title: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R

abstract: Gaussian process (GP) regression models make for powerful predictors in out of sample exercises, but cubic runtimes for dense matrix decompositions severely limit the size of data - training and testing - on which they can be deployed. That means that in computer experiment, spatial/geo-physical, and machine learning contexts, GPs no longer enjoy privileged status as data sets continue to balloon in size. We discuss an implementation of local approximate Gaussian process models, in the laGP package for R, that offers a particular sparse-matrix remedy uniquely positioned to leverage modern parallel computing architectures. The laGP approach can be seen as an update on the spatial statistical method of local kriging neighborhoods. We briefly review the method, and provide extensive illustrations of the features in the package through worked-code examples. The appendix covers custom building options for symmetric multi-processor and graphical processing units, and built-in wrapper routines that automate distribution over a simple network of workstations.


title: Issues in implementing ERP: A case study

abstract: Abstract Information technology/systems play a major role in improving the competitiveness of organizations. There are numerous enterprise information software packages available in the market. One of them is enterprise resource planning (ERP). ERP can provide significant improvements in efficiency across a company, but only when implemented correctly. Otherwise, an ERP system could be a curse and drag the whole enterprise into spiraling inefficiency. Planning for ERP systems and their implementations requires an integrated approach to meet the requirements of various functional areas. With a brief overview of ERP implementations, this paper describes some experiences of an ERP implementation in a water corporation. The case study reveals some of the intricacies during the planning and implementation stages that may occur in any company in any part of the world. Suggestions are offered in resolving the issues of implementing ERP.


title: Ethical Dilemmas at the End of Life: Islamic Perspective

abstract: Many Muslim patients and families are often reluctant to accept fatal diagnoses and prognoses. Not infrequently, aggressive therapy is sought by the patient or his/her family, to prolong the life of the patient at all costs. A series of searches were conducted of Medline databases published in English between January 2000 and January 2015 with the following Keywords: End-of-life, Ethics and Islam. Islamic law permits the withdrawal of futile treatment, including all kinds of life support, from terminally ill patients leaving death to take its natural course. However, such decision should only take place when the physicians are confident that death is inevitable. All interventions ensuring patient’s comfort and dignity should be maintained. This topic is quite challenging for the health care providers of Muslim patients in the Western World.


title: Limited effects of activated protein C on red blood cell deformability

abstract: Alterations of the microcirculation are common in critically ill patients and persistence of these alterations is associated with poor outcome. Infusion of high doses of activated protein C can ameliorate the microcirculation. Because red blood cells (RBCs) play an essential role in oxygen transport to the tissues, we studied the effects of activated protein C on RBC deformability in septic patients.


title: What Can OpenStack Adopt from a Ganeti-Based Open-Source IaaS?

abstract: Cloud computing is becoming a widely adopted paradigm for the provision of different types of ICT services. Building on a set of combined technologies, it enables service provision following on-demand usage patterns. Different vendors aim at addressing the needs of private and public large and small organizations. To this end, several solutions have been developed tackling varying service, platform and infrastructure needs. In this paper, we present the outcomes of our experimentation with respect to two open-source infrastructure-level solutions, namely OpenStack and Synnefo which have attracted the attention both of the research community and of the industrial one. The goal of our qualitative and quantitative survey and experimentation is to identify shortcomings and areas for improvement in the widely adopted OpenStack project.


title: Statistical Machine Translation: A Guide for Linguists and Translators

abstract: This paper presents an overview of Statistical Machine Translation (SMT), which is currently the dominant approach in Machine Translation (MT) research. In Way and Hearne (2010), we describe how central linguists and translators are to the MT process, so that SMT developers and researchers may better understand how to include these groups in continuing to advance the stateof-the-art. If these constituencies are to make an impact in the field of MT, they need to know how their input is used by SMT systems. Accordingly, our objective in this paper is to present the basic principles underpinning SMT in a way that linguists and translators will find accessible and useful.


title: A MATLAB Implementation of Elliptic Curve Cryptography

abstract: The ultimate purpose of this project has been the implementation in MATLAB of an Elliptic Curve Cryptography (ECC) system, primarily the Elliptic Curve Diffie-Hellman (ECDH) key exchange. We first introduce the fundamentals of Elliptic Curves, over both the real numbers and the integers modulo p where p is prime. Then the theoretical underpinnings of the ECDH system are covered, including a brief look at how this system is broken. Next we develop the individual elements that will be needed in the implementation of ECDH, such as functions for calculating modular square roots and the addition of points on an EO. We then bring these elements together to create a working ECDH program, and discuss the limitations of the MATLAB environment in which it was created. Finally we look at the real world application of ECC and its future in the realm of cryptography.


title: Deep Graph Attention Model

abstract: Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or sub-graphs. In the real-world, however, graphs can be both large and noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attentional processing for graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of"interesting"nodes. The model is equipped with an external memory component which allows it to integrate information gathered from different parts of the graph. We demonstrate the effectiveness of the model through various experiments.


title: Impending skin necrosis after dermal filler injection: A “golden time” for first‐aid intervention

abstract: Vascular compromise with impending skin necrosis is one of the most serious potential complications. Early recognition of vascular occlusion and swift and aggressive treatment are required to avoid any irreversible changes. However, initial symptoms of a vascular event are often dismissed as simple post-procedural discomfort. If more than 3 days pass after filler injection, crust formation is initiated over the erythematous base along with a rim of fibrous tissue. Due to the replacement of normal tissues by fibrous material, the healing process may result in scar formation in spite of debridement and aggressive dressing changes. Scars often cause contracture and subsequent cosmetic disfigurement, which results in a traumatic burden to the patient. By sharing our experience of the patients with filler induced skin necrosis, we suggest that treatment should be initiated no later than 3 days after the procedure.


title: Sparse regularization for semi-supervised classification

abstract: Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for classification. It has been shown that the performance of the MR algorithms depends highly on the design of the additional penalty term on manifolds. In this paper, we propose a new approach to define the penalty term on manifolds by the sparse representations instead of the adjacency graphs of data. The process to build this novel penalty term has two steps. First, the best sparse linear reconstruction coefficients for each data point are computed by the l^1-norm minimization. Secondly, the learner is subject to a cost function which aims to preserve the sparse coefficients. The cost function is utilized as the new penalty term for regularization algorithms. Compared with previous semi-supervised learning algorithms, the new penalty term needs less input parameters and has strong discriminative power for classification. The least square classifier using our novel penalty term is proposed in this paper, which is called the Sparse Regularized Least Square Classification (S-RLSC) algorithm. Experiments on real-world data sets show that our algorithm is very effective.


title: Comparative Analysis of Software Effort Estimation Techniques

abstract: Project Failure is the major problem undergoing nowadays as seen by software project managers. Imprecision of the estimation is the reason for this problem. As software grew in size and importance it also grew in complexity, making it very difficult to accurately predict the cost of software development. This was the dilemma in past years. The greatest pitfall of software industry was the fast changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. Development of useful models that accurately predict the cost of developing a software product. It is a very important objective of software industry. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed. This paper summarizes several classes of software cost estimation models and techniques. To achieve all these goals we implement the simulators. No single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.


title: Use of Fourier and Karhunen-Loeve Decomposition for Fast Pattern Matching With a Large Set of Templates

abstract: We present a fast pattern matching algorithm with a large set of templates. The algorithm is based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform. The proposed algorithm is appropriate for the search of an object with unknown distortion within a short period. Patterns with different distortion differ slightly from each other and are highly correlated. The image vector subspace required for effective representation can be defined by a small number of eigenvectors derived by the Karhunen-Loeve transform. A vector subspace spanned by the eigenvectors is generated, and any image vector in the subspace is considered as a pattern to be recognized. The pattern matching of objects with unknown distortion is formulated as the process to extract the portion of the input image, find the pattern most similar to the extracted portion in the subspace, compute normalized correlation between them at each location in the input image, and find the location with the best score. Searching for objects with unknown distortion requires vast computation. The formulation above makes it possible to decompose highly correlated reference images into eigenvectors, as well as to decompose images in frequency domain, and to speed up the process significantly.


