Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.


A 2.45 GHz harmonic suppression array antenna for rectenna application

In this paper, a 2.45 GHz harmonic suppression capability of array antenna has been presented for wireless power transfer applications. Harmonic suppression up to a fourth order of the fundamental operating frequency has been achieved by using rectangular slot Defected Ground Structure (DGS). The designed antenna uses an FR4 material having thickness 1.6 mm as a substrate which has loss tangent tanδ) 0.02. The gain and radiation efficiency are 4.5dB, 75% respectively. This antenna is suitable for rectenna applications.


A Study of Skew in MapReduce Applications

This paper presents a study of skew — highly variable task runtimes — in MapReduce applications. We describe various causes and manifestations of skew as observed in real world Hadoop applications. Runtime task distributions from these applications demonstrate the presence and negative impact of skew on performance behavior. We discuss best practices recommended for avoiding such behavior and their limitations.


Towards a Trust Management System for Cloud Computing

Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.


Utility of MRI after inconclusive ultrasound in pediatric patients with suspected appendicitis: retrospective review of 60 consecutive patients.

OBJECTIVE
The purpose of this study is to examine the utility of appendix MRI in evaluation of pediatric patients with right lower quadrant pain and inconclusive appendix sonography findings.

MATERIALS AND METHODS
A search of the radiology electronic database was performed for all appendix MRI examinations performed of pediatric patients within 24 hours after inconclusive appendix sonography from December 1, 2009, through April 26, 2012. Sixty patients underwent appendix MRI within 24 hours of inconclusive sonography and represented the study cohort. MRI examinations were reviewed independently by two radiologists blinded to the diagnosis and were graded as "positive," "negative," or "indeterminate" for acute appendicitis. The final diagnosis was established by review of the surgical and pathology reports and patients' electronic medical records.

RESULTS
Ten of 60 patients (17%) had acute appendicitis. Both readers graded the same 12 examinations as positive and the same 48 examinations as negative for acute appendicitis, with a kappa value of 1.00 (expected agreement, 0.695). No MRI examination was interpreted as indeterminate. The sensitivity and specificity of MRI for acute appendicitis in children with inconclusive appendix ultrasound findings were 100% (95% CI, 0.72-1.00) and 96% (95% CI, 0.87-0.98), respectively. The positive predictive value for the examination was 83%, the negative predictive value was 100%, and overall test accuracy was 97%.

CONCLUSION
Our study shows that MRI has a sensitivity of 100% and specificity of 96% for appendicitis in pediatric patients after inconclusive appendix sonography. We think that MRI may supplant CT as the secondary modality to follow inconclusive appendix sonography.


Knowledge-Based Integrated Aircraft Design An Applied Approach from Design to Concept Demonstration

The design and development of new aircraft are becoming increasingly expensive and time-consuming. To assist the design process in reducing the development cost, time, and late design changes, the conceptual design needs enhancement using new tools and methods. Integration of several disciplines in the conceptual design as one entity enables the design process to be kept intact at every step and a high understanding of the aircraft concepts obtained at early stages. This thesis presents a Knowledge-Based Engineering (KBE) approach and integration of several disciplines in a holistic approach for use in aircraft conceptual design. KBE allows the reuse of obtained aircrafts’ data, information, and knowledge to gain more awareness and a better understanding of the concept under consideration at early stages of design. For this purpose, Knowledge-Based (KB) methodologies are investigated for enhanced geometrical representation and to enable variable fidelity tools and Multidisciplinary Design Optimization (MDO). The geometry parameterization techniques are qualitative approaches that produce quantitative results in terms of both robustness and flexibility of the design parameterization. The information/parameters from all tools/disciplines and the design intent of the generated concepts are saved and shared via a central database. The integrated framework facilitates multi-fidelity analysis, combining low-fidelity models with high-fidelity models for a quick estimation, enabling a rapid analysis and enhancing the time for a MDO process. The geometry is further propagated to other disciplines [Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA)] for analysis. This is possible with an automated streamlined process (for CFD, FEM, system simulation) to analyze and increase knowledge early in the design process. Several processes were studied to streamline the geometry for


Impact Selective Incivility as Modern Discrimination in Organizations : Evidence and


RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning

In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.


A Distributed Hybrid Recommendation Framework to Address the New-User Cold-Start Problem

With the development of electronic commerce, recommender system is becoming an important research topic. Existing methods of recommender systems such as collaborative filtering and content analysis are suitable for different cases. Not every recommender system is capable of handling all kinds of situations. In this paper, we bring forward a distributed hybrid recommendation framework to address the new-user cold-start problem based on user classification. First, current user characteristics, user context and operating records are used to classify the user type. Then, suitable recommendation algorithms are dynamically selected based on the current user type, and executed in parallel. Finally, the recommendation results are merged into a consolidated list. In the experiment on movie ratings dataset Movie Lens, the proposed framework can enhance the accuracy of recommendation system. Also, a 100% coverage can be achieved in the case of new users. This shows the potential of the proposed framework in addressing new-user cold-start problem.


Ring-Light Photometric Stereo

We propose a novel algorithm for uncalibrated photometric stereo. While most of previous methods rely on various assumptions on scene properties, we exploit constraints in lighting configurations. We first derive an ambiguous reconstruction by requiring lights to lie on a view centered cone. This reconstruction is upgraded to Euclidean by constraints derived from lights of equal intensity and multiple view geometry. Compared to previous methods, our algorithm deals with more general data and achieves high accuracy. Another advantage of our method is that we can model weak perspective effects of lighting, while previous methods often assume orthographical illumination. We use both synthetic and real data to evaluate our algorithm. We further build a hardware prototype to demonstrate our approach.


Opinion and Perspectivesy Why We Need Quantum Physics for Cognitive Neuroscience

For the past 20 years and more, arguments about the role of quantum mechanics in consciousness and mind theory have been mounting. On the one side are traditional neuroscientists who believe that the way to understanding the brain is through looking at the nerve cells. On the other side are various physicists who suggest that the laws of quantum mechanics may have an influence on the dynamics of consciousness and the mind. At the same time however, consciousness and the mind cannot be separated from matter. They originate in the microscopic world of the human brain. There can be no definite separation between mind and matter; there is no 'mind' without 'matter', and no 'matter' without 'mind'. In terms of cognitive neuroscience, we know a great deal about the working of nerve cells. For example, we understand quite well about the formation of action potential, ion exchange, energy use, axonal transport, the vesicle cycle, and formation, oscillation and breakdown in nerve transmission. However, we still do not understand how experience is formed in our material brain (color, sound, smell, taste, pain, imagination, decision, dreams, love, or orgasm) and how consciousness arises in an unconscious material organ. The insufficiency of these answers no doubt arises from the insufficiency of the methods used by cognitive science.


