title: Continuous Positive Airway Pressure in Clinically Stable Patients with Mild-To-Moderate Obesity-Hypoventilation Syndrome and Obstructive Sleep Apnea

abstract: BACKGROUND AND OBJECTIVE ::: The use of continuous positive airway pressure (CPAP) treatment in patients with obesity hypoventilation syndrome (OHS) and obstructive sleep apnoea (OSA) was evaluated, and factors that might predict CPAP treatment failure were determined. ::: ::: ::: METHODS ::: A sleep study was performed in 29 newly diagnosed, clinically stable OHS patients. CPAP treatment was commenced if the apnoea-hypopnoea index was >15. Lung function, night-time oximetry, blood adipokine and C-reactive protein levels were assessed prospectively on enrollment and after 3 months. Treatment failure at 3 months was defined as daytime arterial partial pressure of carbon dioxide (PaCO(2)) >45 mm Hg and/or oxygen saturation (SpO(2)) <90% for >30% of the night-time oximetry study. ::: ::: ::: RESULTS ::: All patients had severe OSA (median apnoea-hypopnoea index = 74.7 (62-100) with a nocturnal mean SpO(2) of 81.4 ± 7), and all patients were treated with CPAP. The percentage of time spent below 90% saturation improved from 8.4% (0.0-39.0%) to 0.3% (0.4-4.0%). Awake PaCO(2) decreased from 50 (47-53) mm Hg to 43 (40-45) mm Hg. Seven patients failed CPAP treatment after 3 months. PaCO(2) at 1 month and mean night-time SpO(2) during the first night of optimal CPAP were associated with treatment failure at 3 months (odds ratio 1.4 (1.03-1.98); P = 0.034 and 0.6 (0.34-0.93); P = 0.027). ::: ::: ::: CONCLUSIONS ::: CPAP treatment improves night-time oxygenation and daytime hypoventilation in selected clinically stable OHS patients who also have OSA. Patients with worse night-time saturation while on CPAP and higher daytime PaCO(2) at 1 month were more likely to fail CPAP treatment.


title: Should glycemic index and glycemic load be considered in dietary recommendations?

abstract: High glycemic index (GI) and glycemic load (GL) have been proposed to be associated with increased risk of lifestyle diseases. Since protein intake varies little in humans, adherence to the common recommendation to reduce fat intake probably leads to increases in carbohydrate intake, which emphasizes the need to investigate the effects of carbohydrate on diet-related conditions and diseases. This review examines the epidemiological literature linking GI and GL to heart disease, insulin sensitivity, type 2 diabetes, dyslipidemia, and obesity among initially healthy people. The evidence for associations between GI and particularly GL and health among free-living populations is mixed. Only the positive association between GI and development of type 2 diabetes was consistent across cross-sectional and longitudinal studies for both sexes. Low GI/GL may protect against heart disease in women, and cross-sectional studies indicate low GI/GL may reduce high-density-lipoprotein cholesterol and triacylglycerol levels in both sexes. Based on the evidence found in this review, it seems premature to include GI/GL in dietary recommendations.


title: Indoor Navigation Validation Framework for Visually Impaired Users

abstract: In this paper, we introduce the first validation framework of an indoor navigation system for blind and visually impaired (BVI) users, which is a significant step toward the development of cost effective indoor way-finding solutions for BVI users. The BVI users require detailed landmark-based navigation instructions that will help them arrive at the chosen destination independently. These users will interact with the navigation instructions on a smartphone using an accessible user interface. The validation framework includes the following three main components: 1) virtual reality-based simulation that simulates a BVI user traversing and interacting with the physical environment, developed using Unity game engine; 2) generation of action codes that emulate the avatar movement in the virtual environment, developed using a natural language processing parser of the navigation instructions; and 3) accessible user interface, which enables the user to access the navigation instructions, developed using Sikuli script library. We introduce a case study that illustrates the use of the validation tool using PERCEPT system. It is our strong belief that the validation framework we provide in this paper will encourage other developers to invent indoor navigation systems for BVI users. In addition, we would like to mention that this tool is the first step of validating an indoor navigation system and should be followed by trials with human subjects.


title: United Kingdom national confidential enquiry into perioperative deaths.

abstract: The enquiry (NCEPOD) was established to examine simultaneously the anaesthetic and surgical circumstances surrounding the death of a patient undergoing surgery, it reviewed clinical practice and identified remedial factors in the practice of anaesthesia and surgery in order to improve the quality of care. Data were reported on every death occurred within 30 days of a procedure performed by a surgeon or gynaecologist under general or local anaesthesia, excepting obstetric deaths, and were collected int he sample of questionnaires to surgeons and anaesthesists involved. In high risk patient the optimisation of cardiovascular system with fluids, inotropic agents, beta-blockers and invasive monitoring may improve mortality (a speculative calculation reported 1,700 out 19,000 lives saved in 1994/1995 in UK). The majority of deaths occurred in emergency operations, urgent operations delayed for no operating theatre free during the day, training personnel which performed emergency operations during the night and/or postoperative care provided by non specialized clinicians, hospital not equipped with high dependency units (HDU), surgery undertaken although the predictable infaust outcome, they were all problems outlooked by the investigation. In conclusion improvements are necessary in hospital facilities and working pattern of surgeons and anaesthesists.


title: Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis

abstract: In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts. Polypus is a modular framework that provides the following functionalities: (1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and (7) a web interface to analyze results visually. The whole architecture is self-deployable and based on Docker containers.


title: Platform Rules: Multi-Sided Platforms as Regulators

abstract: This paper provides a basic conceptual framework for interpreting non-price instruments used by multi-sided platforms (MSPs) by analogizing MSPs as "private regulators" who regulate access to and interactions around the platform. We present evidence on Facebook, TopCoder, Roppongi Hills and Harvard Business School to document the "regulatory" role played by MSPs. We find MSPs use nuanced combinations of legal, technological, informational and other instruments (including price-setting) to implement desired outcomes. Non-price instruments were very much at the core of MSP strategies.


title: Time-series clustering - A decade review

abstract: Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works. Anatomy of time-series clustering is revealed by introducing its 4 main component.Research works in each of the four main components are reviewed in detail and compared.Analysis of research works published in the last decade.Enlighten new paths for future works for time-series clustering and its components.


title: Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)

abstract: Abstract Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.


Heart Disease Prediction System using Data Mining Clustering Techniques


title: Data investigation based on XFS file system metadata

abstract: At present, as the amount of digitized information is increasing geometrically, the importance of digitized information as critical clues from the perspective of criminal investigation is also increasing. The importance of digital forensics has also magnified accordingly. As a fundamental technique of digital forensics, recovery of data deleted to conceal information is extremely important and needs to be studied. Although file system types and versions are diversified, sufficient studies have not been performed. The XFS file system was designed to process huge files, and thus it is expected to be applied in server systems. In this paper, we analyze the XFS file system using its structure and metadata information; then, we propose a technique to recover the deleted files through previous analysis results, and verify it through testing.


