Delta-9-tetrahydrocannabinol may palliate altered chemosensory perception in cancer patients: results of a randomized, double-blind, placebo-controlled pilot trial.

BACKGROUND
A pilot study (NCT00316563) to determine if delta-9-tetrahydrocannabinol (THC) can improve taste and smell (chemosensory) perception as well as appetite, caloric intake, and quality of life (QOL) for cancer patients with chemosensory alterations.

PATIENTS AND METHODS
Adult advanced cancer patients, with poor appetite and chemosensory alterations, were recruited from two sites and randomized in a double-blinded manner to receive either THC (2.5 mg, Marinol(®); Solvay Pharma Inc., n = 24) or placebo oral capsules (n = 22) twice daily for 18 days. Twenty-one patients completed the trial. At baseline and posttreatment, patients completed a panel of patient-reported outcomes: Taste and Smell Survey, 3-day food record, appetite and macronutrient preference assessments, QOL questionnaire, and an interview.

RESULTS
THC and placebo groups were comparable at baseline. Compared with placebo, THC-treated patients reported improved (P = 0.026) and enhanced (P < 0.001) chemosensory perception and food 'tasted better' (P = 0.04). Premeal appetite (P = 0.05) and proportion of calories consumed as protein increased compared with placebo (P = 0.008). THC-treated patients reported increased quality of sleep (P = 0.025) and relaxation (P = 0.045). QOL scores and total caloric intake were improved in both THC and placebo groups.

CONCLUSIONS
THC may be useful in the palliation of chemosensory alterations and to improve food enjoyment for cancer patients.


Completely Derandomized Self-Adaptation in Evolution Strategies

This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equiv-alent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigor-ously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is ob-served. On moderately mis-scaled functions a speed up factor of three to ten can be expected.


Handbook of Multibiometrics (International Series on Biometrics)

Will reading habit influence your life? Many say yes. Reading handbook of multibiometrics 6 international series on biometrics is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.


Photonic ADC: overcoming the bottleneck of electronic jitter.

Accurate conversion of wideband multi-GHz analog signals into the digital domain has long been a target of analog-to-digital converter (ADC) developers, driven by applications in radar systems, software radio, medical imaging, and communication systems. Aperture jitter has been a major bottleneck on the way towards higher speeds and better accuracy. Photonic ADCs, which perform sampling using ultra-stable optical pulse trains generated by mode-locked lasers, have been investigated for many years as a promising approach to overcome the jitter problem and bring ADC performance to new levels. This work demonstrates that the photonic approach can deliver on its promise by digitizing a 41 GHz signal with 7.0 effective bits using a photonic ADC built from discrete components. This accuracy corresponds to a timing jitter of 15 fs - a 4-5 times improvement over the performance of the best electronic ADCs which exist today. On the way towards an integrated photonic ADC, a silicon photonic chip with core photonic components was fabricated and used to digitize a 10 GHz signal with 3.5 effective bits. In these experiments, two wavelength channels were implemented, providing the overall sampling rate of 2.1 GSa/s. To show that photonic ADCs with larger channel counts are possible, a dual 20-channel silicon filter bank has been demonstrated.


Full-body illusions and minimal phenomenal selfhood

We highlight the latest research on body perception and self-consciousness, but argue that despite these achievements, central aspects have remained unexplored, namely, global aspects of bodily self-consciousness. Researchers investigated central representations of body parts and actions involving these, but neglected the global and unitary character of self-consciousness, the 'I' of experience and behaviour. We ask, what are the minimally sufficient conditions for the appearance of a phenomenal self, that is, the fundamental conscious experience of being someone? What are necessary conditions for self-consciousness in any type of system? We offer conceptual clarifications, discuss recent empirical evidence from neurology and cognitive science and argue that these findings offer a new entry point for the systematic study of global and more fundamental aspects of self-consciousness.


A Fuzzy-based approach to programming language independent source-code plagiarism detection

Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.


Unsupervised local deep feature for image recognition

Unsupervised feature learning is an important problem in computer vision and machine learning. Various unsupervised learning methods, e.g., PCA, KMeans, autoencoder, have been applied for visual recognition tasks. Especially, autoencoder has superior image recognition performance, which is usually used as a global feature extractor. To make better use of autoencoder for image recognition, we propose unsupervised local deep feature (ULDF). We collect patches from training images and use autoencoder to transform vectorized patches into short codes. Then these codes are encoded using Fisher Vector with spatial pyramid. Image recognition with ULDF only requires an efficient linear SVM classifier. Finally, we perform multi-scale voting to achieve more accurate classification performance. In the experiments, we evaluate ULDF on the handwritten digit recognition task and the shape classification task, and show that it achieves highly competitive performance comparing to the state-of-the-art image recognition methods. The results suggest that the proposed local deep feature is superior than traditional global autoencoder. © 2016 Elsevier Inc. All rights reserved.


Transanal endoscopic total mesorectal excision: technical aspects of approaching the mesorectal plane from below—a preliminary report

Laparoscopic total mesorectal excision (TME) for low rectal cancer can be technically challenging. This report describes our initial experience with a hybrid laparoscopic and transanal endoscopic technique for TME in low rectal cancer. Between December 2012 and October 2013, we identified patients with rectal cancer < 5 cm from the anorectal junction (ARJ) who underwent laparoscopic-assisted TME with a transanal minimally invasive surgery (TAMIS) technique. A standardized stepwise approach was used in all patients. Resection specimens were examined for completeness and measurement of margins. Preoperative magnetic resonance imaging (MRI) characteristics and short-term postoperative outcomes were examined. All values are mean ± standard deviation. Ten patients (8 males; median age: 60.5 (range 36–70) years) were included. On initial MRI, all tumors were T2 or T3, mean tumor height from the ARJ was 28.9 ± 12.2 mm, mean circumferential resection margin was 5.3 ± 3.1 mm , and the mean angle between the anal canal and the levator ani was 83.9° ± 9.7°. All patients had had preoperative chemoradiotherapy, TME via TAMIS, and distal anastomosis. There were no intraoperative complications, anastomotic leaks, or 30-day mortality. The pathologic quality of all mesorectal specimens was excellent. The distal resection margin was 19.4 ± 10.4 mm, the mean circumferential resection margin was 13.8 ± 5.1 mm, and the median lymph node harvest was 10.5 (range 5–15) nodes. A combined laparoscopic and transanal approach can achieve a safe and oncologically complete TME dissection for low rectal tumors. This approach may improve clinical outcomes in these technically difficult cases, but larger prospective studies are needed.


TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation.

