Query: Secu Wear: An Open Source, Multi-component Hardware/Software Platform for Exploring Wearable Security

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Applications, challenges, and prospective in emerging body area networking technologies <=SEP=> Advances in wireless technology and supporting infrastructure provide unprecedented opportunity for ubiquitous real-time healthcare and fitness monitoring without constraining the activities of the user. Wirelessly connected miniaturized sensors and actuators placed in, on, and around the body form a body area network for continuous, automated, and unobtrusive monitoring of physiological signs to support medical, lifestyle and entertainment applications. BAN technology is in the early stage of development, and several research challenges have to be overcome for it to be widely accepted. In this article we study the core set of application, functional, and technical requirements of the BAN. We also discuss fundamental research challenges such as scalability (in terms of data rate, power consumption, and duty cycle), antenna design, interference mitigation, coexistence, QoS, reliability, security, privacy, and energy efficiency. Several candidate technologies poised to address the emerging BAN market are evaluated, and their merits and demerits are highlighted. A brief overview of standardization activities relevant to BANs is also presented.

Addressing Security and Privacy Risks in Mobile Applications <=SEP=> Applications for mobile platforms are being developed at a tremendous rate, but often without proper security implementation. Insecure mobile applications can cause serious information security and data privacy issues and can have severe repercussions on users and organizations alike.

Security Analysis of Wearable Fitness Devices ( Fitbit ) <=SEP=> This report describes an analysis of the Fitbit Flex ecosystem. Our objectives are to describe (1) the data Fitbit collects from its users, (2) the data Fitbit provides to its users, and (3) methods of recovering data not made available to device owners. Our analysis covers four distinct attack vectors. First, we analyze the security and privacy properties of the Fitbit device itself. Next, we observe the Bluetooth traffic sent between the Fitbit device and a smartphone or personal computer during synchronization. Third, we analyze the security of the Fitbit Android app. Finally, we study the security properties of the network traffic between the Fitbit smartphone or computer application and the Fitbit web service. We provide evidence that Fitbit unnecessarily obtains information about nearby Flex devices under certain circumstances. We further show that Fitbit does not provide device owners with all of the data collected. In fact, we find evidence of per-minute activity data that is sent to the Fitbit web service but not provided to the owner. We also discovered that MAC addresses on Fitbit devices are never changed, enabling usercorrelation attacks. BTLE credentials are also exposed on the network during device pairing over TLS, which might be intercepted by MITM attacks. Finally, we demonstrate that actual user activity data is authenticated and not provided in plaintext on an end-to-end basis from the device to the Fitbit web service.

Versatile low power media access for wireless sensor networks <=SEP=> We propose <i>B-MAC</i>, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, <i>B-MAC</i> employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. <i>B-MAC</i> supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing <i>B-MAC</i>'s parameters and predict the behavior of sensor network applications. By comparing <i>B-MAC</i> to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of <i>B-MAC</i> over a wide range of network conditions. We show that <i>B-MAC</i>'s flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.

Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications <=SEP=> The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.