title: Modeling and controller design for a voice coil actuated engine valve

abstract: This paper presents a physical model of a spark ignition (SI) engine valve actuated by a voice coil actuator (VCA) and proposes a novel control strategy that achieves fast transitions and low seating velocities with high precision position control. The control strategy is based on applying two different pulse width modulated (PWM) voltages for transient and steady state performance. This novel switching control strategy performs better than current strategies using electromechanical valve actuators to implement variable valve timing (VVT). The main improvement over electromechanical actuators is the fact that the novel control strategy achieves the conflicting performance requirements of very fast transition times while simultaneously exhibiting low contact velocities


title: Links between Adolescents' Deep and Surface Learning Approaches, Problematic Internet Use, and Fear of Missing Out (FoMO)

abstract: Abstract This study was aimed at exploring links between adolescents' deep and surface approaches to learning, Fear of Missing Out (FoMO), and Problematic Internet Use (PIU) by using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis corroborated the postulated positive links between surface learning, FoMO, and PIU. Moreover, the FoMO construct represented a complimentary mediation between the surface learning approach and PIU constructs. This study may lead to a plausible inference according to which both FoMO and surface learning share a common core characteristic of decreased levels of self-regulation that might lead to PIU. Having students acquire and practice skills of self-regulation might help them control their levels of FoMO, and consequently their PIU at schools or out-of-school learning environments.


title: STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration

abstract: Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard. Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports.


title: Effective Worm Detection for Various Scan Techniques

abstract: In recent years, the threats and damages caused by active worms have become more and more serious. In order to reduce the loss caused by fast-spreading active worms, an effective detection mechanism to quickly detect worms is desired. In this paper, we first explore various scan strategies used by worms on finding vulnerable hosts. We show that targeted worms spread much faster than random scan worms. We then present a generic worm detection architecture to monitor malicious worm activities. We propose and evaluate our detection mechanism called Victim Number Based Algorithm. We show that our detection algorithm is effective and able to detect worm events before 2% of vulnerable hosts are infected for most scenarios. Furthermore, in order to reduce false alarms, we propose an integrated approach using multiple parameters as indicators to detect worm events. The results suggest that our integrated approach can differentiate worm attacks from DDoS attacks and benign scans.


title: CoreDB: a Data Lake Service

abstract: The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from sensors, social-media, news, user-generated, government and private data sources. Accordingly, in a modern data-oriented landscape, with the advent of various data capture and management technologies, organizations are rapidly shifting to datafication of their processes. In such an environment, analysts may need to deal with a collection of datasets, from relational to NoSQL, that holds a vast amount of data gathered from various private/open data islands, i.e. Data Lake. Organizing, indexing and querying the growing volume of internal data and metadata, in a data lake, is challenging and requires various skills and experiences to deal with dozens of new databases and indexing technologies: How to store information items? What technology to use for persisting the data? How to deal with the large volume of streaming data? How to trace and persist information about data? What technology to use for indexing the data? How to query the data lake? To address the above mentioned challenges, we present CoreDB - an open source data lake service - which offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and offers a built-in design for security and tracing.


title: Automatic Annotation of Text with Pictures

abstract: The vast array of information available on the Internet makes it challenging to quickly determine the importance and relevance of content. Text picturing is a cognitive aid that can help with text understanding, as it helps users decide if the text deserves a closer look by showing relevant pictures along with the text.


title: 3-D Object Recognition of a Robotic Navigation Aid for the Visually Impaired

abstract: This paper presents a 3-D object recognition method and its implementation on a robotic navigation aid to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous planar patches and extracts their inter-plane relationships (IPRs).Based on the existing IPRs of the object models, the method defines six high level features (HLFs) and determines the HLFs for each patch. A Gaussian-mixture-model-based plane classifier is then devised to classify each planar patch into one belonging to a particular object model. Finally, a recursive plane clustering procedure is used to cluster the classified planes into the model objects. As the proposed method uses geometric context to detect an object, it is robust to the object’s visual appearance change. As a result, it is ideal for detecting structural objects (e.g., stairways, doorways, and so on). In addition, it has high scalability and parallelism. The method is also capable of detecting some indoor non-structural objects. Experimental results demonstrate that the proposed method has a high success rate in object recognition.


title: Emaciated mannequins: a study of mannequin body size in high street fashion stores

abstract: BACKGROUND ::: There is concern that the body size of fashion store mannequins are too thin and promote unrealistic body ideals. To date there has been no systematic examination of the size of high street fashion store mannequins. ::: ::: ::: METHODS ::: We surveyed national fashion retailers located on the high street of two English cities. The body size of 'male' and 'female' mannequins was assessed by two blinded research assistants using visual rating scales. ::: ::: ::: RESULTS ::: The average female mannequin body size was representative of a very underweight woman and 100% of female mannequins represented an underweight body size. The average male mannequin body size was significantly larger than the average female mannequin body size. Only 8% of male mannequins represented an underweight body size. ::: ::: ::: CONCLUSIONS ::: The body size of mannequins used to advertise female fashion is unrealistic and would be considered medically unhealthy in humans.


title: Localizing Handle-Like Grasp Affordances in 3D Point Clouds

abstract: We propose a new approach to localizing handle-like grasp affordances in 3-D point clouds. The main idea is to identify a set of sufficient geometric conditions for the existence of a grasp affordance and to search the point cloud for neighborhoods that satisfy these conditions. Our goal is not to find all possible grasp affordances, but instead to develop a method of localizing important types of grasp affordances quickly and reliably. The strength of this method relative to other current approaches is that it is very practical: it can have good precision/recall for the types of affordances under consideration, it runs in real-time, and it is easy to adapt to different robots and operating scenarios. We validate with a set of experiments where the approach is used to enable the Rethink Baxter robot to localize and grasp unmodelled objects.


Fuzzy Logic-Based Natural Language Processing and Its Application to Speech Recognition


Persuasion bias, social influence, and unidimensional opinions. The Quarterly


title: The Process Model of Group-Based Emotion

abstract: Scholars interested in emotion regulation have documented the different goals and strategies individuals have for regulating their emotions. However, little attention has been paid to the regulation of group-based emotions, which are based on individuals’ self-categorization as a group member and occur in response to situations perceived as relevant for that group. We propose a model for examining group-based emotion regulation that integrates intergroup emotions theory and the process model of emotion regulation. This synergy expands intergroup emotion theory by facilitating further investigation of different goals (i.e., hedonic or instrumental) and strategies (e.g., situation selection and modification strategies) used to regulate group-based emotions. It also expands emotion regulation research by emphasizing the role of self-categorization (e.g., as an individual or a group member) in the emotional process. Finally, we discuss the promise of this theoretical synergy and suggest several directions for...


Recent Developments in Indian Sign Language Recognition: An Analysis


title: Exploiting Domain Knowledge in Aspect Extraction

abstract: Aspect extraction is one of the key tasks in sentiment analysis. In recent years, statistical models have been used for the task. However, such models without any domain knowledge often produce aspects that are not interpretable in applications. To tackle the issue, some knowledge-based topic models have been proposed, which allow the user to input some prior domain knowledge to generate coherent aspects. However, existing knowledge-based topic models have several major shortcomings, e.g., little work has been done to incorporate the cannot-link type of knowledge or to automatically adjust the number of topics based on domain knowledge. This paper proposes a more advanced topic model, called MC-LDA (LDA with m-set and c-set), to address these problems, which is based on an Extended generalized Polya urn (E-GPU) model (which is also proposed in this paper). Experiments on real-life product reviews from a variety of domains show that MCLDA outperforms the existing state-of-the-art models markedly.