For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other  Corresponding author. Tel.: +86 18911938368. E-mail address: ilydouble@gmail.com TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation 2 published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.


A sub-µW, 1.0V CMOS temperature sensor circuit insensitive to device parameters

In this paper, we firstly propose the CMOS temperature sensor circuit operating in the subthreshold region. The circuit has advantages of 1.0 V operation and sub-µW power consumption. Furthermore, it is insensitive to the device parameter of the fabrication process. Next, we design the high sensitivity temperature sensor circuit that can be realized by cascade-connection of the proposed temperature sensor circuits. The proposed circuits were evaluated through HSPICE with a standard 0.35 µm CMOS device parameter. The simulation demonstrate the maximum power consumption is 0.27 µW under the condition that the temperature range is from 0 °C to 100 °C, the sensitivity is 1.01 mV/ °C and the maximum differential error is 0.87 %.


Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store

Mobile app stores provide a unique platform for developers to rapidly deploy new updates of their apps. We studied the frequency of updates of 10,713 mobile apps (the top free 400 apps at the start of 2014 in each of the 30 categories in the Google Play store). We find that a small subset of these apps (98 apps representing ˜1 % of the studied apps) are updated at a very frequent rate — more than one update per week and 14 % of the studied apps are updated on a bi-weekly basis (or more frequently). We observed that 45 % of the frequently-updated apps do not provide the users with any information about the rationale for the new updates and updates exhibit a median growth in size of 6 %. This paper provides information regarding the update strategies employed by the top mobile apps. The results of our study show that 1) developers should not shy away from updating their apps very frequently, however the frequency varies across store categories. 2) Developers do not need to be too concerned about detailing the content of new updates. It appears that users are not too concerned about such information. 3) Users highly rank frequently-updated apps instead of being annoyed about the high update frequency.


Energy Efficient Clustering Algorithms for Wireless Sensor Networks

Energy efficiency is a major concern in wireless sensor networks (WSNs). Many clustering algorithms have been proposed for such a purpose. This paper investigates the existing clustering algorithms. The algorithms have been classified and some representatives are described in each category. After analyzing the strengths and the weaknesses of each category, an important characteristic of WSNs is pointed out for further improvement of energy efficiency for WSNs.


Graded Entailment for Compositional Distributional Semantics

The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until now it has lacked the crucial feature of lexical entailment – as do other distributional models of meaning. In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.


Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives/profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24% and recall of 94.34% in sentence offensive detection, as well as precision of 77.9% and recall of 77.8% in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media.


A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty one articles.


Offensive Language Detection Using Multi-level Classification

Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool. Although a human could recognize these sorts of useless annoying texts among the useful ones, it is not an easy task for computer programs. In this paper, we describe an automatic flame detection method which extracts features at different conceptual levels and applies multilevel classification for flame detection. While the system is taking advantage of a variety of statistical models and rule-based patterns, there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries.


Movie-DiC: a Movie Dialogue Corpus for Research and Development

This paper describes Movie-DiC a Movie Dialogue Corpus recently collected for research and development purposes. The collected dataset comprises 132,229 dialogues containing a total of 764,146 turns that have been extracted from 753 movies. Details on how the data collection has been created and how it is structured are provided along with its main statistics and characteristics.


A bibliometric analysis on rural studies in human geography and related disciplines

Although the world has experienced rapid urbanization, rural areas have always been and are still an important research field in human geography. This paper performed a bibliometric analysis on rural geography studies based on the peer-reviewed articles concerning rural geography published in the SSCI-listed journals from 1990 to 2012. Our analysis examines publication patterns (document types and publishing languages, article outputs and their categories, major journals and their publication, most productive authors, geographic distribution and international collaboration) and demonstrates the evolution of intellectual development of rural geography by studying highly cited papers and their citation networks and temporal evolution of keywords. Our research findings include: The article number has been increasing since the 1900s, and went through three phases, and the rural geography research is dominated in size by UK and USA. The USA is the most productive in rural geography, but the UK had more impact than other countries in the terms of the average citation of articles. Three distinct but loosely linked research streams of rural geography were identified and predominated by the UK rural geographers. The keywords frequencies evolved according to contexts of rural development and academic advances of human geography, but they were loosely and scattered since the rural researches in different regions or different systems faced with different problems.


ColorBars: increasing data rate of LED-to-camera communication using color shift keying

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (≈ 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (< 10--3), making them a desirable choice for reliable LED-to-camera communication.


Accelerating ODE-Based Simulation of General and Heterogeneous Biophysical Models Using a GPU

Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.


BatTracker: High Precision Infrastructure-free Mobile Device Tracking in Indoor Environments

Continuous tracking of the device location in 3D space is a popular form of user input, especially for virtual/augmented reality (VR/AR), video games and health rehabilitation. Conventional inertial based approaches are well known for inaccuracy caused by large error drifts. Computer vision approaches can produce accuracy tracking but have privacy concerns and are subject to lighting conditions and computation complexity. Recent work exploits accurate acoustic distance measurements for high precision tracking. However, they require additional hardware (e.g., multiple external speakers), which adds to the costs and installation efforts, thus limiting the convenience and usability. In this paper, we propose BatTracker, which incorporates inertial and acoustic data for robust, high precision and infrastructure-free tracking in indoor environments. BatTracker leverages echoes from nearby objects and uses distance measurements from them to correct error accumulation in inertial based device position prediction. It incorporates Doppler shifts and echo amplitudes to reliably identify the association between echoes and objects despite noisy signals from multi-path reflection and cluttered environment. A probabilistic algorithm creates, prunes and evolves multiple hypotheses based on measurement evidences to accommodate uncertainty in device position. Experiments in real environments show that BatTracker can track a mobile device's movements in 3D space at sub-cm level accuracy, comparable to the state-of-the-art infrastructure based approaches, while eliminating the needs of any additional hardware.


ConTagNet: Exploiting User Context for Image Tag Recommendation

In recent years, deep convolutional neural networks have shown great success in single-label image classification. However, images usually have multiple labels associated with them which may correspond to different objects or actions present in the image. In addition, a user assigns tags to a photo not merely based on the visual content but also the context in which the photo has been captured. Inspired by this, we propose a deep neural network which can predict multiple tags for an image based on the content as well as the context in which the image is captured. The proposed model can be trained end-to-end and solves a multi-label classification problem. We evaluate the model on a dataset of 1,965,232 images which is drawn from the YFCC100M dataset provided by the organizers of Yahoo-Flickr Grand Challenge. We observe a significant improvement in the prediction accuracy after integrating user-context and the proposed model performs very well in the Grand Challenge.