The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.


Modeling and Control of Marine Diesel Generator System With Active Protection

On-board dc grid systems have been proposed in the marine industry to reduce specific fuel consumption and to improve dynamic performance and manoeuvrability. The primary energy sources of an on-board dc system are variable frequency operating diesel engine generators with an active front-end converter system. Such systems might suffer disturbances from the engine side as well as from the electrical load side. These dynamic conditions result in torque overloads in the mechanical system, resulting in failures to sensitive driveline components. To understand the dynamics associated with disturbances and to propose a robust and reliable system, a detailed modeling approach is required. This paper presents complete modeling of a diesel generator system, including cylinder pressure, mechanical driveline system, electrical generator characteristics, and control system of an active front-end converter. The detailed model is used to develop a novel power electronic-based controller to reduce elevated load levels induced during disturbances. The controller increases the damping ratio of the first natural frequency by using the speed difference between a generator and a flywheel. The generator speed is estimated while fly wheel inertia speed is measured from an existing speed sensor used for monitoring and electronic control, and their difference is used to improve the reliability under disturbance conditions. Simulation with the detailed model has indicated excessive drivetrain loads and potential damage to critical components. The proposed controller enhances the damping of the system by transferring the torsional energy to the electrical system, reducing the shaft torque amplitude during resonance. This paper presents simulation results demonstrating the fidelity of the diesel generator model as well as the effectiveness of the proposed controller.


Digital Entrepreneurship of Born Digital and Grown Digital Firms: Comparing the Effectuation Process of Yihaodian and Suning

This paper examines digital entrepreneurship in born digital and grown digital firms. Of late, digital entrepreneurship has garnered renewed attention from national leaders, practitioners, and academics due to the phenomenal success of a number of digital startups and the radical disruptions brought by the innovative use of technologies. Despite the significance and the promises of digital technology, especially amid the gloomy economy, digital entrepreneurship has remained inadequately analyzed for its predominant focus on entrepreneurs (human agency) and newly founded firms (context). This study offers an in-depth analysis of two entrepreneurial firms, Yihaodian and Suning, which are among the largest e-commerce players in China. By adopting effectuation as our theoretical lens, this study aims to generate an understanding of the digital entrepreneurial process in both born digital and grown digital firms.


Automatic Text Categorization of Mathematical Word Problems

This paper describes a novel application of text categorization for mathematical word problems, namely Multiplicative Compare and Equal Group problems. The empirical results and analysis show that common text processing techniques such as stopword removal and stemming should be selectively used. It is highly beneficial not to remove stopwords and not to do stemming. Part of speech tagging should also be used to distinguish words in discriminative parts of speech from the non-discriminative parts of speech which not only fail to help but even mislead the categorization decision for mathematical word problems. An SVM classifier with these selectively used text processing techniques outperforms an SVM classifier with a default setting of text processing techniques (i.e. stopword removal and stemming). Furthermore, a probabilistic meta classifier is proposed to combine the weighted results of two SVM classifiers with different word problem representations generated by different text preprocessing techniques. The empirical results show that the probabilistic meta classifier further improves the categorization accuracy.


Facebook and texting made me do it: Media-induced task-switching while studying

Electronic communication is emotionally gratifying, but how do such technological distractions impact academic learning? The current study observed 263 middle school, high school and university students studying for 15 min in their homes. Observers noted technologies present and computer windows open in the learning environment prior to studying plus a minute-by-minute assessment of on-task behavior, off-task technology use and open computer windows during studying. A questionnaire assessed study strategies, task-switching preference, technology attitudes, media usage, monthly texting and phone calling, social networking use and grade point average (GPA). Participants averaged less than six minutes on task prior to switching most often due to technological distractions including social media, texting and preference for task-switching. Having a positive attitude toward technology did not affect being on-task during studying. However, those who preferred to task-switch had more distracting technologies available and were more likely to be off-task than others. Also, those who accessed Facebook had lower GPAs than those who avoided it. Finally, students with relatively high use of study strategies were more likely to stay on-task than other students. The educational implications include allowing students short ‘‘technology breaks’’ to reduce distractions and teaching students metacognitive strategies regarding when interruptions negatively impact learning. 2012 Elsevier Ltd. All rights reserved.


Design and implementation of microcontroller based PWM technique for sine wave inverter

A microcontroller based advanced technique of generating sine wave with minimized harmonics is implemented in this paper. The proposed technique “impulse-sine product” aims to design and implement a voltage regulated inverter with ripple free and glitch free output sine wave that can operate electronic devices efficiently. The output voltage of the inverter is regulated by a feedback loop using analog to digital protocol of PIC16f877 microcontroller. The design is essentially focused upon low power electronic appliances such as personal computers, chargers, television sets. The inverter output is regulated from 16-volt rms to 220-volt rms for a variation of load between 1-Ω to 180-Ω loads. The designed inverter provides a range of operation from 25 Watts to 250 Watts. The design is mathematically modeled which is simulated in Matlab, Proteus and finally the results are practically verified.


CYUT Short Text Conversation System for NTCIR-12 STC

In this paper, we report how we build the system for Chinese subtask in NTCIR12 Short Text Conversation (STC) shared task. Our approach is to find the most related sentences for a given input sentence. The system is implemented based on the Lucene search engine. The result shows that our system can deal with the conversation that involves related sentences.


On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing e cient deep architectures to analyse volumetric medical images remains challenging. This work investigates e cient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its e ciency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.


Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories

The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed. # 2002 Elsevier Science B.V. All rights reserved.


A Review of Information Quality Research - Develop a Research Agenda

Recognizing the substantial development of information quality research, this review article analyzes three major aspects of information quality research: information quality assessment, information quality management and contextual information quality. Information quality assessment is analyzed by three components: information quality problem, dimension and assessment methodology. Information quality management is analyzed from three perspectives: quality management, information management and knowledge management. Following an overview of contextual information quality, this article analyzes information quality research in the context of information system and decision making. The analyzing results reveal the potential research streams and current research limitations of information quality. Aiming at bridging the research gaps, we conclude by providing the research issues for future information quality research and implications for empirical applications.


A tutorial on support vector regression

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention somemodifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.


An introduction to hidden Markov models

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.


A sample size calculator for SMART pilot studies

In clinical practice, as well as in other areas where interventions are provided, a sequential individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object ′s response. An adaptive intervention is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an adaptive intervention, scientists are increasingly interested in the use of sequential multiple assignment randomized trials (SMART), which is a type of multistage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of SMART and in the development of adaptive interventions, both are relatively new to themedical and behavioral sciences. As a result, many clinical researchers will first implement a SMART pilot study (i.e., a small-scale version of a SMART) to examine feasibility and acceptability considerations prior to conducting a full-scale SMART study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a SMART pilot.


