title: Distributed Learning in Multi-Armed Bandit with Multiple Players

abstract: We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N independent arms. Each arm, when played, offers i.i.d. reward according to a distribution with an unknown parameter. At each time, each player chooses one arm to play without exchanging observations or any information with other players. Players choosing the same arm collide, and, depending on the collision model, either no one receives reward or the colliding players share the reward in an arbitrary way. We show that the minimum system regret of the decentralized MAB grows with time at the same logarithmic order as in the centralized counterpart where players act collectively as a single entity by exchanging observations and making decisions jointly. A decentralized policy is constructed to achieve this optimal order while ensuring fairness among players and without assuming any pre-agreement or information exchange among players. Based on a Time Division Fair Sharing (TDFS) of the M best arms, the proposed policy is constructed and its order optimality is proven under a general reward model. Furthermore, the basic structure of the TDFS policy can be used with any order-optimal single-player policy to achieve order optimality in the decentralized setting. We also establish a lower bound on the system regret growth rate for a general class of decentralized polices, to which the proposed policy belongs. This problem finds potential applications in cognitive radio networks, multi-channel communication systems, multi-agent systems, web search and advertising, and social networks.


Teaching and learning human-computer interaction: past, present, and future


title: Bayesian Warped Gaussian Processes

abstract: Warped Gaussian processes (WGP) [1] model output observations in regression tasks as a parametric nonlinear transformation of a Gaussian process (GP). The use of this nonlinear transformation, which is included as part of the probabilistic model, was shown to enhance performance by providing a better prior model on several data sets. In order to learn its parameters, maximum likelihood was used. In this work we show that it is possible to use a non-parametric nonlinear transformation in WGP and variationally integrate it out. The resulting Bayesian WGP is then able to work in scenarios in which the maximum likelihood WGP failed: Low data regime, data with censored values, classification, etc. We demonstrate the superior performance of Bayesian warped GPs on several real data sets.


title: Low-cost interactive whiteboard using the Kinect

abstract: An interactive whiteboard is a large interactive display that connects to a computer and projector. A projector projects the computer's desktop onto the board's surface where users control the computer using a pen, finger, stylus, or other device. The board is typically mounted to a wall or floor stand. The majority of IWBs sold globally involve one of four forms of interaction between the user and the content projected on the whiteboard. These are an infrared scan technology, a resistive, touch-based board, an electromagnetic pen and associated software, and an ultrasonic pen. This paper's purpose is researching the infrared LED pen, and it starts at the foundational infrared scan technology. Taking the interactive whiteboard's advanced achievement using the Kinect as reference, this paper confirms its research routes and innovative points. This paper presents a lowcost interactive whiteboard by positioning the infrared LED pen with particular sign accurately by analyzing depth and IR data of Kinect. The system excluded the interference of Kinect's infrared light and implemented the right-mouse simulation by identifying the infrared LED pen's two brightness modes. It is combined with CUDA platform and OpenCV Library to speed-up image process and obtain high real-time performance. This lowcost interactive whiteboard using the Kinect can be used in a variety of settings, including classrooms at all levels of education, in corporate board rooms and work groups, in training rooms for professional sports coaching, in broadcasting studios and many others. Furthermore, this study demonstrates the potential application of good scientific and practical values.


title: Strategic leadership and executive innovation influence: an international multi-cluster comparative study

abstract: This study investigates the relationship of strategic leadership behaviors with executive innovation influence and the moderating effects of top management team (TMT)'s tenure heterogeneity and social culture on that relationship. Using survey data from six countries comprising three social cultures, strategic leadership behaviors were found to have a strong positive relationship with executive influence on both product–market and administrative innovations. In addition, TMT tenure heterogeneity moderated the relationship of strategic leadership behaviors with executive innovation influence for both types of innovation, while social culture moderated that relationship only in the case of administrative innovation. Copyright © 2005 John Wiley & Sons, Ltd.


title: A Low Cost FPGA based Cryptosystem Design for High Throughput Area Ratio

abstract: Over many years, Field Programmable Gated Ar-rays (FPGA) have been used as a target device for various prototyping and cryptographic algorithm applications. Due to the parallel architecture of FPGAs, the flexibility of cryptographic algorithms can be exploited to achieve high throughputs at the expense of very low chip area. In this research, we propose a low cost FPGA based cryptosystem named as Secure Cipher for high throughput to area ratio. The proposed Secure Cipher is implemented using full loop unroll technique in order to exploit the parallelism of the proposed algorithm. The proposed cryp-tosystem implementation achieved a throughput of 4600Mbps for encryption. The logic resource utilization of this implementation is 802 logic elements(LE) which yields a throughput to area ratio of 5.735Mbps/LE.


