelines for synthesizing research efforts toward combining SDN and NFV in future networks. Based on this framework, we also discuss key technical challenges to realizing SDN-NFV integration and identify some important topics for future research, with a hope to arouse the research community's interest in this emerging area.


title: Fast detection of the optic disc and fovea in color fundus photographs

abstract: A fully automated, fast method to detect the fovea and the optic disc in digital color photographs of the retina is presented. The method makes few assumptions about the location of both structures in the image. We define the problem of localizing structures in a retinal image as a regression problem. A kNN regressor is utilized to predict the distance in pixels in the image to the object of interest at any given location in the image based on a set of features measured at that location. The method combines cues measured directly in the image with cues derived from a segmentation of the retinal vasculature. A distance prediction is made for a limited number of image locations and the point with the lowest predicted distance to the optic disc is selected as the optic disc center. Based on this location the search area for the fovea is defined. The location with the lowest predicted distance to the fovea within the foveal search area is selected as the fovea location. The method is trained with 500 images for which the optic disc and fovea locations are known. An extensive evaluation was done on 500 images from a diabetic retinopathy screening program and 100 specially selected images containing gross abnormalities. The method found the optic disc in 99.4% and the fovea in 96.8% of regular screening images and for the images with abnormalities these numbers were 93.0% and 89.0% respectively.


title: Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment

abstract: Under the Connected Vehicles (CV) environment, it is possible to create a Cooperative Vehicle Intersection Control (CVIC) system that enables cooperation between vehicles and infrastructure for effective intersection operations and management when all vehicles are fully automated. Assuming such a CVIC environment, this paper proposed a CVIC algorithm that does not require a traffic signal. The CVIC algorithm was designed to manipulate individual vehicles' maneuvers so that vehicles can safely cross the intersection without colliding with other vehicles. By eliminating the potential overlaps of vehicular trajectories coming from all conflicting approaches at the intersection, the CVIC algorithm seeks a safe maneuver for every vehicle approaching the intersection and manipulates each of them. An additional algorithm was designed to deal with the system failure cases resulting from inevitable trajectory overlaps at the intersection and infeasible solutions. A simulation-based case study implemented on a hypothetical four-way single-lane approach intersection under varying congestion conditions showed that the CVIC algorithm significantly improved intersection performance compared with conventional actuated intersection control: 99% and 33% of stop delay and total travel time reductions, respectively, were achieved. In addition, the CVIC algorithm significantly improved air quality and energy savings: 44% reductions of CO2 and 44% savings of fuel consumption.


title: Computer Architecture and Parallel Processing

abstract: The book is intended as a text to support two semesters of courses in computer architecture at the college senior and graduate levels. There are excellent problems for students at the end of each chapter. The authors have divided the use of computers into the following four levels of sophistication: data processing, information processing, knowledge processing, and intelligence processing.


title: Mindfulness: Theoretical Foundations and Evidence for its Salutary Effects

abstract: Interest in mindfulness and its enhancement has burgeoned in recent years. In this article, we discuss in detail the nature of mindfulness and its relation to other, established theories of attention and awareness in day-to-day life. We then examine theory and evidence for the role of mindfulness in curtailing negative functioning and enhancing positive outcomes in several important life domains, including mental health, physical health, behavioral regulation, and interpersonal relationships. The processes through which mindfulness is theorized to have its beneficial effects are then discussed, along with proposed directions for theoretical development and empirical research.


title: Internet of Things in Industries: A Survey

abstract: Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.


title: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms

abstract: The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.


title: Virtual environments: Three-dimensional menus: A survey and taxonomy

abstract: Various interaction techniques have been developed in the field of virtual and augmented reality. Whereas techniques for object selection, manipulation, travel, and wayfinding have already been covered in existing taxonomies in some detail, application control techniques have not yet been sufficiently considered. However, they are needed by almost every mixed reality application, e.g. for choosing from alternative objects or options. For this purpose a great variety of distinct three-dimensional (3D) menu selection techniques is available. This paper surveys existing 3D menus from the corpus of literature and classifies them according to various criteria. The taxonomy introduced here assists developers of interactive 3D applications to better evaluate their options when choosing, optimizing, and implementing a 3D menu technique. Since the taxonomy spans the design space for 3D menu solutions, it also aids researchers in identifying opportunities to improve or create novel virtual menu techniques.


title: Evaluation techniques for storage hierarchies

abstract: The design of efficient storage hierarchies generally involves the repeated running of "typical" program address traces through a simulated storage system while various hierarchy design parameters are adjusted. ::: ::: This paper describes a new and efficient method of determining, in one pass of an address trace, performance measures for a large class of demand-paged, multilevel storage systems utilizing a variety of mapping schemes and replacement algorithms. ::: ::: The technique depends on an algorithm classification, called "stack algorithms," examples of which are "least frequently used," "least recently used," "optimal," and "random replacement" algorithms. The techniques yield the exact access frequency to each storage device, which can be used to estimate the overall performance of actual storage hierarchies.


title: Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression

abstract: There are a variety of applications where user behavior consists of a combination of both repeat item consumption and new item consumption, such as listening to music artists, visiting Web sites, purchasing groceries, and so on. In this paper we address the problem of building user models that can predict the rate at which individuals consume both old and new items. We use zero-inflated Poisson (ZIP) regression models as the basis for our modeling approach, leading to a general framework for modeling user-item consumption rates over time. We show that these models are more flexible in capturing user behavior than alternatives such as well-known latent factor and embedding models. We compare the performance of ZIP regression and latent factor and embedding models on three different data sets involving music, restaurant reviews, and social media. The ZIP regression models are systematically more accurate across all three data sets across different prediction metrics.


title: Finding Local Anomalies in Very High Dimensional Space

abstract: Time, cost and energy efficiency are critical factors for many data analysis techniques when the size and dimensionality of data is very large. We investigate the use of Local Outlier Factor (LOF) for data of this type, providing a motivating example from real world data. We propose Projection-Indexed Nearest-Neighbours (PINN), a novel technique that exploits extended nearest neighbour sets in the a reduced dimensional space to create an accurate approximation for k-nearest-neighbour distances, which is used as the core density measurement within LOF. The reduced dimensionality allows for efficient sub-quadratic indexing in the number of items in the data set, where previously only quadratic performance was possible. A detailed theoretical analysis of Random Projection(RP) and PINN shows that we are able to preserve the density of the intrinsic manifold of the data set after projection. Experimental results show that PINN outperforms the standard projection methods RP and PCA when measuring LOF for many high-dimensional real-world data sets of up to 300000 elements and 102600 dimensions.


title: Security/privacy of wearable fitness tracking IoT devices

abstract: As wearable fitness trackers gain widespread acceptance among the general population, there is a concomitant need to ensure that associated privacy and security vulnerabilities are kept to a minimum. We discuss potential vulnerabilities of these trackers, in general, and specific vulnerabilities in one such tracker - Fitbit - identified by Rahman et al. (2013) who then proposed means to address identified vulnerabilities. However, the `fix' has its own vulnerabilities. We discuss possible means to alleviate related issues.