title: The Dipmeter Advisor System - A Case Study in Commercial Expert System Development

abstract: We discuss commercial expert system development, using our experience with the Dipmeter Advisor system as a case study. While the data is too sparse for definitive results, several ideas have emerged as important and suggestive as guidelines for subsequent commercial expert system undertakings. ::: ::: During the past four years, the Dipmeter Advisor system has migrated from an initial experiment in application of expert system techniques in well-log interpretation to a candidate commercial interpretation system. The system has undergone substantial change: It has been implemented in different configurations, in different languages, and on different computer systems. Our ability to experiment with the system has been greatly enhanced by the tools and ideas of rapid prototyping. We have also observed an oscillation in thrust over time between (i) expansion and change in the domain knowledge, and 00 selection and design of appropriate expert system tools. Finally, we have found several of the maxims of expert system development to be valid, but question a number of others.


title: Considerations on Selecting Fractional-Slot Nonoverlapped Coil Windings

abstract: This paper focuses on the selection of a fractional-slot winding for permanent-magnet (PM) machines. The choice of the proper combination of the number of slots and number of poles, together with the corresponding winding layout, has a strong impact on the PM machine performance, in terms of torque density, torque ripple, magnetomotive force harmonic content, and induced rotor losses, as well as the capability to limit the short-circuit current and other fault-tolerance features. Considering these characteristics, this paper aims to help the PM machine designer to select the proper winding configuration, giving useful indications. The winding choice criteria are given using analytical equations, so that their implementation is easy. In this way, the collection of such criteria becomes a helpful tool in the design process.


title: Linear, machine learning and probabilistic approaches for time series analysis

abstract: In this paper we study different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.


title: Investigation of InGaAsP/InP DFB and FP Laser Diodes Noise Characteristic

abstract: In the paper, there are presented comprehensive noise characteristics (electrical and optical noise and correlation between electrical and optical fluctuations) investigations that were performed with the aim to clear up the reasons of short lifetime of laser diode (LD) and to find out LD design and fabrication features that can increase laser diode quality. Groups of different quality InGaAsP laser diodes have been investigated. Investigation has shown that Fabry-Perot (FP) laser facet coating with antireflection layer suppresses the mode hopping effect (that is characteristic for FP laser operation): LD facet coating reduces resonator quality and leads to the more stable laser operation. It is shown that noise characteristic features that indicate laser diode quality and reliability problems are the most significant at LD operation below and in the vicinity of the threshold region. These noise features are related to additional generation-recombination processes and current leakage out of the active region: some of the investigated laser structures have defects that lead to the leakage current, generation-recombination noise, worse operation characteristics and rapid device degradation.


Author ' s personal copy Using fuzzy logic for robust event detection in wireless sensor networks


title: MOOC Dropout Prediction: How to Measure Accuracy?

abstract: In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can significantly overestimate accuracy. Moreover, (2) training dropout classifiers using proxy labels based on students' persistence -- which are available before a MOOC finishes -- is surprisingly competitive with post-hoc training (87.33% v.~90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs) and can support real-time MOOC interventions.


title: Noun Classification From Predicate-Argument Structures

abstract: A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification.


On Five Definitions of Data Integrity


An Official American Thoracic Society Clinical Policy Statement: Palliative Care for Patients with Respiratory Diseases and Critical Illnesses


title: Home automation using GSM

abstract: In recent years, the home environment has seen a rapid introduction of network enabled digital technology. This technology offers new and exciting opportunities to increase the connectivity of devices within the home for the purpose of home automation. Moreover, with the rapid expansion of the Internet, there is the added potential for the remote control and monitoring of such network enabled devices. However, the adoption of home automation systems has been slow. This paper identifies the reasons for this slow adoption and evaluates the potential of Zig-Bee for addressing these problems through the design and implementation of flexible home automation architecture. Device control is a process that is done in the day to day life of mankind. Usually there are a number of devices associated with home and an efficient control of these systems is a tedious task. The rapidly advancing mobile communication technology and the decrease in costs make it possible to incorporate mobile technology into home automation systems.


title: REA and XBRL GL, Synergies for the 21st Century Business Reporting System

abstract: Incorporating an instance document example, we suggest a framework linking the ::: Resource-Event-Agent (REA) model and eXtensible Business Reporting Language Global Ledger ::: (XBRL GL) as a way to extend the generalized XBRL GL taxonomy. Using the REA semantic ::: model to extend the existing XBRL GL taxonomy provides an ontology and associated process ::: that is reusable. The resulting framework can provide uniform access to information and more ::: reporting and query permutations, thereby facilitating more comprehensive and timely business ::: reporting.


title: Towards Automated Driving: Unmanned Protective Vehicle for Highway Hard Shoulder Road Works

abstract: Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards the road works against the moving traffic. The project "Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works" aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation -- strongly related to the general challenges in the field of automated driving -- are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.


title: ESD (Electrostatic Discharge) Protection Design for Nanoelectronics in CMOS Technology

abstract: In this tutorial, we teach useful on-chip ESD protection designs for CMOS integrated circuits. The contents include (1) Introduction to Electrostatic Discharge, (2) Design Techniques of ESD Protection Circuit, (3) Whole-Chip ESD Protection Design, and (4) ESD Protection for Mixed-Voltage I/O Interface. The clear ESD protection design concepts and detailed circuit implementations are presented in this course. ESD protection design is more important in the nanoscale CMOS technology. High ESD robustness can not be achieved with only process solutions. The circuit design solutions should be added into the chips with suitable layout arrangement to achieve the purpose of whole-chip ESD protection for IC products.


title: Real-time nonintrusive monitoring and prediction of driver fatigue

abstract: This paper describes a real-time online prototype driver-fatigue monitor. It uses remotely located charge-coupled-device cameras equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization.


title: The effect of eye dominancy on patients' cooperation and perceived pain during photorefractive keratectomy

abstract: Abstract Purpose To find a possible association between patients’ cooperation, perceived pain, and ocular dominance in patients who undergo photorefractive keratectomy (PRK). Methods One hundred-one eligible candidates for PRK refractive surgery were recruited. Preoperative exams were performed for all patients, and the dominant eye was specified. The surgeon was unaware about which eye was dominant. After surgery, the surgeon completed a cooperation score form for each patient. Ocular cyclotorsion, cooperation, and perceived pain scores were compared between the first-second eye surgeries and between dominant-non-dominant eyes surgeries. Results The dominant eye was the right eye in 68 patients and the left eye in 33 patients. First, eye surgery was performed on the dominant eye in 56 patients and on the non-dominant eye in 45 patients. Cooperation score and perceived pain were not significantly different between the first and second eye surgeries ( P = 0.902 and P = 0.223, respectively), but cyclotorsion was more in the second eye ( P = 0.031). Cooperation score, pain score, and cyclotorsion were not significantly different between dominant and non-dominant eye surgeries ( P = 0.538, P = 0.581, and P = 0.193, respectively). Also, there was no correlation between cooperation score and duration of the surgery for the first or second eye ( P = 0.12 and P = 0.78). Conclusion During PRK surgery, the patients’ cooperation and perceived pain did not seem to be associated with eye laterality or dominancy.