title: The heroism of women and men

abstract: Heroism consists of actions undertaken to help others, despite the possibility that they may result in the helper's death or injury. The authors examine heroism by women and men in 2 extremely dangerous settings: the emergency situations in which Carnegie medalists rescued others and the holocaust in which some non-Jews risked their lives to rescue Jews. The authors also consider 3 risky but less dangerous prosocial actions: living kidney donations, volunteering for the Peace Corps, and volunteering for Doctors of the World. Although the Carnegie medalists were disproportionately men, the other actions yielded representations of women that were at least equal to and in most cases higher than those of men. These findings have important implications for the psychology of heroism and of gender. Language: en


title: Reading aloud to children: the evidence

abstract: Promoting healthy child development lies at the heart of paediatric practice, yet a major challenge facing the field is applying evidence based standards. However, the evidence is clear as regards reading aloud to children. Ample research demonstrates that reading aloud to young children promotes the development of language and other emergent literacy skills,1–4 which in turn help children prepare for school.3 5 ::: ::: Reading aloud to children or shared bookreading has been linked to young children’s emergent literacy ability, which can be defined as the skills or knowledge that children develop before learning the more conventional skills of reading and writing6–8 which affect children’s later success in reading.9 ::: ::: During shared bookreading, children learn to recognise letters, understand that print represents the spoken word, and learn how to hold a book, turn the page and start at the beginning.10–12 Shared bookreading is also associated with learning print concepts11 and exposing children to the written language register, which is different from spoken language,13 as well as story structures (eg, stories have a beginning, middle and end) and literacy conventions such as syntax and grammar which are essential for understanding texts.14 These emergent literacy skills are important for later success in reading.2 15 ::: ::: Phonological awareness (the ability to manipulate the sounds of spoken language1 16–18) is another important prerequisite for learning to read. To read words, children need to know the rules for translating print into meaningful sounds.8 16 For example, preschoolers’ sensitivity to alliteration and rhyme at age 4–5 contributed to progress in reading and spelling at age 6–7.19 Children’s knowledge of nursery rhymes at age 3–4 is related to detecting alliteration and rhyme at ages 4–7.20 Many parents naturally promote awareness …


title: A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms

abstract: This study aims to contribute a comparison of various simultaneous localization and mapping (SLAM) algorithms that have been proposed in literature. The performance of Extended Kalman Filter (EKF) SLAM, Unscented Kalman Filter (UKF) SLAM, EKF-based FastSLAM version 2.0, and UKF-based FastSLAM (uFastSLAM) algorithms are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on Player/Stage environment. The results show that the UKF-based FastSLAM has the best performance in terms of accuracy of localization and mapping. Unlike most of the previous applications of FastSLAM in literature, no waypoints are used in this study.


title: MDIS cloth system: virtual reality technology for firefighter training

abstract: Fire accidents can cause numerous casualties and heavy property losses, especially, in petrochemical industry, such accidents are likely to cause secondary disasters. However, common fire drill training would cause loss of resources and pollution. We designed a multi-dimensional interactive somatosensory (MDIS) cloth system based on virtual reality technology to simulate fire accidents in petrochemical industry. It provides a vivid visual and somatosensory experience. A thermal radiation model is built in a virtual environment, and it could predict the destruction radius of a fire. The participant position changes are got from Kinect, and shown in virtual environment synchronously. The somatosensory cloth, which could both heat and refrigerant, provides temperature feedback based on thermal radiation results and actual distance. In this paper, we demonstrate the details of the design, and then verified its basic function. Heating deviation from model target is lower than 3.3 °C and refrigerant efficiency is approximately two times faster than heating efficiency.


title: Flapless postextraction socket implant placement in the esthetic zone: part 1. The effect of bone grafting and/or provisional restoration on facial-palatal ridge dimensional change-a retrospective cohort study.

abstract: The dental literature has reported vertical soft tissue changes that can occur with immediate implant placement, bone grafting, and provisional restoration ranging from a gain or loss of 1.0 mm. However, little is known of the effects of facial-palatal collapse of the ridge due to these clinical procedures. Based upon treatment modalities rendered, an ensuing contour change can occur with significant negative esthetic consequences. The results of a retrospective clinical cohort study evaluating the change in horizontal ridge dimension associated with implant placement in anterior postextraction sockets are presented for four treatment groups: (1) group no BGPR = no bone graft and no provisional restoration; (2) group PR = no bone graft, provisional restoration; (3) group BG = bone graft, no provisional restoration; and (4) group BGPR = bone graft, provisional restoration. Bone grafting at the time of implant placement into the gap in combination with a contoured healing abutment or a provisional restoration resulted in the smallest amount of ridge contour change. Therefore, it is recommended to place a bone graft and contoured healing abutment or provisional restoration at the time of flapless postextraction socket implant placement.


title: Design of High-Speed Power-Efficient MOS Current-Mode Logic Frequency Dividers

abstract: A methodology to design high-speed power-efficient MOS current-mode logic (MCML) static frequency dividers is proposed. Analytical criteria to exploit the speed potential of MCML gates are first introduced. Then, an analytical strategy is formulated to progressively reduce the bias currents through the stages without affecting the divider operation speed, thereby reducing the overall power consumption. The proposed design approach is general and independent of the process adopted. Due to its simplicity, it can be used in a pencil-and-paper approach, avoiding a tedious and time-consuming trial-and-error approach based on simulations. Moreover, the analytical approach allows for a deeper understanding of the power-delay tradeoff involved in the design. As a design example, a 1:8 frequency divider is designed and simulated by using a 0.18-mum CMOS process


title: ReDeBug: Finding Unpatched Code Clones in Entire OS Distributions

abstract: Programmers often copy code from one program to another. Unfortunately, when ::: patches to buggy code are not propagated to all code clones, this leaves one or more ::: programs still vulnerable. In this article, we present ReDeBug, a tool to identify ::: unpatched code clones automatically that we have made available for use.


title: The cohesin subunit Rad21 is a negative regulator of hematopoietic self‐renewal through epigenetic repression of Hoxa7 and Hoxa9

abstract: Acute myelogenous leukemia (AML) is a high-risk hematopoietic malignancy caused by a variety of mutations, including genes encoding the cohesin complex. Recent studies have demonstrated that reduction in cohesin complex levels leads to enhanced self-renewal in hematopoietic stem and progenitors (HSPCs). We sought to delineate the molecular mechanisms by which cohesin mutations promote enhanced HSPC self-renewal as this represents a critical initial step during leukemic transformation. We verified that RNAi against the cohesin subunit Rad21 causes enhanced self-renewal of HSPCs in vitro through derepression of polycomb repressive complex 2 (PRC2) target genes, including Hoxa7 and Hoxa9. Importantly, knockdown of either Hoxa7 or Hoxa9 suppressed self-renewal, implying that both are critical downstream effectors of reduced cohesin levels. We further demonstrate that the cohesin and PRC2 complexes interact and are bound in close proximity to Hoxa7 and Hoxa9. Rad21 depletion resulted in decreased levels of H3K27me3 at the Hoxa7 and Hoxa9 promoters, consistent with Rad21 being critical to proper gene silencing by recruiting the PRC2 complex. Our data demonstrates that the cohesin complex regulates PRC2 targeting to silence Hoxa7 and Hoxa9 and negatively regulate self-renewal. Our studies identify a novel epigenetic mechanism underlying leukemogenesis in AML patients with cohesin mutations.