title: Three-dimensional point cloud alignment detecting fiducial markers by structured light stereo imaging

abstract: In recent years, various methodologies of shape reconstruction have been proposed with the aim at creating Computer-Aided Design models by digitising physical objects using optical sensors. Generally, the acquisition of 3D geometrical data includes crucial tasks, such as planning scanning strategies and aligning different point clouds by multiple view approaches, which differ for user’s interaction and hardware cost. This paper describes a methodology to automatically measure three-dimensional coordinates of fiducial markers to be used as references to align point clouds obtained by an active stereo vision system based on structured light projection. Intensity-based algorithms and stereo vision principles are combined to detect passive fiducial markers localised in a scene. 3D markers are uniquely recognised on the basis of geometrical similarities. The correlation between fiducial markers and point clouds allows the digital creation of complete object surfaces. The technology has been validated by experimental tests based on nominal benchmarks and reconstructions of target objects with complex shapes.


title: Translation as the Catalyst of Cultural Transfer

abstract: This essay reflects on the many different strategies involved in translation, which is both a linguistic and a cultural-historical strategy. Examples from the Middle Ages and the Modern Age are adduced to illustrate the huge impact which translations have had on peoples and societies throughout time.


title: A fast algorithm for integrating connected-component labeling and euler number computation

abstract: This paper proposes a fast algorithm for integrating connected-component labeling and Euler number computation. Based on graph theory, the Euler number of a binary image in the proposed algorithm is calculated by counting the occurrences of four patterns of the mask for processing foreground pixels in the first scan of a connected-component labeling process, where these four patterns can be found directly without any additional calculation; thus, connected-component labeling and Euler number computation can be integrated more efficiently. Moreover, when computing the Euler number, unlike other conventional algorithms, the proposed algorithm does not need to process background pixels. Experimental results demonstrate that the proposed algorithm is much more efficient than conventional algorithms either for calculating the Euler number alone or simultaneously calculating the Euler number and labeling connected components.


title: A Study on Techniques and Architecture of Sentiment Analysis

abstract: Today most of the products and the services are available online so that direct interaction between the service provider and the customer is established. Now the feedback of the products and the services is taken in the form of online reviews. Sentiment Analysis is the technique comes under opinion mining to analyze these reviews and concludes the review sentiment. In this paper, a study on different types of sentiment analysis techniques is defined along with the exploration of the architecture of sentiment analysis.


title: Learning question classifiers: The role of semantic information

abstract: To respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11.


title: Robust Automatic Parking without Odometry Using Enhanced Fuzzy Logic Controller

abstract: This paper develops a novel automatic parking algorithm based on a fuzzy logic controller with the vehicle pose for the input and the steering rate for the output. It localizes the vehicle by using only external sensors -a vision sensor and ultrasonic sensors. Then it automatically learns an optimal fuzzy if-then rule set from training data. This is possible using a genetic fuzzy system which optimizes the parameters for the fuzzy logic controller. Furthermore, it also finds the green zone of the ready-to-reverse position where parking is possible just by reversing. It has been tested on a Pioneer mobile robot which emulates the real vehicle.


title: The vicious cycle of dental fear: exploring the interplay between oral health, service utilization and dental fear

abstract: BackgroundBased on the hypothesis that a vicious cycle of dental fear exists, whereby the consequences of fear tend to maintain that fear, the relationship between dental fear, self-reported oral health status and the use of dental services was explored.MethodsThe study used a telephone interview survey with interviews predominantly conducted in 2002. A random sample of 6,112 Australian residents aged 16 years and over was selected from 13 strata across all States and Territories. Data were weighted across strata and by age and sex to obtain unbiased population estimates.ResultsPeople with higher dental fear visited the dentist less often and indicated a longer expected time before visiting a dentist in the future. Higher dental fear was associated with greater perceived need for dental treatment, increased social impact of oral ill-health and worse self-rated oral health. Visiting patterns associated with higher dental fear were more likely to be symptom driven with dental visits more likely to be for a problem or for the relief of pain. All the relationships assumed by a vicious cycle of dental fear were significant. In all, 29.2% of people who were very afraid of going to the dentist had delayed dental visiting, poor oral health and symptom-driven treatment seeking compared to 11.6% of people with no dental fear.ConclusionResults are consistent with a hypothesised vicious cycle of dental fear whereby people with high dental fear are more likely to delay treatment, leading to more extensive dental problems and symptomatic visiting patterns which feed back into the maintenance or exacerbation of existing dental fear.


title: Power, leadership and transformation: the doctor's potential for influence

abstract: Medical Education 2012: 46: 1152–1160 ::: ::: ::: ::: Context Power and leadership are concepts that are linked. Both are studied too infrequently in medical and health care settings, given the responsibilities and opportunities doctors and other health care personnel have to exert leadership and power appropriately to foster patient-centred and health care organisational goals. ::: ::: ::: ::: Methods This paper reviews Raven’s concept of power, clarifies the bases of power that are available to doctors in different roles and provides illustrations of the application of the bases of power in medical practice. The relationship between power and leadership is explored, with an emphasis on how power and leadership are linked through the personal characteristics and competencies of the leader. ::: ::: ::: ::: Results Transformational leadership illustrates the incorporation and elaboration of power strategies into a principles-driven, relationship-oriented and empirically grounded form of leadership. Illustrations of the appropriate and inappropriate use of power and leadership in health care settings are provided. ::: ::: ::: ::: Conclusions The study of power, the study of leadership and their linkage should be incorporated to a greater degree into medical education at all levels. Strategies to achieve this end are suggested.


title: GPS-free Positioning in Mobile Ad Hoc Networks

abstract: We consider the problem of node positioning in ad hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on GPS (Global Positioning System). Instead, the algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. Despite the distance measurement errors and the motion of the nodes, the algorithm provides sufficient location information and accuracy to support basic network functions. Examples of applications where this algorithm can be used include Location Aided Routing [10] and Geodesic Packet Forwarding [2]. Another example are sensor networks, where mobility is less of a problem. The main contribution of this work is to define and compute relative positions of the nodes in an ad hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad hoc networks.


title: Efficient integration of inertial observations into visual SLAM without initialization

abstract: The use of accelerometer and gyro observations in a visual SLAM implementation is beneficial especially in high dynamic situations. The downside of using inertial is that traditionally high prediction rates are required as observations are provided at high sample rates. An accurate orientation and velocity estimate must also be maintained at all times in order to integrate the inertial observations and correct for the effect of gravity.