New Beam Tracking Technique for Millimeter Wave-band Communications <=SEP=> In this paper, we propose an efficient beam tracking method for mobility scenario in mmWave-band communications. When the position of the mobile changes in mobility scenario , the base-station needs to perform beam training frequently to t rack the time-varying channel, thereby spending significant res ources for training beams. In order to reduce the training overhead, we propose a new beam training approach called “beam tracking” which exploits the continuous nature of time varying angle of departure (AoD) for beam selection. We show that transmissi on of only two training beams is enough to track the time-varying AoD at good accuracy. We derive the optimal selection of beam pai r which minimizes Cramer-Rao Lower Bound (CRLB) for AoD estimation averaged over statistical distribution of the AoD. Our numerical results demonstrate that the proposed beam track ing scheme produces better AoD estimation than the conventiona l beam training protocol with less training overhead. I. I NTRODUCTION The next generation wireless communication systems aim to achieve Giga bit/s throughput to support high speed multimedia data service [1], [2]. Since there exist ample amount of unutilized frequency spectrum in millimeter Wave (mmWave) band (30 GHz-300 GHz), wireless communication over mmWave band is considered as a promising solution to achieve significant leap in spectral efficiency [3]. However , one major limitation of mmWave communications is significant free space path loss, which causes large attenuation of sign al power at the receiver. Furthermore, the overall path loss ge t worse when the signal goes through obstacles, rain, foliage , and any blockage to mobile devices. Recently, active resear ch on mmWave communication has been conducted in order to overcome these limitations [1]–[5]. In mmWave band, many antenna elements can be integrated in a small form factor and hence, we can employ high directional beamforming using a large number of antennas to compensate high path loss. In order to perform high directional beamforming, it is necessary to estimate channels for all transmitter and rece iver antenna pair. While this step requires high computational c omplexity due to large number of antennas, channel estimation can be performed efficiently by using the angular domain representation of channels [6]. In angular domain, only a fe w angular bins contain the most of the received energy. Hence, if we identify the dominant angular bins (which correspond to the angle of arrival (AoA) and the angle of departure (AoD)), we can obtain the channel estimate without incurrin g computational complexity. Basically, both AoD and AoA can be estimated using so called “beam training” procedure. The base-station sends t he training beams at the designated direction and the receiver estimates the AoD/AoA based on the received signals. Widely used beam training method (called “beam cycling method”) is to allow the base-station to transmit N training beams one by one at the equally spaced directions. However, to ensure good estimate of AoD/AoA, N should be large, leading to significant training overhead. This problem becomes even more serious for the mobility scenario in mmWave communications. Since the location of mobiles keeps changing, th e base-station should transmit training beams more frequent ly to update AoD/AoA estimates, causing significant drop in data throughput [7]. Recently, several adaptive beam train ing schemes have been proposed to improve the conventional beam training method [10]–[14]. In this paper, we introduce a novel beam training method for mobility scenario in mmWave communications. Our idea is based on the observation that for mobility scenario, the AoD of the particular user does not change drastically so that continuous nature of the AoD change can be accounted to improve the efficacy of the beam training. Since this approach exploits temporal dynamics of AoD, we call such beam training scheme “beam tracking”. While the convenional method makes no assumption on the state of AoD, we use statistical distribution of the AoD given the previousl y state of AoD. Using the probabilistic model on AoD change, we derive effective beam tracking strategy which employs transmission of two training beams from the base-station. Optimal placement of two training beams in angular domain is sought by minimizing (the lower bound of) variance of the estimation error for AoD. As a result, we choose the best beam p ir from the beam codebook for the given prior knowledge on AoD. Our simulation results show that the proposed beam tracking method offers the channel estimation performance comparable to the conventional beam training methods with significantly reduced training overhead. The rest of this paper is organized as follows; In section II, we introduce the system and channel models for mmWave communications and in section III, we describe the proposed b am tracking method and the simulation results are provide d in section IV. Finally, the paper is concluded in section V. II. SYSTEM MODEL In this section, we describe the system model for mmWave communications. First, we describe the angular domain repr esentation of the mmWave channel and then we introduce the procedure for beam training and channel estimation. A. Channel Model Consider single user mmWave MIMO systems with the base-station withNb antennas and the mobile with Nm antennas. The MIMO channel model with L paths at timet is described by [8]

Curvature-controlled curve editing using piecewise clothoid curves <=SEP=> Two-dimensional curves are conventionally designed using splines or Bézier curves. Although formally they are C or higher, the variation of the curvature of (piecewise) polynomial curves is difficult to control; in some cases it is practically impossible to obtain the desired curvature. As an alternative we propose piecewise clothoid curves (PCCs). We show that from the design point of view they have many advantages: control points are interpolated, curvature extrema lie in the control points, and adding control points does not change the curve. We present a fast localized clothoid interpolation algorithm that can also be used for curvature smoothing, for curve fitting, for curvature blending, and even for directly editing the curvature. We give a physical interpretation of variational curvature minimization, from which we derive our scheme. Finally, we demonstrate the achievable quality with a range of examples. & 2013 Elsevier Ltd. All rights reserved.

Linking received packet to the transmitter through physical-fingerprinting of controller area network <=SEP=> The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.

Multi-objective model predictive control for grid-tied 15-level packed U cells inverter <=SEP=> This paper presents a multi-objective Model Predictive Control (MPC) for grid-tied 4-cell 15-level Packed U Cells (PUC) inverter. This challenging topology is characterized by high power quality with reduced number of components compared to conventional multilevel inverters. Compared to traditional PI controller, MPC is attracting more interest due to its good dynamic response and high accuracy of reference tracking, through the minimization of a flexible user-defined cost function. For the presented PUC topology, the grid current should be jointly controlled with the capacitors' voltages for ensuring proper operation of the inverter, which leads to an additional requirement of pre-charge circuits for the capacitors in case of using PI current controller (or using additional PI controllers). The proposed MPC achieves grid current injection, low current THD, unity PF, while balancing the capacitors' voltages.