title: Introducing a New Benchmarked Dataset for Activity Monitoring

abstract: This paper addresses the lack of a commonly used, standard dataset and established benchmarking problems for physical activity monitoring. A new dataset -- recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor -- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes new challenges for physical activity monitoring.


title: Negative Refractive Index in Chiral Metamaterials

abstract: We experimentally demonstrate a chiral metamaterial exhibiting negative refractive index at terahertz frequencies. The presence of strong chirality in the terahertz metamaterial lifts the degeneracy for the two circularly polarized waves and allows for the achievement of negative refractive index without requiring simultaneously negative permittivity and negative permeability. The realization of terahertz chiral negative index metamaterials offers opportunities for investigation of their novel electromagnetic properties, such as negative refraction and negative reflection, as well as important terahertz device applications.


title: A Global Hypothesis Verification Framework for 3D Object Recognition in Clutter

abstract: Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D object recognition pipeline deploying both global and local 3D features as well as shape and color. Thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets.


title: Wide Table Layout Optimization based on Column Ordering and Duplication

abstract: Modern data analytical tasks often witness very wide tables, from a few hundred columns to a few thousand. While it is commonly agreed that column stores are an appropriate data format for wide tables and analytical workloads, the physical order of columns has not been investigated. Column ordering plays a critical role in I/O performance, because in wide tables accessing the columns in a single horizontal partition may involve multiple disk seeks. An optimal column ordering will incur minimal cumulative disk seek costs for the set of queries applied to the data. In this paper, we aim to find such an optimal column layout to maximize I/O performance. Specifically, we study two problems for column stores on HDFS: column ordering and column duplication. Column ordering seeks an approximately optimal order of columns; column duplication complements column ordering in that some columns may be duplicated multiple times to reduce contention among the queries' diverse requirements on the column order. We consider an actual fine-grained cost model for column accesses and propose algorithms that take a query workload as input and output a column ordering strategy with or without storage redundancy that significantly improves the overall I/O performance. Experimental results over real-life data and production query workloads confirm the effectiveness of the proposed algorithms in diverse settings.


Comparison of buccal corridor and dental arch width changes in extraction and non-extraction orthodontic treatment


title: Profile for estimating risk of heart failure.

abstract: CONTEXT ::: We devised a risk appraisal function to assess the hazard of heart failure in persons who are predisposed by coronary disease, hypertension, or valvular heart disease. ::: ::: ::: OBJECTIVE ::: To provide general practitioners and internists with a cost-effective method to select people at high risk who are likely to have impaired left ventricular systolic function and may therefore require further evaluation and aggressive preventive measures. ::: ::: ::: METHODS ::: The routinely measured risk factors used in constructing the heart failure profile include age, electrocardiographic left ventricular hypertrophy, cardiomegaly on chest x-ray film, heart rate, systolic blood pressure, vital capacity, diabetes mellitus, evidence of myocardial infarction, and valvular disease or hypertension. Based on 486 heart failure cases during 38 years of follow-up, 4-year probabilities of failure were computed using the pooled logistic regression model for each sex; a simple point score system was employed. A multivariate profile was also produced without the vital capacity or chest x-ray film because these may not be readily available in some clinical settings. ::: ::: ::: RESULTS ::: Using the risk factors that make up the multivariate risk formulation-derived from ordinary office procedures-the probability of developing heart failure can be estimated and compared with the average risk for persons of the same age and sex. Using this risk profile, 60% of events in men and 73% in women occurred in subjects in the top quintile of multivariate risk. ::: ::: ::: CONCLUSIONS ::: Using this multivariate risk formulation, it is possible to identify high-risk candidates for heart failure who are likely to have a substantial yield of positive findings when tested for objective evidence of presymptomatic left ventricular dysfunction. The risk profile may also identify candidates who are at high risk for heart failure because of multiple, marginal risk factor abnormalities that might otherwise be overlooked.


title: Detection and localization of faces on digital images

abstract: Abstract A method for automatic detection and localization of faces on digital images is proposed. The method is based on learning by example and multi-resolution analysis of digital images. Special emphasis is put on the management of the learning data, in order to improve the performances. Various experimental results, obtained by using a Multi-Layer Perceptron (MLP) as a classifier, are provided.


title: Image Tampering Detection Based on Local Texture Descriptor and Extreme Learning Machine

abstract: This paper proposed a new image tampering detection method based on local texture descriptor and extreme learning machine (ELM). The image tampering includes both splicing and copy-move forgery. First, the image was decomposed into three color channels (one luminance and two Chroma), and each channel was divided into non-overlapping blocks. Local textures in the form of local binary pattern (LBP) were extracted from each block. The histograms of the patterns of all the blocks were concatenated to form a feature vector. The feature vector was then fed to an ELM for classification. The ELM is a powerful and fast classification approach. The experiments was performed using two publicly available databases. The experimental results showed that the proposed method achieved a high detection accuracy in both the databases.