title: Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

abstract: Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. Methods: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. Results: We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. Conclusions: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping. [J Med Internet Res 2018;20(6):e210]


title: Undiscovered Worlds – Towards a Framework for Real-Time

abstract: ABSTRACT: With advances in computer hardware, 3D game worlds are becoming larger and more complex. Consequently the development of game worlds becomes increasingly time and resource intensive. This paper presents a framework for generation of entire virtual worlds using procedural generation. The approach is demonstrated with the example of a virtual city. KEYWORDS: Games Culture, Games System Design, Games Theory, Interactive Architecture, MUDs, Virtual Reality, Virtual Worlds INTRODUCTION The visual quality of three-dimensional, polygon based computer games improves with each new generation of graphics hardware. In fact the processing speed of graphic hardware doubles every year. Each new generation of computer hardware facilitates computer games that feature game worlds in higher detail and larger extent. All object geometries and textures in a game world need to be created in a process that can be very time consuming. As game worlds grow, more time and resources are required for their creation. Where do we go from here? Approaches to minimize the costs for the creation of game worlds include reusing textures and geometries for objects that occur frequently throughout the game, such as trees and crates. However, the repetitive use of artwork is often obvious to the user, and counteracts the verisimilitude of a believably realistic virtual world. Another approach is to generate recurring objects or even entire game worlds in a so-called procedural approach, which not only adds a controllable randomness to the virtual world but also yields outcomes that are unexpected in form and appearance. This paper presents a framework for the real-time procedural generation of virtual worlds, and is aimed at a non-technical audience. The virtual worlds are self contained and neither import nor export data. The worlds can expand to an extent that would require many lifetimes to explore, which we term ‘pseudo infinite’ [1]. The framework is suitable for real-time virtual worlds running on commodity hardware and has been prototyped with the example of a virtual city. A more technical description of our work [1] appeared recently at Graphite 2003.


title: Bio-signal based control in assistive Robots: A survey

abstract: Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized.


title: Menu-Match: Restaurant-Specific Food Logging from Images

abstract: Logging food and calorie intake has been shown to facilitate weight management. Unfortunately, current food logging methods are time-consuming and cumbersome, which limits their effectiveness. To address this limitation, we present an automated computer vision system for logging food and calorie intake using images. We focus on the "restaurant" scenario, which is often a challenging aspect of diet management. We introduce a key insight that addresses this problem specifically: restaurant plates are often both nutritionally and visually consistent across many servings. This insight provides a path to robust calorie estimation from a single RGB photograph: using a database of known food items together with restaurant-specific classifiers, calorie estimation can be achieved through identification followed by calorie lookup. As demonstrated on a challenging Menu-Match dataset and an existing third party dataset, our approach outperforms previous computer vision methods and a commercial calorie estimation app. Our Menu-Match dataset of realistic restaurant meals is made publicly available.


title: Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer

abstract: Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.


title: Menstrual-Cycle and Menstruation Disorders in Episodic vs Chronic Migraine: An Exploratory Study.

abstract: OBJECTIVE ::: Migraine is a chronic condition of recurring moderate-to-severe headaches that affects an estimated 6% of men and 18% of women. The highest prevalence is in those 18-49 years of age, generally when women menstruate. It is divided into episodic and chronic migraine depending on the total number of headache days per month being 14 or less or 15 or more, respectively. Migraine has been associated with menorrhagia, dysmenorrhea, and endometriosis, the latter particularly in chronic migraine. ::: ::: ::: METHODS ::: We conducted a questionnaire survey of 96 women with migraine, 18-45 years old, to determine the occurrence of the menstrual-cycle disorders, oligomenorrhea, polymenorrhea, and irregular cycle, and the menstruation disorders, dysmenorrhea and menorrhagia, in episodic vs chronic migraine. ::: ::: ::: RESULTS ::: The prevalence of menstrual-cycle disorders in general (41.2 vs 22.2%) and dysmenorrhea (51.0 vs 28.9%) was statistically significantly higher in the women with chronic migraine than in those with episodic migraine (P ≤ 0.05) (not corrected for multiple comparisons). Whether the migraine was menstruation sensitive, that is, the headaches consistently occurred or worsened with menstruation, did not impact the prevalence of menstrual disorders. ::: ::: ::: CONCLUSION ::: We conclude that chronic migraine is possibly more often than episodic migraine associated with menstrual-cycle disorders in general and dysmenorrhea, without impact on menstruation sensitivity of the headaches.


title: Using data mining approaches to build credit scoring model: Case study — Implementation of credit scoring model in microfinance institution

abstract: The aim of this paper is to present a Credit Scoring Model applied by one Microfinance Institution in Bosnia and Herzegovina as well as to show how the most relevant attributes for its implementation were identified. The purpose of created Credit Scoring Model was to predict default clients and reduce credit risk of Microfinance Institution by applying data mining algorithm in order to find patterns for recognition of default clients and, thus, support decision making process of credit approval. Credit Scoring Model was build using Oracle Data Miner software package that uses Generalized Linear Model for classification. Created model showed great predictive confidence and accuracy, but also gave trustworthy results regarding feature selection, so the Microfinance institution decided to adopt this model as help in decision making process.


title: Swing-up of the inertia wheel pendulum using impulsive torques

abstract: The swing-up control problem of the inertia wheel pendulum is revisited in this paper. As different from existing methods, purely impulsive control inputs are used for swing-up. A mathematical formulation of the problem indicates that swing-up can be achieved using impulsive inputs applied at two discrete time instants. Two different swing-up schemes are presented and validated using numerical simulations where the impulsive inputs are approximated by high-gain feedback. The results of numerical simulations establish a direct link between high wheel velocities during swing-up and control strategies that take the pendulum directly to the upright configuration. They also indicate that the time required for swing-up is much less than that reported in the literature.


title: UbiCrawler: a scalable fully distributed Web crawler

abstract: We report our experience in implementing UbiCrawler, a scalable distributed Web crawler, using the Java programming language. The main features of UbiCrawler are platform independence, linear scalability, graceful degradation in the presence of faults, a very effective assignment function (based on consistent hashing) for partitioning the domain to crawl, and more in general the complete decentralization of every task. The necessity of handling very large sets of data has highlighted some limitations of the Java APIs, which prompted the authors to partially reimplement them.


title: Analysis and Prediction of Museum Visitors’ Behavioral Pattern Types

abstract: Personalization in the “museum visit” scenario is extremely challenging, especially since in many cases visitors come to the museum for the first time, and it may be the last time in their life. There is therefore a need to generate an effective user model quickly without any prior knowledge. Furthermore, the initial definition of a user model is also challenging since it should be built in a non-intrusive manner. Understanding visitors’ behavioral patterns may help in initializing their user models and supporting them better. This chapter reports three stages of analysis of behavior patterns of museum visitors. The first step assesses, following past ethnographic research, whether a distinct stereotype of behavior can be identified; the second shows that visitors’ behavior is not always consistent; the third shows that, in spite of the inconsistency, prediction of visitor type, is possible.


title: Rapid Eye Movement Sleep Behavior Disorder and Neurodegenerative Disease.