Nodular pulmonary calcification in renal failure. Report of a case.


title: ADHD in girls and boys – gender differences in co-existing symptoms and executive function measures

abstract: ADHD is diagnosed and treated more often in males than in females. Research on gender differences suggests that girls may be consistently underidentified and underdiagnosed because of differences in the expression of the disorder among boys and girls. One aim of the present study was to assess in a clinical sample of medication naïve boys and girls with ADHD, whether there were significant gender x diagnosis interactions in co-existing symptom severity and executive function (EF) impairment. The second aim was to delineate specific symptom ratings and measures of EF that were most important in distinguishing ADHD from healthy controls (HC) of the same gender. Thirty-seven females with ADHD, 43 males with ADHD, 18 HC females and 32 HC males between 8 and 17 years were included. Co-existing symptoms were assessed with self-report scales and parent ratings. EF was assessed with parent ratings of executive skills in everyday situations (BRIEF), and neuropsychological tests. The three measurement domains (co-existing symptoms, BRIEF, neuropsychological EF tests) were investigated using analysis of variance (ANOVA) and random forest classification. ANOVAs revealed only one significant diagnosis x gender interaction, with higher rates of self-reported anxiety symptoms in females with ADHD. Random forest classification indicated that co-existing symptom ratings was substantially better in distinguishing subjects with ADHD from HC in females (93% accuracy) than in males (86% accuracy). The most important distinguishing variable was self-reported anxiety in females, and parent ratings of rule breaking in males. Parent ratings of EF skills were better in distinguishing subjects with ADHD from HC in males (96% accuracy) than in females (92% accuracy). Neuropsychological EF tests had only a modest ability to categorize subjects as ADHD or HC in males (73% accuracy) and females (79% accuracy). Our findings emphasize the combination of self-report and parent rating scales for the identification of different comorbid symptom expression in boys and girls already diagnosed with ADHD. Self-report scales may increase awareness of internalizing problems particularly salient in females with ADHD.


title: Planning and spatial working memory following frontal lobe lesions in man

abstract: Abstract Twenty-six patients with unilateral or bilateral frontal lobe excisions were compared with age and IQ matched controls on a computerized battery of tests of spatial working memory and planning. A computerized test of spatial short term memory capacity revealed no significant impairment in the patients' ability to execute a given sequence of visuo-spatial moves. In contrast, a paradigm designed to assess spatial working memory capacity, revealed significant impairments in the patient group in both possible types of search errors. Furthermore, additional analysis showed that the frontal lobe patients were less efficient than controls in their usage of a strategy for improving performance on this test. Higher level planning was also investigated using a test based on the “Tower of London” problem [S hallice , T. Phil. Trans. R. Soc. Lond. B. 298, 199–209, 1982]. Patients with frontal lobe damage required more moves to complete the problems and a yoked motor control condition revealed that movement times were significantly increased in this group. Taking both of these factors into consideration, initial thinking (planning) time was unimpaired in the patient group although the thinking time subsequent to the first move was significantly prolonged. These data are compared to previous findings from patients with idiopathic Parkinson's disease and are discussed in terms of an impairment of higher cognitive functioning following frontal lobe damage.


BVFG An Unbiased Detector of Curvilinear Structures


title: Segmentation Of Multitemporal Side-Looking Airborne Radar (SLAR) Images

abstract: This paper describes a procedure developed for the automated segmentation of multiple side-looking airborne radar (SLAR) images, i.e. the detection of the boundaries of agricultural fields. The segmentation procedure is based on a split-and-merge algorithm which may process up to three input images simultaneously. The input images must be in registration. A registration method is proposed which uses coarse structural descriptions of the input images as represented by attributed quartic picture trees (QPT).


title: A random graph model of kidney exchanges: efficiency, individual-rationality and incentives

abstract: In kidney exchanges, hospitals share patient lists and receive transplantations. A kidney-paired donation (KPD) mechanism needs to promote full sharing of information about donor-patient pairs, and identify a Pareto efficient outcome that also satisfies participation constraints of hospitals. We introduce a random graph model of the KPD exchange and then fully characterize the structure of the efficient outcome and the expected number of transplantations that can be performed. Random graph theory allows early experimental results to be explained analytically, and enables the study of participation incentives in a methodological way. We derive a square-root law between the welfare gains from sharing patient-donor pairs in a central pool and the individual sizes of hospitals, illustrating the urgent need for the nationwide expansion of such programs. Finally, we establish through theoretical and computational analysis that enforcing simple individual rationality constraints on the outcome can mitigate the negative impact of strategic behavior by hospitals.


title: Topical Keyphrase Extraction from Twitter

abstract: Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.


title: Bitcoin's Security Model Revisited

abstract: We revisit the fundamental question of Bitcoin's security against double spending attacks. While previous work has bounded the probability that a transaction is reversed, we show that no such guarantee can be effectively given if the attacker can choose when to launch the attack. Other approaches that bound the cost of an attack have erred in considering only limited attack scenarios, and in fact it is easy to show that attacks may not cost the attacker at all. We therefore provide a different interpretation of the results presented in previous papers and correct them in several ways. We provide different notions of the security of transactions that provide guarantees to different classes of defenders: merchants who regularly receive payments, miners, and recipients of large one-time payments. We additionally consider an attack that can be launched against lightweight clients, and show that these are less secure than their full node counterparts and provide the right strategy for defenders in this case as well. Our results, overall, improve the understanding of Bitcoin's security guarantees and provide correct bounds for those wishing to safely accept transactions.


title: Autonomic control of the eye.

abstract: The autonomic nervous system influences numerous ocular functions. It does this by way of parasympathetic innervation from postganglionic fibers that originate from neurons in the ciliary and pterygopalatine ganglia, and by way of sympathetic innervation from postganglionic fibers that originate from neurons in the superior cervical ganglion. Ciliary ganglion neurons project to the ciliary body and the sphincter pupillae muscle of the iris to control ocular accommodation and pupil constriction, respectively. Superior cervical ganglion neurons project to the dilator pupillae muscle of the iris to control pupil dilation. Ocular blood flow is controlled both via direct autonomic influences on the vasculature of the optic nerve, choroid, ciliary body, and iris, as well as via indirect influences on retinal blood flow. In mammals, this vasculature is innervated by vasodilatory fibers from the pterygopalatine ganglion, and by vasoconstrictive fibers from the superior cervical ganglion. Intraocular pressure is regulated primarily through the balance of aqueous humor formation and outflow. Autonomic regulation of ciliary body blood vessels and the ciliary epithelium is an important determinant of aqueous humor formation; autonomic regulation of the trabecular meshwork and episcleral blood vessels is an important determinant of aqueous humor outflow. These tissues are all innervated by fibers from the pterygopalatine and superior cervical ganglia. In addition to these classical autonomic pathways, trigeminal sensory fibers exert local, intrinsic influences on many of these regions of the eye, as well as on some neurons within the ciliary and pterygopalatine ganglia.


title: Electromagnetic Modeling of Through-Silicon Via (TSV) Interconnections Using Cylindrical Modal Basis Functions

abstract: This paper proposes an efficient method to model through-silicon via (TSV) interconnections, an essential building block for the realization of silicon-based 3-D systems. The proposed method results in equivalent network parameters that include the combined effect of conductor, insulator, and silicon substrate. Although the modeling method is based on solving Maxwell's equation in integral form, the method uses a small number of global modal basis functions and can be much faster than discretization-based integral-equation methods. Through comparison with 3-D full-wave simulations, this paper validates the accuracy and the efficiency of the proposed modeling method.


title: DRETOM: developer recommendation based on topic models for bug resolution

abstract: Background: In most cases, bug resolution is a collaborative activity among developers in software development where each developer contributes his or her ideas on how to resolve the bug. Although only one developer is recorded as the actual fixer for the bug, the contribution of the developers who participated in the collaboration cannot be neglected. Aims: This paper proposes a new approach, called DRETOM (Developer REcommendation based on TOpic Models), to recommending developers for bug resolution in collaborative behavior. Method: The proposed approach models developers' interest in and expertise on bug resolving activities based on topic models that are built from their historical bug resolving records. Given a new bug report, DRETOM recommends a ranked list of developers who are potential to participate in and contribute to resolving the new bug according to these developers' interest in and expertise on resolving it. Results: Experimental results on Eclipse JDT and Mozilla Firefox projects show that DRETOM can achieve high recall up to 82% and 50% with top 5 and top 7 recommendations respectively. Conclusion: Developers' interest in bug resolving activities should be taken into consideration. On condition that the parameter θ of DRETOM is set properly with trials, the proposed approach is practically useful in terms of recall.