A Quantitative and Qualitative Assessment of Automatic Text Summarization Systems

Text summarization is the process of automatically creating a shorter version of one or more text documents. This paper presents a qualitative and quantitative assessment of the 22 state-of-the-art extractive summarization systems using the CNN corpus, a dataset of 3,000 news articles.


A foundation for representing and querying moving objects

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.


Free Form based active contours for image segmentation and free space perception

In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular Bézier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of the active contour. We demonstrate the effectiveness of the new active contour scheme for visual free space perception and segmentation using omnidirectional images acquired by a robot exploring unknown indoor and outdoor environments. Several experiments validate the approach with comparison to state-of-the art parametric and geometric active contours and provide fast and real-time robot free space segmentation and navigation.


Neon: A (Big) (Fast) Single-Chip 3D Workstation Graphics Accelerator

High-performance 3D graphics accelerators traditionally require multiple chips on multiple boards. Specialized chips perform geometry transformations and lighting computations, rasterizing, pixel processing, and texture mapping. Multiple chip designs are often scalable: they can increase performance by using more chips. Scalability has obvious costs: a minimal configuration needs several chips, and some configurations must replicate texture maps. A less obvious cost is the almost irresistible temptation to replicate chips to increase performance, rather than to design individual chips for higher performance in the first place. In contrast, Neon is a single chip that performs like a multichip design. Neon accelerates OpenGL 3D rendering, as well as X11 and Windows/NT 2D rendering. Since our pin budget limited peak memory bandwidth, we designed Neon from the memory system upward in order to reduce bandwidth requirements. Neon has no special-purpose memories; its eight independent 32-bit memory controllers can access color buffers, Z depth buffers, stencil buffers, and texture data. To fit our gate budget, we shared logic among different operations with similar implementation requirements, and left floating point calculations to Digital's Alpha CPUs. Neon’s performance is between HP’s Visualize fx and fx, and is well above SGI’s MXE for most operations. Neon-based boards cost much less than these competitors, due to a small part count and use of commodity SDRAMs.


Boosting for transfer learning

Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.


Publicity Trends in Arrests of " Online Predators " How the National Juvenile Online Victimization (n‐jov) Study Was Conducted

about " online predators " * – sex of‐ fenders who use the Internet to meet juvenile victims – has raised considerable alarm about the extent to which Internet use may be put‐ ting children and adolescents at risk for sexual abuse and exploitation. Media stories and Internet safety messages have raised fears by describing violent offenders who use the Inter‐ net to prey on naïve children by tricking them into face‐to‐face meetings or tracking them down through information posted online. Law enforcement has mobilized on a number of fronts, setting up task forces to identify and prosecute online predators, developing under‐ cover operations, and urging social networking sites to protect young users. Unfortunately, however, reliable information on the scope and nature of the online predator problem remains scarce. Established criminal justice data collection systems do not gather detailed data on such crimes that could help inform public policy and education. To remedy this information vacuum, the Crimes against Children Research Center at the University of New Hampshire conducted two waves of a The N‐JOV Study collected information from a national sample of law en‐ forcement agencies about the prevalence of arrests for and characteristics of online sex crimes against minors during two 12 month periods: July 1, 2000 through June 30, 2001 (Wave 1) and calendar year 2006 (Wave 2). For both Waves, we used a two‐phase process of mail surveys followed by telephone interviews to collect data from a national sample of the same lo‐ cal, county, state, and federal law enforcement agencies. First, we sent the mail surveys to a national sample of more than 2,500 agencies. These sur‐ veys asked if agencies had made arrests for online sex crimes against minors during the respective one‐year timeframes. Then we conducted detailed telephone interviews with law enforcement investigators about a random sample of arrest cases reported in the mail surveys. For the telephone interviews, we designed a sampling procedure that took into account the number of arrests reported by an agency, so that we would not unduly burden respondents in agencies with many cases. If an agency reported between one and three arrests for online sex crimes, we conducted follow‐up interviews for every case. For agencies that reported more than three arrests, we conducted interviews for all cases that involved youth vic‐ tims (victims who were located and contacted during the investigation), and sampled other arrest cases (i.e., …


AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics

This paper describes AutoBrief, an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief’s research contributions include (1) a design enabling a new human–computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration, (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human–computer ARTICLE IN PRESS *Corresponding author. Tel.: +1-336-2561133; fax: +1-336-3345949. E-mail addresses: nlgreen@uncg.edu (N.L. Green), carenini@cs.ubc.ca (G. Carenini), kerpedjiev@maya.com (S. Kerpedjiev), mattis@mayaviz.com (J. Mattis), j.moore@ed.ac.uk (J.D. Moore), roth+@cs.cmu.edu (S.F. Roth). 1071-5819/$ see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2003.10.007 interaction and natural language generation, presents AutoBrief’s design and design rationale. r 2003 Elsevier Ltd. All rights reserved.


Failure Analysis of SGI XFS File System

Commodity file systems expect a fail stop disk. But todays disks fail in unexpected ways. Disks exhibit latent sector errors, silent corruption and transient failures to name a few. In this paper we study the behavior of SGI XFS to such errors. File systems play a key role in handling most data and hence the failure handling policy of the file system plays a major role in ensuring the integrity of the data. XFS is a highly scalable journaling file system developed by SGI. We analyze the failure handling policy of XFS file system. We fail reads and writes of various blocks that originate from the file system and analyze the file system behavior. Some such blocks include super block, journal header block, commit block, data block, index blocks. We classify our errors according to the extended IRON Taxonomy. We see that XFS is vulnerable to these failures and does not possess a uniform failure handling policy. Further the file system at times silently corrupts data. We also see that XFS has very little internal redundancy even for critical structures such as the super block and B+Tree root. Finally we see that XFS, like most commodity file systems, fails to recover from these failures putting the data at risk.


The Wnt signaling pathway in development and disease.

Tight control of cell-cell communication is essential for the generation of a normally patterned embryo. A critical mediator of key cell-cell signaling events during embryogenesis is the highly conserved Wnt family of secreted proteins. Recent biochemical and genetic analyses have greatly enriched our understanding of how Wnts signal, and the list of canonical Wnt signaling components has exploded. The data reveal that multiple extracellular, cytoplasmic, and nuclear regulators intricately modulate Wnt signaling levels. In addition, receptor-ligand specificity and feedback loops help to determine Wnt signaling outputs. Wnts are required for adult tissue maintenance, and perturbations in Wnt signaling promote both human degenerative diseases and cancer. The next few years are likely to see novel therapeutic reagents aimed at controlling Wnt signaling in order to alleviate these conditions.