abstract: IMPORTANCE ::: The dream enactment of rapid eye movement sleep behavior disorder (RBD) is often the first indication of an impending α-synuclein disorder, such as Parkinson disease, multiple-system atrophy, or dementia with Lewy bodies. ::: ::: ::: OBJECTIVE ::: To provide an overview of RBD from the onset of dream enactment through the emergence of a parkinsonian disorder. ::: ::: ::: EVIDENCE REVIEW ::: Peer-reviewed articles, including case reports, case series, retrospective reviews, prospective randomized trials, and basic science investigations, were identified in a PubMed search of articles on RBD from January 1, 1986, through July 31, 2014. ::: ::: ::: FINDINGS ::: Under normal conditions, vivid dream mentation combined with skeletal muscle paralysis characterizes rapid eye movement sleep. In RBD, α-synuclein abnormalities in the brainstem disinhibit rapid eye movement sleep motor activity, leading to dream enactment. The behaviors of RBD are often theatrical, with complexity, aggression, and violence; fighting and fleeing actions can be injurious to patients as well as bed partners. Rapid eye movement sleep behavior disorder is distinguished from other parasomnias by clinical features and the demonstration of rapid eye movement sleep without atonia on polysomnography. Consistent with early neurodegeneration, patients with RBD demonstrate subtle motor, cognitive, and autonomic impairments. Approximately 50% of patients with spontaneous RBD will convert to a parkinsonian disorder within a decade. Ultimately, nearly all (81%-90%) patients with RBD develop a neurodegenerative disorder. Among patients with Parkinson disease, RBD predicts a non-tremor-predominant subtype, gait freezing, and an aggressive clinical course. The most commonly cited RBD treatments include low-dose clonazepam or high-dose melatonin taken orally at bedtime. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Treatment of RBD can prevent injury to patients and bed partners. Because RBD is a prodromal syndrome of Parkinson disease (or related disorder), it represents a unique opportunity for developing and testing disease-modifying therapies.


title: Dynamic HDR environment capture for mixed reality

abstract: Rendering accurate and convincing virtual content into mixed reality (MR) scenes requires detailed illumination information about the real environment. In existing MR systems, this information is often captured using light probes [1, 8, 9, 17, 19--21], or by reconstructing the real environment as a preprocess [31, 38, 54]. We present a method for capturing and updating a HDR radiance map of the real environment and tracking camera motion in real time using a self-contained camera system, without prior knowledge about the real scene. The method is capable of producing plausible results immediately and improving in quality as more of the scene is reconstructed. We demonstrate how this can be used to render convincing virtual objects whose illumination changes dynamically to reflect the changing real environment around them.


Singer Traits Identification using Deep Neural Network


title: Towards Integrated Model-Driven Testing of SCADA Systems Using the Eclipse Modeling Framework and Modelica

abstract: Testing SCADA (supervisory control and data acquisition) near real-time systems is challenging, as it involves complex interactions and the simulation of the supervised and controlled environment. Model-driven testing techniques can help to achieve clarity about the inner workings of the system and facilitate test construction, but these models are currently disconnected from those of the environmental simulation, leading to a paradigm break. This paper presents a strategy to remedy this situation. To this end, it leverages Modelica and the Eclipse Modeling Framework. Modelica is an object-oriented mathematical modeling language for component-oriented modeling of complex physical systems. It is an open standard and implementation, and provides a rendering of its input language in Ecore, the meta-language of the Eclipse Modeling Framework (EMF). It also offers convenient visual editors, whose notation via the Modelica ML profile is consistent with the SysML standard, a restricted version of UML The strategy presented here leverages EMF as a common basis for model-driven development, reusing Modelica's powerful simulation features in integration with a custom-designed testing process. With this tooling, a test engineer can model all aspects of a SCADA test within one workbench and enjoy full traceability between the proprietary test model, and its surrounding environment simulation.


title: Optimization of commercial net spacers in spiral wound membrane modules

abstract: CFD simulations have been used to determine mass transfer coefficients and power consumption of commercial net spacers. The simulations show transversal and longitudinal vortices, vortex shedding and instationary flow behavior leading to the enhanced mass transfer in spacer filled-channels compared to empty channels. The results of the simulations were validated with experiments and compared with data reported in literature, showing satisfactory agreement. Furthermore, CFD simulations were used to optimize the geometry of commercial net spacers in terms of mass transfer and power consumption. The performance of these optimized spacer geometries will be used as reference for future work on the development of new high-performance spacer shapes.


title: Driver's style classification using jerk analysis

abstract: This paper presents an innovative approach to classifying the driver's driving style by analyzing the jerk profile of the driver. Driving style is a dynamic behavior of a driver on the road. At times a driver can be calm but aggressive at others. The information about driver's dynamic driving style can be used to better control fuel economy. We propose to classify driver's style based on the measure of how fast a driver is accelerating and decelerating. We developed an algorithm that classifies driver's style utilizing the statistical information from the jerk profile and the road way type and traffic congestion level prediction. Our experiment results show that our approach generates more reasonable results than those generated by using other published methods.


A Cost-Effective 3D Interaction Approach for Immersive Virtual Reality


title: A specimen MPI application: N-Queens in parallel

abstract: The generalized problem of placing n queens on an n-by-n board provides an "embarrassingly parallel" problem for parallel solution. This paper expands on the discussion presented in the May 2005 issue of Dr. Dobb's Journal [1], specifically taking the parallel execution through Java threads and bringing it into an application in C taking advantage of MPI.


title: Multi-floor Indoor Navigation with Geomagnetic Field Positioning and Ant Colony Optimization Algorithm

abstract: We here illustrate a new indoor navigation system. It is an outcome of creativity, which merges an imaginative scenario and new technologies. The system intends to guide a person in unknown building by relying on technologies which do not depend on infrastructures. The system includes two key components, namely positioning and path planning. Positioning is based on geomagnetic fields, and it overcomes the several limits of WIFI and Bluetooth, etc. Path planning is based on a new and optimized Ant Colony algorithm, called Ant Colony Optimization (ACO), which offers better performances than the classic A* algorithms. The paper illustrates the logic and the architecture of the system, and also presents experimental results.


title: Neuropsychiatric symptoms as early manifestations of emergent dementia: Provisional diagnostic criteria for mild behavioral impairment

abstract: Abstract Neuropsychiatric symptoms (NPS) are common in dementia and in predementia syndromes such as mild cognitive impairment (MCI). NPS in MCI confer a greater risk for conversion to dementia in comparison to MCI patients without NPS. NPS in older adults with normal cognition also confers a greater risk of cognitive decline in comparison to older adults without NPS. Mild behavioral impairment (MBI) has been proposed as a diagnostic construct aimed to identify patients with an increased risk of developing dementia, but who may or may not have cognitive symptoms. We propose criteria that include MCI in the MBI framework, in contrast to prior definitions of MBI. Although MBI and MCI can co-occur, we suggest that they are different and that both portend a higher risk of dementia. These MBI criteria extend the previous literature in this area and will serve as a template for validation of the MBI construct from epidemiologic, neurobiological, treatment, and prevention perspectives.