Towards a Wireless Building Management System Requiring no Change to Upper-layer Protocols


title: Transient cardio-respiratory responses to visually induced tilt illusions

abstract: Although the orthostatic cardio-respiratory response is primarily mediated by the baroreflex, studies have shown that vestibular cues also contribute in both humans and animals. We have demonstrated a visually mediated response to illusory tilt in some human subjects. Blood pressure, heart and respiration rate, and lung volume were monitored in 16 supine human subjects during two types of visual stimulation, and compared with responses to real passive whole body tilt from supine to head 80° upright. Visual tilt stimuli consisted of either a static scene from an overhead mirror or constant velocity scene motion along different body axes generated by an ultra-wide dome projection system. Visual vertical cues were initially aligned with the longitudinal body axis. Subjective tilt and self-motion were reported verbally. Although significant changes in cardio-respiratory parameters to illusory tilts could not be demonstrated for the entire group, several subjects showed significant transient decreases in mean blood pressure resembling their initial response to passive head-up tilt. Changes in pulse pressure and a slight elevation in heart rate were noted. These transient responses are consistent with the hypothesis that visual-vestibular input contributes to the initial cardiovascular adjustment to a change in posture in humans. On average the static scene elicited perceived tilt without rotation. Dome scene pitch and yaw elicited perceived tilt and rotation, and dome roll motion elicited perceived rotation without tilt. A significant correlation between the magnitude of physiological and subjective reports could not be demonstrated.


title: The predictive power of ranking systems in association football

abstract: We provide an overview and comparison of predictive capabilities of several methods for ranking association football teams. The main benchmark used is the official FIFA ranking for national teams. The ranking points of teams are turned into predictions that are next evaluated based on their accuracy. This enables us to determine which ranking method is more accurate. ::: ::: The best performing algorithm is a version of the famous Elo rating system that originates from chess player ratings, but several other methods (and method versions) provide better predictive performance than the official ranking method. Being able to predict match outcomes better than the official method might have implications for, e.g., a team’s strategy to schedule friendly games.


title: The cartesian product algorithm: Simple and precise type inference of parametric polymorphism

abstract: Concrete types and abstract types are different and serve different purposes. Concrete types, the focus of this paper, are essential to support compilation, application delivery, and debugging in object-oriented environments. Concrete types should not be obtained from explicit type declarations because their presence limits polymorphism unacceptably. This leaves us with type inference. Unfortunately, while polymorphism demands the use of type inference, it has also been the hardest challenge for type inference.We review previous type inference algorithms that analyze code with parametric polymorphism and then present a new one: the cartesian product algorithm. It improves precision and efficiency over previous algorithms and deals directly with inheritance, rather than relying on a preprocessor to expand it away. Last, but not least, it is conceptually simple.The cartesian product algorithm has been used in the Self system since late 1993. We present measurements to document its performance and compare it against several previous algorithms.


title: On the Feasibility of Carrying Out Live Real-Time Forensics for Modern Intelligent Vehicles

abstract: Modern vehicular systems exhibit a number of networked electronic components ranging from sensors and actuators to dedicated vehicular subsystems. These components/systems, and the fact that they are interconnected, raise questions as to whether they are suitable for digital forensic investigations. We found that this is indeed the case especially when the data produced by such components are properly obtained and fused (such as fusing location with audio/video data). In this paper we therefore investigate the relevant advanced automotive electronic components and their respective network configurations and functions with particular emphasis on the suitability for live (real time) forensic investigations and surveillance based on augmented software and/or hardware configurations related to passenger behaviour analysis. To this end, we describe subsystems from which sensor data can be obtained directly or with suitable modifications; we also discuss different automotive network and bus structures, and then proceed by describing several scenarios for the application of such behavioural analysis.


title: A close look on n-grams in intrusion detection: anomaly detection vs. classification

abstract: Detection methods based on n-gram models have been widely studied for the identification of attacks and malicious software. These methods usually build on one of two learning schemes: anomaly detection, where a model of normality is constructed from n-grams, or classification, where a discrimination between benign and malicious n-grams is learned. Although successful in many security domains, previous work falls short of explaining why a particular scheme is used and more importantly what renders one favorable over the other for a given type of data. In this paper we provide a close look on n-gram models for intrusion detection. We specifically study anomaly detection and classification using n-grams and develop criteria for data being used in one or the other scheme. Furthermore, we apply these criteria in the scope of web intrusion detection and empirically validate their effectiveness with different learning-based detection methods for client-side and service-side attacks.


title: A Sequential Decision Formulation of the Interface Card Model for Interactive IR

abstract: The Interface Card model is a promising new theoretical framework for modeling and optimizing interactive retrieval interfaces, but how to systematically instantiate it to solve concrete interface optimization problems remains an open challenge. We propose a novel formulation of the Interface Card model based on sequential decision theory, leading to a general framework for formal modeling of user states and stopping actions. The proposed framework naturally connects optimization of interactive retrieval with Markov Decision Processes and Partially Observable Markov Decision Processes, and enables the use of reinforcement learning algorithms for optimizing interactive retrieval interfaces. Simulation and user study experiments demonstrate the effectiveness of the proposed model in automatically adjusting the interface layout in adaptation to inferred user stopping tendencies in addition to user interaction and screen size.


title: Superimposed Sparse Parameter Classifiers for Face Recognition

abstract: In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.


title: Analytical modeling and self-tuned fuzzy-PID logic based control for quarter car suspension system using Simulink

abstract: A suspension system is responsible for driver comfort and safety. It isolates car body from road disturbances. A quarter car suspension is a suspension of 1/4th car body mass. Suspension system has three types: passive, semi-active and active. A passive suspension has fixed damping rates determined by their design. Semi-active and active suspension uses selective damping rates. In active suspension, energy is added externally to change damping coefficient. This paper focuses on the development of modeling for 2 DOF and control algorithm in order to adjust damping rates of damper according to road disturbances. This paper proposes advanced Fuzzy Logic Controller (FLC) to minimize sprung mass displacement and Suspension Working Space (SWS) using MATLAB Simulink.


title: Toward an Efficient C-RAN Optical Fronthaul for the Future Networks: A Tutorial on Technologies, Requirements, Challenges, and Solutions

abstract: The exponential traffic growth, demand for high speed wireless data communications, as well as incessant deployment of innovative wireless technologies, services, and applications, have put considerable pressure on the mobile network operators (MNOs). Consequently, cellular access network performance in terms of capacity, quality of service, and network coverage needs further considerations. In order to address the challenges, MNOs, as well as equipment vendors, have given significant attention to the small-cell schemes based on cloud radio access network (C-RAN). This is due to its beneficial features in terms of performance optimization, cost-effectiveness, easier infrastructure deployment, and network management. Nevertheless, the C-RAN architecture imposes stringent requirements on the fronthaul link for seamless connectivity. Digital radio over fiber-based common public radio interface (CPRI) is the fundamental means of distributing baseband samples in the C-RAN fronthaul. However, optical links which are based on CPRI have bandwidth and flexibility limitations. Therefore, these limitations might constrain or make them impractical for the next generation mobile systems which are envisaged not only to support carrier aggregation and multi-band but also envisioned to integrate technologies like millimeter-wave (mm-wave) and massive multiple-input multiple-output antennas into the base stations. In this paper, we present comprehensive tutorial on technologies, requirements, architectures, challenges, and proffer potential solutions on means of achieving an efficient C-RAN optical fronthaul for the next-generation network such as the fifth generation network and beyond. A number of viable fronthauling technologies such as mm-wave and wireless fidelity are considered and this paper mainly focuses on optical technologies such as optical fiber and free-space optical. We also present feasible means of reducing the system complexity, cost, bandwidth requirement, and latency in the fronthaul. Furthermore, means of achieving the goal of green communication networks through reduction in the power consumption by the system are considered.


title: Changes in vertebral neural arch morphometry and functional tethering of spinal cord in adolescent idiopathic scoliosis--study with multi-planar reformat magnetic resonance imaging.

abstract: With the use of multiplanar reformat Magnetic Resonance imaging, AIS patients were found to have significantly reduced pedicle widths on concavity. Pattern of vertebral asymmetry was also exaggerated with smaller pedicle width, length and area on concavity. The cord appeared more roundish and was deviated to the concavity at apical vertebra in AIS. A tethering force might therefore be present on the cord along the transverse axis in AIS, accounted by the relatively fixed position of the exit nerve roots and deviation of the cord from the exit foraminae of the corresponding vertebra.


title: Adaptive piezoelectric energy harvesting circuit for wireless remote power supply

abstract: This paper describes an approach to harvesting electrical energy from a mechanically excited piezoelectric element. A vibrating piezoelectric device differs from a typical electrical power source in that it has a capacitive rather than inductive source impedance, and may be driven by mechanical vibrations of varying amplitude. An analytical expression for the optimal power flow from a rectified piezoelectric device is derived, and an "energy harvesting" circuit is proposed which can achieve this optimal power flow. The harvesting circuit consists of an AC-DC rectifier with an output capacitor, an electrochemical battery, and a switch-mode DC-DC converter that controls the energy flow into the battery. An adaptive control technique for the DC-DC converter is used to continuously implement the optimal power transfer theory and maximize the power stored by the battery. Experimental results reveal that use of the adaptive DC-DC converter increases power transfer by over 400% as compared to when the DC-DC converter is not used.