Magnetic Optimization Algorithms a new synthesis

A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO.


Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code

Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.


Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.


Automated Verification of Electrum Wallet

We introduce a formal modeling in ASLan++ of the twofactor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints.


A Semi-Supervised Approach for Kernel-Based Temporal Clustering

Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications of temporal clustering in those areas are diverse, and include human-motion imitation and recognition, emotion analysis, human activity segmentation, automated rehabilitation exercise analysis, and human-computer interaction. However, temporal clustering using a completely unsupervised method may not produce satisfactory results. Similar to regular clustering, temporal clustering also benefits from some expert knowledge that may be available. The type of approach that utilizes a small amount of knowledge to “guide” the clustering process is known as “semi-supervised clustering.” Semi-supervised temporal clustering is a strategy in which extra knowledge, in the form of pairwise constraints, is incorporated into the temporal data to help with the partitioning problem. This thesis proposes a process to adapt and transform two kernel-based methods into semi-supervised temporal clustering methods. The proposed process is exclusive to kernel-based clustering methods, and is based on two concepts. First, it uses the idea of instance-level constraints, in the form of must-link and cannot-link, to supervise the clustering methods. Second, it uses a dynamic-programming method to search for the optimal temporal clusters. The proposed process is applied to two algorithms, aligned cluster analysis (ACA) and spectral clustering. To validate the advantages of the proposed temporal semi-supervised clustering methods, a comparative analysis was performed, using the original versions of the algorithm and another semi-supervised temporal cluster. This evaluation was conducted with both synthetic data and two real-world applications. The first application includes two naturalistic audio-visual human emotion datasets, and the second application focuses on human-motion segmentation. Results show substantial improvements in accuracy, with minimal supervision, compared to unsupervised and other temporal semi-supervised approaches, without compromising time performance.


BRAINIAC: Bringing reliable accuracy into neurally-implemented approximate computing

Applications with large amounts of data, real-time constraints, ultra-low power requirements, and heavy computational complexity present significant challenges for modern computing systems, and often fall within the category of high performance computing (HPC). As such, computer architects have looked to high performance single instruction multiple data (SIMD) architectures, such as accelerator-rich platforms, for handling these workloads. However, since the results of these applications do not always require exact precision, approximate computing may also be leveraged. In this work, we introduce BRAINIAC, a heterogeneous platform that combines precise accelerators with neural-network-based approximate accelerators. These reconfigurable accelerators are leveraged in a multi-stage flow that begins with simple approximations and resorts to more complex ones as needed. We employ high-level, application-specific light-weight checks (LWCs) to throttle this multi-stage acceleration flow and reliably ensure user-specified accuracy at runtime. Evaluation of the performance and energy of our heterogeneous platform for error tolerance thresholds of 5%-25% demonstrates an average of 3× gain over computation that only includes precise acceleration, and 15×-35× gain over software-based computation.


The folly of forecasting : The effects of a disaggregated sales forecasting system on sales forecast error , sales forecast positive bias , and inventory levels

In this study we provide field evidence of the role that sales forecasts play as the coordination mechanism between sales managers and production managers. An extensive body of operations research documents the negative consequences of sales forecast error and investigates how to respond to sales forecast error by optimizing inventory “buffer” stocks. In contrast, we focus on whether a change in the sales forecasting information environment implemented at our research site reduces forecast error and, hence, the need for those buffer stocks. The newly implemented sales forecast “contingency system” disaggregated the sales forecast into two components: (i) an official sales forecast that reflected relatively more certain expected demand, and (ii) a separate report that provided the probability of a contingent demand “event” occurring and the expected volume impact of that event. We predict and find that the system had the intended effect of a reduction in inventory levels, both through better timing of production via a production “postponement” strategy and through a decrease in absolute forecast error. We further consider the incentives of the self-interested sales managers and predict and find that the inventory reduction benefits of the sales forecast system gained through postponement and a decline in absolute forecast error were partially offset by an increase in positive sales forecast bias. Our study provides novel insights regarding the role of forecasting within the organizational context. While the operations literature uses analytic and simulation methods to examine sales and operations planning in a very mechanistic way, we examine the role that changes in the sales forecast information environment play and the opportunistic responses of self-interested managers. (266 words)


Creating people-aware IoT applications by combining design thinking and user-centered design methods

This article presents a methodology based on design thinking and user experience design methods for creating what we call `people-aware' IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.


A Survey of Surface-Based Illustrative Rendering for Visualization

In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub-area of visualization. For the remainder of the survey, we then focus on surface-based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods’ implementations. We then provide an overview of low-level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.


Recovering the interleaver of an unknown turbo-code

We give here an efficient algorithm for recovering the permutation of an unknown turbo-code when several noisy codewords are given. The algorithm presented here uses the same information as some other algorithms given previously for this problem but in an optimal fashion. This paper also clarifies the link between this problem and the BCJR decoding algorithm.


Digital Libraries and Autonomous Citation Indexing

The Web is revolutionizing the way researchers access scientific literature, however scientific literature on the Web is largely disorganized. Autonomous citation indexing can help organize the literature by automating the construction of citation indices. Autonomous citation indexing aims to improve the dissemination and retrieval of scientific literature, and provides improvements in cost, availability, comprehensiveness, efficiency, and timeliness.