title: Where color rests: Spontaneous brain activity of bilateral fusiform and lingual regions predicts object color knowledge performance

abstract: Abstract Knowledge of the physical attributes of objects is commonly assumed to be distributed near their respective modality-specific brain regions. The exact neural correlates for such knowledge, especially how it is maintained in the resting state, are largely unknown. In the current study, we explored the intrinsic neural basis related to a specific type of object knowledge — color — by investigating the relationship between spontaneous brain activity and color knowledge behavioral performance. We correlated the regional amplitude of spontaneous low-frequency fluctuations (ALFF, a resting-state fMRI parameter) with healthy participants' performance on two object color knowledge tasks (object color verification and color attribute judgment). We found that ALFF in bilateral lingual and fusiform gyri and right inferior occipital gyrus reliably predicted participants' color knowledge performance (correlation coefficients = 0.55–0.70), and that calcarine cortex showed a similar trend, although less stable. Furthermore, the ALFF-behavior correlations for other types of object knowledge (i.e. form, motion and sound) in these regions were minimal and significantly lower than those for color knowledge, suggesting that the effects in the observed regions were not merely due to general object processing. Furthermore, we showed that functional connectivity strengths of the lingual/fusiform and inferior occipital regions are significantly associated with color knowledge performance, indicating that they work as a network to support color knowledge processing or the acquisition of such knowledge. Our findings show the critical role of ventral medial occipito-temporal regions in processing or acquiring color knowledge and highlight the behavioral significance of spontaneous brain activity in the resting state.


title: A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

abstract: Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.


title: The neural bases of social pain: Evidence for shared representations with physical pain

abstract: Experiences of social rejection or loss have been described as some of the most ‘painful’ experiences that we, as humans, face and perhaps for good reason. Because of our prolonged period of immaturity, the social attachment system may have co-opted the pain system, borrowing the pain signal to prevent the detrimental consequences of social separation. This review summarizes a program of research that has explored the idea that experiences of physical and social pain rely on shared neural substrates. First, evidence showing that social pain activates pain-related neural regions is reviewed. Then, studies exploring some of the expected consequences of such a physical-social pain overlap are summarized. These studies demonstrate: 1) that individuals who are more sensitive to one kind of pain are also more sensitive to the other and 2) that factors that increase or decrease one kind of pain alter the other in a similar manner. Finally, what these shared neural substrates mean for our understanding of socially painful experience is discussed.


title: AppInk: watermarking android apps for repackaging deterrence

abstract: With increased popularity and wide adoption of smartphones and mobile devices, recent years have seen a new burgeoning economy model centered around mobile apps. However, app repackaging, among many other threats, brings tremendous risk to the ecosystem, including app developers, app market operators, and end users. To mitigate such threat, we propose and develop a watermarking mechanism for Android apps. First, towards automatic watermark embedding and extraction, we introduce the novel concept of manifest app, which is a companion of a target Android app under protection. We then design and develop a tool named AppInk, which takes the source code of an app as input to automatically generate a new app with a transparently-embedded watermark and the associated manifest app. The manifest app can be later used to reliably recognize embedded watermark with zero user intervention. To demonstrate the effectiveness of AppInk in preventing app repackaging, we analyze its robustness in defending against distortive, subtractive, and additive attacks, and then evaluate its resistance against two open source repackaging tools. Our results show that AppInk is easy to use, effective in defending against current known repackaging threats on Android platform, and introduces small performance overhead.


title: Fast and robust level shifters in 65 nm CMOS

abstract: This paper presents two fast level shifters with low power consumption used for controlling the output stage of 5 V DC-DC buck converters. The propagation delays of the level shifters in nominal cases are lower than 0.61 ns, which allows controlling the power switches very accurately. The level shifters are very robust against power supply ringing and are evaluated in a DC-DC buck converter test chip in a 65 nm CMOS technology.


title: Sentiment of Emojis

abstract: There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.


title: Forecasting Stock Market Trends by Logistic Regression and Neural Networks: Evidence from KSA Stock Market

abstract: Forecasting stock market trends is very vital for investors to take action for the next period for sustainable competition. It is especially important for policy makers to predict actions for development. KSA stock market is evolving rapidly. Due to increasing importance; the aim of this study is to forecast the stock market trends by using logistic model and artificial neural network. Logistic model is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable(i.e., a class label) based on one or more predictor variables (features). Artificial neural networks are models which are used for forecasting because of their capabilities of pattern recognition and machine learning. Both methods are used to forecast the stock prices of upcoming period. The model has used the preprocessed data set of closing value of TASA Index. The data set encompassed the trading days from 5th April, 2007 to 1st January, 2015. Both methods give us estimation with up to 80% accuracy.


title: Convolutional neural network-based real-time ROV detection using forward-looking sonar image

abstract: Agent system is strategy to enhance the underwater manipulation. The conventional manipulation is generally robot arm-based configuration which has singular points. On the other hand, the agent system is an armless manipulation that the agent vehicle works as the end-effector. If the location of the agent can be measured, the end effector is able to be place to any position. To implement this system, the method of an agent vehicle localization is proposed. The method uses the sonar images of moving agent obtained by forward-looking sonar. To detect the location of the agent in the sonar images, the convolutional neural network is applied. We applied the state-of-art object-detection algorithm to the agent vehicle system. The fast object-detection algorithm based on neural network can fulfil the real-time detection and show the remarkable validity. It means the underwater robot can begin navigation under its feed-back. Through field experiment, we confirm the proposed method can detect and track the agent in the successive sonar images.


title: Design of compressive imaging masks for human activity perception based on binary convolutional neural network

abstract: Many applications demand proper design and implementation of 0-1 binary compressive sensing (CS) measurement matrices. This paper presents a construction method for such binary CS measurement matrices by training a convolutional neural network (CNN) with 0-1 weights. The desired CS performance of resultant binary measurement matrices can be achieved by designing a proper CNN training procedure. For human activity recognition applications, such a sensing system is implemented with a small number of optical sensors and optical masks, which can achieve a high recognition capability with a far smaller amount of data than traditional cameras. In the experiments, the compressive sensory readings are classified using a basic K-Nearest Neighbor (KNN) algorithm to demonstrate the high sampling efficiency of hardware without compromising much the recognition performance.


