title: The Influence of Base Rate and Case Information on Health-Risk Perceptions : A Unified Model of Self-Positivity and Self-Negativity Author ( s ) :

abstract: This research examines how consumers use base rate (e.g., disease prevalence in a population) and case information (e.g., an individual's disease symptoms) to estimate health risks. Drawing on construal level theory, we propose that consumers' reliance on base rate (case information) will be enhanced (weakened) by psychological distance. A corollary of this premise is that self-positivity (i.e., underestimating self-risk vs. other-risk) is likely when the disease base rate is high but the case information suggests low risk. In contrast, self-negativity (i.e., overestimating self-risk vs. other-risk) is likely when the disease base rate is low, but case information implies high risk. Six experiments provide convergent support for this thesis, using different operationalizations of construal level, base rate, and case risk across multiple health domains. Our findings inform the extant literature on health-risk perception and also provide theoretical implications for research on social comparisons, as well as that on the use of base rate versus case information.


title: Improving naive Bayes classifier using conditional probabilities

abstract: Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness.


title: Using the theory of inventive problem solving to brainstorm innovative ideas for assessing varieties of phone-cameras

abstract: TRIZ (the theory of inventive problem solving) is used to design phone-cameras.ARM (association rule mining) is employed to identify critical product features.CA (conjoint analysis) is conducted to evaluate varieties of design alternatives.Samsung's S4 zoom, K zoom and galaxy camera are used for benchmarking.Asus's Zenfone zoom justifies the validity of the suggested periscopic lenses. Today, to survive in an extremely turbulent business environment, traditional products characterized by limited capabilities cannot satisfy diverse customer requirements. In particular, it is observed that the boundaries between smart phones, digital cameras, and tablets are becoming more and more blurred than before. Therefore, for attracting the ad-hoc segments, global companies began to develop hybrid pad-phones and phone-cameras. Unfortunately, some of these products are facing poor sales without incurring much market attention. In order to overcome the aforementioned difficulty, this paper presents a novel framework to reduce the gaps between producer expectation and user perception. By means of the TRIZ (the theory of inventive problem solving), a contradiction matrix is applied to handle engineering conflicts among multi-functional alternatives to seek inventive solutions. Then, ARM (association rule mining) is conducted to identify critical features that formulate customer dissatisfaction (purchase intention). Finally, CA (conjoint analysis) is employed to derive customer utilities for benchmarking varieties of design concepts. In summary, the proposed framework cannot only help product planners efficiently generate innovative ideas, but also effectively justify the validity of design concepts.


title: Mind-Reading System - A Cutting-Edge Technology

abstract: In this paper, we describe a human-computer interface (HCI) system that includes an enabler for controlling gadgets based on signal analysis of brain activities transmitted from the enabler to the gadgets. The enabler is insertable in a user’s ear and includes a recorder that records brain signals. A processing unit of the system, which is inserted in a gadget, commands the gadget based on decoding the recorded brain signals. The proposed device and system could facilitate a brain-machine interface to control the gadget from electroencephalography signals in the user’s brain.


title: Integrated NFV/SDN Architectures: A Systematic Literature Review

abstract: Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware. While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes. NFV and SDN are complementary technologies capable of providing one network solution. SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain. There are many studies designing NFV/SDN architectures in different environments. Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs. This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: (i) to investigate and provide an in-depth review of the state of the art of NFV/SDN architectures, (ii) to synthesize their architectural designs, and (iii) to identify areas for further improvements. Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state of the practice) of integrated NFV/SDN architectures and shed some light on future research efforts and the challenges faced.


title: A Hybrid Multilevel Inverter Topology for an Open-End Winding Induction-Motor Drive Using Two-Level Inverters in Series With a Capacitor-Fed H-Bridge Cell

abstract: In this paper, a new five-level inverter topology for open-end winding induction-motor (IM) drive is proposed. The open-end winding IM is fed from one end with a two-level inverter in series with a capacitor-fed H-bridge cell, while the other end is connected to a conventional two-level inverter. The combined inverter system produces voltage space-vector locations identical to that of a conventional five-level inverter. A total of 2744 space-vector combinations are distributed over 61 space-vector locations in the proposed scheme. With such a high number of switching state redundancies, it is possible to balance the H-bridge capacitor voltages under all operating conditions including overmodulation region. In addition to that, the proposed topology eliminates 18 clamping diodes having different voltage ratings compared with the neutral point clamped inverter. On the other hand, it requires only one capacitor bank per phase, whereas the flying-capacitor scheme for a five-level topology requires more than one capacitor bank per phase. The proposed inverter topology can be operated as a three-level inverter for full modulation range, in case of any switch failure in the capacitor-fed H-bridge cell. This will increase the reliability of the system. The proposed scheme is experimentally verified on a four-pole 5-hp IM drive.


What Makes a Leader ? The Impact of Cognitive and


title: Large-Scale Linear RankSVM

abstract: Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.


title: Power Assist Recuperation System (PARS™) for automotive fuel saving and e-boost

abstract: A novel Power Assist Recuperation System consisted of motor, inverter, DC-DC converter and 48V Li-ion battery has been presented for fuel saving of conventional Internal Combustion Engine and hybrid vehicles. As a total solution of the next-generation 48V PARS, key components such as Permanent Magnet motor, MOSFET power switching inverter and bi-directional DC-DC converter with cost competitiveness from in-house power IC's and power modules have been optimized based on the 48V battery pack for quick charge and discharge. A deep flux weakening control has been evaluated for the PM motor's high speed operation. The specific value of how much the proposed 48V PARS can reduce CO ::: 2 ::: emission and fuel consumption was numerically simulated by using both motoring and generating efficiency maps as input data. It is noticed that the present 48V PARS gives a considerably enhanced performance in fuel saving by as high as 17% for a commercial 1.8L gasoline engine during the Urban Dynamometer Driving Schedule mode.


title: ZOE: A Cloud-less Dialog-enabled Continuous Sensing Wearable Exploiting Heterogeneous Computation

abstract: The wearable revolution, as a mass-market phenomenon, has finally arrived. As a result, the question of how wearables should evolve over the next 5 to 10 years is assuming an increasing level of societal and commercial importance. A range of open design and system questions are emerging, for instance: How can wearables shift from being largely health and fitness focused to tracking a wider range of life events? What will become the dominant methods through which users interact with wearables and consume the data collected? Are wearables destined to be cloud and/or smartphone dependent for their operation? Towards building the critical mass of understanding and experience necessary to tackle such questions, we have designed and implemented ZOE - a match-box sized (49g) collar- or lapel-worn sensor that pushes the boundary of wearables in an important set of new directions. First, ZOE aims to perform multiple deep sensor inferences that span key aspects of everyday life (viz. personal, social and place information) on continuously sensed data; while also offering this data not only within conventional analytics but also through a speech dialog system that is able to answer impromptu casual questions from users. (Am I more stressed this week than normal?) Crucially, and unlike other rich-sensing or dialog supporting wearables, ZOE achieves this without cloud or smartphone support - this has important side-effects for privacy since all user information can remain on the device. Second, ZOE incorporates the latest innovations in system-on-a-chip technology together with a custom daughter-board to realize a three-tier low-power processor hierarchy. We pair this hardware design with software techniques that manage system latency while still allowing ZOE to remain energy efficient (with a typical lifespan of 30 hours), despite its high sensing workload, small form-factor, and need to remain responsive to user dialog requests.


title: Analysis of IR spectrum and crystal morphology of polyalumina-silica chlorid(PASC) prepared by various method

abstract: With the technology of IR spectrum and crystal morphology,studying the effect on interatomic bonding action and molecular shape of coagulating agent PASC when it is prepared by various methods.It shows that as PASC is produced by basifying polymerization,comparing to that by others way,the IR spectrum of PASC shows,965cm -1 peak proportion reflecting Si O Al bond accrete 118%,266%,875cm -1 peak proportion reflecting Al O Al bond accrete 23%,30%.It may be caused by multinuclear hydroxy aluminum's content and Zeta electric potential increases in the process hydrolyze concrete with PASC.It disappear in crystal morphology,molecular chain is longer and branching further,so that PASC have better potentiality of arching and charge neutralization.