title: Hybrid PWM Strategy of SVPWM and VSVPWM for NPC Three-Level Voltage-Source Inverter

abstract: Neutral-point (NP) voltage drift is the main technical drawback of NP-clamped (NPC) three-level inverters. Traditional space vector pulsewidth modulation (SVPWM) is incapable of controlling the NP voltage for high modulation indexes and low power factors. Virtual SVPWM (VSVPWM) is capable of controlling the NP voltage under full modulation indexes and full power factors. However, this modulation strategy is more complex than SVPWM, increases the switching frequency, and deteriorates the output waveforms of the inverter. A novel PWM concept that includes NP voltage-balancing conditions is proposed. Based on this concept, a hybrid modulation scheme that uses both SVPWM and VSVPWM is presented for complete control of the NP voltage in NPC three-level inverters. The performance of this new modulation approach and its benefits over SVPWM and VSVPWM are verified by simulation and experiments.


title: Traffic forecasting for mobile networks with multiplicative seasonal ARIMA models

abstract: Traffic forecasting is an important task which is required by overload warning and capacity planning for mobile networks. Based on analysis of real data collected by China Mobile Communications Corporation (CMCC) Heilongjiang Co. Ltd, this paper proposes to use the multiplicative seasonal ARIMA models for mobile communication traffic forecasting. Experiments and test results show that the whole solution presented in this paper is feasible and effective to fulfill the requirements in traffic forecasting application for mobile networks.


title: Text classification with heterogeneous information network kernels

abstract: Text classification is an important problem with many applications. Traditional approaches represent text as a bag-of-words and build classifiers based on this representation. Rather than words, entity phrases, the relations between the entities, as well as the types of the entities and relations carry much more information to represent the texts. This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. We show that with the new representation and links of texts, the structured and typed information of entities and relations can be incorporated into kernels. Particularly, we develop both simple linear kernel and indefinite kernel based on meta-paths in the HIN representation of texts, where we call them HIN-kernels. Using Freebase, a well-known world knowledge base, to construct HIN for texts, our experiments on two benchmark datasets show that the indefinite HIN-kernel based on weighted meta-paths outperforms the state-of-the-art methods and other HIN-kernels.


title: Nonverbal behavior in clinician—patient interaction

abstract: Empirical studies on nonverbal communication in clinician-patient interaction are reviewed for both the psychotherapeutic and medical settings. Nonverbal behavior is considered both as the consequence of antecedent variables, such as individual or relationship characteristics, and as a predictor of clinical effectiveness and patient outcomes. The concept of rapport is considered in relation to nonverbal behavior, both theoretically and empirically. Also, the relevance of a patient's nonverbal behavior to the diagnostic goals of the clinical visit is demonstrated by research on nonverbal cues to psychopathology, Type A/B personality, and pain. Although the topic of nonverbal behavior and clinician-patient interaction has had a promising start, much research is still needed, especially that which experimentally manipulates either antecedent variables or nonverbal behavior itself to demonstrate cause and effect relations.


title: Active filter design using operational transconductance amplifiers: A tutorial

abstract: Basic properties of the operational transconductance amplifier (OTA) are discussed. Applications of the OTA in voltage-controlled amplifiers, filters, and impedances are presented. A versatile family of voltage-controlled filter sections suitable for systematic design requirements is described. The total number of components used in these circuits is small, and the design equations and voltage-control characteristics are attractive. Limitations as well as practical considerations of OTA-based filters using commercially available bipolar OTAs are discussed. Applications of OTAs in continuous-time monolithic filters are considered.


title: Vision and driving

abstract: Driving is the primary means of personal travel in many countries and relies heavily on vision for its successful execution. Research over the past few decades has addressed the role of vision in driver safety (motor vehicle collision involvement) and in driver performance (both on-road and using interactive simulators in the laboratory). Here we critically review what is currently known about the role of various aspects of visual function in driving. We also discuss translational research issues on vision screening for licensure and re-licensure and rehabilitation of visually impaired persons who want to drive.


title: Phrase-based document similarity based on an index graph model

abstract: Document clustering techniques mostly rely on single term analysis of the document data set, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the document index graph, which indexes web documents based on phrases, rather than single terms only. The semi-structured web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The document index graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The similarity between documents is based on both single term weights and matching phrases weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, enhances web document clustering quality significantly.


title: A Real-Time Linux

abstract: The paper describes the design, implementation, experimental results, and possible applications of a real-time version of the Linux operating system. We have used the idea of virtual machines for running a standard time-sharing OS and a real-time executive on the same computer. Services provided by the real-time kernel are described. The comparison of our solution with other work on real-time UNIXes is also presented.


title: Face Recognition Methods&Applications

abstract: Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.


Workload-aware Parameter Selection and Performance Prediction for In-memory Databases


title: End-to-End Neural Entity Linking

abstract: Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.


title: Capsule Networks for Protein Structure Classification and Prediction

abstract: Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. The proposed Capsule Network trained on 2D and 3D structural encodings can successfully classify HRAS and KRAS structures. The Capsule Network can also classify a protein-based dataset derived from a PSI-BLAST search on sequences of KRAS and HRAS mutations. Our results show an accuracy improvement compared to traditional convolutional networks, while improving interpretability through visualization of activation vectors.


title: Learning Context for Text Categorization

abstract: This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from sports domain. Our experimental results indicate that the learned context greatly improves the categorization performance as compared to traditional categorization approaches.


title: Predicting the Parts Weight in Plastic Injection Molding Using Least Squares Support Vector Regression

abstract: To achieve the desired quality in plastic injection molding, advanced monitoring techniques are often recommended in the workshop. Unfortunately, the signal in plastic injection modeling process such as nozzle pressure that is relevant to part quality is not easy to obtain because of the cost of sensors. The sensor-based modeling idea is therefore adopted. In this paper, a new method for predicting the parts weight in plastic injection molding using least squares support vector regression (LS-SVR) is proposed, which is composed of two steps. The first step is to estimate the nozzle pressure with the hydraulic system pressure using an LS-SVR model. The second step is to predict product weight using the estimated nozzle pressure, which is done using another LS-SVR model. The experimental results show that the new method is very effective.


title: The epidemiology of rheumatoid arthritis in Indonesia.

abstract: The prevalence of RA was investigated as part of a house-to-house survey of musculoskeletal pain in a total population of 4683 rural and 1071 urban subjects 15 years of age and over in Central Java. Those identified as having peripheral joint pain of more than 6 weeks duration (82 men and 129 women) were examined by a rheumatologist (JD) and serology tests and X-rays arranged. The prevalence of definite RA by ARA criteria was 0.2% in rural and 0.3% in urban subjects. The severity of diagnosed cases was indicated by Steinbrocker's functional classification of grades 2 and 3 and erosive arthritis on hand X-rays of grades 2-4. The low prevalence rate of RA compared with that found in developed countries is due partly to the different age structure of the population and lower life expectancy. There is also evidence of high mortality from the disease. This is thought to be due to the deprived socio-economic circumstances, the intermittent use of high dose corticosteroids and the frequent presence of severe infections in these communities. These factors should be considered when assessing the low prevalence of RA in surveys in other developing countries.