title: Diversity-Driven Extensible Hierarchical Reinforcement Learning

abstract: Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive. Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels. DEHRL follows a popular assumption: diverse subpolicies are useful, i.e., subpolicies are believed to be more useful if they are more diverse. However, existing implementations of this diversity assumption usually have their own drawbacks, which makes them inapplicable to HRL with multiple levels. Consequently, we further propose a novel diversity-driven solution to achieve this assumption in DEHRL. Experimental studies evaluate DEHRL with nine baselines from four perspectives in two domains; the results show that DEHRL outperforms the state-of-the-art baselines in all four aspects.


title: M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

abstract: Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MSCOCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new stateof-the-art results among one-stage detectors. The code will be made available on https://github.com/qijiezhao/M2Det.


title: Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models

abstract: Recent developments in applying deep learning techniques to train end-to-end communication systems have shown great promise in improving the overall performance of the system. However, most of the current methods for applying deep learning to train physical-layer characteristics assume the availability of the explicit channel model. Training a neural network requires the availability of the functional form all the layers in the network to calculate gradients for optimization. The unavailability of gradients in a physical channel forced previous works to adopt simulation-based strategies to train the network and then fine tune only the receiver part with the actual channel. In this letter, we present a practical method to train an end-to-end communication system without relying on explicit channel models. By utilizing stochastic perturbation techniques, we show that the proposed method can train a deep learning-based communication system in real channel without any assumption on channel models.


title: Skeleton-Based Action Recognition with Synchronous Local and Non-Local Spatio-Temporal Learning and Frequency Attention

abstract: Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention. Most existing methods utilize local networks (e.g recurrent, convolutional, and graph convolutional networks) to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which contain more details and semantics respectively, are asynchronously captured in different level of layers. Moreover, existing methods are limited to the spatio-temporal domain and ignore information in the frequency domain. To better extract synchronous detailed and semantic information from multi-domains, we propose a residual frequency attention (rFA) block to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. Besides, a soft-margin focal loss (SMFL) is proposed to optimize the learning whole process, which automatically conducts data selection and encourages intrinsic margins in classifiers. Our approach significantly outperforms other state-of-the-art methods on several large-scale datasets.


title: Design and optimization of concentric saddle shaped coils for permanent magnet transverse flux machine in segmented construction

abstract: Design of multi-turns concentric saddle windings are required for flux concentrated permanent magnet transverse flux machine, FCPM-TFM in a sector wise configuration. In the segmented construction of FCPM-TFM, saddle coils or concentric type of windings are used to replace the ring coils, which are normally utilised in the conventional layered-phase structures for generating fields' perpendicular to the axis of cylindrical shell to which the coils are confined. This paper investigates the optimum design of saddle phase windings for this special construction of TFM that is constructed as an outer-rotor for in-wheel motor applications. Meshed and non-meshed coils are designed and their parameters effects are studied via finite element method (FEM). Throughout the study, optimisation of two stator structures, i.e., laminated and soft magnetic composite, SMC-pressed, are shown.


title: Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features

abstract: Cover song detection is a very relevant task in Music Information Retrieval (MIR) studies and has been mainly addressed using audio-based systems. Despite its potential impact in industrial contexts, low performances and lack of scalability have prevented such systems from being adopted in practice for large applications. In this work, we investigate whether textual music information (such as metadata and lyrics) can be used along with audio for large-scale cover identification problem in a wide digital music library. We benchmark this problem using standard text and state of the art audio similarity measures. Our studies shows that these methods can significantly increase the accuracy and scalability of cover detection systems on Million Song Dataset (MSD) and Second Hand Song (SHS) datasets. By only leveraging standard tf-idf based text similarity measures on song titles and lyrics, we achieved 35.5% of absolute increase in mean average precision compared to the current scalable audio content-based state of the art methods on MSD. These experimental results suggests that new methodologies can be encouraged among researchers to leverage and identify more sophisticated NLP-based techniques to improve current cover song identification systems in digital music libraries with metadata.


title: Design and Describe REST API without Violating REST: A Petri Net Based Approach

abstract: As REST architectural style gains popularity in the web service community, there is a growing concern and debate on how to design Restful web services (REST API) in a proper way. We attribute this problem to lack of a standard model and language to describe a REST API that respects all the REST constraints. As a result, many web services that claim to be REST API are not hypermedia driven as prescribed by REST. This situation may lead to REST APIs that are not as scalable, extensible, and interoperable as promised by REST. To address this issue, this paper proposes REST Chart as a model and language to design and describe REST API without violating the REST constraints. REST Chart models a REST API as a special type of Colored Petri Net whose topology defines the REST API and whose token markings define the representational state space of user agents using that API. We demonstrate REST Chart with an example REST API. We also show how REST Chart can support efficient content negotiation and reuse hybrid representations to broaden design choices. Furthermore, we argue that the REST constraints, such as hypermedia driven and statelessness, can either be enforced naturally or checked automatically in REST Chart.


The Antecedents of Customer Loyalty: An Empirical Investigation in Life Insurance Context


title: Integrated Framework Incorporating Optimization for Plant-Wide Control of Industrial Processes

abstract: Numerous methodologies have been developed for the plant-wide control (PWC) of industrial processes. These methodologies can be classified into heuristics, optimization, mathematical, and mixed approaches. However, most of these methodologies do not involve the extensive usage of rigorous process simulators as part of the control system development. Also, in recent years, the focus has shifted toward the use of mixed approaches, which builds synergies between different techniques. As part of the continuing search for more effective PWC system design methods, an integrated methodology is proposed in this paper. This eight-level framework incorporates heuristics and optimization, together with the use of simulation throughout the procedure. The main intuitive feature of this framework is the identification of key controlled variables for optimal operation; this is achieved by detailed analysis of disturbances, computation of the new optimal set points by reoptimization, and cost breakdown analysis for throu...


title: Nursing on empty: compassion fatigue signs, symptoms, and system interventions.

abstract: Few healthcare organizations acknowledge, discuss, or provide interventions for assisting with compassion fatigue. Yet, it is an important concept due to its individual, professional, and financial costs. This article defines compassion fatigue, differentiates it from burnout, and offers system interventions for supporting nurses and reducing compassion fatigue.


title: Development of Self Balance Transporter : A Segway

abstract: This paper deals with constructing the vehicle which is fully functional to transport the one person only and movement of it is achieve by naturally. Forward and backwards motion is achieved by leaning forwards and backwards. Segway uses handlebar for turning purpose. In Segway technique uses principle of inverted pendulum in which it keeps an angle of Zero degrees with vertical at all times. The rider motion enables this technique to accelerate, brake or steer the vehicle, for this it uses Gyroscopic Sensor and it is Eco-friendly and non polluting. Index Terms—Gyroscopic, Inverted Pendulum, Segway


title: Polymers for Drug Delivery Systems

abstract: Polymers have played an integral role in the advancement of drug delivery technology by providing controlled release of therapeutic agents in constant doses over long periods, cyclic dosage, and tunable release of both hydrophilic and hydrophobic drugs. From early beginnings using off-the-shelf materials, the field has grown tremendously, driven in part by the innovations of chemical engineers. Modern advances in drug delivery are now predicated upon the rational design of polymers tailored for specific cargo and engineered to exert distinct biological functions. In this review, we highlight the fundamental drug delivery systems and their mathematical foundations and discuss the physiological barriers to drug delivery. We review the origins and applications of stimuli-responsive polymer systems and polymer therapeutics such as polymer-protein and polymer-drug conjugates. The latest developments in polymers capable of molecular recognition or directing intracellular delivery are surveyed to illustrate areas of research advancing the frontiers of drug delivery.