title: Location privacy preferences: A survey-based analysis of consumer awareness, trade-off and decision-making

abstract: With the advent and rapid dissemination of location-sensing information technology, the issue of location information privacy is receiving growing attention. Perhaps of greatest concern is ensuring that potential users of mobile Information and Communications Technologies (e.g., Location-Based Services and Intelligent Transportation Systems) are comfortable with the levels of privacy protection afforded them, as well as with the benefits they will receive in return for providing private location information. This paper explores the concepts of privacy risks, benefits, willingness to trade, and compensation in relationship to mobile and locational technologies using a stated preference survey to ascertain areas of interest in determining the trade-offs that consumers will be willing to make in return for mobility enhancements. Analysis of the survey leads to findings that while respondents believe that sharing data in the mobile environment may pose privacy risks, they do not generally take steps necessary to address these risks; that privacy preferences are impacted by a range of factors, including both personal and contextual considerations (such as factors arising from their specific situation at the time of information seeking); and that willingness to trade private location data is dependent upon a number of factors related to context, personal characteristics, expected benefits and degree of trust in the collecting organization.


title: Substrate Integrated Waveguide Loaded by Complementary Split-Ring Resonators for Miniaturized Diplexer Design

abstract: A novel high-performance planar diplexer is developed based on the substrate integrated waveguide (SIW) with complementary split-ring resonators (CSRRs) etched on the waveguide surface. The proposed diplexer is operated below the characteristic cutoff frequency of the waveguide. The filtering response of the SIW-CSRR combined structure is investigated first. The diplexer is then implemented based on two cascaded two-pole bandpass filters. Equivalent circuit, simulated results and experimental verification are all provided. This diplexer shows advantages in terms of the compact size, low loss, high isolation, easy fabrication and integration with other circuits.


title: A Temporal Order Modeling Approach to Human Action Recognition from Multimodal Sensor Data

abstract: From wearable devices to depth cameras, researchers have exploited various multimodal data to recognize human actions for applications, such as video gaming, education, and healthcare. Although there many successful techniques have been presented in the literature, most current approaches have focused on statistical or local spatiotemporal features and do not explicitly explore the temporal dynamics of the sensor data. However, human action data contain rich temporal structure information that can characterize the unique underlying patterns of different action categories. From this perspective, we propose a novel temporal order modeling approach to human action recognition. Specifically, we explore subspace projections to extract the latent temporal patterns from different human action sequences. The temporal order between these patterns are compared, and the index of the pattern that appears first is used to encode the entire sequence. This process is repeated multiple times and produces a compact feature vector representing the temporal dynamics of the sequence. Human action recognition can then be efficiently solved by the nearest neighbor search based on the Hamming distance between these compact feature vectors. We further introduce a sequential optimization algorithm to learn the optimized projections that preserve the pairwise label similarity of the action sequences. Experimental results on two public human action datasets demonstrate the superior performance of the proposed technique in both accuracy and efficiency.


title: A Novel Methodology for Designing Linear Phase IIR Filters

abstract: This paper presents a novel technique for designing an Infinite Impulse Response (IIR) Filter with Linear Phase Response. The design of IIR filter is always a challenging task due to the reason that a Linear Phase Response is not realizable in this kind. The conventional techniques involve large number of samples and higher order filter for better approximation resulting in complex hardware for implementing the same. In addition, an extensive computational resource for obtaining the inverse of huge matrices is required. However, we propose a technique, which uses the frequency domain sampling along with the linear programming concept to achieve a filter design, which gives a best approximation for the linear phase response. The proposed method can give the closest response with less number of samples (only 10) and is computationally simple. We have presented the filter design along with its formulation and solving methodology. Numerical results are used to substantiate the efficiency of the proposed method.


title: A new isolated SEPIC converter with coupled inductors for photovoltaic applications

abstract: Summary from only given. In this paper a new isolated SEPIC converter which is a proper choice for PV applications, is introduced and analyzed. The proposed converter has the advantage of high voltage gain while the switch voltage stress is same as a regular SEPIC converter. The converter operating modes are discussed and design considerations are presented. Also simulation results are illustrated which justifies the theoretical analysis. Finally the proposed converter is improved using active clamp technique.


title: Stereotype or grammar? The representation of gender when two-year-old and three-year-old French-speaking toddlers listen to role nouns.

abstract: Using a preferential looking paradigm, the current study examined the role that grammatical gender plays when preschool French-speaking toddlers process role nouns in the masculine form (e.g., chanteursmasculine ‘singers’). While being auditorily prompted with “Look at the ‘a role noun’!”, two- and three-year-olds were presented with two pictures of two characters (‘boy–boy’ versus ‘girl–boy’) with attributes of the given role noun (e.g., singers with microphone and music notes). All role nouns were presented in the masculine plural form, which, despite its use to refer to mixed-gender groups, can be interpreted as referring to men. We expected toddlers to be biased by stereotypes, yet when non-stereotypical role nouns were presented, toddlers were not influenced by grammatical gender, but by their own sex (even more so for three-year-old toddlers). The absence of sensitivity to grammatical cues for either age group is discussed in terms of the developmental awareness of grammatical gender.


title: Incremental regression testing

abstract: The purpose of regression testing is to ensure that bug fixes and new functionality introduced in a new version of a software do not adversely affect the correct functionality inherited from the previous version. Efficient methods of selecting small subsets of regression test sets that can be used to ensure correct functionality are explored. >


Weight Loss with a Low-Carbohydrate, Mediterranean, or Low-Fat Diet


title: Probabilistic encryption & how to play mental poker keeping secret all partial information

abstract: This paper proposes an Encryption Scheme that possess the following property : An adversary, who knows the encryption algorithm and is given the cyphertext, cannot obtain any information about the clear-text. ::: Any implementation of a Public Key Cryptosystem, as proposed by Diffie and Hellman in [8], should possess this property. ::: Our Encryption Scheme follows the ideas in the number theoretic implementations of a Public Key Cryptosystem due to Rivest, Shamir and Adleman [13], and Rabin [12].


title: Design of a double-deck automatic guided vehicle with magnetic traction for material transporting

abstract: Automatic guided vehicles (AGVs) are pervasively used in plants, warehouses, institutes, and ports. Research on AGVs for foodstuff, biochemistry, medical testing, etc. however, due to their speciality, attracted little attention. In this paper, a novel double-deck AGV for material transporting in a narrow enclosed channel is proposed. This vehicle is composed of a trailer (the upper deck) and a tractor (the lower deck), which are separated by the channel floor. The upper and lower decks move synchronously by magnetic attraction induced by unaligned position of two sets of permanent magnets. The shape and layout of the magnets are analyzed to maximize the magnetic traction and minimize the friction of wheels. The AGV is designed in compact and modular structure, manoeuvrable and convenient to maintain. Application results show that the AGV system works reliable, operates simple and is easy to control, etc., which makes it particularly suitable to accomplish tasks of material transporting in narrow enclosed channels.


title: LASSO: A feature selection technique in predictive modeling for machine learning

abstract: Feature selection is one of the techniques in machine learning for selecting a subset of relevant features namely variables for the construction of models. The feature selection technique aims at removing the redundant or irrelevant features or features which are strongly correlated in the data without much loss of information. It is broadly used for making the model much easier to interpret and increase generalization by reducing the variance. Regression analysis plays a vital role in statistical modeling and in turn for performing machine learning tasks. The traditional procedures such as Ordinary Least Squares (OLS) regression, Stepwise regression and partial least squares regression are very sensitive to random errors. Many alternatives have been established in the literature during the past few decades such as Ridge regression and LASSO and its variants. This paper explores the features of the popular regression methods, OLS regression, ridge regression and the LASSO regression. The performance of these procedures has been studied in terms of model fitting and prediction accuracy using real data and simulated environment with the help of R package.