title: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

abstract: In this paper, a new learning framework - probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge,) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting connections to a number of existing methods such as the A* algorithm, decision tree algorithms, generative models, and cascade approaches. In this paper, we show the applications of PBT for classification, detection, and object recognition. We have also applied the framework in segmentation


title: A 1.2-V 2.7-mW 160MHz continuous-time delta-sigma modulator with input-feedforward structure

abstract: A power and area efficient continuous-time inputfeedforward delta-sigma modulator (DSM) structure is proposed. The coefficients are optimized to increase the input range and reduce the power. The feedforward paths and the summer are embedded into the quantizer, hence the circuit is simplified, and the power consumption and area are reduced. The prototype chip, fabricated in a 0.13-µm CMOS technology, achieves a 68-dB DR (Dynamic Range) and 66.1-dB SNDR (signal-to-noise-and-distortion ratio) over a 1.25-MHz signal bandwidth with a 160-MHz clock. The power consumption of the modulator is 2.7 mW under a 1.2-V supply, and the chip core area is 0.082mm2.


title: Structural racism and myocardial infarction in the United States

abstract: There is a growing research literature suggesting that racism is an important risk factor undermining the health of Blacks in the United States. Racism can take many forms, ranging from interpersonal interactions to institutional/structural conditions and practices. Existing research, however, tends to focus on individual forms of racial discrimination using self-report measures. Far less attention has been paid to whether structural racism may disadvantage the health of Blacks in the United States. The current study addresses gaps in the existing research by using novel measures of structural racism and by explicitly testing the hypothesis that structural racism is a risk factor for myocardial infarction among Blacks in the United States. State-level indicators of structural racism included four domains: (1) political participation; (2) employment and job status; (3) educational attainment; and (4) judicial treatment. State-level racial disparities across these domains were proposed to represent the systematic exclusion of Blacks from resources and mobility in society. Data on past-year myocardial infarction were obtained from the National Epidemiologic Survey on Alcohol and Related Conditions (non-Hispanic Black: N = 8245; non-Hispanic White: N = 24,507), a nationally representative survey of the U.S. civilian, non-institutionalized population aged 18 and older. Models were adjusted for individual-level confounders (age, sex, education, household income, medical insurance) as well as for state-level disparities in poverty. Results indicated that Blacks living in states with high levels of structural racism were generally more likely to report past-year myocardial infarction than Blacks living in low-structural racism states. Conversely, Whites living in high structural racism states experienced null or lower odds of myocardial infarction compared to Whites living in low-structural racism states. These results raise the provocative possibility that structural racism may not only harm the targets of stigma but also benefit those who wield the power to enact stigma and discrimination.


title: Age, Dietary Fiber, Breath Methane, and Fecal Short Chain Fatty Acids Are Interrelated in Archaea-Positive Humans

abstract: Recent attention has focused on the significance of colonic Archaea in human health and energy metabolism. The main objectives of this study were to determine the associations among the number of fecal Archaea, body mass index (BMI), fecal short chain fatty acid (SCFA) concentrations, and dietary intakes of healthy humans. We collected demographic information, 3-d diet records, and breath and fecal samples from 95 healthy participants who were divided into 2 groups: detectable Archaea (>10(6) copies/g; Arch+ve) and undetectable Archaea. Dietary intakes, BMI, and fecal SCFAs were similar in both groups. The mean number of Archaea 16S rRNA gene copies detected in Arch+ve participants' feces was 8.9 ± 0.2 log/g wet weight. In Arch+ve participants, there were positive correlations between breath methane and age (r = 0.52; P = 0.001), total dietary fiber (TDF) intake (r = 0.57; P = 0.0003), and log number of fecal Archaea 16S rRNA gene copies (r = 0.35; P = 0.03). In the Arch+ve group, negative correlations were observed between TDF/1000 kcal and fecal total SCFA (r = -0.46; P ≤ 0.01) and between breath methane and fecal total SCFA (r = -0.42; P = 0.01). Principal component analysis identified a distinct Archaea factor with positive loadings of age, breath methane, TDF, TDF/1000 kcal, and number of log Archaea 16S rRNA gene copies. The results suggest that colonic Archaea is not associated with obesity in healthy humans. The presence of Archaea in humans may influence colonic fermentation by altering SCFA metabolism and fecal SCFA profile.


title: Distributed denial of service attacks

abstract: We discuss distributed denial of service attacks in the Internet. We were motivated by the widely known February 2000 distributed attacks on Yahoo!, Amazon.com, CNN.com, and other major Web sites. A denial of service is characterized by an explicit attempt by an attacker to prevent legitimate users from using resources. An attacker may attempt to: "flood" a network and thus reduce a legitimate user's bandwidth, prevent access to a service, or disrupt service to a specific system or a user. We describe methods and techniques used in denial of service attacks, and we list possible defences. In our study, we simulate a distributed denial of service attack using ns-2 network simulator. We examine how various queuing algorithms implemented in a network router perform during an attack, and whether legitimate users can obtain desired bandwidth. We find that under persistent denial of service attacks, class based queuing algorithms can guarantee bandwidth for certain classes of input flows.