Test anxiety and direction of attention. <=SEP=> The literature reviewed suggests an attentional interpretation, of the adverse effects which test anxiety has on task performance. During task performance the highly test-anxious person divides his attention between self-relevant and task-relevant variables, in contrast to the low-test-anxious person who focuses his attention more fully on the task. This interpretation was supported by literature from diverse areas suggesting that (a) highly anxious persons are generally more self-preoccupied than are people low in anxiety; (b) the selffocusing tendencies of highly test-anxious persons are activated in testing situations; (c) those situational conditions in which the greatest performance differences occur are ones which elicit the self-focusing tendencies of highly test-anxious subjects, and the task-focusing tendencies of low-anxious subjects; (d) research examining the relationship between anxiety and task variables suggests that anxiety reduces the range of task cues utilized in performance; (e) "worry," an attentionally demanding cognitive activity, is more debilitating of task performance than is autonomic arousal. Treatment and research implications of this attentional interpretation of test anxiety are briefly discussed.

GPS positioning in a multipath environment <=SEP=> We address the problem of GPS signal delay estimation in a multipath environment with a low-complexity constraint. After recalling the usual early–late estimator and its bias in a multipath propagation context, we study the maximum-likelihood estimator (MLE) based on a signal model including the parametric contribution of reflected components. It results in an efficient algorithm using the existing architecture, which is also very simple and cheap to implement. Simulations show that the results of the proposed algorithm, in a multipath environment, are similar to these of the early–late in a single-path environment. The performance are further characterized, for both MLEs (based on the single-path and multipath propagation) in terms of bias and standard deviation. The expressions of the corresponding Cramér—Rao (CR) bounds are derived in both cases to show the good performance of the estimators when unbiased.

Counter-forensics in machine learning based forgery detection <=SEP=> With the powerful image editing tools available today, it is very easy to create forgeries without leaving visible traces. Boundaries between host image and forgery can be concealed, illumination changed, and so on, in a naive form of counter-forensics. For this reason, most modern techniques for forgery detection rely on the statistical distribution of micro-patterns, enhanced through high-level filtering, and summarized in some image descriptor used for the final classification. In this work we propose a strategy to modify the forged image at the level of micro-patterns to fool a state-of-the-art forgery detector. Then, we investigate on the effectiveness of the proposed strategy as a function of the level of knowledge on the forgery detection algorithm. Experiments show this approach to be quite effective especially if a good prior knowledge on the detector is available.

A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment <=SEP=> Recent progress in autonomous and semiautonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we provide a comprehensive description of our heavily sensorized, panoramic testbed capable of providing high quality data from a slew of synchronized and calibrated sensors such as cameras, LIDARs, radars, and the IMU/GPS. The vehicle has recorded over 100 hours of real world data for a very diverse set of weather, traffic and daylight conditions. All captured data is accurately calibrated and synchronized using timestamps, and stored safely in high performance servers mounted inside the vehicle itself. Details on the testbed instrumentation, sensor layout, sensor outputs, calibration and synchronization are described in this paper.

Prototypical Networks for Few-shot Learning <=SEP=> We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-ofthe-art results on the CU-Birds dataset.