title: Walkie-Markie: Indoor Pathway Mapping Made Easy

abstract: We present Walkie-Markie - an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users' mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available. The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively.


title: PAC Reinforcement Learning with Rich Observations

abstract: We propose and study a new model for reinforcement learning with rich observations, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To avoid barriers to sample-efficient learning associated with large observation spaces and general POMDPs, we focus on problems that can be summarized by a small number of hidden states and have long-term rewards that are predictable by a reactive function class. In this setting, we design and analyze a new reinforcement learning algorithm, Least Squares Value Elimination by Exploration. We prove that the algorithm learns near optimal behavior after a number of episodes that is polynomial in all relevant parameters, logarithmic in the number of policies, and independent of the size of the observation space. Our result provides theoretical justification for reinforcement learning with function approximation.


title: IoT Security Framework for Smart Cyber Infrastructures

abstract: The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.


title: Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features

abstract: Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images. Support vector machine, sequential minimal optimization, naive Bayesian, and random-forest classifiers are used to perform supervised classification. Our results demonstrate that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 91%. The impact of feature ranking and normalization is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.


title: Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data

abstract: We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.


title: An Exploratory Study of Factors Affecting the Internet Banking Adoption: A Qualitative Study among Postgraduate Students

abstract: This paper aims to explore and understand the factors that affect the internet banking adoption among postgraduateâ€™ students in International Islamic University Malaysia (IIUM).Approach- Semi-structured interviews with eight informants; four adopters and four non-adopters on postgraduateâ€™ students were conducted to explore this issue. The results revealed that adopters and non-adopters realized that internet banking (IB) has several benefits and conveniences. However, non adopters were concerned about some factors like trust, ease of use, awareness and security. The results also showed that adopters had positive influence on use of online banking and they did not have problems with these factors because they had sufficient knowledge and experience in using online banking. This study has several limitations such as the sample used for the study was from a specific group of respondents in Malaysia (postgraduateâ€™ students at IIUM). The second limitation was that informants were postgraduate â€™students who had limitation on banking transactions. The findings are important to enable bank executives to have a better understanding of clientsâ€™ perception to adopt internet banking. This will help banksâ€™ managers and owners formulate strategies that could significantly affect IBA among their customers.


title: A Practical Approach for Content Mining of Tweets

abstract: Abstract Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.


title: You Watch, You Give, and You Engage: A Study of Live Streaming Practices in China

abstract: Despite gaining traction in North America, live streaming has not reached the popularity it has in China, where livestreaming has a tremendous impact on the social behaviors of users. To better understand this socio-technological phenomenon, we conducted a mixed methods study of live streaming practices in China. We present the results of an online survey of 527 live streaming users, focusing on their broadcasting or viewing practices and the experiences they find most engaging. We also interviewed 14 active users to explore their motivations and experiences. Our data revealed the different categories of content that was broadcasted and how varying aspects of this content engaged viewers. We also gained insight into the role reward systems and fan group-chat play in engaging users, while also finding evidence that both viewers and streamers desire deeper channels and mechanisms for interaction in addition to the commenting, gifting, and fan groups that are available today.


IoT-based Efficient Tamper Detection Mechanism for Healthcare Application


title: Attribute-based encryption with non-monotonic access structures

abstract: We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.


title: Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials

abstract: aBackground: Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores), using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials). Methods: Analysis of covariance (ANCOVA) was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline. Results: All four methods showed similar direction of effect; however, ANCOVA (−3.9; 95% confidence interval [CI ]: −9.5, 1.6; P=0.15) and the posttreatment score (−4.3; 95% CI: −9.8, 1.2; P=0.12) method provided the highest precision of estimate compared with the change score (−3.0; 95% CI: −9.9, 3.8; P=0.38) and percent change (–0.019; 95% CI: −0.087, 0.050; P=0.58). Conclusion: ANCOVA, through both simulation and empirical studies, provides the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.


title: Fibonacci Exposure Bracketing for High Dynamic Range Imaging

abstract: Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.


title: Folding and unfolding in computational geometry

abstract: We survey results in a recent branch of computational geometry: folding and unfolding of linkages, paper, and polyhedra.


title: Interpretation and Identification of Causal Mediation

abstract: This paper reviews the foundations of causal mediation analysis and offers a general and transparent account of the conditions necessary for the identification of natural direct and indirect effects, thus facilitating a more informed judgment of the plausibility of these conditions in specific applications. We show that the conditions usually cited in the literature are overly restricted, and can be relaxed substantially, without compromising identification. In particular, we show that natural effects can be identified by methods that go beyond standard adjustment for confounders, applicable to observational studies in which treatment assignment remains confounded with the mediator or with the outcome. These identification conditions can be validated algorithmically from the diagramatic description of one's model, and are guaranteed to produce unbiased results whenever the description is correct. The identification conditions can be further relaxed in parametric models, possibly including interactions, and permit us to compare the relative importance of several pathways, mediated by interdependent variables.


title: Periodization Paradigms in the 21st Century: Evidence-Led or Tradition-Driven?

abstract: The planning and organization of athletic training have historically been much discussed and debated in the coaching and sports science literature. Various influential periodization theorists have devised, promoted, and substantiated particular training-planning models based on interpretation of the scientific evidence and individual beliefs and experiences. Superficially, these proposed planning models appear to differ substantially. However, at a deeper level, it can be suggested that such models share a deep-rooted cultural heritage underpinned by a common set of historically pervasive planning beliefs and assumptions. A concern with certain of these formative assumptions is that, although no longer scientifically justifiable, their shaping influence remains deeply embedded. In recent years substantial evidence has emerged demonstrating that training responses vary extensively, depending upon multiple underlying factors. Such findings challenge the appropriateness of applying generic methodologies, founded in overly simplistic rule-based decision making, to the planning problems posed by inherently complex biological systems. The purpose of this review is not to suggest a whole-scale rejection of periodization theories but to promote a refined awareness of their various strengths and weaknesses. Eminent periodization theorists—and their variously proposed periodization models—have contributed substantially to the evolution of training-planning practice. However, there is a logical line of reasoning suggesting an urgent need for periodization theories to be realigned with contemporary elite practice and modern scientific conceptual models. In concluding, it is recommended that increased emphasis be placed on the design and implementation of sensitive and responsive training systems that facilitate the guided emergence of customized context-specific training-planning solutions.


title: A Wavelet Based Image Watermarking Scheme

abstract: A novel robust watermarking scheme based on wavelet transform is presented in this paper. The watermark is embedded into significant triplet of wavelet coefficients in the three lowest detail subband images. The embedding algorithm only needs to modify a small amount of the wavelet coefficients, which leads to very small distortion and guarantees the visual quality of the watermarked image. The performance of the proposed algorithm is tested on a series of normal attacks in image processing, such as JPEG compression, Gaussian noise addition, median filter, etc. The corresponding experimental results show that the proposed watermarking scheme has better performance.


title: Clustering the Vélib' origin-destinations flows by means of Poisson mixture models

abstract: Studies based on human mobility, including Bicycle Sharing System analysis, has expanded over the past few years. They aim to give insight of the underlying urban phenomena linked to city dynamics. This paper presents a generative count-series model using adapted Poisson mixtures to automatically analyse and find temporal-based clusters over the Velib' origin-destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over the time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graph with respect to their temporal profiles.