A Statistical Approach to Anaphora Resolution

This paper presents an algorithm for identifying pronominal anaphora and two experiments based upon this algorithm. We incorporate multiple anaphora resolution factors into a statistical framework specifically the distance between the pronoun and the proposed antecedent, gender/number/animaticity of the proposed antecedent, governing head information and noun phrase repetition. We combine them into a single probability that enables us to identify the referent. Our first experiment shows the relative contribution of each source Of information and demonstrates a success rate of 82.9% for all sources combined. The second experiment investigates a method for unsupervised learning of gender/number/animaticity information. We present some experiments illustrating the accuracy of the method and note that with this information added, our pronoun resolution method achieves 84.2% accuracy. 1 I n t r o d u c t i o n We present a statistical method for determining pronoun anaphora. This program differs from earlier work in its almost complete lack of hand-crafting, relying instead on a very small corpus of Penn Wall Street Journal Tree-bank text (Marcus et al., 1993) that has been marked with co-reference information. The first sections of this paper describe this program: the probabilistic model behind it, its implementation, and its performance. The second half of the paper describes a method for using (portions of) t~e aforementioned program to learn automatically the typical gender of English words, information that is itself used in the pronoun resolution program. In particular, the scheme infers the gender of a referent from the gender of the pronouns that 161 refer to it and selects referents using the pronoun anaphora program. We present some typical results as well as the more rigorous results of a blind evaluation of its output. 2 A P r o b a b i l i s t i c M o d e l There are many factors, both syntactic and semantic, upon which a pronoun resolution system relies. (Mitkov (1997) does a detailed study on factors in anaphora resolution.) We first discuss the training features we use and then derive the probability equations from them. The first piece of useful information we consider is the distance between the pronoun and the candidate antecedent. Obviously the greater the distance the lower the probability. Secondly, we look at the syntactic situation in which the pronoun finds itself. The most well studied constraints are those involving reflexive pronouns. One classical approach to resolving pronouns in text that takes some syntactic factors into consideration is that of Hobbs (1976). This algorithm searches the parse tree in a leftto-right, breadth-first fashion that obeys the major reflexive pronoun constraints while giving a preference to antecedents that are closer to the pronoun. In resolving inter-sentential pronouns, the algorithm searches the previous sentence, again in left-to-right, breadth-first order. This implements the observed preference for subject position antecedents. Next, the actual words in a proposed nounphrase antecedent give us information regarding the gender, number, and animaticity of the proposed referent. For example: M a r i e Giraud carries historical significance as one of the last women to be ezecuted in France. S h e became an abortionist because it enabled her to buy jam, cocoa and other war-rationed goodies. Here it is helpful to recognize that "Marie" is probably female and thus is unlikely to be referred to by "he" or "it". Given the words in the proposed antecedent we want to find the probability that it is the referent of the pronoun in question. We collect these probabilities on the training data, which are marked with reference links. The words in the antecedent sometimes also let us test for number agreement. Generally, a singular pronoun cannot refer to a plural noun phrase, so that in resolving such a pronoun any plural candidates should be ruled out. However a singular noun phrase can be the referent of a plural pronoun, as illustrated by the following example: "I think if I tell Viacom I need more time, they will take 'Cosby' across the street," says the general manager ol a network a~liate. It is also useful to note the interaction between the head constituent of the pronoun p and the antecedent. For example: A Japanese company might make television picture tubes in Japan, assemble the T V sets in Malaysia and extort them to Indonesia. Here we would compare the degree to which each possible candidate antecedent (A Japanese company, television picture tubes, Japan, T V sets, and Malaysia in this example) could serve as the direct object of "export". These probabilities give us a way to implement selectional restriction. A canonical example of selectional restriction is that of the verb "eat", which selects food as its direct object. In the case of "export" the restriction is not as clearcut. Nevertheless it can still give us guidance on which candidates are more probable than others. The last factor we consider is referents' mention count. Noun phrases that are mentioned repeatedly are preferred. The training corpus is marked with the number of times a referent has been mentioned up to that point in the story. Here we are concerned with the probability that a proposed antecedent is correct given that it has been repeated a certain number of times. 162 In effect, we use this probability information to identify the topic of the segment with the belief that the topic is more likely to be referred to by a pronoun. The idea is similar to tha t used in the centering approach (Brennan et al., 1987) where a continued topic is the highest-ranked candidate for pronominalization. Given the above possible sources of informar tion, we arrive at the following equation, where F(p) denotes a function from pronouns to their antecedents: F(p) = argmaxP( A(p) = alp, h, l~', t, l, so, d~ A~') where A(p) is a random variable denoting the referent of the pronoun p and a is a proposed antecedent. In the conditioning events, h is the head constituent above p, l~ r is the list of candidate antecedents to be considered, t is the type of phrase of the proposed antecedent (always a noun-phrase in this s tudy), I is the type of the head constituent, sp describes the syntactic structure in which p appears, dspecifies the distance of each antecedent from p and M" is the number of times the referent is mentioned. Note that 17r ", d'~ and A~ are vector quantities in which each entry corresponds to a possible antecedent. When viewed in this way, a can be regarded as an index into these vectors that specifies which value is relevant to the particular choice of antecedent. This equation is decomposed into pieces that correspond to all the above factors but are more statistically manageable. The decomposition makes use of Bayes' theorem and is based on certain independence assumptions discussed below. P( A(p) = alp, h, fir, t, l, sp, d~ .Q') = P(alA~)P(p,h, fir, t,l, sp,~a, 2~) (1) P(p, h, fir, t, t, sp, diM ) o¢ PCalM)P(p, h, fir, t, l, sp, ~a, .Q') (2) = P(a[:Q)P(.%, ~a, :~'I) P(p,h, fir, t, l la ,~ ,sp , i) (3) = P(all~)P(sp, d~a,.Q ) PCh, t, Zla, ~'0", so, i) PC.. ~ la , .~', so, d, h, t, l) (4) oc P(a]l~)P(So,~a,M') P(p, 14tin, ]Q, s o, d, h, t, I) (5) = P(al.Q)P(sp, d~a, 3~r) P(ffrla, I~, s o, d, h, t, I). (6) P(pla. l~, sf,, d. h, t, l, l~) cx P(a163P(dtt la)P(f f ' lh , t, I, a) P(plw°) (7) Equation (1) is simply an application of Bayes' rule. The denominator is eliminated in the usual fashion, resulting in equation (2). Selectively applying the chain rule results in equations (3) and (4). In equation (4), the term P(h. t, lla, .~, So, d) is the same for every antecedent and is thus removed. Equat ion (6) follows when we break the last component of (5) into two probability distributions. In equation (7) we make the following independence assumptions: • Given a particular choice of the antecedent candidates, the distance is independent of distances of candidates other than the antecedent (and the distance to non-referents can be ignored): P(so, d~a, 2~) o¢ P(so, dola , IC4) • The syntnctic s t ructure st, and the distance from the pronoun da are independent of the number of times the referent is mentioned. Thus P(sp, dola, M) = P(sp, d.la) Then we combine sp and de into one variable dIt, Hobbs distance, since the Hobbs algorithm takes both the syntax and distance into account. The words in the antecedent depend only on the parent consti tuent h, the type of the words t, and the type of the parent I. Hence e(ff'la, M, sp, ~, h, t, l) = P ( ~ l h , t, l, a) • The choice pronoun depends only on the words in the antecedent, i.e. P{pla, M, sp, d, h, t, l, ~ = P(pla, W) 163 • If we treat a as an index into the vector 1~, then (a, I.V') is simply the a th candidate in the list ffz. We assume the selection of the pronoun is independent of the candidates other than the antecedent. Hence P(pla, W) = P(plw,~) Since I~" is a vector, we need to normalize P(ff ' lh, t,l, a) to obtain the probability of each element in the vector. It is reasonable to assume tha t the antecedents in W are independent of each other; in other words, P(wo+llwo, h , t , l ,a ) = P(wo+llh, t , l ,a}. Thus,


An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.