Reveal, a general reverse engineering algorithm for inference of genetic network architectures.

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.


Barrel menu: a new mobile phone menu for feature rich devices

Mobile phones have ceased to be devices that people merely use to make phone calls. Today, modern mobile phones offer their users a large selection of features which are accessed via hierarchical menus. These hierarchical menus typically result in deep nested menus that require numerous clicks to navigate through, often resulting in usability issues. This paper presents a prototype of a new menu style (Barrel menu) for mobile phones, and compares the usability of this menu style with that of a traditional hierarchical (Hub-and-Spoke) design. The Barrel menu was found to be as efficient as the Hub-and-Spoke menu in terms of time-on-task and key presses, but was found to cause fewer user errors. In addition, the Barrel menu was found to be better in terms of ease of use, orientation, user satisfaction and learnability. Thus, the Barrel menu shows the potential to be a feasible alternative for mobile phone manufacturers to overcome some of the usability issues associated with Hub-and-Spoke menus.


Simultaneous robot-world and hand-eye calibration

1 Simultaneous Robot-World and Hand-Eye Calibration Fadi Dornaika and Radu Horaud, Member, IEEE Abstract|Recently, Zhuang, Roth, & Sudhakar [1] proposed a method that allows simultaneous computation of the rigid transformations from world frame to robot base frame and from hand frame to camera frame. Their method attempts to solve a homogeneous matrix equation of the form AX = ZB. They use quaternions to derive explicit linear solutions for X and Z. In this short paper, we present two new solutions that attempt to solve the homogeneous matrix equation mentioned above: (i) a closed-form method which uses quaternion algebra and a positive quadratic error function associated with this representation and (ii) a method based on non-linear constrained minimization and which simultaneously solves for rotations and translations. These results may be useful to other problems that can be formulated in the same mathematical form. We perform a sensitivity analysis for both our two methods and the linear method developed by Zhuang et al. [1]. This analysis allows the comparison of the three methods. In the light of this comparison the non-linear optimization method, which solves for rotations and translations simultaneously, seems to be the most stable one with respect to noise and to measurement errors. Keywords| hand/eye calibration, robot/world calibration, quaternion algebra.


Modern electrical machine analysis and design techniques applied to hybrid vehicle drive machines

In this paper a review is made of the different analysis and design techniques that can be used in modern electrical machine design, and references them to the developing area of hybrid vehicle drives. These machines are characterized by high transient torque requirement, compactness and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets. An example is made of the published Toyota Prius permanent-magnet machine specification. An alternative induction motor design is addressed.


BP: Profiling Vulnerabilities on the Attack Surface

Security practitioners use the attack surface of software systems to prioritize areas of systems to test and analyze. To date, approaches for predicting which code artifacts are vulnerable have utilized a binary classification of code as vulnerable or not vulnerable. To better understand the strengths and weaknesses of vulnerability prediction approaches, vulnerability datasets with classification and severity data are needed. The goal of this paper is to help researchers and practitioners make security effort prioritization decisions by evaluating which classifications and severities of vulnerabilities are on an attack surface approximated using crash dump stack traces. In this work, we use crash dump stack traces to approximate the attack surface of Mozilla Firefox. We then generate a dataset of 271 vulnerable files in Firefox, classified using the Common Weakness Enumeration (CWE) system. We use these files as an oracle for the evaluation of the attack surface generated using crash data. In the Firefox vulnerability dataset, 14 different classifications of vulnerabilities appeared at least once. In our study, 85.3% of vulnerable files were on the attack surface generated using crash data. We found no difference between the severity of vulnerabilities found on the attack surface generated using crash data and vulnerabilities not occurring on the attack surface. Additionally, we discuss lessons learned during the development of this vulnerability dataset.


Subspace clustering based on low rank representation and weighted nuclear norm minimization

Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces. Aiming at the subspace clustering problem, various subspace clustering algorithms have been proposed and low rank representation based subspace clustering is a very promising and efficient subspace clustering algorithm. Low rank representation method seeks the lowest rank representation among all the candidates that can represent the data points as linear combinations of the bases in a given dictionary. Nuclear norm minimization is adopted to minimize the rank of the representation matrix. However, nuclear norm is not a very good approximation of the rank of a matrix and the representation matrix thus obtained can be of high rank which will affect the final clustering accuracy. Weighted nuclear norm (WNN) is a better approximation of the rank of a matrix and WNN is adopted in this paper to describe the rank of the representation matrix. The convex program is solved via conventional alternation direction method of multipliers (ADMM) and linearized alternating direction method of multipliers (LADMM) and they are respectively refer to as WNNM-LRR and WNNM-LRR(L). Experimental results show that, compared with low rank representation method and several other state-of-the-art subspace clustering methods, WNNM-LRR and WNNM-LRR(L) can get higher clustering accuracy.


iHadoop: Asynchronous Iterations for MapReduce

MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a variant Hadoop implementation that caches invariant data between iterations, reduces execution time by 38% on average.


A Quaternion-based Unscented Kalman Filter for Orientation Tracking

This paper describes a Kalman filter for the real-time estimation of a rigid body orientation from measurements of acceleration, angular velocity and magnetic field strength. A quaternion representation of the orientation is computationally effective and avoids problems with singularities. The nonlinear relationship between estima ed orientation and expected measurement prevent the usage of a classical Kalman filter. This problem is solved by an Unscented Kalman filter which allows nonlinear process and measurement models and is more accurate and less costly than the common Extended Kalman filter. Several extensions to the original Unscented Kalman filter are necessary to treat the inherent properties of unit quaternions. Resul ts with simulated and measured data are discussed.


Graph-wavelet filterbanks for edge-aware image processing

In our recent work, we proposed the construction of critically-sampled wavelet filterbanks for analyzing functions defined on the vertices of arbitrary undirected graphs. These graph based functions, referred to as graph-signals, provide a flexible model for representing many datasets with arbitrary location and connectivity. An application area considered in that work is image-processing, where pixels can be connected with their neighbors to form undirected graphs. In this paper, we propose various graph-formulations of images, which capture both directionality and intrinsic edge-information. The proposed graph-wavelet filterbanks provide a sparse, edge-aware representation of image-signals. Our preliminary results in non-linear approximation and denoising using graphs show promising gains over standard separable wavelet filterbank designs.