title: Entity Extraction: From Unstructured Text to DBpedia RDF triples

abstract: In this paper, we describe an end-to-end system that automatically extracts RDF triples describing entity relations and properties from unstructured text. This system is based on a pipeline of text processing modules that includes a semantic parser and a coreference solver. By using coreference chains, we group entity actions and properties described in different sentences and convert them into entity triples. We applied our system to over 114,000 Wikipedia articles and we could extract more than 1,000,000 triples. Using an ontology-mapping system that we bootstrapped using existing DBpedia triples, we mapped 189,000 extracted triples onto the DBpedia namespace. These extracted entities are availableonline in the N-Triple format. 1 1 http://semantica.cs.lth.se/ (Less)


title: A Sub-nW Multi-stage Temperature Compensated Timer for Ultra-Low-Power Sensor Nodes

abstract: Accurate measurement of synchronization cycle time is required for ultra-low power wireless sensor nodes with stringent power budgets. A multi-stage gate-leakage-based timer with boosted charging is proposed to address the high jitter of prior-art gate-leakage-based timers. The key approaches are faster load capacitor charging, wider voltage swing, and an improved gain sensing inverter. The proposed timer reduces RMS jitter by 8.1× and synchronization uncertainty by 4.1×, which allows hourly tracking with 200 ms uncertainty while consuming 660 pW. A novel closed-loop temperature compensation scheme with dynamic leakage adjustment is also proposed to achieve temperature sensitivity of 31 ppm/°C.


title: Complementary Learning Systems

abstract: This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles.


title: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases

abstract: Research highlights? We propose the improved F-score. Our F-score can measure the discrimination between sets of real numbers. We propose a hybrid feature selection method based on our F-score and SFS and SVM. We develop a diagnosis model to diagnose erythemato-squamous diseases. The model can get up to 98.61% classification accuracy with only 21 features. In this paper, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F-score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature set. In our IFSFS, we improved the original F-score from measuring the discrimination of two sets of real numbers to measuring the discrimination between more than two sets of real numbers. The improved F-score and Sequential Forward Search (SFS) are combined to find the optimal feature subset in the process of feature selection, where, the improved F-score is an evaluation criterion of filter method, and SFS is an evaluation system of wrapper method. The best parameters of kernel function of SVM are found out by grid search technique. Experiments have been conducted on different training-test partitions of the erythemato-squamous diseases dataset taken from UCI (University of California Irvine) machine learning database. Our experimental results show that the proposed SVM-based model with IFSFS achieves 98.61% classification accuracy and contains 21 features. With these results, we conclude our method is very promising compared to the previously reported results.


title: Markov Chains on Orbits of Permutation Groups

abstract: We present a novel approach to detecting and utilizing symmetries in probabilistic graphical models with two main contributions. First, we present a scalable approach to computing generating sets of permutation groups representing the symmetries of graphical models. Second, we introduce orbital Markov chains, a novel family of Markov chains leveraging model symmetries to reduce mixing times. We establish an insightful connection between model symmetries and rapid mixing of orbital Markov chains. Thus, we present the first lifted MCMC algorithm for probabilistic graphical models. Both analytical and empirical results demonstrate the effectiveness and efficiency of the approach.


title: Position Paper: Provenance Data Visualisation for Neuroimaging Analysis

abstract: Visualisation facilitates the understanding of scientific data both through exploration and explanation of visualised data. Provenance contributes to the understanding of data by containing the contributing factors behind a result. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. Data coming from various heterogeneous sources (multiple sources with varying level of trust) in a collaborative environment adds to the uncertainty of the scientific output. Systems are required that offer provenance data capture and visualisation support for analyses. We present an account for the need to visualise provenance information in order to aid the process of verification of scientific outputs, comparison of analyses, progression and evolution of results for neuroimaging analysis.


title: A complete solution to asymmetric S-curve motion profile: Theory & experiments

abstract: In this paper, we propose the asymmetric S-curve motion profile that enables easy manipulation of jerks during the arrival time in order to effectively reduce the residual vibration. To derive the complete closed form solution to the asymmetric S-curve, a design parameter the so-called jerk ratio is introduced to scale down the jerks during the deceleration period so that the velocity profile is in an asymmetric S-curve. Thanks to the jerk ratio, the motion parameters are remarkably simplified in analytic forms for short, medium and long distances. The effectiveness of the proposed approach is validated by experiments with the AC motor control system. As the jerk ratio increases, the residual vibration decreases, but, the motion profile is lengthened as expected by simulations.


title: Dopaminergic dynamics underlying sex-specific cocaine reward

abstract: Sex differences in reward processing are at present poorly understood. Calipari and Juarez et al. report oestrous cycle-dependent fluctuations in firing of VTA dopamine neurons that drive alterations in DAT function expressed in terminals in the NAc. These differences underlie enhanced cocaine reward processing during oestrus.


title: How Hard is a Commercial Puzzle: the Eternity II Challenge

abstract: Recently, edge matching puzzles, an NP-complete problem, have received, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.


title: Small UWB Planar Monopole Antenna With Added GPS/GSM/WLAN Bands

abstract: A small-size microstrip-fed multi-band planar monopole antenna is presented. The base of the proposed antenna is a diamond-shaped-patch (DSP) that covers the ultrawideband (UWB) frequency range. To create a multi-band antenna, several narrow strips, acting as resonance paths, can be integrated with the DSP antenna. It is shown that by removing the centre part of the DSP antenna, without distorting the UWB behavior, quarter-wavelength strips can be added to the notched region. This will not affect the dimension of the base antenna. The designed quad-band antenna has a substrate size of 16 × 22 mm2 and covers the frequency bands 1.3, 1.8, 2.4 and 3.1-10.6 GHz which includes GPS, GSM, WLAN and UWB. Dual-, triple- and quad-band antennas are simulated and good results are obtained. The antennas have omnidirectional and stable radiation patterns across all the relevant bands. Moreover, relatively consistent group delays across the UWB frequencies are noticed for the base, dual- and triple-band antennas, and slightly distorted for the quad-band antenna. A prototype of the quad-band antenna is fabricated and measured results are compared with simulated results.


title: An Efficient Statistical Method for Image Noise Level Estimation

abstract: In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that many state-of-the-art noise estimation methods underestimate the noise level of an image. To this end, we derive a new nonparametric algorithm for efficient noise level estimation based on the observation that patches decomposed from a clean image often lie around a low-dimensional subspace. The performance of our method has been guaranteed both theoretically and empirically. Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments. We further demonstrate that the denoising algorithm BM3D algorithm achieves optimal performance using noise variance estimated by our algorithm.


title: A simple Photovoltaic simulator based on a one-diode equivalent circuit model

abstract: This paper proposes a simple circuit-based Photovoltaic (PV) simulator for testing Maximum Power Point Tracker (MPPT). The main advantage of proposed simulator is that being able to perform with low cost instead of the expensive commercial solar array simulators. In summary, the structure of the simulator is very simple due to not requirement extra components and complex control circuits, it only requires a few basic components available for all laboratories. The I–V and P-V characteristics of the proposed simulator were obtained experimentally and simulated on MATLAB/Simulink. The characteristics have shown in agreement with actual PV output characteristics. Therefore, the proposed simulator can be used instead of real photovoltaic modules or commercial solar array simulators for MPPT studies.


title: Multi area state estimation using synchronized phasor measurements

abstract: This paper investigates the problem of state estimation in very large power systems, which may contain several control areas. An estimation approach which coordinates locally obtained decentralized estimates while improving bad data processing capability at the area boundaries is presented. Each area is held responsible for maintaining a sufficiently redundant measurement set to allow bad data processing among its internal measurements. It is assumed that synchronized phasor measurements from different area buses are available in addition to the conventional measurements provided by the substation remote terminal units. The estimator is implemented and tested using different measurement configurations for the IEEE 118-bus test system and the 4520-bus ERCOT system.


title: Data Clustering Using a Modified Kuwahara Filter

abstract: In this paper we propose a new density based clustering algorithm. As with other density based clustering algorithms our approach does not require the number of clusters as input. A modification of the Kuwahara filter, used in image processing, is used to generate a special density map in which the brightness of pixels is indicative of the density of the data points. A framework for clustering is derived and its performance is demonstrated on a number of different data sets.