title: What's in a name?: an unsupervised approach to link users across communities

abstract: In this paper, we consider the problem of linking users across multiple online communities. Specifically, we focus on the alias-disambiguation step of this user linking task, which is meant to differentiate users with the same usernames. We start quantitatively analyzing the importance of the alias-disambiguation step by conducting a survey on 153 volunteers and an experimental analysis on a large dataset of About.me (75,472 users). The analysis shows that the alias-disambiguation solution can address a major part of the user linking problem in terms of the coverage of true pairwise decisions (46.8%). To the best of our knowledge, this is the first study on human behaviors with regards to the usages of online usernames. We then cast the alias-disambiguation step as a pairwise classification problem and propose a novel unsupervised approach. The key idea of our approach is to automatically label training instances based on two observations: (a) rare usernames are likely owned by a single natural person, e.g. pennystar88 as a positive instance; (b) common usernames are likely owned by different natural persons, e.g. tank as a negative instance. We propose using the n-gram probabilities of usernames to estimate the rareness or commonness of usernames. Moreover, these two observations are verified by using the dataset of Yahoo! Answers. The empirical evaluations on 53 forums verify: (a) the effectiveness of the classifiers with the automatically generated training data and (b) that the rareness and commonness of usernames can help user linking. We also analyze the cases where the classifiers fail.


An Anomaly-Based Approach for Intrusion Detection in Web Traffic


title: Linear Active Stabilization of Converter-Dominated DC Microgrids

abstract: DC microgrids are gaining high momentum under the smart grid environment. DC microgrid stability can be an issue under high penetration of tightly regulated power converters used to interface distributed resources and loads. This paper addresses dc microgrid stability under high penetration of tightly regulated power electronic converters; and proposes three simple and computationally efficient active damping solutions that can be implemented to stabilize a controlled voltage-source converter (VSC) interfacing a dc- microgrid to an ac system. The proposed active damping methods depend on reshaping the VSC impedance by injecting internal-model-based active damping signal at the outer, intermediate and inner control loops of the voltage-oriented VSC interface. Small signal analysis is conducted to assess the system stability under different compensation schemes. Moreover, the reshaped source impedance of the VSC interface and the modified voltage-tracking dynamics are derived under different compensation schemes. Sensitivity and robustness analyses are provided to assess the dynamic coupling among active damping and voltage tracking controllers. Evaluation results, based on a detailed model of a dc microgrid with multiple tightly regulated converter-interfaced loads, are provided to validate the developed models and demonstrate the effectiveness and robustness of proposed techniques.


title: Load Theory of Selective Attention and Cognitive Control

abstract: A load theory of attention in which distractor rejection depends on the level and type of load involved in current processing was tested. A series of experiments demonstrates that whereas high perceptual load reduces distractor interference, working memory load or dual-task coordination load increases distractor interference. These findings suggest 2 selective attention mechanisms: a perceptual selection mechanism serving to reduce distractor perception in situations of high perceptual load that exhaust perceptual capacity in processing relevant stimuli and a cognitive control mechanism that reduces interference from perceived distractors as long as cognitive control functions are available to maintain current priorities (low cognitive load). This theory resolves the long-standing early versus late selection debate and clarifies the role of cognitive control in selective attention. (PsycINFO Database Record (c) 2009 APA, all rights reserved)


title: Hybris: Robust Hybrid Cloud Storage

abstract: Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macrobenchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.


title: Space–time image layout

abstract: Cameras are now ubiquitous in our lives. A given activity is often captured by multiple people from different viewpoints resulting in a sizable collection of photograph footage. We present a method that effectively organizes this spatiotemporal content. Given an unorganized collection of photographs taken by a number of photographers, capturing some dynamic event at a number of time steps, we would like to organize the collection into a space–time table. The organization is an embedding of the photographs into clusters that preserve the viewpoint and time order. Our method relies on a self-organizing map (SOM), which is a neural network that embeds the training data (the set of images) into a discrete domain. We introduce BiSOM, which is a variation of SOM that considers two features (space and time) rather than a single one, to layout the given photograph collection into a table. We demonstrate our method on several challenging datasets, using different space and time descriptors.