Mixed Methods Research Designs in Counseling Psychology

With the increased popularity of qualitative research, researchers in counseling psychology are expanding their methodologies to include mixed methods designs. These designs involve the collection, analysis, and integration of quantitative and qualitative data in a single or multiphase study. This article presents an overview of mixed methods research designs. It defines mixed methods research, discusses its origins and philosophical basis, advances steps and procedures used in these designs, and identifies 6 different types of designs. Important design features are illustrated using studies published in the counseling literature. Finally, the article ends with recommendations for designing, implementing, and reporting mixed methods studies in the literature and for discussing their viability and continued usefulness in the field of counseling psychology.


Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.


Content-boosted Matrix Factorization Techniques for Recommender Systems

Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable.


NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter

The massive growth of the micro-blogging service Twitter has shed the light on the challenging problem of summarizing a collection of large number of tweets. This paper attempts to extract topical key phrases that would represent topics in tweets. Due to the informality, noise, and short length of tweets, such research is nontrivial. We tackle such challenges with extensive preprocessing approach. Followed by, introduction of new features that improve topical key phrase extraction in Twitter. We start by proposing a novel unsupervised graph-based keyword ranking method, called NE-Rank, that considers word weights in addition to edge weights when calculating the ranking. Then we introduce a new approach of leveraging hash tags when extracting key phrases. We have conducted a set of experiments showing the potential of both approaches with 16% to 39% improvement for NE-Rank and 20% improvement for hash tag enhanced extraction.


Defending continuous collision detection against errors

Numerical errors and rounding errors in continuous collision detection (CCD) can easily cause collision detection failures if they are not handled properly. A simple and effective approach is to use error tolerances, as shown in many existing CCD systems. Unfortunately, finding the optimal tolerance values is a difficult problem for users. Larger tolerance values will introduce false positive artifacts, while smaller tolerance values may cause collisions to be undetected. The biggest issue here is that we do not know whether or when CCD will fail, even though failures are extremely rare. In this paper, we demonstrate a set of simple modifications to make a basic CCD implementation failure-proof. Using error analysis, we prove the safety of this method and we formulate suggested tolerance values to reduce false positives. The resulting algorithms are safe, automatic, efficient, and easy to implement.


BGPmon: A Real-Time, Scalable, Extensible Monitoring System

This paper presents a new system, called BGPmon, for monitoring the Border Gateway Protocol (BGP). BGP is the routing protocol for the global Internet. Monitoring BGP is important for both operations and research; a number of public and private BGP monitors are deployed and widely used. These existing monitors typically collect data using a full implementation of a BGP router. In contrast, BGPmon eliminates the unnecessary functions of route selection and data forwarding to focus solely on the monitoring function. BGPmon uses a publish/subscribe overlay network to provide real-time access to vast numbers of peers and clients. All routing events are consolidated into a single XML stream. XML allows us to add additional features such as labeling updates to allow easy identification of useful data by clients. Clients subscribe to BGPmon and receive the XML stream, performing tasks such as archiving, filtering, or real-time data analysis. BGPmon enables scalable real-time monitoring data distribution by allowing monitors to peer with each other and form an overlay network to provide new services and features without modifying the monitors. We illustrate the effectiveness of the BGPmon data using the Cyclops route monitoring system.


Smart Irrigation Decision Support Based on Fuzzy Logic Using Wireless Sensor Network

Smart Irrigation Decision Support (SIDS) system based on fuzzy logic using Wireless Sensor Network (WSN) is considered for precision water irrigation. WSN consists of limited-energy sensor nodes which are equipped with sensing, wireless communication and processing capabilities. SIDS aims to measure the agricultural parameters including the soil temperature and soil moisture using the sensor nodes. The rate of soil moisture reduction is calculated from the current soil moisture reading and the previous one. These soil temperate and the rate of soil moisture reduction are employed as input parameters for fuzzy logic controller to produce the amount of irrigation time as output parameter. The fuzzy logic rules and linguistic values for the input and output parameters of fuzzy logic are carefully selected with the guide of agricultural experts including the farmers. The experimental results show that by using the SIDS system, the amount of irrigation time is precisely calculated based on the measured agricultural parameters. In addition, the water irrigation utilization is improved.


The ‘ dark side ’ of leadership personality and transformational leadership : An exploratory study

There is growing interest in dysfunctional dispositions in the workplace, in particular relating to leaders and managers within organizations. This paper reports an exploratory study investigating the relationship between the ‘dark side’ of leadership personality and transformational leadership. Data were collected from a sample of New Zealand business leaders and senior managers (n = 80) from scores on the Hogan Development Survey (HDS; Hogan & Hogan, 1997) and the Multifactor Leadership Questionnaire (MLQ; Bass & Avolio, 1995). Regression analyses revealed the ‘Colorful’ (histrionic) dimension of the HDS to be a positive predictor of transformational leadership, while the ‘Cautious’ (avoidant) and ‘Bold’ (narcissistic) dimensions of the HDS were revealed as negative predictors of transformational leadership. This is one of the first reports to suggest a relationship between histrionic personality and transformational leadership, in a literature preoccupied with the relationship between leadership and narcissism. 2007 Elsevier Ltd. All rights reserved.


Shedding Light on the Dark Triad of Personality : Narcissism , Machiavellianism , and Psychopathy

Of the offensive yet non-pathological personalities in the literature, three are especially prominent: Machiavellianism, subclinical narcissism, and subclinical psychopathy. We evaluated the recent contention that, in normal samples, this Dark Triad of constructs are one and the same. In a sample of 245 students, we measured the three constructs with standard measures and examined a variety of laboratory and self-report correlates. The measures were moderately inter-correlated, but certainly were not equivalent. Their only common Big Five correlate was disagreeableness. Subclinical psychopaths were distinguished by low neuroticism; Machiavellians, and psychopaths were low in conscientiousness; narcissism showed small positive associations with cognitive ability. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. We conclude that the Dark Triad of personalities, as currently measured, are overlapping but distinct constructs. 2002 Elsevier Science (USA). All rights reserved.


The Narcissistic Personality Inventory: alternative form reliability and further evidence of construct validity.

The aim of this study was twofold: (a)to measure the alternate form reliability of the Narcissistic Personality Inventory, and (b)to determine its construct validity by correlating it with the four scales of the Eysenck Personality Questionnaire (EPQ). The alternate form reliability was .72. The Extraversion and Psychoticism scales of the EPQ were positively and significantly correlated with the narcissism measure, and the Lie scale showed a significant negative correlation. The Neuroticism scale showed a nonsignificant relationship with narcissism. In addition, the combined Extraversion and Psychoticism scales produced[ a Multiple R with the narcissism measure that accounted for significantly more of the variance in narcissism than did either measure alone.