title: CnC-CUDA: declarative programming for GPUs

abstract: The computer industry is at a major inflection point in its hardware roadmap due to the end of a decades-long trend of exponentially increasing clock frequencies. Instead, future computer systems are expected to be built using homogeneous and heterogeneous many-core processors with 10's to 100's of cores per chip, and complex hardware designs to address the challenges of concurrency, energy efficiency and resiliency. Unlike previous generations of hardware evolution, this shift towards many-core computing will have a profound impact on software. These software challenges are further compounded by the need to enable parallelism in workloads and application domains that traditionally did not have to worry about multiprocessor parallelism in the past. A recent trend in mainstream desktop systems is the use of graphics processor units (GPUs) to obtain order-of-magnitude performance improvements relative to general-purpose CPUs. Unfortunately, hybrid programming models that support multithreaded execution on CPUs in parallel with CUDA execution on GPUs prove to be too complex for use by mainstream programmers and domain experts, especially when targeting platforms with multiple CPU cores and multiple GPU devices. ::: ::: In this paper, we extend past work on Intel's Concurrent Collections (CnC) programming model to address the hybrid programming challenge using a model called CnC-CUDA. CnC is a declarative and implicitly parallel coordination language that supports flexible combinations of task and data parallelism while retaining determinism. CnC computations are built using steps that are related by data and control dependence edges, which are represented by a CnC graph. The CnC-CUDA extensions in this paper include the definition of multithreaded steps for execution on GPUs, and automatic generation of data and control flow between CPU steps and GPU steps. Experimental results show that this approach can yield significant performance benefits with both GPU execution and hybrid CPU/GPU execution.


title: AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references

abstract: Motivation: De novo assemblies of genomes remain one of the most challenging applications in next-generation sequencing. Usually, their results are incomplete and fragmented into hundreds of contigs. Repeats in genomes and sequencing errors are the main reasons for these complications. With the rapidly growing number of sequenced genomes, it is now feasible to improve assemblies by guiding them with genomes from related species. Results: Here we introduce AlignGraph, an algorithm for extending and joining de novo -assembled contigs or scaffolds guided by closely related reference genomes. It aligns paired-end (PE) reads and preassembled contigs or scaffolds to a close reference. From the obtained alignments, it builds a novel data structure, called the PE multipositional de Bruijn graph. The incorporated positional information from the alignments and PE reads allows us to extend the initial assemblies, while avoiding incorrect extensions and early terminations. In our performance tests, AlignGraph was able to substantially improve the contigs and scaffolds from several assemblers. For instance, 28.7–62.3% of the contigs of Arabidopsis thaliana and human could be extended, resulting in improvements of common assembly metrics, such as an increase of the N50 of the extendable contigs by 89.9–94.5% and 80.3–165.8%, respectively. In another test, AlignGraph was able to improve the assembly of a published genome (Arabidopsis strain Landsberg) by increasing the N50 of its extendable scaffolds by 86.6%. These results demonstrate AlignGraph’s efficiency in improving genome assemblies by taking advantage of closely related references. Availability and implementation: The AlignGraph software can be downloaded for free from this site: https://github.com/baoe/ AlignGraph.


title: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.


Analysis and Finite Element Modelling of Water Flow in Concrete


title: Wegener's granulomatosis in two sisters.

abstract: We describe two sisters with necrotising granulomas. The intervals between the onset of symptoms and diagnosis were eight and nine years. The pathological findings showed necrotising granulomas in both patients, this being consistent with a diagnosis of pathergic (Wegener's) granulomatosis. The one patient that accepted treatment responded well to cyclophosphamide, though the facial lesions responded only to radiation therapy. Since the second was almost asymptomatic we conclude that relatives of patients with Wegener's disease should be investigated.


title: Modeling and Model Checking Software Product Lines

abstract: Software product line engineering combines the individual developments of systems to the development of a family of systems consisting of common and variable assets.In this paper we introduce the process algebra PL-CCS as a product line extension of CCS and show how to model the overall behavior of an entire family within PL-CCS. PL-CCS models incorporate behavioral variability and allow the derivation of individual systems in a systematic way due to a semantics given in terms of multi-valued modal Kripke structures. Furthermore, we introduce multi-valued modal μ-calculus as a property specification language for system families specified in PL-CCS and show how model checking techniques operate on such structures. In our setting the result of model checking is no longer a simple yesor noanswer but the set of systems of the product line that do meet the specified properties.


title: Alternative Narratives of Crisis Events: Communities and Social Botnets Engaged on Social Media

abstract: This paper presents an early stage exploratory analysis of communities engaging in alternative narratives about crisis events on social media. Over 11,000 user accounts on Twitter engaged in conversations questioning the mainstream narrative of the Paris Attacks and Umpqua Community College Shootings in Autumn 2015. We analyze the social network of communication, examining the composition of clusters of accounts with shared audiences. We describe some of the common traits within communities and note the prevalence of automated accounts. Our results shed light on the nature of the communities espousing alternative narratives and factors in the spread of the content.


title: Toward a structure- and process-integrated view of personality: Traits as density distributions of states.

abstract: Three experience-sampling studies explored the distributions of Big-Five-rele vant states (behavior) across 2 to 3 weeks of everyday life. Within-person variability was high, such that the typical individual regularly and routinely manifested nearly all levels of all traits in his or her everyday behavior. Second, individual differences in central tendencies of behavioral distributions were almost perfectly stable. Third, amount of behavioral variability (and skew and kurtosis) were revealed as stable individual differences. Finally, amount of within-person variability in extraversion was shown to reflect individual differences in reactivity to extraversion-reievant situational cues. Thus, decontextualized and noncontingent Big-Five content is highly useful for descriptions of individuals' density distributions as wholes. Simultaneously, contextualized and contingent personality units (e.g., conditional traits, goals) are needed for describing the considerable within-person variation. The purpose of this article is to identify aspects of individuals' everyday trait-relevant behavior over time that are relevant to and explainable by personality psychology. The presumption is that, because the same individual behaves differently on different occasions, an individual's behavior over time forms a distribution; the central proposal is that the entire distribution and its several components are relevant to—and to be explained by—personality psychology. Specifically, on the basis of a view of individuals as actively reacting to context (Allport, 1937; Brown & Moskowitz,


title: Sports Data Mining: Predicting Results for the College Football Games

abstract: Abstract In many real-life sports games, spectators are interested in predicting the outcomes and watching the games to verify their predictions. Traditional approaches include subjective prediction, objective prediction, and simple statistical methods. However, these approaches may not be too reliable in many situations. In this paper, we present a sports data mining approach, which helps discover interesting knowledge and predict outcomes of sports games such as college football. Our approach makes predictions based on a combination of four different measures on the historical results of the games. Evaluation results on real-life college football data shows that our approach leads to relatively high accuracy in result prediction.


title: Analytical Processing for Forensic Analysis

abstract: The current status of Information Technology has prompted several countries to adopt technologies based on internationally accepted standards for the disclosure of their financial statements. In order to facilitate data exchange and increase the transparency of financial information available on the Internet, the XBRL technology has been adopted as standard by many regulators of the financial market. However, the occurrence of financial crimes in big corporations fosters research on fraud detection using computational resources. With such aim, this paper proposes a support tool for forensic financial analysis, based on OLAP query languages for the digital detection of fraud in financial reports represented in XBRL. We propose an extension of the LMDQL language, which supports both queries in XML documents connected by XLink and XML Schema, and also relational databases. For this reason, forensic operators are presented, extending those defined in LMDQL, and its use is demonstrated on a relational repository of multidimensional data, based on XBRL. To review the forensic operators a case study was realized using XBRL documents provided by US- SEC, to which an ETL processing was applied for the loading of the data, making it possible to analyze the occurrence of fraud in large volumes of data presented.