Prozessmodellierung in eGovernment-Projekten mit der eEPK <=SEP=> Im Rahmen der Informationssystemgestaltung unterscheiden sich die Projektbeteiligten insbesondere hinsichtlich ihrer Anforderungen an die Inhalte und Repräsentationsformen der für die Anwendungssystemund Organisationsgestaltung verwendeten Modellen. Die multiperspektivische Modellierung stellt einen viel versprechenden Ansatz dar, um diesem Umstand durch die Bereitstellung perspektivenspezifischer Sichten auf Informationsmodelle gerecht zu werden. Der Beitrag stellt eine Erweiterung ereignisgesteuerter Prozessketten durch Konfigurationsmechanismen vor, welche die Erstellung und Nutzung von Perspektiven auf Prozessmodelle unterstützt. 1 Perspektiven auf Prozessmodelle Die wissenschaftliche Diskussion über die Qualität von Prozessmodellen wurde in den letzten Jahren wesentlich durch die Entwicklung von allgemeinen Modellierungsempfehlungen, welche auf die syntaktische Korrektheit der Modelle und der Einhaltung der Grundsätze ordnungsmäßiger Modellierung bei der Erstellung von Prozessmodellen abzielen [BRS95; Ro96; BRU00], geprägt. Ein dominierendes Kriterium, das die Qualität von Prozessmodellen determiniert, ist dabei die Entsprechung der Modellkonzeption und -repräsentation mit den Anforderungen der jeweiligen Modellnutzergruppe [RS99, S. 25f.; Be02, S. 28ff.; RSD03, S. 49]. So werden z. B. innerhalb eines prozessbasierten Reorganisationsprojektes diese Modellnutzergruppen durch die verschiedenen organisatorischen Rollen der Projektteilnehmer bestimmt. Ein Sachbearbeiter benötigt hierbei lediglich die für ihn relevanten Prozessmodelle und deren Schnittstellen. Aus der Sicht des Managers der zu reorganisierenden Unternehmung sollten die Modelle in aggregierter Form vorliegen und durch erweiterte visuelle Merkmale, wie z. B. ansprechender Farboder Symboldarstellung, gekennzeichnet sein. Einem Anwendungsentwickler sollten sehr detaillierte Modelle vorgelegt werden, damit sich die aus der Reorganisation ergebenden Konsequenzen im Anwendungssystem berücksichtigt werden können. Die verschiedenen Anforderungen der Nutzergruppen resultieren aus den Differenzen ihrer subjektabhängigen Vorstellungswelten und den daraus resultierenden Problemlösungsansätzen [Be01a; Be02, S. 30ff.]. Ein Ansatz zur expliziten Vermittlung zwischen diesen Vorstellungswelten findet sich in der Bereitstellung von anforderungsgerechten Perspektiven wieder [Fr94, S. 36f.]. Perspektiven werden dadurch determiniert, welcher Modellierungszweck verfolgt wird, welche organisatorische Rolle die Nutzer einnehmen und welche persönlichen Präferenzen bzgl. der Modellkonzeption und -repräsentation bestehen [Be02, S. 38ff., RSD03, S. 49]. Die Entwicklung und Bereitstellung perspektivenspezifischer Modelle wird unter dem Begriff der multiperspektivischen Informationsmodellierung diskutiert [Fi92; RS99, S. 25f.; RSD03, S. 52]. Soll den Anforderungen mehrerer Nutzergruppen entsprochen werden, d. h. sollen mehrere Perspektiven berücksichtigt werden, ergeben sich für den Modellersteller zwei Alternativen. Modelle könnten einerseits für unterschiedliche Perspektiven redundant vorgehalten werden. Nachteile dieser Vorgehensweise sind die üblichen, mit Redundanzen verbundenen Zusatzaufwände wie erhöhter Pflegeaufwand und Gefahr von Inkonsistenzen. Andererseits kann ein Gesamtmodell derartig erstellt werden, dass es die für sämtliche Perspektiven relevanten Elemente redundanzfrei