Loving yourself abundantly: relationship of the narcissistic personality to self- and other perceptions of workplace deviance, leadership, and task and contextual performance.

The authors report results from 2 studies assessing the extent to which narcissism is related to self- and other ratings of leadership, workplace deviance, and task and contextual performance. Study 1 results revealed that narcissism was related to enhanced self-ratings of leadership, even when controlling for the Big Five traits. Study 2 results also revealed that narcissism was related to enhanced leadership self-perceptions; indeed, whereas narcissism was significantly positively correlated with self-ratings of leadership, it was significantly negatively related to other ratings of leadership. Study 2 also revealed that narcissism was related to more favorable self-ratings of workplace deviance and contextual performance compared to other (supervisor) ratings. Finally, as hypothesized, narcissism was more strongly negatively related to contextual performance than to task performance.


Integrating the 3+1 SysML view model with safety engineering

System safety is the property of the system that characterizes its ability to prevent from hazards, which may lead to accidents or losses. Traditionally, system developers are not familiar with system safety analysis processes which are performed by safety engineers. One reason for this is the gap that exists between the traditional development processes, methodologies, notations and tools and the ones used in safety engineering. This gap makes the development of safety aware systems a very complicated task. Several approaches based on UML have been proposed to address this gap. In this paper, an approach to integrate safety engineering with a SysML based development process that is expressed in the form of the V-model, is presented. Preliminary hazard analysis is adopted and applied to a SysML based requirements specification of the mechatronic system that exploits essential use cases. A case study from the railway domain is used to illustrate the proposed approach.


The Search for Quasi-Periodicity in Islamic 5-fold Ornament

T he Penrose tilings are remarkable in that they are non-periodic (have no translational symmetry) but are clearly organised. Their structure, called quasiperiodicity, can be described in several ways, including via self-similar subdivision, tiles with matching rules, and projection of a slice of a cubic lattice in R. The tilings are also unusual for their many centres of local 5-fold and 10fold rotational symmetry, features shared by some Islamic geometric patterns. This resemblance has prompted comparison, and has led some to see precursors of the Penrose tilings and even evidence of quasi-periodicity in traditional Islamic designs. Bonner [2] identified three styles of self-similarity; Makovicky [20] was inspired to develop new variants of the Penrose tiles and later, with colleagues [24], overlaid Penrose-type tilings on traditional Moorish designs; more recently, Lu and Steinhardt [17] observed the use of subdivision in traditional Islamic design systems and overlaid Penrose kites and darts on Iranian designs. The latter article received widespread exposure in the world’s press, although some of the coverage overstated and misrepresented the actual findings. The desire to search for examples of quasi-periodicity in traditional Islamic patterns is understandable, but we must take care not to project modern motivations and abstractions into the past. An intuitive knowledge of group theory is sometimes attributed to any culture that has produced repeating patterns displaying a wide range of symmetry types, even though they had no abstract notion of a group. There are two fallacies to avoid: • abstraction: P knew about X and X is an example of Y therefore P knew Y. • deduction: P knew X and X implies Y therefore P knew Y.


Social Choice for Partial Preferences Using Imputation

Within the field of multiagent systems, the area of computational social choice considers the problems arising when decisions must be made collectively by a group of agents. Usually such systems collect a ranking of the alternatives from each member of the group in turn, and aggregate these individual rankings to arrive at a collective decision. However, when there are many alternatives to consider, individual agents may be unwilling, or unable, to rank all of them, leading to decisions that must be made on the basis of incomplete information. While earlier approaches attempt to work with the provided rankings by making assumptions about the nature of the missing information, this can lead to undesirable outcomes when the assumptions do not hold, and is ill-suited to certain problem domains. In this thesis, we propose a new approach that uses machine learning algorithms (both conventional and purpose-built) to generate plausible completions of each agent’s rankings on the basis of the partial rankings the agent provided (imputations), in a way that reflects the agents’ true preferences. We show that the combination of existing social choice functions with certain classes of imputation algorithms, which forms the core of our proposed solution, is equivalent to a form of social choice. Our system then undergoes an extensive empirical validation under 40 different test conditions, involving more than 50,000 group decision problems generated from real-world electoral data, and is found to outperform existing competitors significantly, leading to better group decisions overall. Detailed empirical findings are also used to characterize the behaviour of the system, and illustrate the circumstances in which it is most advantageous. A general testbed for comparing solutions using real-world and artificial data (Prefmine) is then described, in conjunction with results that justify its design decisions. We move on to propose a new machine learning algorithm intended specifically to learn and impute the preferences of agents, and validate its effectiveness. This Markov-Tree approach is demonstrated to be superior to imputation using conventional machine learning, and has a simple interpretation that characterizes the problems on which it will perform well. Later chapters contain an axiomatic validation of both of our new approaches, as well as techniques for mitigating their manipulability. The thesis concludes with a discussion of the applicability of its contributions, both for multiagent systems and for settings involving human elections. In all, we reveal an interesting connection between machine learning and computational social choice, and introduce a testbed which facilitates future research efforts on computational social choice for partial preferences, by allowing empirical comparisons between competing approaches to be conducted easily, accurately, and quickly. Perhaps most importantly, we offer an important and effective new direction for enabling group decision making when preferences are not completely specified, using imputation methods.


DRAPE: DRessing Any PErson

We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.


Cooperation and Competition

Some time ago, in the garden of a friend’s house, my 5-year-old son and his chum were struggling over possession of a water hose. (They were in conflict.) Each wanted to use it first to water the garden. (They had a competitive orientation.) Each was trying to tug it away from the other and both were crying. Each was very frustrated, and neither was able to use the hose to sprinkle the flowers as he’d desired. After reaching a deadlock in this tug-of-war, they began to punch one another and call each other names. (As a result of their competitive approach, the conflict took a destructive course for both of them—producing frustration, crying, and violence.) Now imagine a different scenario. The garden consists mainly of two sections, flowers and vegetables. Each kid wants to use the hose first. Let’s suppose they want to resolve their conflict amicably. (They have a cooperative orientation.) One says to the other, “Let’s flip a coin to see who uses the hose first.” (A fair procedure for resolving the conflict is suggested.) The other agrees and suggests that the loser be given the right to select which section of the garden he waters. They both agree to the suggestion. (A cooperative, win-win agreement is reached.) Their agreements are implemented and both kids feel happy and good about one another. (These are common effects of a cooperative or constructive approach to a conflict.) As this example illustrates, whether the participants in a conflict have a cooperative orientation or a competitive one is decisive in determining its course and outcomes. This chapter is concerned with understanding the processes involved in cooperation and competition, their effects, and the factors that contribute to developing a cooperative or competitive relationship. It is important to understand the nature of cooperation and competition since almost all conflicts are mixed-motive, containing elements of both cooperation and competition.