title: Far voxels: a multiresolution framework for interactive rendering of huge complex 3D models on commodity graphics platforms

abstract: We present an efficient approach for end-to-end out-of-core construction and interactive inspection of very large arbitrary surface models. The method tightly integrates visibility culling and out-of-core data management with a level-of-detail framework. At preprocessing time, we generate a coarse volume hierarchy by binary space partitioning the input triangle soup. Leaf nodes partition the original data into chunks of a fixed maximum number of triangles, while inner nodes are discretized into a fixed number of cubical voxels. Each voxel contains a compact direction dependent approximation of the appearance of the associated volumetric subpart of the model when viewed from a distance. The approximation is constructed by a visibility aware algorithm that fits parametric shaders to samples obtained by casting rays against the full resolution dataset. At rendering time, the volumetric structure, maintained off-core, is refined and rendered in front-to-back order, exploiting vertex programs for GPU evaluation of view-dependent voxel representations, hardware occlusion queries for culling occluded subtrees, and asynchronous I/O for detecting and avoiding data access latencies. Since the granularity of the multiresolution structure is coarse, data management, traversal and occlusion culling cost is amortized over many graphics primitives. The efficiency and generality of the approach is demonstrated with the interactive rendering of extremely complex heterogeneous surface models on current commodity graphics platforms.


Automatic Language Identification using Long Short-Term Memory Recurrent Neural Networks


title: Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

abstract: Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.


title: Suspicious URL Filtering Based on Logistic Regression with Multi-view Analysis

abstract: The current malicious URLs detecting techniques based on whole URL information are hard to detect the obfuscated malicious URLs. The most precise way to identify a malicious URL is verifying the corresponding web page contents. However, it costs very much in time, traffic and computing resource. Therefore, a filtering process that detecting more suspicious URLs which should be further verified is required in practice. In this work, we propose a suspicious URL filtering approach based on multi-view analysis in order to reduce the impact from URL obfuscation techniques. URLs are composed of several portions, each portion has a specific use. The proposed method intends to learn the characteristics from multiple portions (multi-view) of URLs for giving the suspicion level of each portion. Adjusting the suspicion threshold of each portion, the proposed system would select the most suspicious URLs. This work uses the real dataset from T. Co. to evaluate the proposed system. The requests from T. Co. are (1) detection rate should be less than 25%, (2) missing rate should be lower than 25%, and (3) the process with one hour data should be end in an hour. The experiment results show that our approach is effective, is capable to reserve more malicious URLs in the selected suspicious ones and satisfy the requests given by practical environment, such as T. Co. daily works.


title: Design, analysis and experiments of an omni-directional spherical robot

abstract: This paper presents a novel design of an omni-directional spherical robot that is mainly composed of a lucent ball-shaped shell and an internal driving unit. Two motors installed on the internal driving unit are used to realize the omni-directional motion of the robot, one motor is used to make the robot move straight and another is used to make it steer. Its motion analysis, kinematics modeling and controllability analysis are presented. Two typical motion simulations show that the unevenness of ground has big influence on the open-loop trajectory tracking of the robot. At last, motion performance of this spherical robot in several typical environments is presented with prototype experiments.


title: Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications

abstract: Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving constrained optimization problems in image restoration. Among many useful features, one critical feature of the ADMM algorithm is its modular structure which allows one to plug in any off-the-shelf image denoising algorithm for a subproblem in the ADMM algorithm. Because of the plug-in nature, this type of ADMM algorithms is coined the name "Plug-and-Play ADMM". Plug-and-Play ADMM has demonstrated promising empirical results in a number of recent papers. However, it is unclear under what conditions and by using what denoising algorithms would it guarantee convergence. Also, since Plug-and-Play ADMM uses a specific way to split the variables, it is unclear if fast implementation can be made for common Gaussian and Poissonian image restoration problems. ::: In this paper, we propose a Plug-and-Play ADMM algorithm with provable fixed point convergence. We show that for any denoising algorithm satisfying an asymptotic criteria, called bounded denoisers, Plug-and-Play ADMM converges to a fixed point under a continuation scheme. We also present fast implementations for two image restoration problems on super-resolution and single-photon imaging. We compare Plug-and-Play ADMM with state-of-the-art algorithms in each problem type, and demonstrate promising experimental results of the algorithm.


title: Who Cares? Revisiting Empathy in Asperger Syndrome

abstract: A deficit in empathy has consistently been cited as a central characteristic of Asperger syndrome (AS), but previous research on adults has predominantly focused on cognitive empathy, effectively ignoring the role of affective empathy. We administered the Interpersonal Reactivity Index (IRI), a multi-dimensional measure of empathy, and the Strange Stories test to 21 adults with AS and 21 matched controls. Our data show that while the AS group scored lower on the measures of cognitive empathy and theory of mind, they were no different from controls on one affective empathy scale of the IRI (empathic concern), and scored higher than controls on the other (personal distress). Therefore, we propose that the issue of empathy in AS should be revisited.


title: Ocr error correction using a noisy channel model

abstract: In this paper, we take a pattern recognition approach to correcting errors in text generated from printed documents using optical character recognition (OCR). We apply a very general, theoretically optimal model to the problem of OCR word correction, introduce practical methods for parameter estimation, and evaluate performance on real data.