enthält. Den einzelnen Vertretern der Perspektiven werden die für sie relevanten Modelle bereitgestellt, indem ihnen eine View auf das Gesamtmodell zur Verfügung gestellt wird, die das Gesamtmodell um die nicht relevanten Inhalte reduziert. Die Maßnahmen, welche vom Modellierer zur Erstellung dieser Views durchgeführt werden müssen, können hierbei durchaus zu einer komplexen Aufgabe werden. Im Folgenden wird anhand ereignisgesteuerter Prozessketten (EPKs) [KNS92] die Umsetzung der zweiten Variante diskutiert. Dabei wird in Abschnitt 2 zunächst eine metamodellbasierte Formalisierung der EPK vorgenommen, um eine konzeptionelle Basis für die multiperspektivische Erweiterung der Modellierungstechnik durch Konfigurationsmechanismen zu schaffen. Darauf aufbauend werden in Abschnitt 3 die notwendigen Erweiterungen der Modellierungstechnik vorgenommen. Abschnitt 4 behandelt die Problematik und Notwendigkeit von Konsistenzsicherungsmechanismen, die bei der Verwendung der Konfigurationsmechanismen auftreten können. Nach einem Praxisbeispiel in Abschnitt 5 schließt der Beitrag mit einem Ausblick in Abschnitt 6. 2 Metamodellbasierte Formalisierung der EPK Das hier vorgestellte Verfahren zur Erstellung multiperspektivischer ereignisgesteuerter Prozessketten basiert auf der Manipulation von Modellstrukturen durch die Anwendung von Konfigurationsmechanismen. Da hierbei die strukturellen Veränderungen auf Ebene der Sprachspezifikation sowie deren Auswirkungen auf die Struktur der Modelle im Zentrum der Betrachtung stehen, empfiehlt es sich, die Sprache der EPK formalisiert darzustellen. Dies kann wiederum durch Informationsmodelle – sogenannte sprachorientierte Metamodelle – erfolgen [St96, S. 24ff.]. Als Notationsform eignen sich insbesondere Struktursprachen wie das Entity-Relationship-Modell [Ch76]. Ein erweiterter Dialekt dieser Modellierungssprache wird im vorliegenden Beitrag zur Metamodellierung verwendet. 2 Das zentrale Element der EPK Sprache ist das Prozesselement (vgl. Abbildung 1). Alle am Prozessgraphen beteiligten Knoten werden als Spezialisierung dieses Elements mo1 Der Begriff EPK wird im Folgenden synonym zur erweiterten ereignisgesteuerten Prozesskette (eEPK) verwendet, die neben der Ablauflogik die Annotation von benötigten Ressourcen erlaubt. 2 Es handelt sich hierbei um einen Dialekt, der die perspektivenabhängige Modifikation von Sprachen erlaubt (vgl. ausführlich [Be02, S. 77ff.]). delliert. Hierzu gehören die Elemente Prozessfunktion, Prozessereignis und Operator (vgl. Spezialisierung des Entitytyps Prozesselement). Eine Prozessfunktion beschreibt eine Aktivität innerhalb eines Prozesses. Diese Aktivitäten werden beim Eintreten eines oder einer Kombination von Ereignissen ausgeführt und führen nach ihrer Beendigung selbst wieder zu dem Eintreten von Ereignissen. Somit kann ein Ereignis als Zustandswechsel interpretiert werden. Die notwendige Kombination der Ereignisse für die Ausführung einer Aktivität sowie die Definition der Menge der eintretenden Ereignisse nach erfolgreicher Ausführung werden über die Operatoren definiert. Hierdurch lassen sich parallele Prozessstränge und deren Wiederzusammenführung im Prozessmodell abbilden. Prozessfunktion Operator Prozessereignis Prozesselement D,T (0,n) (0,n) Ressource (0,n) (0,n) D,P Organisationseinheit Fachbegriff Anwendungssystemtyp Vorgänger/ Nachfolger PERessourcenZuO (0,n) ProzessRessourcenBeziehungstyp Typ PRBeziehungstyphierarchie

Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task <=SEP=> The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.

Algorithmic Prediction of Health-Care Costs <=SEP=> The rising cost of health care is one of the world’s most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data-mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 200,000 members. The key findings are: (a) our data-mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health-care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high-cost members.

Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization <=SEP=> Scene background initialization is the process by which a method tries to recover the background image of a video without foreground objects in it. Having a clear understanding about which approach is more robust and/or more suited to a given scenario is of great interest to many end users or practitioners. The aim of this paper is to provide an extensive survey of scene background initialization methods as well as a novel benchmarking framework. The proposed framework involves several evaluation metrics and state-of-the-art methods, as well as the largest video data set ever made for this purpose. The data set consists of several camera-captured videos that: 1) span categories focused on various background initialization challenges; 2) are obtained with different cameras of different lengths, frame rates, spatial resolutions, lighting conditions, and levels of compression; and 3) contain indoor and outdoor scenes. The wide variety of our data set prevents our analysis from favoring a certain family of background initialization methods over others. Our evaluation framework allows us to quantitatively identify solved and unsolved issues related to scene background initialization. We also identify scenarios for which state-of-the-art methods systematically fail.

PUMA: Planning Under Uncertainty with Macro-Actions <=SEP=> Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporallyextended sequences of actions, or “macro-actions.” In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an ǫ-optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperforms existing state-of-the art solvers. Most partially observable Markov decision process (POMDP) planners select actions conditioned on the prior observation at each timestep: we refer to such planners as fully-conditional. When good performance relies on considering different possible observations far into the future, both online and offline fully-conditional planners typically struggle. An extreme alternative is unconditional (or “openloop”) planning where a sequence of actions is fixed and does not depend on the observations that will be received during execution. While open-loop planning can be extremely fast and perform surprisingly well in certain domains, acting well in most real-world domains requires plans where at least some action choices are conditional on the obtained observations. This paper focuses on the significant subset of POMDP domains, including scientific exploration, target surveillance, and chronic care management, where it is possible to act well by planning using conditional sequences of openloop, fixed-length action chains, or “macro-actions.” We call this approach semi-conditional planning, in that actions are chosen based on the received observations only at the end of each macro-action. Copyright c © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. For a discussion of using open-loop planning for multi-robot tag for open-loop planning see Yu et al. (2005). We demonstrate that for certain domains, planning with macro-actions can offer performance close to fullyconditional planning at a dramatically reduced computational cost. In comparison to prior macro-action work, where a domain expert often hand-coded a good set of macro-actions for each problem, we present a technique for automatically constructing finite-length open-loop macroactions. Our approach uses sub-goal states based on immediate reward and potential information gain. We then describe how to incrementally refine an initial macro-action plan by incorporating successively shorter macro-actions. We combine these two contributions in a forward-search algorithm for planning under uncertainty with macro-actions (PUMA). PUMA is an anytime algorithm which guarantees eventual convergence to an ǫ-optimal policy, even in domains that may require close to fully-conditional plans. PUMA outperforms a state-of-the-art POMDP planner both in terms of plan quality and computational cost on two large simulated POMDP problems. However, semiconditional planning does not yield an advantage in all domains, and we provide preliminary experimental analysis towards determining a priori when planning in a semiconditional manner will be helpful. However, even in domains that are not well suited to semi-conditional planning, our anytime improvement allows PUMA to still eventually compute a good policy, suggesting that PUMA may be viable as a generic planner for large POMDP problems.

Revising Perceptual Linear Prediction (PLP) <=SEP=> Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) are the most popular acoustic features used in speech recognition. Often it depends on the task, which of the two methods leads to a better performance. In this work we develop acoustic features that combine the advantages of MFCC and PLP. Based on the observation that the techniques have many similarities, we revise the processing steps of PLP. In particular, the filter-bank, the equal-loudness pre-emphasis and the input for the linear prediction are improved. It is shown for a broadcast news transcription task and a corpus of children’s speech that the new variant of PLP performs better than both MFCC and conventional PLP for a wide range of clean and noisy acoustic conditions.

Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis <=SEP=> In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent highly-complex transformation compactly, but also take advantage of huge amount of training data. In this paper, we propose an approach to model multiple speakers TTS with a general DNN, where the same hidden layers are shared among different speakers while the output layers are composed of speaker-dependent nodes explaining the target of each speaker. The experimental results show that our approach can significantly improve the quality of synthesized speech objectively and subjectively, comparing with speech synthesized from the individual, speaker-dependent DNN-based TTS. We further transfer the hidden layers for a new speaker with limited training data and the resultant synthesized speech of the new speaker can also achieve a good quality in term of naturalness and speaker similarity.

Classroom Response Systems: A Review of the Literature. <=SEP=> As the frequency with which Classroom Response Systems (CRSs) are used is increasing, it becomes more and more important to define the affordances and limitations of these tools. Currently existing literature is largely either anecdotal or focuses on comparing CRS and non-CRS environments that are unequal in other aspects as well. In addition, the literature primarily describes situations in which the CRS is used to provide an individual as opposed to a group response. This article points to the need for a concerted research effort, one that rigorously explores conditions of use across diverse settings and pedagogies.

An energy-efficient dual sampling SAR ADC with reduced capacitive DAC <=SEP=> This paper presents an energy-efficient SAR ADC which adopts reduced MSB cycling step with dual sampling of the analog signal. By sampling and holding the analog signal asymmetrically at both input sides of comparator, the MSB cycling step can be hidden by hold mode. Benefits from this technique, not only the total capacitance of DAC is reduced by half, but also the average switching energy is reduced by 68% compared with conventional SAR ADC. Moreover, switching energy distribution is more uniform over entire output code compared with previous works.