Review of ontology matching with background knowledge

The ontology matching process with background knowledge is more suitable to match heterogeneous ontologies, since background knowledge is used as a mediator or a reference to identify relation between two concepts being matched. This method is called indirect matching and the system is called indirect matching system. This paper reviews the motivation that described the urgency of ontology matching, the various background knowledge and their strengths, also indirect matching process. At the end we provide the comparison of indirect matching system. Based on the comparison, mapping repair function were added to improve the quality of mapping. The purpose of this paper is to help in guiding new practitioners get a general idea on the ontology matching field and to determine possible research lines.


Development of sEMG-driven assistive devices based on twisted string actuation

A twisted string actuation module with an integrated force sensor based on optoelectronic components is presented in this paper. This solution is especially suited for very compact, light-weight and wearable robotic devices, such as exoskeletons, but is appropriate for various robotic applications. An in-depth presentation of the proposed actuation module, the description and the basic sensor working principle of the integrated force sensor are portrayed and discussed. Extensive experimental measures have been carried out to verify the actuation module compliant frame design and the related finite element analysis results. Therefore, the proposed actuation module has been used for the implementation of a simple assistive application where surface-electromyographic signals are used to detect the user's muscle activity in order to control the support action provided by the actuator, thus reducing his/her effort.


Variational inference in nonconjugate models

Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate updates are easily derived and in closed form. However, many models of interest—like the correlated topic model and Bayesian logistic regression—are nonconjugate. In these models, mean-field methods cannot be directly applied and practitioners have had to develop variational algorithms on a case-by-case basis. In this paper, we develop two generic methods for nonconjugate models, Laplace variational inference and delta method variational inference. Our methods have several advantages: they allow for easily derived variational algorithms with a wide class of nonconjugate models; they extend and unify some of the existing algorithms that have been derived for specific models; and they work well on real-world data sets. We studied our methods on the correlated topic model, Bayesian logistic regression, and hierarchical Bayesian logistic regression.


Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.


Contrast-Guided Image Interpolation

In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45° and 135° CDMs for interpolating the diagonal pixels and 2) the 0° and 90° CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications.


Increased frontal and paralimbic activation following ayahuasca, the pan-amazonian inebriant

Ayahuasca is a South American psychoactive plant tea which contains the serotonergic psychedelic N,N-dimethyltryptamine (DMT) and monoamine-oxidase inhibitors that render DMT orally active. Previous investigations with ayahuasca have highlighted a psychotropic effect profile characterized by enhanced introspective attention, with individuals reporting altered somatic perceptions and intense emotional modifications, frequently accompanied by visual imagery. Despite recent advances in the study of ayahuasca pharmacology, the neural correlates of acute ayahuasca intoxication remain largely unknown. To investigate the effects of ayahuasca administration on regional cerebral blood flow. Fifteen male volunteers with prior experience in the use of psychedelics received a single oral dose of encapsulated freeze-dried ayahuasca equivalent to 1.0 mg DMT/kg body weight and a placebo in a randomized double-blind clinical trial. Regional cerebral blood flow was measured 100–110 min after drug administration by means of single photon emission tomography (SPECT). Ayahuasca administration led to significant activation of frontal and paralimbic brain regions. Increased blood perfusion was observed bilaterally in the anterior insula, with greater intensity in the right hemisphere, and in the anterior cingulate/frontomedial cortex of the right hemisphere, areas previously implicated in somatic awareness, subjective feeling states, and emotional arousal. Additional increases were observed in the left amygdala/parahippocampal gyrus, a structure also involved in emotional arousal. The present results suggest that ayahuasca interacts with neural systems that are central to interoception and emotional processing and point to a modulatory role of serotonergic neurotransmission in these processes.


Semi-supervised deep learning by metric embedding

Deep networks are successfully used as classification models yielding state-ofthe-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data. This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set. The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.


Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.


Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics

Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.


Kijken naar porno: subcorticale hersenactiviteit weerspiegelt negatieve maar niet positieve seksuele associaties

Aan de ene kant kunnen mensen visuele seksuele stimulatie (VSS) ervaren als plezierig, maar aan de andere kant kunnen mensen VSS ook ervaren als iets “viezigs” en negatief. Dit onderzoek richt zich op de vraag hoe het brein bij vrouwen reageert op VSS en hoe de hersenactiviteit samenhangt met hun impliciete en expliciete attitudes ten aanzien van VSS. Vrouwen zonder seksuele klachten (N = 20) bekeken een reeks afbeeldingen uit diverse categorieën (seksuele penetratie, walging, neutraal) in een fMRI-scanner. Met behulp van een reactietijd-taak (Implicit Association Test) werd vervolgens de sterkte van hun impliciete seks-vies versus seks-lekker associaties gemeten. Daarnaast beoordeelden ze alle afbeeldingen op een visuele analoge schaal als meting van hun expliciete attitude. De hersenactiviteit in reactie op afbeeldingen van seksuele penetratie vertoonde grote overeenkomst met die op afbeeldingen van walgelijke plaatjes. Specifiek de hersenactiviteit in reactie op de seksuele penetratieafbeeldingen bleek te variëren als functie van de impliciete attitude ten aanzien van de seksuele penetratie-plaatjes. Hoe sterker de impliciete associatie van seks met walging des te sterker de activiteit in de basale voorhersenen (inclusief de nucleus accumbens en de bed nucleus van de stria terminalis), de middenhersenen, en amygdala in reactie op het zien van de seksuele penetratie plaatjes. De betrokken hersengebieden zijn veelal geassocieerd met visuele seksuele verwerkingsprocessen. De huidige resultaten laten zien dat de activatie van deze gebieden niet zondermeer een positieve appreciatie van seksuele stimuli impliceert, maar ook kan duiden op een negatieve of ambivalente houding ten aanzien van seksuele stimuli.


Automatic detection of motion artifacts in MR images using CNNS

Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis.


D Region Proposal U-Net with Dense and Residual Learning for Lung Nodule Detection


Semi-supervised Deep Learning with Memory

We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semisupervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.


A real-time object detecting and tracking system for outdoor night surveillance

Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking. 2007 Published by Elsevier Ltd on behalf of Pattern Recognition Society.