title: Time Series Prediction and Neural Networks

abstract: Neural Network approaches to time series prediction are briefly discussed, and the need to find the appropriate sample rate and an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the appropriate sampling rate and embedding dimension, and thence window size, are discussed. The method is applied to several time series and the resulting generalisation performance of the trained feed-forward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architecture.


title: The Performance of Laboratories Analysing Heavy Metals in the Workplace Analysis Scheme for Proficiency (WASP)

abstract: This paper discusses the proficiency of laboratories analysing identical test samples representative of metals in air in the Workplace Analysis Scheme for Proficiency (WASP) and the variability of results from laboratories used to assess personal exposure in the workplace. Over 11 years, the performance of laboratories has significantly improved for lead, cadmium and chromium. Laboratories show better agreement when using inductively coupled plasmaatomic emission spectroscopy (ICP-AES) than flame atomic absorption spectroscopy (FAAS) when analysing chromium, but not when analysing lead and cadmium. Data from the proficiency testing scheme show that for a customer to have confidence that a measurement value doesnotexceedthe workplace exposure limit theanalytical result wouldhaveto be <134mgm � 3 for lead and 23 m gm � 3 for cadmium for an 8 h sample and 129 m gm � 3 for lead, 21 m gm � 3 for cadmium and 429 m gm � 3 for chromium for a 4 h sample (for samples with relatively soluble matrices), before considering the further uncertainties due to sampling. The performance criterion for satisfactory performance (–8.3%) was found to be appropriate as long as laboratories participated consecutively in all rounds. Estimates of the overall uncertainty of results from ICP-AES and FAAS analysis were shown to meet the criteria in EN482. Samples from some types of workplace environments are more difficult than the WASP test material to dissolve into solution, so it is expected that estimates of uncertainty are larger for less soluble analytes and matrices. WASP has now initiated a programme to help laboratories assess their performance with more complex matrices.


title: Technical Analysis on Financial Forecasting

abstract: Financial forecasting is an estimation of future financial outcomes for a company, industry, country using historical internal accounting and sales data. We may predict the future outcome of BSE_SENSEX practically by some soft computing techniques and can also optimized using PSO (Particle Swarm Optimization), EA (Evolutionary Algorithm) or DEA (Differential Evolutionary Algorithm) etc. PSO is a biologically inspired computational search & optimization method developed in 1995 by Dr. Eberhart and Dr. Kennedy based on the social behaviors of fish schooling or birds flocking. PSO is a promising method to train Artificial Neural Network (ANN). It is easy to implement then Genetic Algorithm except few parameters are adjusted. PSO is a random & pattern search technique based on populating of particle. In PSO, the particles are having some position and velocity in the search space. Two terms are used in PSO one is Local Best and another one is Global Best. To optimize problems that are like Irregular, Noisy, Change over time, Static etc. PSO uses a classic optimization method such as Gradient Decent & Quasi-Newton Methods. The observation and review of few related studies in the last few years, focusing on function of PSO, modification of PSO and operation that have implemented using PSO like function optimization, ANN Training & Fuzzy Control etc. Differential Evolution is an efficient EA technique for optimization of numerical problems, financial problems etc. PSO technique is introduced due to the swarming behavior of animals which is the collective behavior of similar size that aggregates together.


title: Black-box Importance Sampling

abstract: Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method allows us to use better and richer proposals to solve difficult problems, and (somewhat counter-intuitively) also has the additional benefit of improving the estimation accuracy beyond typical importance sampling. Both theoretical and empirical analyses are provided.


title: A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment

abstract: Heavy metal pollution has pervaded many parts of the world, especially developing countries such as China. This review summarizes available data in the literature (2005-2012) on heavy metal polluted soils originating from mining areas in China. Based on these obtained data, this paper then evaluates the soil pollution levels of these collected mines and quantifies the risks these pollutants pose to human health. To assess these potential threat levels, the geoaccumulation index was applied, along with the US Environmental Protection Agency (USEPA) recommended method for health risk assessment. The results demonstrate not only the severity of heavy metal pollution from the examined mines, but also the high carcinogenic and non-carcinogenic risks that soil heavy metal pollution poses to the public, especially to children and those living in the vicinity of heavily polluted mining areas. In order to provide key management targets for relevant government agencies, based on the results of the pollution and health risk assessments, Cd, Pb, Cu, Zn, Hg, As, and Ni are selected as the priority control heavy metals; tungsten, manganese, lead-zinc, and antimony mines are selected as the priority control mine categories; and southern provinces and Liaoning province are selected as the priority control provinces. This review, therefore, provides a comprehensive assessment of soil heavy metal pollution derived from mines in China, while identifying policy recommendations for pollution mitigation and environmental management of these mines.