LEAST SQUARES FITTING OF QUADRATIC CURVES AND SURFACES


title: Time-aware Personalized Hashtag Recommendation on Social Media

abstract: The task of recommending hashtags for microblogs has been received considerable attention in recent years, and many applications can reap enormous benefits from it. Various approaches have been proposed to study the problem from different aspects. However, the impacts of temporal and personal factors have rarely been considered in the existing methods. In this paper, we propose a novel method that extends the translation based model and incorporates the temporal and personal factors. To overcome the limitation of only being able to recommend hashtags that exist in the training data of the existing methods, the proposed method also incorporates extraction strategies into it. The results of experiments on the data collected from real world microblogging services by crawling demonstrate that the proposed method outperforms state-of-the-art methods that do not consider these aspects. The relative improvement of the proposed method over the method without considering these aspects is around 47.8% in F1-score.


title: Using Operations Research to Reduce Delays for Healthcare

abstract: The Institute of Medicine identified "timeliness" as one of six key "aims for improve- ment" in its most recent report on quality. Yet patient delays remain prevalent, resulting in dissatisfaction, adverse clinical consequences, and often, higher costs. This tutorial describes several areas in which patients routinely experience significant and potentially dangerous delays and presents operations research (OR) models that have been developed to help reduce these delays, often at little or no cost. Ialso describe the difficulties in developing and implementing models as well as the factors that increase the likelihood of success. Finally, Idiscuss the opportunities, large and small, for using OR methodologies to significantly impact practices and policies that will affect timely access to healthcare. Healthcare is riddled with delays. The Institute of Medicine (IOM), the major advisory body to the government and the private sector on health-related issues, identified timeliness as one of the six key aims for improvement in its major report on quality of healthcare in 2001. This was in response to the growing evidence of significant delays across the healthcare industry and the impact of these delays on clinical outcomes. Almost all of us have waited for days or weeks to get an appointment with a physician or schedule a procedure, and upon arrival we wait some more until being seen. In hospitals, it is not unusual to find patients backlogged in the emergency department waiting room and on stretchers in hallways waiting for beds. Inpatient falls, which often result in broken bones and serious complications, are known to be more prevalent when nurses are delayed in answering call buttons. Although waiting for service is inconvenient and annoying in other situations, in healthcare, delays can be dangerous or even deadly. This tutorial will describe some of the major types and sources of delay for healthcare, why they exist, and how operations research (OR) approaches can help provide insights and guidance for reducing delays, often with little or no increase in costs. In presenting specific examples, this tutorial will focus more on the contextual issues that are important to consider in developing, implementing, and interpreting the results of models than on the technical details of the models themselves. I will also discuss the regulatory, institutional, and systemic obstacles to using these methodologies to improve patient access to healthcare and look at efforts to reduce or eliminate these obstacles in the future. Finally, drawing from experiences across a range of applications, I will discuss the factors that appear to be most important in the adoption of OR approaches for reducing healthcare delays and how OR can play a role in influencing healthcare policy. 1. Three Sources of Dangerous Healthcare Delays


title: Obesity stigma in online news: A visual content analysis

abstract: This study conducted a content analysis to examine the types of images that accompany online news stories about obesity and to determine how obese people are portrayed in news photographs. Images were selected from news articles about obesity obtained from 5 major news Web sites, during a 2-week period in September of 2009. Images accompanying news stories about obesity (N = 549) were systematically coded. Of 441 individuals identified in news photographs, 65% were overweight/obese and 27% were nonoverweight. Overall, 72% of images that depicted an overweight or obese person were portrayed in a negative, stigmatizing manner. Overweight/obese individuals were significantly more likely to have their heads cut out of the photos, to be portrayed showing only their abdomens or lower bodies, and to be shown eating or drinking than were nonoverweight individuals. Overweight/obese individuals were significantly less likely to be shown fully clothed, wearing professional clothing, or exercising than were nonoverweight individuals. Obese individuals are frequently stigmatized in online news photographs; this phenomenon has important implications for public perceptions of obese persons and may reinforce pervasive prejudice and discrimination.


title: Prison Conditions , Capital Punishment , and Deterrence

abstract: Previous research has attempted to identify a deterrent effect of capital punishment. We argue that the quality of life in prison is likely to have a greater impact on criminal behavior than the death penalty. Using state-level panel data covering the period 1950--90, we demonstrate that the death rate among prisoners (the best available proxy for prison conditions) is negatively correlated with crime rates, consistent with deterrence. This finding is shown to be quite robust. In contrast, there is little systematic evidence that the execution rate influences crime rates in this time period. Copyright 2003, Oxford University Press.


title: Exploring Static and Live Digital Forensics: Methods, Practices and Tools

abstract: Analysis and examination of data is performed in digital forensics. Nowadays computer is the major source of communication which can also be used by the investigators to gain forensically relevant information. Forensic analysis can be done in static and live modes. Traditional approach pro- vides incomplete evidentiary data, while live analysis tools can provide the investigators a more accurate and consistent picture of the current and pre- viously running processes. Many important system related information present in volatile memory cannot be effectively recovered by using static analysis techniques. In this paper, we present a critical review of static and live analysis approaches and we evaluate the reliability of different tools and tech- niques used in static and live digital forensic analysis.


title: Application of Deep Learning Neural Network for Classification of TB lung CT Images based on Patches

abstract: In this work, convolutional neural network (CNN) is applied to classify the five types of Tuberculosis (TB) lung CT images. In doing so, each image has been segmented into rectangular patches with side width and high varying between 20 and 55 pixels, which are later normalised into 30x30 pixels. While classifying TB types, six instead of five categories are distinguished. Group 6 houses those patches/segments that are common to most of the other types, or background. In this way, while each 3D dataset only has less than 10% distinguishable volumes that are applied to perform the training, the rest remains part of the learning cycle by participating to the classification, leading to an automated process to differentiation of five types of TB. When tested against 300 datasets, the Kappa value is 0.2187, ranking 5 among 23 submissions. However, the accuracy value of ACC is 0.4067, the highest in this competition of classification of TB types.


title: TG-FTIR analysis of bio-oil and its pyrolysis/gasification property

abstract: The process of bio-oil pyrolysis/gasification and gas evolution characteristic was studied using a thermogravimetric analyzer coupled with Fourier transform infrared spectroscopy (TG-FTIR). Pyrolysis/gasification of bio-oil and its fractions were also performed in a fixed bed. As a result, the process of bio-oil pyrolysis/gasification can be divided into two stages. The first is volatilization and pyrolysis of the light compounds at low temperature and the second is cracking and polymerization of the heavy compounds at high temperature. The values of activation energy are 35–38 kJ/mol in the first stage and 15–22 kJ/mol in the second stage, respectively. With temperature increasing, the conversion of pyrolysis/gasification grows higher and the yield of synthesis gas (syngas) increases. However, the calorific value of the gas has an inverse correlation with the temperature. In comparison, the light fraction (LF) makes more contribution to the overall H2 release; while CO and CH4 are mainly generated from the heavy fraction (HF).


title: Learning-Aided Sub-Band Selection Algorithms for Spectrum Sensing in Wide-Band Cognitive Radios

abstract: We propose wide-band spectrum sensing scheduling solutions for cognitive radios that are equipped with reconfigurable RF front-ends. The wide frequency spectrum of interest is segmented into frequency sub-bands due to software and hardware limitations. These sub-bands can be non-contiguous, and each may contain an arbitrary number of channels from an arbitrary number of systems. It is assumed that the CR can only sense one sub-band at a time. Three sub-band selection policies are proposed to find spectrum opportunities taking into account realistic hardware reconfiguration energy consumptions and time delays. Two of the proposed policies rely on the individual channel Markov properties and the sub-band Markov properties, respectively. Although these two policies may achieve good performance, they rely on complete knowledge of RF environment dynamics and thus may become computationally demanding. The third sub-band selection policy based on Q-learning is proposed to circumvent this. Performance of the three policies are compared and discussed against a performance upper-bound of the optimal solution to the corresponding partially observable Markov decision process formulation. The suitability of the Q-learning technique is validated by showing that it achieves good performance through numerical results in both simulated and real measured RF environments.


title: Prenatal diagnosis ofde novo isochromosome 13q associated with microcephaly, alobar holoprosencephaly and cebocephaly in a fetus

abstract: We report on the prenatal diagnosis, genetic studies, and pathology of a case with de novo isochromosome 13q. A 31-year-old primigravida was referred for genetic counselling at 26 weeks' gestation due to the sonographic findings of intrauterine growth retardation and microcephaly. Level II ultrasonograms further demonstrated alobar holoprosencephaly, hypotelorism, polydactyly, a ventricular septal defect, and a single nostril. A diagnosis of cebocephaly was made. Genetic amniocentesis and cord blood sampling revealed translocation trisomy 13 with a de novo t(13q13q) rearrangement. Chromosomal analysis using G- and C-banding techniques and fluorescence in situ hybridization (FISH) showed an apparent monocentric isochromosome. Molecular analyses using polymorphic molecular markers showed that the rearrangement was consistent with an isochromosome of maternal chromosome 13q[46,XX,i(13)(q10)]. Necropsy confirmed cebocephaly and the prenatally detected anomalies.


title: Precog: prefetching for image recognition applications at the edge

abstract: Image recognition applications are on the rise. Increasingly, applications on edge devices such as mobile smartphones, drones and cars, are relying on recognition techniques to provide interactive and intelligent functionality. Given the complexity of these techniques, and resource constrained nature of edge devices, applications rely on offloading compute intensive recognition tasks to the cloud. This has also lead to the rise of cloud-based recognition services. This involves sending captured images to remote servers across the Internet, which leads to slower responses. With the rising numbers of edge devices, both, the network and such centralized cloud-based solutions, are likely to be under stress, and lead to further slower responses. To reduce the recognition latency, and provide better scalability to the cloud-based solutions, we propose Precog. Precog employs selective computation on the devices to reduce the need to offload images to the cloud. In coordination with edge servers, it uses prediction to prefetch parts of the trained classifiers used for recognition onto the devices, and uses these smaller models to accelerate recognition on devices. Our evaluation shows that Precog can reduce latency by up to 5×, better utilize edge and cloud resources and also increase accuracy. We believe that Precog is the first system to use devices and edge servers collaboratively to enable prefetching and caching on the devices, and drive down recognition latency for mobile applications.