Thermal management and cooling of windings in electrical machines for electric vehicle and traction application

Conventional electrical machine cooling includes housing fins, shaft-mounted fan, and water jacket. In these cases, heat from the copper loss of windings needs to pass through either the stator core or the air between windings and the housing. Because of the large thermal resistance in the path, these methods are sometimes not efficient enough for high torque density machines. Overheat in windings causes failure in the insulation and damages the machine. Many technologies that facilitate winding cooling have been investigated in the literature, such as winding topologies with more efficient heat dissipation capability, impregnation material with high thermal conductivity, and advanced direct winding. This paper reviews and classifies thermal management and cooling methods to provide a guideline for high torque density electrical machine design for a better winding thermal management.


Multisensor data fusion: Target tracking with a doppler radar and an Electro-Optic camera

This paper addresses the problem of multisensor data fusion for target tracking using a Doppler radar with range rate measurements and an Electro-Optic (EO) camera. We present three fusion architectures, named FA1-FA3, to perform data fusion using the above mentioned sensors. FA1 and FA2 are distributed fusion architectures employing the information matrix fusion method with dynamic feedback. In FA1, radar and camera pseudo measurements are formed that allow us to make use of a linear Kalman Filter (KF) for the radar local filter and an Extended Kalman Filter (EKF) for the EO camera local filter. In FA2, the radar and camera measurements are used directly and therefore the system comprises two EKFs. FA3 is a centralised architecture where the data fusion is performed by way of the measurement fusion method. The final contribution of this paper is a performance comparison of these sensor data fusion techniques when making use of range rate measurements. In order to evaluate the performance of the fusion architectures, Monte Carlo simulations are performed and two filter metrics are presented: an absolute metric - the root mean squared error (RMSE) and a performance metric - the average normalised estimation error squared (ANEES). The results show that the fusion architectures presented are accurate, stable and credible.


Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin

Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.


Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem

We present an empirical investigation into the prevalence and impact of distributed denial-of-service (DDoS) attacks on operators in the Bitcoin economy. To that end, we gather and analyze posts mentioning “DDoS” on the popular Bitcoin forum bitcointalk.org. Starting from around 3 000 different posts made between May 2011 and October 2013, we document 142 unique DDoS attacks on 40 Bitcoin services. We find that 7% of all known operators have been attacked, but that currency exchanges, mining pools, gambling operators, eWallets, and financial services are much more likely to be attacked than other services. Not coincidentally, we find currency exchanges and mining pools are much more likely to have DDoS protection such as CloudFlare, Incapsula, or Amazon Cloud. We show that those services that have been attacked are more than three times as likely to buy anti-DDoS services than operators who have not been attacked. We find that big mining pools (those with historical hashrate shares of at least 5%) are much more likely to be DDoSed than small pools. We investigate Mt. Gox as a case study for DDoS attacks on currency exchanges and find a disproportionate amount of DDoS reports made during the large spike in trading volume and exchange rates in spring 2013. We conclude by outlining future opportunities for researching DDoS attacks on Bitcoin.


Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk

Bitcoin has enjoyed wider adoption than any previous crypto-currency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk investors face from Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 40 Bitcoin exchanges established over the past three years, and find that 18 have since closed, with customer account balances often wiped out. Fraudsters are sometimes to blame, but not always. Using a proportional hazards model, we find that an exchange’s transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. We also present a logistic regression showing that popular exchanges are more likely to suffer a security breach.


Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools

One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resourceintensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game’s equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.


Towards Risk Scoring of Bitcoin Transactions

If Bitcoin becomes the prevalent payment system on the Internet, crime fighters will join forces with regulators and enforce blacklisting of transaction prefixes at the parties who offer real products and services in exchange for bitcoin. Blacklisted bitcoins will be hard to spend and therefore less liquid and less valuable. This requires every recipient of Bitcoin payments not only to check all incoming transactions for possible blacklistings, but also to assess the risk of a transaction being blacklisted in the future. We elaborate this scenario, specify a risk model, devise a prediction approach using public knowledge, and present preliminary results using data from selected known thefts. We discuss the implications on markets where bitcoins are traded and critically revisit Bitcoin’s ability to serve as a unit of account.


Mastering Digital Transformation: the Path of a Financial Services Provider towards a Digital Transformation Strategy

To master the challenges of a digital transformation and to systematically address IT’s multifaceted transformative impacts on an organization’s inner and outer environments, top management is increasingly formulating and implementing a digital transformation strategy (DTS). To date, there have been few details of DTS formation concerning its underlying processes and activities. In this study, an interpretive case study approach is employed and DTS formation is investigated from a process/activity perspective. By using an activity-based process model that builds on IS strategizing, an in-depth case study at a large financial services provider was conducted. The results show that this DTS was predominantly shaped by a diversity of emergent strategizing activities through a bottom-up process and prior to the introduction of a holistic approach by top management. Top management then sought to formalize emergent strategy contents by formulating and implementing a DTS that comprised a shared target picture, distinct digital transformation governance, and measures to increase the share of deliberate strategy contents. Besides providing practical implications for DTS formulation and implementation, this study contributes to the literature on digital transformation, IS strategy, and IS strategizing.


A comprehensive physical model for light reflection

A new general reflectance model for computer graphics is presented. The model is based on physical optics and describes specular, directional diffuse, and uniform diffuse reflection by a surface. The reflected light pattern depends on wavelength, incidence angle, two surface roughness parameters, and surface refractive index. The formulation is self consistent in terms of polarization, surface roughness, masking/shadowing, and energy. The model applies to a wide range of materials and surface finishes and provides a smooth transition from diffuse-like to specular reflection as the wavelength and incidence angle are increased or the surface roughness is decreased. The model is analytic and suitable for Computer Graphics applications. Predicted reflectance distributions compare favorably with experiment. The model is applied to metallic, nonmetallic, and plastic materials, with smooth and rough surfaces.


Labor Costs and Foreign Direct Investment : A Panel VAR Approach

This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the dynamic association between labor costs and FDI. Further, results suggest that labor market policies should focus on productivity-enhancing tools in addition to price hindering tools.


Hairstyle recommendation system for women

A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.