title: Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement Analysis

abstract: This paper combines traditional fundamentals, such as earnings and cash flows, with measures tailored for growth firms, such as earnings stability, growth stability and intensity of R&D, capital expenditure and advertising, to create an index – GSCORE. A long–short strategy based on GSCORE earns significant excess returns, though most of the returns come from the short side. Results are robust in partitions of size, analyst following and liquidity and persist after controlling for momentum, book-to-market, accruals and size. High GSCORE firms have greater market reaction and analyst forecast surprises with respect to future earnings announcements. Further, the results are inconsistent with a risk-based explanation as returns are positive in most years, and firms with lower risk earn higher returns. Finally, a contextual approach towards fundamental analysis works best, with traditional analysis appropriate for high BM stocks and growth oriented fundamental analysis appropriate for low BM stocks.


title: Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

abstract: Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.


Vibration eigenmodes of the Au- (5×2) /Si(111) surface studied by Raman spectroscopy and first-principles calculations


title: LingoTurk: managing crowdsourced tasks for psycholinguistics

abstract: LingoTurk is an open-source, freely available crowdsourcing client/server system aimed primarily at psycholinguistic experimentation where custom and specialized user interfaces are required but not supported by popular crowdsourcing task management platforms. LingoTurk enables user-friendly local hosting of experiments as well as condition management and participant exclusion. It is compatible with Amazon Mechanical Turk and Prolific Academic. New experiments can easily be set up via the Play Framework and the LingoTurk API, while multiple experiments can be managed from a single system.


title: Neural Correlates of Behavioral Preference for Culturally Familiar Drinks

abstract: Abstract Coca-Cola ® (Coke ® ) and Pepsi ® are nearly identical in chemical composition, yet humans routinely display strong subjective preferences for one or the other. This simple observation raises the important question of how cultural messages combine with content to shape our perceptions; even to the point of modifying behavioral preferences for a primary reward like a sugared drink. We delivered Coke and Pepsi to human subjects in behavioral taste tests and also in passive experiments carried out during functional magnetic resonance imaging (fMRI). Two conditions were examined: (1) anonymous delivery of Coke and Pepsi and (2) brand-cued delivery of Coke and Pepsi. For the anonymous task, we report a consistent neural response in the ventromedial prefrontal cortex that correlated with subjects' behavioral preferences for these beverages. In the brand-cued experiment, brand knowledge for one of the drinks had a dramatic influence on expressed behavioral preferences and on the measured brain responses.


title: A Physically-Based Modeling and Simulation Framework for Facial Animation

abstract: Realistic facial animation is important in many graphics applications, like animated feature films and computer games, to enrich human computer interaction. In this paper, we propose a physically-based facial animation approach employing knowledge from the anatomy and biomechanics of human facial muscles. First, the 3D face mesh is generated automatically by commercial software and the facial skin is represented by a nonlinear mass-spring system which simulates realistic elastic dynamics of human dermis. Then, a structure skull is attached to fit the face mesh. A set of anatomically consistent facial muscles are incorporated to model the forces deforming the face mesh. Finally, we extend Waters' muscle model to improve the combination of multiple muscle actions and to generate realistic expression. Experiments show that our method is superior to the traditional geometric model and can achieve comparative results with the commercial software FaceGen.


An approach to discovering temporal association rules


title: Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance

abstract: This paper presents an effective approach for detecting abandoned luggage in surveillance videos. We combine short- and long-term background models to extract foreground objects, where each pixel in an input image is classified as a 2-bit code. Subsequently, we introduce a framework to identify static foreground regions based on the temporal transition of code patterns, and to determine whether the candidate regions contain abandoned objects by analyzing the back-traced trajectories of luggage owners. The experimental results obtained based on video images from 2006 Performance Evaluation of Tracking and Surveillance and 2007 Advanced Video and Signal-based Surveillance databases show that the proposed approach is effective for detecting abandoned luggage, and that it outperforms previous methods.


title: Faster, Practical GLL Parsing

abstract: Generalized LL (GLL) parsing is an extension of recursivedescent (RD) parsing that supports all context-free grammars in cubic time and space. GLL parsers have the direct relationship with the grammar that RD parsers have, and therefore, compared to GLR, are easier to understand, debug, and extend. This makes GLL parsing attractive for parsing programming languages.


title: The history of forensic entomology in German-speaking countries.

abstract: Megnin's book "La fauna des cadaveres" published in 1894 in France is generally accepted as a mile-stone in forensic entomology. It is hardly known that at the same time this topic was likewise explored in the German-speaking countries. Even PMI estimation based on developmental data of blowflies was performed. After a more descriptive period in the first half of the 20th century the complexity and variability of insects' biological behavior were detected and formally investigated. Improved technical facilities, enhanced comprehension of scientific studies and multidisciplinary cooperation, enabled rapid progress in forensic entomology during the last decades. With the European Association for Forensic Entomology founded in 2002 the frame work for a high standard of competency at an international level was constituted.


title: Error Correction for Dense Semantic Image Labeling

abstract: Pixelwise semantic image labeling is an important, yet challenging, task with many applications. Typical approaches to tackle this problem involve either the training of deep networks on vast amounts of images to directly infer the labels or the use of probabilistic graphical models to jointly model the dependencies of the input (i.e. images) and output (i.e. labels). Yet, the former approaches do not capture the structure of the output labels, which is crucial for the performance of dense labeling, and the latter rely on carefully hand-designed priors that require costly parameter tuning via optimization techniques, which in turn leads to long inference times. To alleviate these restrictions, we explore how to arrive at dense semantic pixel labels given both the input image and an initial estimate of the output labels. We propose a parallel architecture that: 1) exploits the context information through a LabelPropagation network to propagate correct labels from nearby pixels to improve the object boundaries, 2) uses a LabelReplacement network to directly replace possibly erroneous, initial labels with new ones, and 3) combines the different intermediate results via a Fusion network to obtain the final per-pixel label. We experimentally validate our approach on two different datasets for the semantic segmentation and face parsing tasks respectively, where we show improvements over the state-of-the-art. We also provide both a quantitative and qualitative analysis of the generated results.