Modified image segmentation method based on region growing and region merging.


title: RRT-connect: An efficient approach to single-query path planning

abstract: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two rapidly-exploring random trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through, the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.


title: An investigation of sparse tensor formats for tensor libraries

abstract: Tensors provide a generalized structure to store ::: arbitrary indexable data, which is applicable in fields such as ::: chemometrics, physics simulations, signal processing and lies at ::: the heart of machine learning. Many naturally occurring tensors are ::: considered sparse as they contain mostly zero values. As with ::: sparse matrices, various techniques can be employed to more ::: efficiently store and compute on these sparse tensors. This work ::: explores several sparse tensor formats while ultimately evaluating ::: two implementations; one based on explicitly storing coordinates ::: and one that compresses these coordinates. The two formats, ::: Coordinate and CSF2, were evaluated by comparing their execution ::: time of tensor-matrix products and the MTTKRP operation on several ::: datasets. We find that the Coordinate format is superior for ::: uniformly distributed sparse tensors or when used in computation ::: that emits a sparse tensor via a mode dependent operation. In all ::: other considered cases for large sparse tensors, the storage ::: savings of the compressed format provide the best ::: results.


title: The benefits of being bilingual: Working memory in bilingual Turkish–Dutch children

abstract: Whether bilingual children outperform monolingual children on visuospatial and verbal working memory tests was investigated. In addition, relations among bilingual proficiency, language use at home, and working memory were explored. The bilingual Turkish–Dutch children (n = 68) in this study were raised in families with lower socioeconomic status (SES) and had smaller Dutch vocabularies than Dutch monolingual controls (n = 52). Having these characteristics, they are part of an under-researched bilingual population. It was found that the bilingual Turkish–Dutch children showed cognitive gains in visuospatial and verbal working memory tests when SES and vocabulary were controlled, in particular on tests that require processing and not merely storage. These findings converge with recent studies that have revealed bilingual cognitive advantages beyond inhibition, and they support the hypothesis that experience with dual language management influences the central executive control system that regulates processing across a wide range of task demands. Furthermore, the results show that bilingual cognitive advantages are found in socioeconomically disadvantaged bilingual populations and suggest that benefits to executive control are moderated by bilingual proficiency.


title: Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application

abstract: The use of artificial neural networks (ANNs) in problems related to water resources has received steadily increasing interest over the last decade or so. The related method of the self-organizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. Consequently, the present paper aims firstly to explain the algorithm and secondly, to review published applications with main emphasis on water resources problems in order to assess how well SOM can be used to solve a particular problem. It is concluded that SOM is a promising technique suitable to investigate, model, and control many types of water resources processes and systems. Unsupervised learning methods have not yet been tested fully in a comprehensive way within, for example water resources engineering. However, over the years, SOM has displayed a steady increase in the number of applications in water resources due to the robustness of the method.


title: Adversarial Semi-Supervised Audio Source Separation Applied to Singing Voice Extraction

abstract: The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With only few datasets available, often extensive data augmentation is used to combat overfitting. Mixing random tracks, however, can even reduce separation performance as instruments in real music are strongly correlated. The key concept in our approach is that source estimates of an optimal separator should be indistinguishable from real source signals. Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples. This way, we can also use unpaired source and mixture recordings without the drawbacks of creating unrealistic music mixtures. Our framework is widely applicable as it does not assume a specific network architecture or number of sources. To our knowledge, this is the first adoption of adversarial training for music source separation. In a prototype experiment for singing voice separation, separation performance increases with our approach compared to purely supervised training.


8 Clinical Decision Support Systems


abstract: The Handbook of Information and Communication Security covers some of the latest advances in fundamentals, cryptography, intrusion detection, access control, networking (including extensive sections on optics and wireless systems), software, forensics, and legal issues. The editors intention, with respect to the presentation and sequencing of the chapters, was to create a reasonably natural flow between the various sub-topics. This handbook will be useful to researchers and graduate students in academia, as well as being an invaluable resource for university instructors who are searching for new material to cover in their security courses. In addition, the topics in this volume are highly relevant to the real world practice of information security, which should make this book a valuable resource for working IT professionals. This handbook will be a valuable resource for a diverse audience for many years to come.


title: Arousal facilitates collision avoidance mediated by a looming sensitive visual neuron in a flying locust

abstract: Locusts have two large collision-detecting neurons, the descending contralateral movement detectors (DCMDs) that signal object approach and trigger evasive glides during flight. We sought to investigate whether vision for action, when the locust is in an aroused state rather than a passive viewer, significantly alters visual processing in this collision-detecting pathway. To do this we used two different approaches to determine how the arousal state of a locust affects the prolonged periods of high-frequency spikes typical of the DCMD response to approaching objects that trigger evasive glides. First, we manipulated arousal state in the locust by applying a brief mechanical stimulation to the hind leg; this type of change of state occurs when gregarious locusts accumulate in high-density swarms. Second, we examined DCMD responses during flight because flight produces a heightened physiological state of arousal in locusts. When arousal was induced by either method we found that the DCMD response recovered from a previously habituated state; that it followed object motion throughout approach; and—most important—that it was significantly more likely to generate the maintained spike frequencies capable of evoking gliding dives even with extremely short intervals (1.8 s) between approaches. Overall, tethered flying locusts responded to 41% of simulated approaching objects (sets of 6 with 1.8 s ISI). When we injected epinastine, the neuronal octopamine receptor antagonist, into the hemolymph responsiveness declined to 12%, suggesting that octopamine plays a significant role in maintaining responsiveness of the DCMD and the locust to visual stimuli during flight.


Varying Linguistic Purposes of Emoji in (Twitter) Context


title: Scarlett: coping with skewed content popularity in mapreduce clusters

abstract: To improve data availability and resilience MapReduce frameworks use file systems that replicate data uniformly. However, analysis of job logs from a large production cluster shows wide disparity in data popularity. Machines and racks storing popular content become bottlenecks; thereby increasing the completion times of jobs accessing this data even when there are machines with spare cycles in the cluster. To address this problem, we present Scarlett, a system that replicates blocks based on their popularity. By accurately predicting file popularity and working within hard bounds on additional storage, Scarlett causes minimal interference to running jobs. Trace driven simulations and experiments in two popular MapReduce frameworks (Hadoop, Dryad) show that Scarlett effectively alleviates hotspots and can speed up jobs by 20.2%.


title: Query-Efficient Imitation Learning for End-to-End Autonomous Driving

abstract: One way to approach end-to-end autonomous driving is to learn a policy function that maps from a sensory input, such as an image frame from a front-facing camera, to a driving action, by imitating an expert driver, or a reference policy. This can be done by supervised learning, where a policy function is tuned to minimize the difference between the predicted and ground-truth actions. A policy function trained in this way however is known to suffer from unexpected behaviours due to the mismatch between the states reachable by the reference policy and trained policy functions. More advanced algorithms for imitation learning, such as DAgger, addresses this issue by iteratively collecting training examples from both reference and trained policies. These algorithms often requires a large number of queries to a reference policy, which is undesirable as the reference policy is often expensive. In this paper, we propose an extension of the DAgger, called SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous driving. We evaluate the proposed SafeDAgger in a car racing simulator and show that it indeed requires less queries to a reference policy. We observe a significant speed up in convergence, which we conjecture to be due to the effect of automated curriculum learning.