title: A modal characterization of defeasible deontic conditionals and conditional goals

abstract: We explore the notions of permission and obligation and their role in knowledge representation, especially as guides to action for planning systems. We first present a simple conditional deontic logic (or more accurately a preference logic) of the type common in the literature and demonstrate its equivalence to a number of modal and conditional systems for default reasoning. We show how the techniques of conditional default reasoning can be used to derive factual preferences from conditional preferences. We extend the system to account for the effect of beliefs on an agent’s obligations, including beliefs held by default. This leads us to the notion of a conditional goal, goals toward which an agent should strive according to its belief state. We then extend the system (somewhat naively) to model the ability of an agent to perform actions. Even with this simple account, we are able to show that the deontic slogan "make the best of a bad situation" gives rise to several interpretations or strategies for determining goals (and actions). We show that an agent can improve its decisions and focus its goals by making observations, or increasing its knowledge of the world. Finally, we discuss how this model might be extended and used in the planning process, especially to represent planning under uncertainty in a qualitative manner.


title: Boosting-like Deep Learning For Pedestrian Detection

abstract: This paper proposes boosting-like deep learning (BDL) framework for pedestrian detection. Due to overtraining on the limited training samples, overfitting is a major problem of deep learning. We incorporate a boosting-like technique into deep learning to weigh the training samples, and thus prevent overtraining in the iterative process. We theoretically give the details of derivation of our algorithm, and report the experimental results on open data sets showing that BDL achieves a better stable performance than the state-of-the-arts. Our approach achieves 15.85% and 3.81% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech benchmark dataset, respectively.


title: Heterogeneous Silicon Photonic Integrated Circuits

abstract: We review recent breakthroughs in the silicon photonic technology and components, and describe progress in silicon photonic integrated circuits. Heterogeneous silicon photonics has recently demonstrated performance that significantly outperforms native III/V components. The impact active silicon photonic integrated circuits could have on interconnects, telecommunications, sensors, and silicon electronics is reviewed.


title: Enforceable Data Sharing Agreements Using Smart Contracts

abstract: As more and more data is collected for various reasons, the sharing of such data becomes paramount to increasing its value. Many applications ranging from smart cities to personalized health care require individuals and organizations to share data at an unprecedented scale. Data sharing is crucial in today's world, but due to privacy reasons, security concerns and regulation issues, the conditions under which the sharing occurs needs to be carefully specified. Currently, this process is done by lawyers and requires the costly signing of legal agreements. In many cases, these data sharing agreements are hard to track, manage or enforce. In this work, we propose a novel alternative for tracking, managing and especially enforcing such data sharing agreements using smart contracts and blockchain technology. We design a framework that generates smart contracts from parameters based on legal data sharing agreements. The terms in these agreements are automatically enforced by the system. Monetary punishment can be employed using secure voting by external auditors to hold the violators accountable. Our experimental evaluation shows that our proposed framework is efficient and low-cost.


Unbounded Innovation with Digitalization : A Case of Digital Camera *


title: Analysis of Scores, Datasets, and Models in Visual Saliency Prediction

abstract: Significant recent progress has been made in developing high-quality saliency models. However, less effort has been undertaken on fair assessment of these models, over large standardized datasets and correctly addressing confounding factors. In this study, we pursue a critical and quantitative look at challenges (e.g., center-bias, map smoothing) in saliency modeling and the way they affect model accuracy. We quantitatively compare 32 state-of-the-art models (using the shuffled AUC score to discount center-bias) on 4 benchmark eye movement datasets, for prediction of human fixation locations and scan path sequence. We also account for the role of map smoothing. We find that, although model rankings vary, some (e.g., AWS, LG, AIM, and HouNIPS) consistently outperform other models over all datasets. Some models work well for prediction of both fixation locations and scan path sequence (e.g., Judd, GBVS). Our results show low prediction accuracy for models over emotional stimuli from the NUSEF dataset. Our last benchmark, for the first time, gauges the ability of models to decode the stimulus category from statistics of fixations, saccades, and model saliency values at fixated locations. In this test, ITTI and AIM models win over other models. Our benchmark provides a comprehensive high-level picture of the strengths and weaknesses of many popular models, and suggests future research directions in saliency modeling.


title: Regression shrinkage and selection via the lasso : a retrospective

abstract: Summary. In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.


title: Cone-Beam Computed Tomography and Radiographs in Dentistry: Aspects Related to Radiation Dose

abstract: Introduction. The aim of this study was to discuss the radiation doses associated with plain radiographs, cone-beam computed tomography (CBCT), and conventional computed tomography (CT) in dentistry, with a special focus on orthodontics. Methods. A systematic search for articles was realized by MEDLINE from 1997-March 2011. Results. Twenty-seven articles met the established criteria. The data of these papers were grouped in a table and discussed. Conclusions. Increases in kV, mA, exposure time, and field of view (FOV) increase the radiation dose. The dose for CT is greater than other modalities. When the full-mouth series (FMX) is performed with round collimation, the orthodontic radiographs transmit higher dose than most of the large FOV CBCT, but it can be reduced if used rectangular collimation, showing lower effective dose than large FOV CBCT. Despite the image quality, the CBCT does not replace the FMX. In addition to the radiation dose, image quality and diagnostic needs should be strongly taken into account.


title: Prosthetic gingival reconstruction in a fixed partial restoration. Part 1: introduction to artificial gingiva as an alternative therapy.

abstract: The Class III defect environment entails a vertical and horizontal deficiency in the edentulous ridge. Often, bone and soft tissue surgical procedures fall short of achieving a natural esthetic result. Alternative surgical and restorative protocols for these types of prosthetic gingival restorations are presented in this three-part series, which highlights the diagnostic and treatment aspects as well as the lab and maintenance challenges. A complete philosophical approach involves both a biologic understanding of the limitations of the hard and soft tissue healing process as well as that of multiple adjacent implants in the esthetic zone. These limitations may often necessitate the use of gingiva-colored "pink" restorative materials and essential preemptive planning via three-dimensional computer-aided design/computer-assisted manufacture to achieve the desired esthetic outcome. The present report outlines a rationale for consideration of artificial gingiva when planning dental prostheses. Prosthetic gingiva can overcome the limitations of grafting and should be a consideration in the initial treatment plan. (Int J Periodontics Restorative Dent 2009;29:471-477.).


title: The SWAL-QOL Outcomes Tool for Oropharyngeal Dysphagia in Adults: I. Conceptual Foundation and Item Development

abstract: In the past two decades, noteworthy advances have been made in measuring the physiologic outcomes of dysphagia, including measurement of duration of structure and bolus movements, stasis, and penetration–aspiration. However, there is a paucity of data on health outcomes from the patients' perspective, such as quality of life and patient satisfaction. A patient-based, dysphagia-specific outcomes tool is needed to enhance information on treatment variations and treatment effectiveness. We present the conceptual foundation and item generation process for the SWAL-QOL, a quality of life and quality of care outcomes tool under development for dysphagia researchers and clinicians.


title: SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis

abstract: While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements. Recently, there has been greater exploration into small deep neural network architectures that are more suitable for edge devices, with one of the most popular architectures being SqueezeNet, with an incredibly small model size of 4.8MB. Taking further advantage of the notion that many applications of machine learning on edge devices are often characterized by a low number of target classes, this study explores the utility of combining architectural modifications and an evolutionary synthesis strategy for synthesizing even smaller deep neural architectures based on the more recent SqueezeNet v1.1 macroarchitecture for applications with fewer target classes. In particular, architectural modifications are first made to SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an evolutionary synthesis strategy is leveraged to synthesize more efficient deep neural networks based on this modified macroarchitecture. The resulting SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (~5.17X smaller than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%, and able to process at speeds of 156 images/sec to as much as 256 images/sec on a Nvidia Jetson TX1 embedded chip. These preliminary results show that a combination of architectural modifications and an evolutionary synthesis strategy can be a useful tool for producing very small deep neural network architectures that are well-suited for edge device scenarios.