Authentication Algorithm for Intrusion Detection in Wireless Networks

Security has been a major issue in wireless networks. In a wireless network, wireless devices are prone to be unauthorized accessing data or resources. Hence it becomes necessary to consider issues of security such as : 1. Authentication, 2. Access Control. Traditional methods of Authentication has been to assign user names and passwords. This is extremely vulnerable to be accessed and misused. Therefore, to overcome these problems, This paper proposes a method by which the Username and Password are stored in an Hashed format, with the help of Neural Network learning the hashes instead of storing them in the tables. Thus, reducing the risk of being accessed. Therefore, a combination of authentication and access control could make a good tool for intrusion detection.


Design equations for the Quasi Yagi-Uda antenna operating in the UHF band

Design equations for the standard Quasi Yagi-Uda antenna built on 60 mil FR4 are presented to be used on fixed wireless telecommunication systems, Wi-Fi, WiMAX and LTE. In this work, the design equations are systematically proposed for the band between 1 GHz and 3 GHz, where relative bandwidth is better than 39%, the cross-polarization is lower than - 15dB for every angle and frequency, the cross-polarization is lower than -25dB in the main lobe and a front to back ratio better than 12dB in all the bandwidth. Three antennas where designed, built and measured with the proposed equations at 1.5GHz, 2GHz and 3 GHz, showing good agreement with the simulation results.


The burden of allergic contact dermatitis caused by acrylates.

Acrylates and methacrylates are esters of acrylic acid and methacrylic acid, respectively, that polymerize either spontaneously or on ultraviolet (UV) light exposure to form products that are commonly used in adhesives in healthcare settings, and in plastics and textiles (1). They were commonly considered to be occupational contact allergens, particularly in dentists and orthopaedic surgeons, owing to the use of (meth)acrylates in dental materials and bone cement (2). However, more recently, the demand for long-lasting cosmetic nails has led to the widespread use of gel nails and acrylic nails. Gel nails are methacrylate-based gel varnishes that rely on UV curing to form a hard nail-coating colour that lasts for up to 2 weeks. This has led to an occupational shift in (metha)crylate allergy, with a rise in incidence among beauticians and nail technicians using these products (1, 3–5). In addition, with home use UV-curing kits now readily available, there is a further shift of contact allergy to consumers. Our aim was to characterize the cases of allergic contact dermatitis caused by meth(acrylates) and cyanoacrylates in our large regional patch testing centre. Because of its significant occupational and health-related sequelae, we highlight the current burden of the serious allergy to these compounds.


A cross-platform analysis of bugs and bug-fixing in open source projects: desktop vs. Android vs. iOS

As smartphones continue to increase in popularity, understanding how software processes associated with the smartphone platform differ from the traditional desktop platform is critical for improving user experience and facilitating software development and maintenance. In this paper we focus specifically on differences in bugs and bug-fixing processes between desktop and smartphone software. Our study covers 444,129 bug reports in 88 open source projects on desktop, Android, and iOS. The study has two main thrusts: a quantitative analysis to discover similarities and differences between desktop and smartphone bug reports/processes; and a qualitative analysis where we extract topics from bug reports to understand bugs' nature, categories, and differences between platforms. Our findings include: during 2011--2013, iOS bugs were fixed three times faster compared to Android and desktop; top smartphone bug fixers are more involved in reporting bugs than top desktop bug fixers; and most frequent high-severity bugs are due to build issues on desktop, concurrency on Android, and application logic on iOS. Our study, findings, and recommendations are potentially useful to smartphone researchers and practitioners.


Modelling the interaction of electromagnetic fields (10 MHz-10 GHz) with the human body: methods and applications.

Numerical modelling of the interaction between electromagnetic fields (EMFs) and the dielectrically inhomogeneous human body provides a unique way of assessing the resulting spatial distributions of internal electric fields, currents and rate of energy deposition. Knowledge of these parameters is of importance in understanding such interactions and is a prerequisite when assessing EMF exposure or when assessing or optimizing therapeutic or diagnostic medical applications that employ EMFs. In this review, computational methods that provide this information through full time-dependent solutions of Maxwell's equations are summarized briefly. This is followed by an overview of safety- and medical-related applications where modelling has contributed significantly to development and understanding of the techniques involved. In particular, applications in the areas of mobile communications, magnetic resonance imaging, hyperthermal therapy and microwave radiometry are highlighted. Finally, examples of modelling the potentially new medical applications of recent technologies such as ultra-wideband microwaves are discussed.


AAAI: An Argument Against Artificial Intelligence

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI.


Reward Systems in the Brain and Nutrition.

The taste cortex in the anterior insula provides separate and combined representations of the taste, temperature, and texture of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are combined by associative learning with olfactory and visual inputs for some neurons, and these neurons encode food reward value in that they respond to food only when hunger is present and in that activations correlate linearly with subjective pleasantness. Cognitive factors, including word-level descriptions and selective attention to affective value, modulate the representation of the reward value of taste, olfactory, and flavor stimuli in the orbitofrontal cortex and a region to which it projects, the anterior cingulate cortex. These food reward representations are important in the control of appetite and food intake. Individual differences in reward representations may contribute to obesity, and there are age-related differences in these reward representations. Implications of how reward systems in the brain operate for understanding, preventing, and treating obesity are described.


Healthcare via cell phones: a systematic review.

Regular care and informational support are helpful in improving disease-related health outcomes. Communication technologies can help in providing such care and support. The purpose of this study was to evaluate the empirical evidence related to the role of cell phones and text messaging interventions in improving health outcomes and processes of care. Scientific literature was searched to identify controlled studies evaluating cell phone voice and text message interventions to provide care and disease management support. Searches identified 25 studies that evaluated cell phone voice and text messaging interventions, with 20 randomized controlled trials and 5 controlled studies. Nineteen studies assessed outcomes of care and six assessed processes of care. Selected studies included 38,060 participants with 10,374 adults and 27,686 children. They covered 12 clinical areas and took place in 13 countries. Frequency of message delivery ranged from 5 times per day for diabetes and smoking cessation support to once a week for advice on how to overcome barriers and maintain regular physical activity. Significant improvements were noted in compliance with medicine taking, asthma symptoms, HbA1C, stress levels, smoking quit rates, and self-efficacy. Process improvements were reported in lower failed appointments, quicker diagnosis and treatment, and improved teaching and training. Cost per text message was provided by two studies. The findings that enhancing standard care with reminders, disease monitoring and management, and education through cell phone voice and short message service can help improve health outcomes and care processes have implications for both patients and providers.


"GrabCut": interactive foreground extraction using iterated graph cuts

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.