title: An Analysis of Persistent Memory Use with WHISPER

abstract: Emerging non-volatile memory (NVM) technologies promise durability with read and write latencies comparable to volatile memory (DRAM). We define Persistent Memory (PM) as NVM accessed with byte addressability at low latency via normal memory instructions. Persistent-memory applications ensure the consistency of persistent data by inserting ordering points between writes to PM allowing the construction of higher-level transaction mechanisms. An epoch is a set of writes to PM between ordering points. To put systems research in PM on a firmer footing, we developed and analyzed a PM benchmark suite called WHISPER (Wisconsin-HP Labs Suite for Persistence) that comprises ten PM applications we gathered to cover all current interfaces to PM. A quantitative analysis reveals several insights: (a) only 4% of writes in PM-aware applications are to PM and the rest are to volatile memory, (b) software transactions are often implemented with 5 to 50 ordering points (c) 75% of epochs update exactly one 64B cache line, (d) 80% of epochs from the same thread depend on previous epochs from the same thread, while few epochs depend on epochs from other threads. Based on our analysis, we propose the Hands-off Persistence System (HOPS) to track updates to PM in hardware. Current hardware design requires applications to force data to PM as each epoch ends. HOPS provides high-level ISA primitives for applications to express durability and ordering constraints separately and enforces them automatically, while achieving 24.3% better performance over current approaches to persistence.


title: Animation of dynamic legged locomotion

abstract: This paper is about the use of control algorithms to animate dynamic legged locomotion. Control could free the animator from specifying the details of joint and limb motion while producing both physically realistic and natural looking results. We implemented computer animations of a biped robot, a quadruped robot, and a kangaroo. Each creature was modeled as a linked set of rigid bodies with compliant actuators at its joints. Control algorithms regulated the running speed, organized use of the legs, and maintained balance. All motions were generated by numerically integrating equations of motion derived from the physical models. The resulting behavior included running at various speeds, traveling with several gaits (run, trot, bound, gallop, and hop), jumping, and traversing simple paths. Whereas the use of control permitted a variety of physically realistic animated behavior to be generated with limited human intervention, the process of designing the control algorithms was not automated: the algorithms were "tweaked" and adjusted for each new creature.


title: Avoiding plagiarism: guidance for nursing students.

abstract: The pressures of study, diversity of source materials, past assumptions relating to good writing practice, ambiguous writing guidance on best practice and students' insecurity about their reasoning ability, can lead to plagiarism. With the use of source checking software, there is an increased chance that plagiarised work will be identified and investigated, and penalties given. In extreme cases, plagiarised work may be reported to the Nursing and Midwifery Council and professional as well as academic penalties may apply. This article provides information on how students can avoid plagiarism when preparing their coursework for submission.


title: The Drug Discovery and Development Industry in India—Two Decades of Proprietary Small‐Molecule R&D

abstract: This review provides a comprehensive survey of proprietary drug discovery and development efforts performed by Indian companies between 1994 and mid-2016. It is based on the identification and detailed analysis of pharmaceutical, biotechnology, and contract research companies active in proprietary new chemical entity (NCE) research and development (R&D) in India. Information on preclinical and clinical development compounds was collected by company, therapeutic indication, mode of action, target class, and development status. The analysis focuses on the overall pipeline and its evolution over two decades, contributions by type of company, therapeutic focus, attrition rates, and contribution to Western pharmaceutical pipelines through licensing agreements. This comprehensive analysis is the first of its kind, and, in our view, represents a significant contribution to the understanding of the current state of the drug discovery and development industry in India.


title: Enhancing and Measuring Consumers' Motivation, Opportunity, and Ability to Process Brand Information From Ads

abstract: Considerable research suggests that advertising executional cues can influence communication effectiveness. Related research indicates that communication effectiveness is in part driven by consumers' motivation, opportunity, and ability (MOA) to process brand information from an ad. However, little research has explicitly linked executional cues to communication effectiveness via their impact on MOA and levels of processing. The authors present a framework that explicitly provides such a linkage. The framework highlights the mediational role of MOA in the relationships among executional cues and communication outcomes, it also provides a theoretical account that links apparently disparate cues to their common effects on motivation, opportunity, or ability. The framework is complemented by a critical review of current measures of MOA and proposed measures based on the review. Research issues raised by the framework and the proposed measures are discussed.


title: A Study on User Adoption of Cloud Storage Service in China: A Revised Unified theory of Acceptance and Use of Technology Model

abstract: Cloud services have been basically popularized in Europe and US, but in China especially in enterprise market the promotion of cloud services is quite difficult, it is necessary to explore the influencing factors of cloud services adoption. Combined with the unique characteristics of cloud storage service, we revised the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluating the adoption of cloud storage service in China. Results of empirical study showed that perceived risk, personal innovativeness, performance expectancy, effort expectancy and social influence had significant effects on adoption intention, and adoption intention and facilitating conditions had significant effects on adoption behavior.


title: DES, AES and Blowfish: Symmetric Key Cryptography Algorithms Simulation Based Performance Analysis

abstract: Security is the most challenging aspects in the internet and network applications. Internet and networks applications are growing very fast, so the importance and the value of the exchanged data over the internet or other media types are increasing. Hence the search for the best solution to offer the necessary protection against the data intruders' attacks along with providing these services in time is one of the most interesting subjects in the security related communities. Cryptography is the one of the main categories of computer security that converts information from its normal form into an unreadable form. The two main characteristics that identify and differentiate one encryption algorithm from another are its ability to secure the protected data against attacks and its speed and efficiency in doing so. This paper provides a fair comparison between three most common symmetric key cryptography algorithms: DES, AES, and Blowfish. Since main concern here is the performance of algorithms under different settings, the presented comparison takes into consideration the behavior and the performance of the algorithm when different data loads are used. The comparison is made on the basis of these parameters: speed, block size, and key size. Simulation program is implemented using Java programming.


title: Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.

abstract: The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R(2)=0.92. Finally, the model was evaluated for application to the semi-targeted identification of pharmaceutical residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83±4% and 73±5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.


Duchenne vs . Non-Duchenne Smiles


title: Fast Similarity Search for Learned Metrics

abstract: We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality makes it infeasible to learn an explicit transformation over the feature dimensions. We demonstrate the approach applied to a variety of image data sets, as well as a systems data set. The learned metrics improve accuracy relative to commonly used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.


title: Abandoned Object Detection Using Pixel-Based Finite State Machine and Single Shot Multibox Detector

abstract: This paper proposes a robust, scalable framework for automatic detection of abandoned, stationary objects in real time surveillance videos that can pose a security threat. We use the sViBe background modeling method to generate a long-term and a short-term background model to extract foreground objects. Subsequently, a pixel-based FSM detects stationary candidate objects based on the temporal transition of code patterns. In order to classify the stationary candidate objects, we use deep learning method (SSD: Single Shot MultiBox Detector) to detect person and some suspected type of objects which include backpack, handbag. In order to suppress any false alarm, we remove other stationary candidate objects other than the suspected stationary objects. After stationary object detection, we also check if there is no person near by the suspected detected objects for a particular time. We tested the system on four standard public datasets. The results show that our method outperforms the performance of existing results while also being robust to temporary occlusions and illumination changes.


title: Multi-User Broadcast Authentication in Wireless Sensor Networks

abstract: Broadcast authentication is a critical security service in wireless sensor networks (WSNs), as it allows the mobile users of WSNs to broadcast messages to multiple sensor nodes in a secure way. Although symmetric-key- based solutions such as muTESLA and multilevel muTESLA have been proposed, they all suffer from severe energy- depletion attacks resulted from the nature of delayed message authentication. This paper presents several efficient public-key-based schemes to achieve immediate broadcast authentication and thus avoid the security vulnerability intrinsic to muTESLA-like schemes. Our schemes are built upon the unique integration of several cryptographic techniques, including the Bloom filter, the partial message recovery signature scheme and the Merkle hash tree. We prove the effectiveness and efficiency of the proposed schemes by a comprehensive quantitative analysis of their energy consumption in both computation and communication.