title: Enhancing Light Fields through Ray-Space Stitching

abstract: Light fields (LFs) have been shown to enable photorealistic visualization of complex scenes. In practice, however, an LF tends to have a relatively small angular range or spatial resolution, which limits the scope of virtual navigation. In this paper, we show how seamless virtual navigation can be enhanced by stitching multiple LFs. Our technique consists of two key components: LF registration and LF stitching. To register LFs, we use what we call the ray-space motion matrix (RSMM) to establish pairwise ray-ray correspondences. Using Plucker coordinates, we show that the RSMM is a $5 \times 6$ matrix, which reduces to a $5 \times 5$ matrix under pure translation and/or in-plane rotation. The final LF stitching is done using multi-resolution, high-dimensional graph-cut in order to account for possible scene motion, imperfect RSMM estimation, and/or undersampling. We show how our technique allows us to create LFs with various enhanced features: extended horizontal and/or vertical field-of-view, larger synthetic aperture and defocus blur, and larger parallax.


title: Compensatory aspects of dreams: An empirical test of Jung's theory.

abstract: Jung's hypothesis that dreams have a compensatory function is clearly stated and well-known, yet has not been empirically tested. Three dream reports for each of 62 students were rated on 15 personality dimensions and compared with scores on the same dimensions as measured by the Edwards Personality Preference Schedule and the Adjective Check List (ACL). Six EPPS scales and 10 ACL scales correlated significantly with their appropriate dream rating scales. However, all significant correlations were positive, a finding that does not supoort Jung but is more in line with the theories of Adler, Fromm, and Hall, who regarded dream content as not substantially different from conscious functioning.


title: Towards a Trait Model of Video Game Preferences

abstract: ABSTRACTTypologies for understanding players’ preferences toward different gameplay styles have gained popularity in research. However, attempts to model players’ preferences are based on type models instead of trait models, contrary to the latest personality research. One such model, BrainHex, was designed as an interim model to enable investigations toward a definitive player trait model. However, it lacks empirical validation in support of its psychometric properties. The present work analyzed a dataset with over 50,000 respondents to devise a player traits model based off the BrainHex scale. Results indicate three player traits: action, esthetic, and goal orientation. Furthermore, we analyzed the games listed by participants as examples of what they enjoy, to understand which factors influence player preferences. Results illustrate that the emergent player traits and participants’ genders and attitudes toward story can partially explain player preferences toward certain games. Finally, we present the ...


title: ABCD rule and pre-trained CNNs for melanoma diagnosis

abstract: Skin cancer is the most common type of cancer and represents more than half of cancer diagnoses. Melanoma is the least frequent among skin cancers, but it is the most serious, with high potential for metastasis and can lead to death. However, melanoma is almost always curable if discovered in the early stages. In this context, computational methods for processing and analysis of skin lesion images have been studied and developed. This work proposes a computational approach to assist dermatologists in the diagnosis of skin lesions in melanoma or non-melanoma by means of dermoscopic images. The proposed methodology classifies skin lesions using a descriptor formed by the combination of the ABCD rule (Asymmetry, Border, Color, and Diameter) and pre-trained Convolutional Neural Networks (CNNs) features. The features were selected according to their gain ratios and used as input to the MultiLayer Perceptron classifier. We built a new database joining two distinct databases presented in the literature to validate the proposed methodology. The proposed method achieved an accuracy rate of 94.9% and Kappa index of 89.2%, which is considered “excellent”.


title: SVM classifier on chip for melanoma detection

abstract: Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.


title: Solving the Rubik's Cube Without Human Knowledge

abstract: A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game, however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge.


title: 3D shape retrieval and classification using multiple kernel learning on extended Reeb graphs

abstract: We propose in this article a new 3D shape classification and retrieval method, based on a supervised selection of the most significant features in a space of attributed extended Reeb graphs encoding different shape characteristics. The similarity between pairs of graphs is addressed through both their representation as set of bags of shortest paths, and the definition of kernels adapted to these descriptions. A multiple kernel learning algorithm is used on this set of kernels to find an optimal linear combination of kernels for classification and retrieval purposes. Results on classical data sets are comparable with the best results of the literature, and the modularity and flexibility of the kernel learning ensure its applicability to a large set of methods.


title: On Measurement Bias in Causal Inference

abstract: This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias free effect estimates in such models.


title: Explaining Inconsistencies in OWL Ontologies

abstract: Justifications play a central role as the basis for explaining entailments in OWL ontologies. While techniques for computing justifications for entailments in consistent ontologies are theoretically and practically well-understood, little is known about the practicalities of computing justifications for inconsistent ontologies. This is despite the fact that justifications are important for repairing inconsistent ontologies, and can be used as a basis for paraconsistent reasoning. This paper presents algorithms, optimisations, and experiments in this area. Surprisingly, it turns out that justifications for inconsistent ontologies are more "difficult" to compute and are often more "numerous" than justifications for entailments in consistent ontologies: whereas it is always possible to compute some justifications, it is often not possible to compute all justifications for real world inconsistent ontologies.


title: Open Problem: The landscape of the loss surfaces of multilayer networks

abstract: Deep learning has enjoyed a resurgence of interest in the last few years for such applications as image and speech recognition, or natural language processing. The vast majority of practical applications of deep learning focus on supervised learning, where the supervised loss function is minimized using stochastic gradient descent. The properties of this highly non-convex loss function, such as its landscape and the behavior of critical points (maxima, minima, and saddle points), as well as the reason why largeand small-size networks achieve radically different practical performance, are however very poorly understood. It was only recently shown that new results in spin-glass theory potentially may provide an explanation for these problems by establishing a connection between the loss function of the neural networks and the Hamiltonian of the spherical spin-glass models. The connection between both models relies on a number of possibly unrealistic assumptions, yet the empirical evidence suggests that the connection may exist in real. The question we pose is whether it is possible to drop some of these assumptions to establish a stronger connection between both models.


title: Augmenting the Refutation Text Effect with Analogies and Graphics

abstract: Refutation texts have been shown to be effective at promoting knowledge revision. It has been suggested that refutation texts are most effective when the misconception and the correct information are co-activated and integrated with causal networks that support the correct information. We explored two augmentations to a refutation text that might enhance the possibility of co-activation, integration, and thus revision: analogies and graphics. Participants (122 college students) were randomly assigned to read one of four experimental texts (refutation text, refutation text+graphic, refutation text+analogy, or refutation text+graphic+analogy). Results showed that refutation text+analogy and refutation text+graphic+analogy were the most successful in promoting revision. Implications for theory and practice are discussed.


title: A Twisted Loop Antenna to enhance HF RFID detection for different tag positioning

abstract: In this paper, a new LF/HF RFID reader loop antenna design is proposed and tested, in order to increase detection areas of the tags. The studied structure is a Twisted Loop Antenna (TLA) which is based on a modified distribution and orientation of the magnetic field lines. Our structure fruitfully uses the complementary antenna principle in a co-planar configuration. This offers more possibilities of tag detection whatever the tag position and orientation. The antenna performances are evaluated by optimizing the equivalent mutual inductance between reader antenna and tag antenna. Results are presented firstly in simulation (MATLAB and HFSS electromagnetic calculator) and secondly by experimental tests at different distances and misalignments, for the two possible tag orientations: parallel and perpendicular.