Aggregate Profile Clustering for Streaming Analytics

Many analytic applications require analyzing user interaction data. In particular, such data can be aggregated over a window to build user activity profiles. Clustering such aggregate profiles is useful for grouping together users with similar behaviors, so that common models could be built for them. In this paper, we present an approach for clustering profiles that are incrementally maintained over a stream of updates. Owing to the potentially large number of users and high rate of interactions, maintaining profile clusters can have high processing and memory resource requirements. To tackle this problem, we apply distributed stream processing. However, in the presence of distributed state, it is a major challenge to partition the profiles over nodes such that memory and computation balance is maintained, while keeping the clustering accuracy high. Furthermore, in order to adapt to potentially changing user interaction patterns, the partitioning of profiles to nodes should be continuously revised, yet one should minimize the migration of profiles so as not to disturb the online processing of updates. We develop a re-partitioning technique that achieves all these goals. To achieve this, we keep micro-cluster summaries at each node and periodically collect these summaries at a central node to perform re-partitioning. We use a greedy algorithm with novel affinity heuristics to revise the partitioning and update the routing tables without introducing a lengthy pause. We showcase the effectiveness of our approach using an application that clusters customers of a telecommunications company based on their aggregate calling profiles.


A compact field weakening controller implementation

This paper proposes a compact field weakening controller (FWC). The FWC was developed during the development of an electrically driven rear axle for a hybrid electric car. The overall control system of the vehicle, the hybrid controller (HC), delivers a reference torque for the rear drive unit (RDU). Depending on the torque reference, instantaneous vehicle speed and battery voltage the task of the FWC is to produce current references for the current controller. The references are chosen to give an optimal current, optimal in the sense that the torque-per-current ratio is maximized, under the restriction of keeping the stator voltage and current limited. The FWC is based on comprehensive measurements of the machine. An algorithm is used to process the data from the measurements. The output from the algorithm is look-up tables. Implementation of the look-up tables is also discussed in the paper


Enhancing the RGR Routing Protocol for Unmanned Aeronautical Ad-hoc Networks

The design of a routing protocol for Unmanned Aeronautical Ad-hoc Networks (UAANETs) is quite challenging, especially due to the high mobility of Unmanned Aerial Vehicles (UAVs) and the low network density. Among all routing protocols that have been designed for UAANETs, the Reactive-Greedy-Reactive (RGR) protocol has been proposed as a promising routing protocol in high mobility and density-variable scenarios. While prior results have shown that RGR is competitive to AODV or other purely reactive routing protocols, it is not fully exploiting all the unique features of UAANETs and the knowledge maintained by intermediate nodes. This report presents a number of different enhancements, namely scoped flooding, delayed route request, and mobility prediction, in order to improve the performance of RGR in terms of overhead, packet delivery ratio and delay respectively. These modifications were implemented in Opnet, and simulation results show that we can reduce the protocol overhead by about 30%, while at the same time increasing PDR by about 3-5% and reducing packet latency. The results further show that there is still significant scope for further performance improvements.


Sensitivity enhancement of MEMS fluid flow rate and flow direction sensor

MEMS based flow rate sensor and flow direction sensor is a crucial to the determination of exact fluid flow path. Conventional rectangular cantilever beam detects based on the surface strain of the beam with respect to the mass flow. Different structured beam will greatly affect the sensitivity. In this paper different geometries of cantilever beam such as rectangular, inverted trapezoidal, triangular are designed and simulated using the finite element software INTELLISUITE 8.7. The simulation results show that the inverted trapezoidal geometries having the more deflection at same stress of the conventional beam and thus increase the sensitivity by approx. 1.45 times.


A study on the software requirements elicitation issues - its causes and effects

Software requirements elicitation is an important and essential pre-requisite to the subsequent phases in software development lifecycle. There is an increasing focus on how industry performs elicitation as this has a direct influence on the overall project success. It is important to understand broader elicitation issues and challenges, and address them on a large-scale, especially on geographically distributed software development. There are studies focusing on elicitation, but they are relatively small. There is no substantial research related to providing a comprehensive view of elicitation issues along with its causes and effects. This paper attempts to provide a summary of the systematic literature review (SLR) findings from 81 papers. The findings are based on causes of poor elicitation, elicitation issues and challenges, consequences of poor elicitation, advisable practices and classification of elicitation issues. The authors have leveraged cause-and-effect diagrams to draw conclusions on SLR.


Scalable Semantics-Based Detection of Similar Android Applications

The popularity and utility of smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. In this paper, we present a scalable approach to detecting similar Android apps based on semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous third-party markets. In contrast with earlier approaches, AnDarwin does not compare apps pairwise, thus greatly increasing its scalability. Additionally, AnDarwin does not rely on information other than the app code — such as the app’s market, signature, or description — thus greatly increasing its reliability. AnDarwin can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers (“clones”) and similar apps from the same developer (“rebranded”). In ten hours, AnDarwin detected at least 4,295 apps which have been the victims of cloning and 36,106 apps that are rebranded. By analyzing the clusters found by AnDarwin, we found 88 new variants of malware and identified 169 malicious apps based on differences in the requested permissions. In contrast to earlier approaches, AnDarwin can detect both full and partial app similarity. Additionally, AnDarwin can automatically detect similar code that is injected into many apps, which may indicate the spread of malware. Our evaluation demonstrates AnDarwin’s ability to accurately detect similar apps on a large scale.


The one-time pad revisited

The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.


Help your mobile applications with fog computing

Cloud computing has paved a way for resource-constrained mobile devices to speed up their computing tasks and to expand their storage capacity. However, cloud computing is not necessary a panacea for all mobile applications. The high network latency to cloud data centers may not be ideal for delay-sensitive applications while storing everything on public clouds risks users' security and privacy. In this paper, we discuss two preliminary ideas, one for mobile application offloading and the other for mobile storage expansion, by leveraging the edge intelligence offered by fog computing to help mobile applications. Preliminary experiments conducted based on implemented prototypes show that fog computing can provide an effective and sometimes better alternative to help mobile applications.


Bayesian Biosurveillance of Disease Outbreaks

Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.


Improving Social Inclusion Using NLP : Tools and Resources

This paper discusses the issue of accessibility to being online and using social media for people with a learning disability, and the challenges to using a co-production approach in an accessible technology project. While an increasing number of daily living tasks are now completed online, people with a learning disability frequently experience digital exclusion due to limited literacy and IT skills. The Able to Include project sought to engage people with a learning disability as active partners to test and feedback on the use and development of a pictogram app used to make social media more accessible. The challenges mainly related to the feedback needing to be sent electronically to the partners; there was only minimal contact with them and no face to face contact. The paper also outlines how other challenges were overcome to enable genuine and meaningful co-production. These included addressing online safety and ethical issues regarding anonymity.