title: Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

abstract: Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.


title: Normalization of Term Weighting Scheme for Sentiment Analysis

abstract: N-gram models with a binary (or tf-idf) weighting scheme and SVM classifiers are commonly used together as a baseline approach in lots of research studies on sentiment analysis and opinion mining. Other advanced methods are used on top of this model to improve the classification accuracy, such as generation of additional features or using supplementary linguistic resources. In this paper, we show how a simple technique can improve both the overall classification accuracy and the classification of minor reviews by normalizing the terms weights in the basic bag-of-words method. Any other term selection scheme may also benefit from this improved weighting scheme, if it is based on the n-gram model. We have tested our approach on the movie review and the product review datasets in English and show that our normalization technique enhances the classification accuracy of the traditional weighting schemes. The question whether we would observe similar performance increases for other language families is still to be investigated, but our weighting scheme can easily address any other language, since it does not use any language specific resource apart from a training corpus.


title: Control of goal-directed and stimulus-driven attention in the brain

abstract: We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a ‘circuit breaker’ for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.


title: Non-SELEX Selection of Aptamers

abstract: Aptamers are typically selected from libraries of random DNA (or RNA) sequences by SELEX, which involves multiple rounds of alternating steps of partitioning and PCR amplification. Here we report, for the first time, non-SELEX selection of aptamersa process that involves repetitive steps of partitioning with no amplification between them. A highly efficient affinity method, non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), was used for partitioning. We found that three steps of NECEEM-based partitioning in the non-SELEX approach were sufficient to improve the affinity of a DNA library to a target protein by more than 4 orders of magnitude. The resulting affinity was higher than that of the enriched library obtained in three rounds of NECEEM-based SELEX. Remarkably, NECEEM-based non-SELEX selection took only 1 h in contrast to several days or several weeks required for a typical SELEX procedure by conventional partitioning methods. In addition, NECEEM-based non-SELEX allowed us to...


title: Navigation-related structural change in the hippocampi of taxi drivers

abstract: Structural MRIs of the brains of humans with extensive navigation experience, licensed London taxi drivers, were analyzed and compared with those of control subjects who did not drive taxis. The posterior hippocampi of taxi drivers were significantly larger relative to those of control subjects. A more anterior hippocampal region was larger in control subjects than in taxi drivers. Hippocampal volume correlated with the amount of time spent as a taxi driver (positively in the posterior and negatively in the anterior hippocampus). These data are in accordance with the idea that the posterior hippocampus stores a spatial representation of the environment and can expand regionally to accommodate elaboration of this representation in people with a high dependence on navigational skills. It seems that there is a capacity for local plastic change in the structure of the healthy adult human brain in response to environmental demands.


title: Fully Convolutional Crowd Counting On Highly Congested Scenes

abstract: In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera perspective is the key objective for research in this area. Techniques developed in the past have generally performed poorly in highly congested scenes with several thousands of people in frame (Rodriguez et al., 2011). Our approach, influenced by the work of (Zhang et al., 2016), consists of the following contributions: (1) A training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance; (2) a deep, single column, fully convolutional network (FCN) architecture; (3) a multi-scale averaging step during inference. The developed technique can analyse images of any resolution or aspect ratio and achieves state-of-the-art counting performance on the Shanghaitech Part B and UCF CC 50 datasets as well as competitive performance on Shanghaitech Part A.


title: RolX: structural role extraction & mining in large graphs

abstract: Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node classification. This paper addresses the question: Given a graph, how can we automatically discover roles for nodes? We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from general network data. We demonstrate the effectiveness of RolX on several network-mining tasks: from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not); and show that the two approaches are complimentary in nature.


title: Trace elements and cancer risk: a review of the epidemiologic evidence

abstract: Worldwide, there are more than 10 million new cancer cases each year, and cancer is the cause of approximately 12% of all deaths. Given this, a large number of epidemiologic studies have been undertaken to identify potential risk factors for cancer, amongst which the association with trace elements has received considerable attention. Trace elements, such as selenium, zinc, arsenic, cadmium, and nickel, are found naturally in the environment, and human exposure derives from a variety of sources, including air, drinking water, and food. Trace elements are of particular interest given that the levels of exposure to them are potentially modifiable. In this review, we focus largely on the association between each of the trace elements noted above and risk of cancers of the lung, breast, colorectum, prostate, urinary bladder, and stomach. Overall, the evidence currently available appears to support an inverse association between selenium exposure and prostate cancer risk, and possibly also a reduction in risk with respect to lung cancer, although additional prospective studies are needed. There is also limited evidence for an inverse association between zinc and breast cancer, and again, prospective studies are needed to confirm this. Most studies have reported no association between selenium and risk of breast, colorectal, and stomach cancer, and between zinc and prostate cancer risk. There is compelling evidence in support of positive associations between arsenic and risk of both lung and bladder cancers, and between cadmium and lung cancer risk.