title: Detecting Malware Domains at the Upper DNS Hierarchy

abstract: In recent years Internet miscreants have been leveraging the DNS to build malicious network infrastructures for malware command and control. In this paper we propose a novel detection system called Kopis for detecting malware-related domain names. Kopis passively monitors DNS traffic at the upper levels of the DNS hierarchy, and is able to accurately detect malware domains by analyzing global DNS query resolution patterns. ::: ::: Compared to previous DNS reputation systems such as Notos [3] and Exposure [4], which rely on monitoring traffic from local recursive DNS servers, Kopis offers a new vantage point and introduces new traffic features specifically chosen to leverage the global visibility obtained by monitoring network traffic at the upper DNS hierarchy. Unlike previous work Kopis enables DNS operators to independently (i.e., without the need of data from other networks) detect malware domains within their authority, so that action can be taken to stop the abuse. Moreover, unlike previous work, Kopis can detect malware domains even when no IP reputation information is available. ::: ::: We developed a proof-of-concept version of Kopis, and experimented with eight months of real-world data. Our experimental results show that Kopis can achieve high detection rates (e.g., 98.4%) and low false positive rates (e.g., 0.3% or 0.5%). In addition Kopis is able to detect new malware domains days or even weeks before they appear in public blacklists and security forums, and allowed us to discover the rise of a previously unknown DDoS botnet based in China.


title: Computer science: The learning machines

abstract: Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence.


title: Human interaction discovery in smartphone proximity networks

abstract: Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.


title: Secure Ranked Keyword Search over Encrypted Cloud Data

abstract: As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys ``as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.


title: Study and analysis of data mining for healthcare

abstract: In this paper Data Mining is introduced as well as big data in the framework of Healthcare. Furthermore, the Data Mining for accumulated data is investigated. Especially, their complexities of the various areas health and medical research. Finally, machine learning algorithms have been used in order to study processing Healthcare data.


title: The Effect of Grammatical Gender on Object Categorization

abstract: In 3 experiments, we investigated the effect of grammatical gender on object categorization. Participants were asked to judge whether 2 objects, whose names did or did not share grammatical gender, belonged to the same semantic category by pressing a key. Monolingual speakers of English (Experiment 1), Italian (Experiments 1 and 2), and Spanish (Experiments 2 and 3) were tested in their native language. Italian and Spanish participants responded faster to pairs of stimuli sharing the same gender, whereas no difference was observed for English participants. In Experiment 2, the pictures were chosen in such a way that the grammatical gender of the names was opposite in Italian and Spanish. Therefore, the same pair of stimuli gave rise to different patterns depending on the gender congruency of the names in the languages. In Experiment 3, Spanish speakers performed the same task under an articulatory suppression condition, showing no grammatical gender effect. The locus where meaning and gender interact can be located at the level of the lexical representation that specifies syntactic information: Nouns sharing the same grammatical gender activate each other, thus facilitating their processing and speeding up responses, either to semantically related pairs or to semantically unrelated pairs.


title: Comparing exploration strategies for Q-learning in random stochastic mazes

abstract: Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.


title: Gender dysphoria in childhood.

abstract: AbstractGender dysphoria (GD) in childhood is a complex phenomenon characterized by clinically significant distress due to the incongruence between assigned gender at birth and experienced gender. The clinical presentation of children who present with gender identity issues can be highly variable; the psychosexual development and future psychosexual outcome can be unclear, and consensus about the best clinical practice is currently under debate.In this paper a clinical picture is provided of children who are referred to gender identity clinics. The clinical criteria are described including what is known about the prevalence of childhood GD. In addition, an overview is presented of the literature on the psychological functioning of children with GD, the current knowledge on the psychosexual development and factors associated with the persistence of GD, and explanatory models for psychopathology in children with GD together with other co-existing problems that are characteristic for children referred for th...


title: Finding related tables

abstract: We consider the problem of finding related tables in a large corpus of heterogenous tables. Detecting related tables provides users a powerful tool for enhancing their tables with additional data and enables effective reuse of available public data. Our first contribution is a framework that captures several types of relatedness, including tables that are candidates for joins and tables that are candidates for union. Our second contribution is a set of algorithms for detecting related tables that can be either unioned or joined. We describe a set of experiments that demonstrate that our algorithms produce highly related tables. We also show that we can often improve the results of table search by pulling up tables that are ranked much lower based on their relatedness to top-ranked tables. Finally, we describe how to scale up our algorithms and show the results of running it on a corpus of over a million tables extracted from Wikipedia.


title: Quality-Based Pricing for Crowdsourced Workers

abstract: The emergence of online paid crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), presents us huge opportunities to distribute tasks to human workers around the world, on-demand and at scale. In such settings, online workers can come and complete tasks posted by a company, and work for as long or as little as they wish. Given the absolute freedom of choice, crowdsourcing eliminates the overhead of the hiring (and dismissal) process. However, this exibility introduces a di erent set of ine ciencies: verifying the quality of every submitted piece of work is an expensive operation, which often requires the same level of e ort as performing the task itself. There are many research challenges that emerge in this paid-crowdsourcing setting. How can we ensure that the submitted work is accurate? How can we estimate the quality of the workers, and the quality of the submitted results? How should we pay online workers that have imperfect quality? We present a comprehensive scheme for managing quality of crowdsourcing processes: First, we present an algorithm for estimating the quality of the participating workers and, by extension, of the generated data. We show how we can separate systematic worker biases from unrecoverable errors and how to generate an unbiased "worker quality" measurement that can be used to objectively rank workers according to their performance. Next, we describe a pricing scheme that identi es the fair payment level for a worker, adjusting the payment level according to the contributed information by each worker. Our pricing policy, which pays workers based on their expected quality, reservation wage, and expected lifetime, estimates not only 1 the payment level but also accommodates measurement uncertainties and allows the workers to receive a fair wage, even in the presence of temporary incorrect estimations of quality. Our experimental results demonstrate that the proposed pricing strategy performs better than the commonly adopted uniform-pricing strategy. We conclude the paper by describing strategies that build on our quality control and pricing framework, to build crowdsourced tasks of increasingly higher complexity, while still maintaining a tight quality control of the process, even if we allow participants of unknown quality to join the process.


title: Hadoop Oriented Smart Cities Architecture

abstract: A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.


title: Biocompatibility analysis of chemomechanical caries removal material Papacárie on cultured fibroblasts and subcutaneous tissue.

abstract: Chemomechanical caries removal allies an atraumatic technique with antimicrobiotic characteristics, minimizing painful stimuli and maximally preserving healthy dental structures. The purpose of this study was to compare the cytotoxic effects of papain-based gel (Papacarie) and another caries-removing substance, Carisolv, to a nontreatment control on cultured fibroblasts in vitro and the biocompatibility in subcutaneous tissue in vivo. The cytotoxicity analysis was performed on fibroblast cultures (NIH-3T3) after 0-, 4-, 8-, and 12-hour exposure (cell viability assay) and after 1-, 3-, 5-, and 7-day exposure (survival assay). In the in vivo study, the 2 compounds were introduced into polyethylene tubes that were implanted into subcutaneous tissues of rats. After 1, 7, 14, 30, and 60 days, tissue samples were examined histologically. Cell viability did not differ between the 2 experimental groups. The control group, however, showed significantly higher percentage viability. There were no differences in cell survival between the control and experimental groups. The histological analysis revealed a moderate inflammatory response at 2 and 7 days and a mild response at 15 days, becoming almost imperceptible by 30 and 60 days in both experimental groups. The 2 tested substances exhibited acceptable biocompatibilities and demonstrated similar responses in the in vitro cytotoxicity and in vivo implantation assay.


RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms


title: Deep Neural Networks for Data-Driven Turbulence Models

abstract: Abstract In this work, we present a novel data-based approach to turbulence modeling for Large Eddy Simulation (LES) by artificial neural networks. We define the perfect LES formulation including the discretization operators and derive the associated perfect closure terms. We then generate training data for these terms from direct numerical simulations of decaying homogeneous isotropic turbulence. We design and train artificial neural networks based on local convolution filters to predict the underlying unknown non-linear mapping from the coarse grid quantities to the closure terms without a priori assumptions. We show that selecting both the coarse grid primitive variables as well as the coarse grid LES operator as input features significantly improves training results. All investigated networks are able to generalize from the data and learn approximations with a cross correlation of up to 47% and even 73% for the inner elements, demonstrating that the perfect closure can indeed be learned from the provided coarse grid data. Since the learned closure terms are approximate, a direct application leads to stability issues. We show how to employ the artificial neural network output to construct stable and accurate models. The best results have been achieved with a data-informed, temporally and spatially adaptive eddy viscosity closure. While further investigations into the generalizability of the approach is warranted, this work thus represents a starting point for further research into data-driven, optimal turbulence models.


title: An Environment for Design and Modeling of Electro-Mechanical Micro-Systems

abstract: This paper presents a comprehensive CAD environment for MEMS/MST design. The design flow includes MEMS mechanical schematic entry, parameterized cell layout generation, LVS style error checking, lumped parameter macro-model generation, and system level modeling of the micro-system. The design environment also provides error checking between different design views. The implemented macromodel extraction technique can be used for micro-systems that can be represented as multi-component massspring-dashpot structures. Examples include accelerometers, gyros, and other structures that have rigid masses and compliant springs. A mass-produced capacitive MEMS accelerometer (Analog Devices, Inc.'s ADXL76), is employed as an illustrative example of this procedure. The accuracy of the developed method is also verified by comparing macro-model results for several other MEMS structures to the results of full 3-D physics simulations. Good accuracy is demonstrated in both spatialdomain and frequency-domain dynamic behavior of the models.


title: High Vgs MOSFET characteristics with thin gate oxide for PMIC application

abstract: We present the characteristics of a newly developed MOSFET which has thin gate oxide but sustains high gate voltage. It consists of MIM capacitor coupled floating gate poly and source/drain junction formed by low voltage well. The characteristics of the MOSFET depend on the choice of capacitance coupling ratio which is determined by the ratio of MIM (Metal-Insulator-Metal) capacitance and gate oxide capacitance. This means that the proper choice of layout can control the device performances; threshold voltage, I D -V D , and I D -V G characteristics. Some of smart power management ICs requires of a low density non-volatile memory for the purpose of PMIC trimming. To meet this purpose, NVM embedding on PMIC solution should be cost effective. This newly proposed high gate voltage MOSFET with thin gate oxide can be an effective solution for the periphery circuits of single poly EEPROM operation.


title: Stock selection using support vector machines

abstract: We used the support vector machines (SVM) in a classification approach to 'beat the market'. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208% over a five years period, significantly outperformed the benchmark of 71%. We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25%.


title: Applying Graph theory to the Internet of Things

abstract: In the Internet of Things (IoT), we all are ``things''. Graph theory, a branch of discrete mathematics, has been proven to be useful and powerful in understanding complex networks in history. By means of graph theory, we define new concepts and terminology, and explore the definition of IoT, and then show that IoT is the union of a topological network, a data-functional network and a domi-functional network.


title: Challenges of Feature Selection for Big Data Analytics

abstract: We are surrounded by huge amounts of large-scale high dimensional data. It is desirable to reduce the dimensionality of data for many learning tasks due to the curse of dimensionality. Feature selection has shown its effectiveness in many applications by building simpler and more comprehensive model, improving learning performance, and preparing clean, understandable data. Recently, some unique characteristics of big data such as data velocity and data variety present challenges to the feature selection problem. In this paper, we envision these challenges of feature selection for big data analytics. In particular, we first give a brief introduction about feature selection and then detail the challenges of feature selection for structured, heterogeneous and streaming data as well as its scalability and stability issues. At last, to facilitate and promote the feature selection research, we present an open-source feature selection repository (scikit-feature), which consists of most of current popular feature selection algorithms.


title: End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering

abstract: We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To effectively exploit the detected words, we also develop a semantic attention mechanism that selectively focuses on the detected concept words and fuse them with the word encoding and decoding in the language model. In order to demonstrate that the proposed approach indeed improves the performance of multiple video-to-language tasks, we participate in all the four tasks of LSMDC 2016 [18]. Our approach has won three of them, including fill-in-the-blank, multiple-choice test, and movie retrieval.


title: Metacognitive strategies in student learning: do students practise retrieval when they study on their own?

abstract: Basic research on human learning and memory has shown that practising retrieval of information (by testing the information) has powerful effects on learning and long-term retention. Repeated testing enhances learning more than repeated reading, which often confers limited benefit beyond that gained from the initial reading of the material. Laboratory research also suggests that students lack metacognitive awareness of the mnemonic benefits of testing. The implication is that in real-world educational settings students may not engage in retrieval practise to enhance learning. To investigate students’ real-world study behaviours, we surveyed 177 college students and asked them (1) to list strategies they used when studying (an open-ended, free report question) and (2) to choose whether they would reread or practise recall after studying a textbook chapter (a forced report question). The results of both questions point to the same conclusion: A majority of students repeatedly read their notes or textbook (despite the limited benefits of this strategy), but relatively few engage in self-testing or retrieval practise while studying. We propose that many students experience illusions of competence while studying and that these illusions have significant consequences for the strategies students select when they monitor and regulate their own learning.


Indistinguishable Predicates: A New Tool for Obfuscation.


title: A composite splitting criterion using random sampling

abstract: The ever growing presence of data lead to a large number of proposed algorithms for classification and especially decision trees over the last few years. However, learning decision trees from large irrelevant datasets is quite different from learning small and moderate sized datasets. In practice, use of only small and moderate sized datasets is rare. Unfortunately, the most popular heuristic function gain ratio has a serious disadvantage towards dealing with large and irrelevant datasets. To tackle these issues, we design a new composite splitting criterion with random sampling approach. Our random sampling method depends on small random subset of attributes and it is computationally cheap to act on such a set in a reasonable time. The empirical and theoretical properties are validated by using 40 UCI datasets. The experimental result supports the efficacy of the proposed method in terms of tree size and accuracy.


title: Sexual life in Pharaonic Egypt: towards a urological view

abstract: Sex is a basic human need, common to all people at all times. It is evident that the ancient Egyptians were real human beings, not only a people who built massive pyramids and made mummies of their dead. The ancient Egyptians had a rich and varied sexual life, which they found an opportunity to describe in words and pictures. As in the other early primitive civilizations, erotic matters were of prime importance and became an integral part of life. In Pharaonic times, the Egyptians described impotence and recorded several methods to increase the sexual power. In the present paper, we will shed light on some aspects of the sexual life in ancient Egypt that may be interesting to the urologists, including ancient Egyptian concepts of sex and erotic matters, their own way of treatment of impotence and Min, the Egyptian fertility God.