The impact of pretend play on children's development: a review of the evidence. <=SEP=> Pretend play has been claimed to be crucial to children's healthy development. Here we examine evidence for this position versus 2 alternatives: Pretend play is 1 of many routes to positive developments (equifinality), and pretend play is an epiphenomenon of other factors that drive development. Evidence from several domains is considered. For language, narrative, and emotion regulation, the research conducted to date is consistent with all 3 positions but insufficient to draw conclusions. For executive function and social skills, existing research leans against the crucial causal position but is insufficient to differentiate the other 2. For reasoning, equifinality is definitely supported, ruling out a crucially causal position but still leaving open the possibility that pretend play is epiphenomenal. For problem solving, there is no compelling evidence that pretend play helps or is even a correlate. For creativity, intelligence, conservation, and theory of mind, inconsistent correlational results from sound studies and nonreplication with masked experimenters are problematic for a causal position, and some good studies favor an epiphenomenon position in which child, adult, and environment characteristics that go along with play are the true causal agents. We end by considering epiphenomenalism more deeply and discussing implications for preschool settings and further research in this domain. Our take-away message is that existing evidence does not support strong causal claims about the unique importance of pretend play for development and that much more and better research is essential for clarifying its possible role.

Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods <=SEP=> As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role. However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks. Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks. We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization method. By introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88.7% and 74.1% on average in different ML-based detection methods.

Block Chain based Searchable Symmetric Encryption <=SEP=> The mechanism for traditional Searchable Symmetric Encryption is pay-then-use. That is to say, if a user wants to search some documents that contain special keywords, he needs to pay to the server firstly, then he can enjoy search service. Under this situation, these kinds of things will happen: After the user paying the service fees, the server may either disappear because of the poor management or returning nothing. As a result, the money that the user paid cannot be brought back quickly. Another case is that the server may return incorrect document sets to the user in order to save his own cost. Once such events happen, it needs the arbitration institution to mediate which will cost a long time. Besides, to settle the disputes the user has to pay to the arbitration institution. Ideally, we deeply hope that when the user realizes the server has a tendency to cheat in the task of searching, he can immediately and automatically withdraw his money to safeguard his right. However, the existing SSE protocols cannot satisfy this demand. To solve this dilemma, we find a compromised method by introducing the block chain into SSE. Our scheme achieves three goals stated below. Firstly, when the server does not return any thing to user after he gets the search token, the user can get some compensation from the server, because the server can infer some important information from the Index and this token. Besides, the user also doesn’t pay the service charge. Secondly, if the documents that the server returns are false, the server cannot receive service fees, meanwhile, he will be punished. Lastly, when the user receives some bitcoin from server at the beginning, he may terminate the protocol. Under this situation, the server is a victim. In order to prevent such thing from happening, the server will broadcast a transaction to redeem his pledge after an appointed time. ∗Corresponding author Email addresses: tianhb@mail.sysu.edu.cn (Haibo Tian), isszhfg@mail.sysu.edu.cn (Fangguo Zhang ) Preprint submitted to Elsevier April 26, 2017

Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users <=SEP=> The goal in designing an ionospheric time-delay correctionalgorithm for the single-frequency global positioning system userwas to include the main features of the complex behavior of theionosphere, yet require a minimum of coefficients and usercomputational time, while still yielding an rms correction of at least50 percent. The algorithm designed for this purpose, andimplemented in the GPS satellites, requires only eight coefficientssent as part of the satellite message, contains numerousapproximations designed to reduce user computationalrequirements, yet preserves the essential elements required to obtaingroup delay values along multiple satellite viewing directions.

Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering <=SEP=> Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures These authors contributed equally to this work as the first author. P. Pirasteh · D. Hwang Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea P. Pirasteh e-mail: parivash63@gmail.com D. Hwang e-mail: dosamhwang@gmail.com J. E. Jung ( ) Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea e-mail: ontology.society@gmail.com significantly improve the results obtained from traditional similarity measures.

Optimization tool for direct water cooling system of high power IGBT modules <=SEP=> Thermal management of power electronic devices is essential for reliable system performance especially at high power levels. Since even the most efficient electronic circuit becomes hot because of ohmic losses, it is clear that cooling is needed in electronics and even more as the power increases. One of the most important activities in the thermal management and reliability improvement is the cooling system design. As industries are developing smaller power devices with higher power densities, optimized design of cooling systems with minimum thermal resistance and pressure drop become important issue for thermal design engineers. This paper aims to present a user friendly optimization tool for direct water cooling system of a high power module which enables the cooling system designer to identify the optimized solution depending on customer load profiles and available pump power. CFD simulations are implemented to find best solution for each scenario.

BER Analysis of MIMO-OFDM System using Alamouti STBC and MRC Diversity Scheme over Rayleigh Multipath Channel <=SEP=> Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Global Journal of Researches in Engineering Electrical and Electronics Engineering Volume 13 Issue 13 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4596 & Print ISSN: 0975-5861