title: Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation

abstract: We consider the following problem: There is a set of items (e.g., movies) and a group of agents (e.g., passengers on a plane); each agent has some intrinsic utility for each of the items. Our goal is to pick a set of K items that maximize the total derived utility of all the agents (i.e., in our example we are to pick K movies that we put on the plane's entertainment system). However, the actual utility that an agent derives from a given item is only a fraction of its intrinsic one, and this fraction depends on how the agent ranks the item among the chosen, available, ones. We provide a formal specification of the model and provide concrete examples and settings where it is applicable. We show that the problem is hard in general, but we show a number of tractability results for its natural special cases.


title: Applying statistical machine learning to multicore voltage & frequency scaling

abstract: Dynamic Voltage/Frequency Scaling (DVFS) is a useful tool for improving system energy efficiency, especially in multi-core chips where energy is more of a limiting factor. Per-core DVFS, where cores can independently scale their voltages and frequencies, is particularly effective. We present a DVFS policy using machine learning, which learns the best frequency choices for a machine as a decision tree. Machine learning is used to predict the frequency which will minimize the expected energy per user-instruction (epui) or energy per (user-instruction)2 (epui2). While each core independently sets its frequency and voltage, a core is sensitive to other cores' frequency settings. Also, we examine the viability of using only partial training to train our policy, rather than full profiling for each program. We evaluate our policy on a 16-core machine running multiprogrammed, multithreaded benchmarks from the PARSEC benchmark suite against a baseline fixed frequency as well as a recently-proposed greedy policy. For 1ms DVFS intervals, our technique improves system epui2 by 14.4% over the baseline no-DVFS policy and 11.3% on average over the greedy policy.


title: One-Step Sneak-Path Free Read Scheme for Resistive Crossbar Memory

abstract: A one-step sneak-path free read scheme for resistive crossbar memory is proposed in this article. During read operation, it configures the crossbar array into a four-terminal resistance network, which is composed of the selected cell and three other resistors corresponding to unselected cells that contribute to the sneak-path. Two sensing voltages with equal potential are applied to three terminals of the network. One is for sensing the resistance of the selected cell; the other is for creating zero-voltage drop across one of the three resistors, which connects the sneak-path to the selected cell. This effectively suppresses the current injected by the sneak-path to the selected cell-sensing loop. This work also proposes a cost-effective data-encoding circuit that guarantees that at least half of the memory cells are in a high-resistance state, which further minimizes sneak-path current. The impact of key design parameters, such as sensing voltage, switch on-resistance, and the ratio of memory cell resistances in different states, as well as nonideal effects are investigated. Equations for estimating the maximum array size to share a single read circuit are derived. The effectiveness of the proposed design has been validated via circuit simulations. Impacts of the word-/bit-line resistance are also analyzed.


title: Development of a wall-climbing robot using a tracked wheel mechanism

abstract: In this paper, a new concept of a wall-climbing robot able to climb a vertical plane is presented. A continuous locomotive motion with a high climbing speed of 15m/min is realized by adopting a series chain on two tracked wheels on which 24 suction pads are installed. While each tracked wheel rotates, the suction pads which attach to the vertical plane are activated in sequence by specially designed mechanical valves. The engineering analysis and detailed mechanism design of the tracked wheel, including mechanical valves and the overall features, are described in this paper. It is a self-contained robot in which a vacuum pump and a power supply are integrated and is controlled remotely. The climbing performance, using the proposed mechanism, is evaluated on a vertical steel plate. Finally, the procedures are presented for an optimization experiment using Taguchi methodology to maximize vacuum pressure which is a critical factor for suction force.


title: The regulation of surrogacy in the United Kingdom: the case for reform

abstract: This article considers the legal regulation of surrogacy in the United Kingdom and examines legislative changes and case law that have emerged over the past three decades. We illustrate how the legal requirements to obtain parental orders (which give parenthood to the intended parents after the birth and extinguish the parental status of the surrogate) are overly restrictive and anachronistic. The legal requirements needed to obtain such orders have resulted in clear anomalies and left children in a state of legal-limbo. In practice some of the legal requirements are often bypassed and this alone is a compelling argument for legal reform. Other requirements, such as parental orders only being available to couples, are discriminatory and violate the individual’s right to respect for theirprivate and family life. The regulation of surrogacy is also incoherent and inconsistent when compared with other aspects of ‘becoming’ a parent via other assisted reproductive technologies. This paper argues that the anomalies that judicial decisions have generated in this domain have intensified the case for reform. We argue that the time is opportune for a new statute governing this contentious area and make the following three proposals for reform: (i) a pre-conception regulatory framework (ii) permitting moderate payment (iii) widening access to parenthood and moving away from the two-parent model.


title: Tracking a head-mounted display in a room-sized environment with head-mounted cameras

abstract: This paper presents our efforts to accurately track a Head-Mounted Display (HMD) in a large ::: environment. We review our current benchtop prototype (introduced in {WCF9O]), then describe our plans ::: for building the full-scale system. Both systems use an inside-oui optical tracking scheme, where lateraleffect ::: photodiodes mounted on the user's helmet view flashing infrared beacons placed in the environment. ::: Church's method uses the measured 2D image positions and the known 3D beacon locations to recover the ::: 3D position and orientation of the helmet in real-time. We discuss the implementation and performance of ::: the benchtop prototype. The full-scale system design includes ceiling panels that hold the infrared beacons ::: and a new sensor arrangement of two photodiodes with holographic lenses. In the full-scale system, the user ::: can walk almost anywhere under the grid of ceiling panels, making the working volume nearly as large as ::: the room.


title: Modeling complex air traffic management systems

abstract: In this work, we propose the use of multi-agent system (MAS) models as the basis for predictive reasoning about various safety conditions and the performance of Air Traffic Management (ATM) Systems. To this end, we describe the engineering of a domain-specific MAS model that provides constructs for creating scenarios related to ATM systems and procedures; we then instantiate the constructs in the ATM model for different scenarios. As a case study we generate a model for a concept that provides the ability to maximize departure throughput at La Guardia airport (LGA) without impacting the flow of the arrival traffic; the model consists of approximately 1.5 hours real time flight data. During this time, between 130 and 150 airplanes are managed by four en- route controllers, three TRACON controllers, and one tower controller at LGA who is responsible for departures and ar- rivals. The planes are landing at approximately 36 to 40 planes an hour. A key contribution of this work is that the model can be extended to various air-traffic management scenarios and can serve as a template for engineering large- scale models in other domains.


title: 5G 3GPP-like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

abstract: For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.


title: Learning Context Cues for Synapse Segmentation

abstract: We present a new approach for the automated segmentation of synapses in image stacks acquired by electron microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifier that can effectively learn cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. Furthermore, as a by-product of the segmentation, our method flawlessly determines synaptic orientation, a crucial element in the interpretation of brain circuits. We evaluate our approach on three different datasets, compare it against the state-of-the-art in synapse segmentation and demonstrate our ability to reliably collect shape, density, and orientation statistics over hundreds of synapses.


title: A simple hybrid 3-level buck-boost DC-DC converter with efficient PWM regulation scheme

abstract: This paper describes a 3-level buck-boost converter with an output voltage range up to double the input voltage. The proposed converter can be used for applications that require stepping up/down the input voltage depending on the operating conditions like solar power harvesting. The converter has a hybrid structure between inductor-based converters and capacitor-based converters by utilizing a one flying capacitor and an inductor in the charge transfer process. The converter features smaller inductor size and current ripples as compared to the traditional buck-boost inductor-based converter while offering a high efficiency as compared to SC converters making it suitable for efficient on-chip integration. A prototype circuit has been designed on a 65nm technology using a 5-nH parasitic bondwire inductor. Simulation results show that the proposed converter features up to 20% improvement in the efficiency over a conventional 1:2 SC converter for a wide range of output voltage values.


title: Graph Echo State Networks

abstract: In this paper we introduce the Graph Echo State Network (GraphESN) model, a generalization of the Echo State Network (ESN) approach to graph domains. GraphESNs allow for an efficient approach to Recursive Neural Networks (RecNNs) modeling extended to deal with cyclic/acyclic, directed/undirected, labeled graphs.