title: Pulmonary Vasculitis: Clinical Presentation, Differential Diagnosis, and Management

abstract: This review focuses on vasculitides with prominent pulmonary manifestations and discusses key contributions from the recent literature. Pulmonary vasculitis should be considered when clinical findings include alveolar hemorrhage, nodular and cavitary lung disease, airway stenosis, pulmonary artery aneurysms, or pulmonary artery stenosis. The differential diagnostic considerations for common clinical presentations of vasculitis in the lung are important, and several recent additions are discussed. Treatment for established pulmonary vasculitis is effective and has decreased the morbidity and mortality associated with these diseases while introducing an increased risk of infectious complications. Advances in immunosuppressive therapy have improved treatment of refractory disease and are likely to change initial treatment strategies in the future.


title: The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students

abstract: This study examined the relationship between three of the ''Big Five'' traits (neuroticism, extraversion, and openness), self-esteem, loneliness and narcissism, and Facebook use. Participants were 393 first year undergraduate psychology students from a medium-sized Australian university who completed an online questionnaire. Negative binomial regression models showed that students with higher openness levels reported spending more time on Facebook and having more friends on Facebook. Interestingly, students with higher levels of loneliness reported having more Facebook friends. Extraversion, neuroticism, self-esteem and narcissism did not have significant associations with Facebook use. It was concluded that students who are high in openness use Facebook to connect with others in order to discuss a wide range of interests, whereas students who are high in loneliness use the site to compensate for their lack of offline relationships.


title: Mining Log Files for Data-Driven System Management

abstract: With advancement in science and technology, computing systems are becoming increasingly more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming increasingly more difficult to monitor, manage and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. This has been well known and experienced as a cumber-some, labor intensive, and error prone process. In addition, this process is difficult to keep up with the rapidly changing environments. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing systems.A popular approach to system management is based on analyzing system log files. However, some new aspects of the log files have been less emphasized in existing methods from data mining and machine learning community. The various formats and relatively short text messages of log files, and temporal characteristics in data representation pose new challenges. In this paper, we will describe our research efforts on mining system log files for automatic management. In particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.


title: Floudas. A new robust optimization approach for scheduling under uncertainty: I. bounded uncertainty

abstract: The problem of scheduling under bounded uncertainty is addressed. We propose a novel robust optimization methodology, which when applied to mixed-integer linear programming (MILP) problems produces “robust” solutions which are in a sense immune against bounded uncertainty. Both the coefficients in the objective function, the left-hand-side parameters and the right-hand-side parameters of the inequalitie s are considered. Robust optimization techniques are developed for two types of uncertain data: bounded uncertainty and bounded and symmetric uncertainty. By introducing a small number of auxiliary variables and constraints, a deterministic robust counterpart problem is formulated to determine the optimal solution given the (relative) magnitude of uncertain data, feasibility tolerance, and “reliability level” when a probabilis tic measurement is applied. The robust optimization approach is then applied to the scheduling under uncertainty problem. Based on a novel and effective continuous-time short-term scheduling model proposed by Floudas and coworkers [Ind. Eng. Chem. Res. 37 (1998a) 4341; Ind. Eng. Chem. Res. 37 (1998b) 4360; Ind. Eng. Chem. Res. 38 (1999) 3446; Comp. Chem. Engng. 25 (2001) 665; Ind. Eng. Chem. Res. 41 (2002) 3884; Ind. Eng. Chem. Res. (2003)], three of the most common sources of bounded uncertainty in scheduling problems are addressed, namely processing times of tasks, market demands for products, and prices of products and raw materials. Computational results on several small examples and an industrial case study are presented to demonstrate the effectiveness of the proposed approach. © 2003 Elsevier Ltd. All rights reserved.


title: High-Resolution Global Maps of 21st-Century Forest Cover Change

abstract: Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.


Ontology Modeling for a Drought Management Information System


title: Movie recommender system for profit maximization

abstract: Traditional recommender systems minimize prediction error with respect to users' choices. Recent studies have shown that recommender systems have a positive effect on the provider's revenue. In this paper we show that by providing a set of recommendations different than the one perceived best according to user acceptance rate, the recommendation system can further increase the business' utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content. We performed a large body of experiments comparing a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible reduce in satisfaction by providing the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Differences in user satisfaction between the lists is negligible, and not statistically significant. We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.


title: LAWBO: a smart lawyer chatbot

abstract: When it comes to conversing and understanding like humans, one of the most intricate domains for chatbots is the judicial system. One needs to really pour into volumes of legal books and judgment papers to analyze and investigate a case. "Justice delayed is justice denied!". With time being a big constraint, LAWBO could draw parallelism between cases and guide the lawyers by giving relevant information for the given queries. We use a combination of heuristics applied on data extracted from Supreme Court judgments using in-house developed, state-of-the-art parsers, dynamic memory networks (DMN) and GloVe word representation for Natural Language Processing (NLP).


title: Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition

abstract: A new progressive lossless 3D triangular mesh encoder is proposed in this work, which can encode any 3D triangular mesh with an arbitrary topological structure. Given a mesh, the quantized 3D vertices are first partitioned into an octree (OT) structure, which is then traversed from the root and gradually to the leaves. During the traversal, each 3D cell in the tree front is subdivided into eight childcells. For each cell subdivision, both local geometry and connectivity changes are encoded, where the connectivity coding is guided by the geometry coding. Furthermore, prioritized cell subdivision is performed in the tree front to provide better rate-distortion (RD) performance. Experiments show that the proposed mesh coder outperforms the kd-tree algorithm in both geometry and connectivity coding efficiency. For the geometry coding part, the range of improvement is typically around 10%~20%, but may go up to 50%~60% for meshes with highly regular geometry data and/or tight clustering of vertices.


title: A vector quantization approach to speaker recognition

abstract: In this study a vector quantization (VQ) codebook was used as an efficient means of characterizing the short-time spectral features of a speaker. A set of such codebooks were then used to recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) classification rule. A series of speaker recognition experiments was performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification accuracy was achieved. The effects, on performance, of different system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions were also studied in detail.


Visit to national cash register corporation of Dayton Ohio Universal Turing Machine


title: 1 “Relationships Between Marketing & Sales: The Role of Power and

abstract: Acidification of impure alkali or ammonium tungstate or molybdate solutions prior to electrodialysis to produce tungstic acid or molybdic acid is carried out by the addition of pure tungstic acid or molybdic acid as the salts of tungsten isopolyacids or molybdenum isopolyacids, respectively. This is less expensive than using pure HCl or H2SO4 and prevents contamination with chloride or sulfate ions. The pure tungstic acid or molybdic acid is isolated from the product.


title: A Wideband Stacked Offset Microstrip Antenna With Improved Gain and Low Cross Polarization

abstract: A broadband printed microstrip antenna having cross polarization level >; 15 dB with improved gain in the entire frequency band is presented. Principle of stacking is implemented on a strip loaded slotted broadband patch antenna for enhancing the gain without affecting the broadband impedance matching characteristics and offsetting the position of the upper patch excites a lower resonance which enhances the bandwidth further. The antenna has a dimension of 42 × 55 × 4.8 mm3 when printed on a substrate of dielectric constant 4.2 and has a 2:1 VSWR bandwidth of 34.9%. The antenna exhibits a peak gain of 8.07 dBi and a good front to back ratio better than 12 dB is observed throughout the entire operating band. Simulated and experimental reflection characteristics of the antenna with and without stacking along with offset variation studies, radiation patterns and gain of the final antenna are presented.


title: Printed circular disc compact planar antenna for UWB applications

abstract: A printed planar antenna having a compact dimension of 27×23.5 mm2, fed by a microstrip line is proposed in this article. The proposed compact antenna consists of a circular disc radiator and partial ground plane with a rectangular slot on its' top edge. Experimental results show that the antenna has an ?10 dB impedance bandwidth ranges from 3.3 GHz to 20 GHz (193%) which almost covers the entire UWB band. A symmetric and stable radiation pattern with an average realized gain of 5.11 dBi makes the proposed antenna suitable for using in different UWB applications. Details of the proposed antenna design are presented and discussed.