title: A circularly polarized slits loaded UHF RFID antenna with parasitic element

abstract: An eccentric annular slot patch antenna is initially studied for UHF (ultrahigh frequency) RFID (radio frequency identification) reader antenna application that can operate within the North America RFID band (902–928 MHz). Circular polarization (CP) radiation is achieved in this band by loading two narrow open-ended slots (slits) into the slot patch. To further enhance the CP bandwidth, a parasitic element is loaded beside the patch antenna that forms the proposed antenna. From the experimental results, the proposed antenna can yield an impedance bandwidth (10-dB return loss) between 618 to 1105 MHz, while good CP bandwidth (3-dB axial ratio, AR) from 886 to 942 MHz is also attained. In addition to that, gain level and efficiency of more than 7.6 dBic and 91%, respectively, were also measured.


title: Power-Efficient and Secure WPCNs With Hardware Impairments and Non-Linear EH Circuit

abstract: In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total consumed power in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a two-dimensional search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is then proposed. ::: Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: (1) residual HWIs create a system performance bottleneck in the high transmit/receive power regimes; (2) increasing the number of transmit antennas can effectively reduce the system power consumption and alleviate the performance degradation due to residual HWIs; (3) imperfect CSI increases the system power consumption and exacerbates the impact of residual HWIs.


title: Network Analysis of Clinical Trials on Depression: Implications for Comparative Effectiveness Research

abstract: A comprehensive understanding of evidence related to treatments for a disease is critical for planning effective clinical care, and for designing future trials. However, it is often difficult to comprehend the available evidence because of the complex combination of interventions across trials, in addition to the limited search and retrieval tools available in databases such as ClinicalTrials.gov. Here we demonstrate the use of networks to visualize and quantitatively analyze the co-occurrence of drug interventions across trials on depression in ClinicalTrials.gov. The analysis identified general co-occurrence patterns of interventions across all depression trials, and specific co-occurrence patterns related to antidepressants and natural supplements. These results led to insights about the current state of depression trials, and to a graph-theoretic measure to categorize interventions for a disease. We conclude by discussing the opportunities and challenges of generalizing our approach to analyze comparative interventional studies for any disease.


title: Friendship 2.0: Adolescents' experiences of belonging and self-disclosure online

abstract: Abstract This study explores the role that digital media technologies play in adolescents' experiences of friendship and identity. The author draws on findings from in-depth interviews with 32 adolescents (15 girls, 17 boys) ages 13–18 ( M = 15.5 years) attending one of seven secondary schools in Bermuda. The adolescents were asked to describe the nature of their online exchanges with friends and the value they ascribe to these conversations. A thematic analysis of their responses revealed that online peer communications promote adolescents' sense of belonging and self-disclosure, two important peer processes that support identity development during adolescence. At the same time, the unique features of computer-mediated communication shape adolescents' experiences of these processes in distinct ways. Gender and age differences show that adolescents' online peer communications are not uniform; the characteristics that distinguish adolescents offline also shape their online activities.


title: Microcontrollers as material: crafting circuits with paper, conductive ink, electronic components, and an "untoolkit"

abstract: Embedded programming is typically made accessible through modular electronics toolkits. In this paper, we explore an alternative approach, combining microcontrollers with craft materials and processes as a means of bringing new groups of people and skills to technology production. We have developed simple and robust techniques for drawing circuits with conductive ink on paper, enabling off-the-shelf electronic components to be embedded directly into interactive artifacts. We have also developed an set of hardware and software tools -- an instance of what we call an "untoolkit" -- to provide an accessible toolchain for the programming of microcontrollers. We evaluated our techniques in a number of workshops, one of which is detailed in the paper. Four broader themes emerge: accessibility and appeal, the integration of craft and technology, microcontrollers vs. electronic toolkits, and the relationship between programming and physical artifacts. We also expand more generally on the idea of an untoolkit, offering a definition and some design principles, as well as suggest potential areas of future research.


title: An over-segmentation method for single-touching Chinese handwriting with learning-based filtering

abstract: The segmentation of touching characters is still a challenging task, posing a bottleneck for offline Chinese handwriting recognition. In this paper, we propose an effective over-segmentation method with learning-based filtering using geometric features for single-touching Chinese handwriting. First, we detect candidate cuts by skeleton and contour analysis to guarantee a high recall rate of character separation. A filter is designed by supervised learning and used to prune implausible cuts to improve the precision. Since the segmentation rules and features are independent of the string length, the proposed method can deal with touching strings with more than two characters. The proposed method is evaluated on both the character segmentation task and the text line recognition task. The results on two large databases demonstrate the superiority of the proposed method in dealing with single-touching Chinese handwriting.


CARDIOVASCULAR DISEASE PREDICTION USING GENETIC ALGORITHM AND NEURO-FUZZY SYSTEM


title: Flexible Tactile Sensors Using Screen-Printed P(VDF-TrFE) and MWCNT/PDMS Composites

abstract: This paper presents and compares two different types of screen-printed flexible and conformable pressure sensors arrays. In both variants, the flexible pressure sensors are in the form of segmental arrays of parallel plate structure—sandwiching the piezoelectric polymer polyvinylidene fluoride trifluoroethylene [P(VDF-TrFE)] between two printed metal layers of silver (Ag) in one case and the piezoresistive [multiwall carbon nanotube (MWCNT) mixed with poly(dimethylsiloxane (PDMS)] layer in the other. Each sensor module consists of $4 \times 4$ sensors array with 1-mm $\times 1$ -mm sensitive area of each sensor. The screen-printed piezoelectric sensors array exploits the change in polarization level of P(VDF-TrFE) to detect dynamic tactile parameter such as contact force. Similarly, the piezoresistive sensors array exploits the change in resistance of the bulk printed layer of MWCNT/PDMS composite. The two variants are compared on the basis of fabrication by printing on plastic substrate, ease of processing and handling of the materials, compatibility of the dissimilar materials in multilayers structure, adhesion, and finally according to the response to the normal compressive forces. The foldable pressure sensors arrays are completely realized using screen-printing technology and are targeted toward realizing low-cost electronic skin.


title: Efficient interpolation of high‐frequency domain data by phase smoothing

abstract: [1] In this paper, a novel phase-smoothing method is proposed to efficiently interpolate the fine resolution frequency data from sparse samples in the high-frequency range. This is achieved by taking the effect of the complex exponential term e−jkr in the electromagnetic field into account, which causes the oscillation in the system response especially in the high-frequency domain. By phase-smoothing, even though the magnitude of the field quantity is unchanged, both real and imaginary parts of the frequency response become smoother. The interpolation is performed separately for both real and imaginary parts so that the sample rate required for accurate reconstruction is significantly reduced. The interpolation is carried out by the matrix pencil method, the coefficients of which are calculated by using the total least squares implementation to improve accuracy. Several numerical examples are presented to illustrate the applicability of this unique phase-smoothing method in ultra-high-frequency bands.