A Neural Model for Language Identification in Code-Switched Tweets


title: New developments in capacitive encoders for servo drives

abstract: Speed and position encoders are essential components in electrical drives. High resolution encoders are necessary to obtain acceptable speed control behaviour. Therefore optical encoders are mainly used in servo drives as position (and speed) feedback. Optical encoders, however, do not provide a robustness comparable to electrical motors - hard environmental influences like mechanical shock and/or vibrations often damage the optical disc inside the encoder. Using resolvers provides better mechanical robustness - their resolution, however, is not sufficient for good speed control behaviour. Capacitive (electrcial) [electrical] encoders are an attempt to combine good robustness with high resolution. Technical results obtained with a first commercial product encourage to proceed with further research and development in this area.


title: Impact of Information Technology on Accounting Systems

abstract: The emergence of information technology in accounting is an innovative system. Nowadays, most ::: business entities, from large corporations down to micro enterprises, are aided by their Accounting ::: Information Systems in managing their operations. This paper provides a review on the impact of ::: information technology on accounting systems. This paper also discusses things about the accounting ::: processes and its types. Accounting is a critical factor for the business, having the touch of IT can ::: enhance speed and accurateness of computations as well as to enhance its flexibility to change and safety ::: storage of information.


title: An end-to-end approach to language identification in short utterances using convolutional neural networks

abstract: This work has been supported by project CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Senal de Voz (TEC2012-37585-C02-01), funded by Ministerio de Economia y Competitividad, Spain.


title: Bacteria and yeast microbiota in milk kefir grains from different Italian regions.

abstract: Kefir grains are a unique symbiotic association of different microrganisms, mainly lactic acid bacteria, yeasts and occasionally acetic acid bacteria, cohabiting in a natural polysaccharide and a protein matrix. The microbial composition of kefir grains can be considered as extremely variable since it is strongly influenced by the geographical origin of the grains and by the sub-culturing method used. The aim of this study was to elucidate the bacteria and yeast species occurring in milk kefir grains collected in some Italian regions by combining the results of scanning electron microscopy analysis, viable counts on selective culture media, PCR-DGGE and pyrosequencing. The main bacterial species found was Lactobacillus kefiranofaciens while Dekkera anomala was the predominant yeast. The presence of sub-dominant species ascribed to Streptococcus thermophilus, Lactococcus lactis and Acetobacter genera was also highlighted. In addition, Lc. lactis, Enterococcus sp., Bacillus sp., Acetobacter fabarum, Acetobacter lovaniensis and Acetobacter orientalis were identified as part of the cultivable community. This work further confirms both the importance of combining culture-independent and culture-dependent approaches to study microbial diversity in food and how the combination of multiple 16S rRNA gene targets strengthens taxonomic identification using sequence-based identification approaches.


title: Fundamental Components of the Gameplay Experience: Analysing Immersion

abstract: This paper presents a gameplay experience model, assesses its potential as a tool for research and presents some directions for future work. The presented model was born from observations among game-playing children and their non-player parents, which directed us to have a closer look at the complex nature of gameplay experience. Our research led into a heuristic gameplay experience model that identifies some of the key components and processes that are relevant in the experience of gameplay, with a particular focus on immersion. The model includes three components: sensory, challenge-based and imaginative immersion (SCI-model). The classification was assessed with self-evaluation questionnaires filled in by informants who played different popular games. It was found that the gameplay experiences related to these games did indeed differ as expected in terms of the identified three immersion components.


title: Continuous Probabilistic Transform for Voice Conversion

abstract: Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic+noise model) system, which allows high-quality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.


title: Case Report: POSTERIOR INTEROSSEOUS NERVE COMPRESSION SYNDROME

abstract: Radial nerve compression or injury can occur at any point along the course of the nerve within the upper extremity. Entrapment of the radial nerve or its branches is most common within the proximal forearm and at the elbow. Variations in anatomic structures at this level are an important cause of radial nerve entrapment syndromes. Compression of the radial nerve and its branches at the elbow can therefore result in motor, sensory, or mixed deficits. The motor branch is particularly vulnerable to compressive injury, and compression of this branch may result in a variety of clinical presentations. Posterior interosseous nerve syndrome can be mistaken for a C7 radiculopathy and lateral epicondylitis which has a burning sensation along the lateral aspect of the forearm. The clinical entities sometime are difficult to distinguish on physical examination. MR imaging plays an important role to depicting nerve entrapment by identifying muscle edema, atrophy, and fatty infiltration as well as detecting the cause of entrapment. This paper reported a case of male 25 year old with persistent pain at the forearm after a fall 2 years before admission. On physical examination, a weakening of the extension of the fingers and wrist was detected. There was no sensory deficit noted and no history of appropriate treatment taken previously. MRI examination was perfomed to detect the abnormality of the forearm and elbow. In conclusion, compression of the posterior interosseous nerve can result in a variety of clinical symptoms. The onset of pain or weakness is often insidious, resulting in a confusing clinical presentation. MRI is useful in identifying muscle signal changes in keeping with denervation, contributory anatomic factors, and masses or other lesions that may result in nerve entrapment. (FMI 2012;48 37-42)


title: A robust inverse kinematics algorithm for animating a joint chain

abstract: The cyclic coordinate descent (CCD) is a well-known algorithm used for inverse kinematics solutions in applications involving joint chains and moving targets. Even though a CCD algorithm can be easily implemented, it can take a series of iterations before converging to a solution and also generate undesirable joint rotations. This paper presents a novel single-pass algorithm that is fast and eliminates problems associated with improper and large angle rotations. Experimental results are presented to show the performance benefits of the proposed algorithm over CCD and the 'triangulation' methods, using different types of cost functions.


title: Surround view based parking lot detection and tracking

abstract: Parking Assistance System (PAS) provides useful help to beginners or less experienced drivers in complicated urban parking scenarios. In recent years, ultrasonic sensor based PAS and rear-view camera based PAS have been proposed from different car manufacturers. However, ultrasonic sensors detection distance is less than 3 meters and results cannot be used to extract further information like obstacle recognition. Rear-view camera based systems cannot provide assistance to the circumstances like parallel parking which need a wider view. In this paper, we proposed a surround view based parking lot detection algorithm. An efficient tracking algorithm was proposed to solve the tracking problem when detected parking slots were falling out of the surround view. Experimental results on simulation and real outdoor environment showed the effectiveness of the proposed algorithm.


title: Influencing the online consumer's behavior: the Web experience

abstract: Addresses one of the fundamental issues of e-marketing: how to attract and win over the consumer in the highly competitive Internet marketplace. Analyses the factors affecting the online consumer's behavior and examines how e-marketers can influence the outcome of the virtual interaction and buying process by focusing their marketing efforts on elements shaping the customer's virtual experience, the Web experience. Identifying the Web experience components and understanding their role as inputs in the online customer's decision-making process are the first step in developing and delivering an attractive online presence likely to have the maximum impact on Internet users. Click-and-mortar firms delivering superior Web experience influence their physical clients’ perceptions and attitudes, driving additional traffic to traditional sales outlets. Provides a contribution to the theoretical debate around the factors influencing the online consumer's behavior and outlines some noticeable similarities and differences between the traditional and virtual consumers.