Mode Regularized Generative Adversarial

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.


Abnormal brain chemistry in chronic back pain: an in vivo proton magnetic resonance spectroscopy study

The neurobiology of chronic pain, including chronic back pain, is unknown. Structural imaging studies of the spine cannot explain all cases of chronic back pain. Functional brain imaging studies indicate that the brain activation patterns are different between chronic pain patients and normal subjects, and the thalamus, and prefrontal and cingulate cortices are involved in some types of chronic pain. Animal models of chronic pain suggest abnormal spinal cord chemistry. Does chronic pain cause brain chemistry changes? We examined brain chemistry changes in patients with chronic back pain using in vivo single- voxel proton magnetic resonance spectroscopy ((1)H-MRS). In vivo (1)H-MRS was used to measure relative concentrations of N-acetyl aspartate, creatine, choline, glutamate, glutamine, gamma-aminobutyric acid, inositol, glucose and lactate in relation to the concentration of creatine. These measurements were performed in six brain regions of nine chronic low back pain patients and 11 normal volunteers. All chronic back pain subjects underwent clinical evaluation and perceptual measures of pain and anxiety. We show that chronic back pain alters the human brain chemistry. Reductions of N-acetyl aspartate and glucose were demonstrated in the dorsolateral prefrontal cortex. Cingulate, sensorimotor, and other brain regions showed no chemical concentration differences. In chronic back pain, the interrelationship between chemicals within and across brain regions was abnormal, and there was a specific relationship between regional chemicals and perceptual measures of pain and anxiety. These findings provide direct evidence of abnormal brain chemistry in chronic back pain, which may be useful in diagnosis and future development of more effective pharmacological treatments.


Overview of the TAC 2010 Knowledge Base Population Track

In this paper we give an overview of the Knowledge Base Population (KBP) track at TAC 2010. The main goal of KBP is to promote research in discovering facts about entities and expanding a structured knowledge base with this information. A large source collection of newswire and web documents is provided for systems to discover information. Attributes (a.k.a. “slots”) derived from Wikipedia infoboxes are used to create the reference knowledge base (KB). KBP2010 includes the following four tasks: (1) Regular Entity Linking, where names must be aligned to entities in the KB; (2) Optional Entity linking, without using Wikipedia texts; (3) Regular Slot Filling, which requires a system to automatically discover the attributes of specified entities from the source document collection and use them to expand the KB; (4) Surprise Slot Filling, which requires a system to return answers regarding new slot types within a short time period. KBP2010 has attracted many participants (over 45 teams registered for KBP 2010 (not including the RTEKBP Validation Pilot task), among which 23 teams submitted results). In this paper we provide an overview of the task definition and annotation challenges associated with KBP2010. Then we summarize the evaluation results and discuss the lessons that we have learned based on detailed analysis.


Probabilistic Similarity Logic

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.


A short introduction to probabilistic soft logic

Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.


Second-order cone programming

Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems that do not fall into these three categories. These latter problems model applications from a broad range of fields from engineering, control and finance to robust optimization and combinatorial optimization. On the other hand semidefinite programming (SDP)—that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices—includes SOCP as a special case. Therefore, SOCP falls between linear (LP) and quadratic (QP) programming and SDP. Like LP, QP and SDP problems, SOCP problems can be solved in polynomial time by interior point methods. The computational effort per iteration required by these methods to solve SOCP problems is greater than that required to solve LP and QP problems but less than that required to solve SDP’s of similar size and structure. Because the set of feasible solutions for an SOCP problem is not polyhedral as it is for LP and QP problems, it is not readily apparent how to develop a simplex or simplex-like method for SOCP. While SOCP problems can be solved as SDP problems, doing so is not advisable both on numerical grounds and computational complexity concerns. For instance, many of the problems presented in the survey paper of Vandenberghe and Boyd [VB96] as examples of SDPs can in fact be formulated as SOCPs and should be solved as such. In §2, 3 below we give SOCP formulations for four of these examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadratic functions such as those that arise in structural optimization, logarithmic Tchebychev approximation and the


Whitening Black-Box Neural Networks

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.


Development of a superior frontal–intraparietal network for visuo-spatial working memory

Working memory capacity increases throughout childhood and adolescence, which is important for the development of a wide range of cognitive abilities, including complex reasoning. The spatial-span task, in which subjects retain information about the order and position of a number of objects, is a sensitive task to measure development of spatial working memory. This review considers results from previous neuroimaging studies investigating the neural correlates of this development. Older children and adolescents, with higher capacity, have been found to have higher brain activity in the intraparietal cortex and in the posterior part of the superior frontal sulcus, during the performance of working memory tasks. The structural maturation of white matter has been investigated by diffusion tensor magnetic resonance imaging (DTI). This has revealed several regions in the frontal lobes in which white matter maturation is correlated with the development of working memory. Among these is a superior fronto-parietal white matter region, located close to the grey matter regions that are implicated in the development of working memory. Furthermore, the degree of white matter maturation is positively correlated with the degree of cortical activation in the frontal and parietal regions. This suggests that during childhood and adolescence, there is development of networks related to specific cognitive functions, such as visuo-spatial working memory. These networks not only consist of cortical areas but also the white matter tracts connecting them. For visuo-spatial working memory, this network could consist of the superior frontal and intraparietal cortex.


Nobel Lecture: The Economic Way of Looking at Behavior

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Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition

Research in person identification and authentication has attracted significant attention from the researchers and scientists. This paper presents a biometric user authentication based on a person's foot step. The advantage of this recognition method over other types of biometrics is that it enables unobtrusive user authentication where other types of biometrics are not available. Firstly the ground reaction force data was extracted using force plate to gather ground reaction force for individuals. Later we utilized the discrete wavelet transform to de-noise the experimental data and in the final step, histograms were used to identify different person's foot step. The experimental results show improvements in identification accuracies compared to previously reported work.


Pharmaceutical cold chain management: Platform based on a distributed ledger

In the process of distributing pharmaceutical products multiple stages are involved until products are delivered to patient. A reliable way for validating and verifying that products had been maintained within a licensed range is required. The paper presents a solution for pharmaceutical cold chain management using distributed ledger technologies. An application framework is proposed for shipment tracking which will deliver information to all stakeholders during the distribution phase of pharmaceutical products. The solution is based on Hyperledger Sawtooth distributed ledger framework, which has been extended with a custom transactions family and a sensors gateway for automatically collecting data from temperature tracking devices. The focus of the paper is to describe the data model and how entities of the system communicate.