Harmonic Resonance Evaluation for Hub Traction Substation Consisting of Multiple High-Speed Railways

In many hub traction power-supply systems (TPSSs), the 27.5 kV busbars in the traction substation (TSS) have supplied several railway lines in a local area for reducing the number of TSSs. However, the multiple railway lines connected to the busbar may affect the resonance behaviors of the entire system. It is therefore necessary to investigate the resonance behaviors under such hub TPSS conditions since the harmonic current collection of all railway lines will aggravate the resonance problem. Thus, resonance-mode assessment and its sensitivity method is adopted for investigating the resonance behaviors of hub TPSS affected by different numbers of connected lines, lengths of supply lines, system capacities, etc. The numerical results of modal sensitivity indices are compared to investigate and quantify the harmonic resonance issue caused by multiple electrical railway lines. In addition, the resonance frequency-shift technique and modal impedance sensitivity index-based method are also employed to mitigate the critical harmonic resonances. The results illustrate that it is effective to analyze resonance issues by adopting the harmonic model and modal methods in this paper. The two types of resonance behaviors (i.e., primary resonance and resonance band) caused from the hub TPSS have been fully investigated, and the resulting harmonic distortion of hub TPSS is suppressed.


Communication-Efficient Distributed Statistical Inference

We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of MCMC algorithms even in a non-distributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation.


Social Interactions in Online Gaming

This paper briefly overviews five studies examining massively multiplayer online role-playing games (MMORPGs). The first study surveyed 540 gamers and showed that the social aspects of the game were the most important factor for many gamers. The second study explored the social interactions of 912 MMORPG players and showed they created strong friendships and emotional relationships. A third study examined the effect of online socializing in the lives of 119 online gamers. Significantly more male gamers than female gamers said that they found it easier to converse online than offline, and 57% of gamers had engaged in gender swapping. A fourth study surveyed 7,069 gamers and found that 12% of gamers fulfilled at least three diagnostic criteria of addiction. Finally, an interview study of 71 gamers explored attitudes, experiences, and feelings about online gaming. They provided detailed descriptions of personal problems that had arisen due to playing MMORPGs. an individualistic character (Griffiths, Davies, & Chappell, 2004). This is the only setting where millions of users voluntarily immerse themselves in a graphical virtual environment and interact with each other through avatars on a daily basis (Yee, 2007). Research suggests that the game play within these virtual worlds is enhanced because players use them as traditional games as well as arenas in which to explore DOI: 10.4018/ijgbl.2011100103 International Journal of Game-Based Learning, 1(4), 20-36, October-December 2011 21 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. new relationships, new places and themselves (Krotoski, 2004). Despite the massive amounts of money spent on online gaming, very little research has been carried out regarding the positive social aspects of these games. Much of the debate over the last 30 years has focused on the dangers of computer gaming in the adolescent population, including increased aggression and addiction. Research has also been carried out examining the potentially harmful effects playing computer games may have on social development, self-esteem, social inadequacy, and social anxiety. MMORPGs are very (virtually) socially interactive but little social interaction in the real world is needed when playing them as only one person can play them at any one time from a single computer, unlike some popular two-player console games such as Mortal Kombat. Yee (2001, 2006, 2007) has carried out research into MMORPGs and notes that they allow new forms of social identity and social interaction. Yee’s research has shown that MMORPGs appeal to adults and teenagers from a wide range of backgrounds, and they spend on average more than half a working week in these environments. In a study by Utz (2000), it was found that 77% of respondents reported that they had some sort of relation with other Multi-User Dungeon (MUD) Gamers. It has also been suggested that college students can develop compulsions to play MMORPGs leading to social isolation, poor academic performance, and sleep deprivation. In 2004, a survey of over 54,000 American students found 11% of females and 20% of males said their recreational computer use had significantly hindered their performance at college and University (American College Health Association, 2005). Players can become fixated on their virtual characters, striving to obtain the best armour, experience and reputation in the game, ignoring the fact that their grades are dropping and their friends have drifted apart. It is clear to see that computer games appear to play a role in the socialisation of heavy game players particularly for those who play MMORPGs. Krotoski (2004) maintains that MMORPGs encourage group interaction and involvement, flexibility and mastery, resulting in significant friendships and personal empowerment. It is important to realise that gaming has shown elements of being a compulsive behaviour, with players feeling addicted, experiencing a strong impulse to play the games and finding it hard to resist the games (Griffiths & Davies, 2005). Positive social interaction is paramount in MMORPGs because they require a large number of players to cooperate together and work as a team at the same time. MMORPGs also have multiple tasks that require different characters with different skills in order to complete a challenge or quest. This teaches gamers to be dependent on one another that reinforce their relationships, providing a good understanding of teamwork. The purpose of the research here is to examine the social interactions that occur both within and outside of MMORPGs. The development of virtual friendships can be very enjoyable for gamers, and anecdotal evidence has suggested they sometimes develop into serious real-life friendships and relationships. Not only do MMORPGs facilitate formation of relationships, they are also windows into and catalysts in existing relationships. To date, there has been relatively little research into massively multiplayer online roleplaying games (MMORPGs). This paper briefly overviews five studies by the authors (Griffiths, Davies & Chappell, 2004; Cole & Griffiths, 2007; Hussain & Griffiths, 200, 2009; Grüsser, Thalemann, & Griffiths, 2007). In all studies outlined below, participants were informed that participation was entirely voluntary and that the research was conducted according to the British Psychological Society’s Ethical Code of Conduct for Psychologists. In the case of the surveys, if participants no longer wished to take part they simply had to close the Internet browser. All players were guaranteed anonymity and confidentiality. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/social-interactions-onlinegaming/60132?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Library Science, Information Studies, and Education, InfoSci-Select, InfoSci-Select, InfoSciCommunications, Online Engagement, and Media eJournal Collection, InfoSci-Educational Leadership, Administration, and Technologies eJournal Collection, InfoSci-Select, InfoSci-Select, InfoSci-Journal Disciplines Communications and Social Science. Recommend this product to your


THE AUDIT RISK MODEL UNDER THE RISK OF FRAUD

In this article, we derive an audit risk formula for a simple situation. This formula closely resembles the SAS 47 model when we assume that no material misstatement due to fraud exists. A simple case illustrates how the risk of material misstatement due to management fraud impacts audit risk and how performing special audit procedures to detect such irregularities can decrease overall audit risk. While SAS 53 requires auditors to assess the risk of errors and irregularities and plan the audit to provide reasonable assurance of detecting errors and irregularities, the audit risk model of SAS 47 does not directly address the risk of management fraud. This article develops an audit risk model using the belief-function framework that considers the risks faced by auditors due to random errors, defalcations (employee fraud) and management fraud. We consider two cases. In the first, we consider only affirmative items of evidence and derive an analytical formula for the audit risk. In the second, we consider mixed items of evidence (both affirmative and negative), which models the situation more frequently faced by auditors. We demonstrate that a serious underestimation of audit risk can occur if the audit risk model is used without specifically considering the risks associated with management fraud.