title: Backhaul-aware adaptive TP selection for virtual cell in ultra-dense networks

abstract: Ultra-dense network (UDN) in 5G provides more potential transmission points (TPs) for users. To take advantage of dense TPs, 5G will shift the network paradigm from traditional cell-centric to user-centric. Virtual cell redefines the cell from the view of users to build user-centric networks. Virtual cell is a logical cell consisting of multi physical TPs. By data synchronization as a way of cooperation between TPs at different protocol layers, virtual cell can enhance spectral efficiency. Data synchronization will utilize backhaul resources. However, non-ideal backhaul will be extensively deployed, which will take a significant share of the energy consumption in future systems. Therefore, TP selection in a virtual cell should take spectral efficiency (SE) and energy efficiency (EE) into account. To trade off between the SE and EE, we present an analysis of the backhaul power consumption and put forward an adaptive algorithm for TP selection in a virtual cell. We define the normalized outage capacity as goodput to represent the SE. Then, we consider the power of backhaul link as the energy consumption. Thus, the objective of TP selection is maximizing the defined energy efficiency. A dynamic ergodic method is used to solve the problem. Our simulation shows the dynamic selection scheme has better performance than static scheme and random scheme. Besides, with the size of virtual cell increasing, the growth rate of energy efficiency is decreasing. Thus, the size of virtual cell and TP numbers should be controlled.


title: Machine-Learning Attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs

abstract: A physically unclonable function (PUF) is a circuit of which the input–output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this paper is the development of efficient impersonation attacks on the following five Arbiter PUF–based authentication protocols: 1) the so-called Poly PUF protocol of Konigsmark et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016; 2) the so-called OB-PUF protocol of Gao et al. as presented at the IEEE Conference PerCom 2016; 3) the so-called RPUF protocol of Ye et al. as presented at the IEEE Conference AsianHOST 2016; 4) the so-called LHS-PUF protocol of Idriss and Bayoumi as presented at the IEEE Conference RFID-TA 2017; and 5) the so-called PUF–FSM protocol of Gao et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2018. The common flaw of all five designs is that the use of lightweight obfuscation logic provides insufficient protection against machine-learning attacks.


title: ×86 computer architecture simulators: A comparative study

abstract: The significance of computer architecture simulators in advancing computer architecture research is widely acknowledged. Computer architects have developed numerous simulators in the past few decades and their number continues to rise. This paper explores different simulation techniques and surveys many ×86 simulators. Comparing simulators with each other and validating their correctness has been a challenging task. In this paper, we compare and contrast ×86 simulators in terms of flexibility, level of details, user friendliness and simulation models. In addition, we measure the experimental error and compare the speed of four contemporary ×86 simulators: gem5, Multi2sim, PTLsim and Sniper. We also discuss the strengths and limitations of these simulators. We believe that this paper provides insights into different simulation strategies and aims to help computer architects understand the differences among existing simulation tools.


title: Isolectin-IB4 as a vascular stain for the study of adult neurogenesis

abstract: It has been proposed that new neurons in the adult brain might preferentially develop in the immediate vicinity of existing vasculature, an area that has been referred to as a "vascular niche". The most common method for identifying neurons that arise as the result of mitotic activity is to label them with the thymidine analogue bromodeoxyuridine (BrdU), and a nuclear marker for mature neurons (NeuN). Unfortunately, commercially available forms of NeuN and the vasculature stain, RECA-1, are incompatible, as both antibodies are raised in the same species. This technical limitation has impeded the analysis of the relationship between new neurons and existing vasculature. In the present work we show that Isolectin-IB 4, a 114 k Da protein isolated from the seeds of the African legume, Griffonia simplicifolia, can be used to identify vasculature in conjunction with both NeuN and BrdU immunohistochemistry. Isolectin-IB 4 serves as an easy to use and robust marker for obtaining spatial information regarding the proximity of new neurons to vasculature in the mammalian brain.


title: Pattern Recognition: Statistical, Structural and Neural Approaches

abstract: STATISTICAL PATTERN RECOGNITION (StatPR). Supervised Learning (Training) Using Parametric and Nonparametric Approaches. Linear Discriminant Functions and the Discrete and Binary Feature Cases. Unsupervised Learning and Clustering. SYNTACTIC PATTERN RECOGNITION (SyntPR). Overview. Syntactic Recognition via Parsing and Other Grammars. Graphical Approaches to SyntPR. Learning via Grammatical Inference. NEURAL PATTERN RECOGNITION (NeurPR). Introduction to Neural Networks. Introduction to Neural Pattern Associators and Matrix Approaches. Feedforward Networks and Training by Backpropagation. Content Addressable Memory Approaches and Unsupervised Learning in NeurPR. Appendices. References. Permission Source Notes. Index.


Interval-based Queries over Multiple Streams with Missing Timestamps


title: Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs)

abstract: Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization. The key step in SNNs is to properly scale the exponential linear unit (referred to as SELU) to inherently incorporate normalization based on central limit theory. SELU is a monotonically increasing function, where it has an approximately constant negative output for large negative input. In this work, we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property. Differently from SELU, the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function, which is referred to as a scaled exponentially-regularized linear unit (SERLU). The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically. To effectively combat over-fitting, we develop a so-called shift-dropout for SERLU, which includes standard dropout as a special case. Experimental results on MNIST, CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU, SELU, Swish, Leakly ReLU and ReLU.


title: A Statistical Semantic Parser That Integrates Syntax And Semantics

abstract: We introduce a learning semantic parser, Scissor, that maps natural-language sentences to a detailed, formal, meaning-representation language. It first uses an integrated statistical parser to produce a semantically augmented parse tree, in which each non-terminal node has both a syntactic and a semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. We evaluate the system in two domains, a natural-language database interface and an interpreter for coaching instructions in robotic soccer. We present experimental results demonstrating that Scissor produces more accurate semantic representations than several previous approaches.


title: Support Vector Machines for Prediction of Futures Prices in Indian Stock Market

abstract: Machine learning methods are being used by several researchers for successfully predicting prices of financial instruments from the financial time series data of different markets. As the nature of markets in different regions are different, in this paper two machine learning techniques: Back Propagation Technique (BP) and Support Vector Machine Technique (SVM) have been used to predict futures prices traded in Indian stock market. The performances of these techniques are compared and it is observed that SVM provides better performance results as compared to BP technique. The implementation is carried out using MATLAB and SVM Tools (LS-SVM Tool Box). General Terms Machine Learning, Forecasting


title: Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms

abstract: Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.


title: High-Efficiency Single-Phase AC–AC Converters Without Commutation Problem

abstract: This paper proposes single-phase direct pulsewidth modulation (PWM) buck-, boost-, and buck–boost-type ac–ac converters. The proposed converters are implemented with a series-connected freewheeling diode and MOSFET pair, which allows to minimize the switching and conduction losses of the semiconductor devices and resolves the reverse-recovery problem of body diode of MOSFET. The proposed converters are highly reliable because they can solve the shoot-through and dead-time problems of traditional ac–ac converters without voltage/current sensing module, lossy resistor–capacitor ( RC ) snubbers, or bulky coupled inductors. In addition, they can achieve high obtainable voltage gain and also produce output voltage waveforms of good quality because they do not use lossy snubbers. Unlike the recently developed switching cell (SC) ac–ac converters, the proposed ac–ac converters have no circulating current and do not require bulky coupled inductors; therefore, the total losses, current stresses, and magnetic volume are reduced and efficiency is improved. Detailed analysis and experimental results are provided to validate the novelty and merit of the proposed converters.


Illness representations in Thai women diagnosed with hypertension and relationships to medication-taking behavior.


title: Visual firewall: real-time network security monitor

abstract: Networked systems still suffer from poor firewall configuration and monitoring. VisualFirewall seeks to aid in the configuration of firewalls and monitoring of networks by providing four simultaneous views that display varying levels of detail and time-scales as well as correctly visualizing firewall reactions to individual packets. The four implemented views, real-time traffic, visual signature, statistics, and IDS alarm, provide the levels of detail and temporality that system administrators need to properly monitor their systems in a passive or an active manner. We have visualized several attacks, and we feel that even individuals unfamiliar with networking concepts can quickly distinguish between benign and malignant traffic patterns with a minimal amount of introduction.