title: Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes

abstract: The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.


title: Learning user profiles for personalized information dissemination

abstract: Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised adaptive resonance theory (ART) system, called fuzzy ARAM (adaptive resonance associative map), can be used to learn user profiles for personalized information dissemination. ARAM learning is online, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system (PIN). Preliminary experiments have verified that PIN is able to provide personalized news by adapting to user's interests in an online manner and generalizing them to new information on-the-fly.


title: The Application of Machine Learning in mmWave-NOMA Systems

abstract: Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non- orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.


title: Delite: A Compiler Architecture for Performance-Oriented Embedded Domain-Specific Languages

abstract: Developing high-performance software is a difficult task that requires the use of low-level, architecture-specific programming models (e.g., OpenMP for CMPs, CUDA for GPUs, MPI for clusters). It is typically not possible to write a single application that can run efficiently in different environments, leading to multiple versions and increased complexity. Domain-Specific Languages (DSLs) are a promising avenue to enable programmers to use high-level abstractions and still achieve good performance on a variety of hardware. This is possible because DSLs have higher-level semantics and restrictions than general-purpose languages, so DSL compilers can perform higher-level optimization and translation. However, the cost of developing performance-oriented DSLs is a substantial roadblock to their development and adoption. In this article, we present an overview of the Delite compiler framework and the DSLs that have been developed with it. Delite simplifies the process of DSL development by providing common components, like parallel patterns, optimizations, and code generators, that can be reused in DSL implementations. Delite DSLs are embedded in Scala, a general-purpose programming language, but use metaprogramming to construct an Intermediate Representation (IR) of user programs and compile to multiple languages (including Cpp, CUDA, and OpenCL). DSL programs are automatically parallelized and different parts of the application can run simultaneously on CPUs and GPUs. We present Delite DSLs for machine learning, data querying, graph analysis, and scientific computing and show that they all achieve performance competitive to or exceeding Cpp code.


title: MixFab: a mixed-reality environment for personal fabrication

abstract: Personal fabrication machines, such as 3D printers and laser cutters, are becoming increasingly ubiquitous. However, designing objects for fabrication still requires 3D modeling skills, thereby rendering such technologies inaccessible to a wide user-group. In this paper, we introduce MixFab, a mixed-reality environment for personal fabrication that lowers the barrier for users to engage in personal fabrication. Users design objects in an immersive augmented reality environment, interact with virtual objects in a direct gestural manner and can introduce existing physical objects effortlessly into their designs. We describe the design and implementation of MixFab, a user-defined gesture study that informed this design, show artifacts designed with the system and describe a user study evaluating the system's prototype.


title: Age Synthesis and Estimation via Faces: A Survey

abstract: Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.


title: Cable-suspended planar parallel robots with redundant cables: controllers with positive cable tensions

abstract: Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots lot more challenging than their counterpart parallel-actuated robots. In the case with redundant cables, feedback control laws can be designed to make all tensions positive while attaining desired control performance. This paper describes these approaches and their effectiveness is demonstrated through simulations of a three degree-of-freedom cable suspended robots with four, five, and six cables.


title: Assessment of the Quality of Shallow Groundwater for Irrigation in the Atankwidi Sub-Basin of the White Volta Basin, Ghana

abstract: The potential use of shallow groundwater for small-scale dry season irrigation is a key issue for irrigation development in the arid zone of Ghana. Farmers within the Atankwidi sub-basin of the White Volta Basin are increasingly adopting groundwater as a source of irrigation water due to the unavailability of surface water during the dry season. However the quality of the groundwater and its suitability for irrigation is not known, hence the need to assess the quality and its suitability in order to ensure sustainability in the application and possible expansion of groundwater irrigation in the area. Two main water types (Ca-Mg-HCO 3 and Na-Mg-Ca-HCO 3 ) have been determined using the Piper diagram. The relative abundance of cations in the groundwater was found to be in the decreasing order of Na + > Ca 2+ > K + > Mg 2+ > Fe 2+ . Similarly, that of anions was found to have a decreasing order of HCO 3 - > SO 4 2- > Cl - > PO 4 2- > NO 3 - > F - . Groundwater in the area had low SAR and low to medium salinity hazard. However, magnesium hazard and alkalinity problems are likely to limit its use for irrigation. Keywords: Shallow Groundwater, Hydrochemical facies, Irrigation Water Quality.


title: Storeoboard: Sketching Stereoscopic Storyboards

abstract: We present Storeoboard, a system for stereo-cinematic conceptualization, via storyboard sketching directly in stereo. The resurgence of stereoscopic media has motivated filmmakers to evolve a new stereo-cinematic vocabulary, as many principles for stereo 3D film are unique. Concepts like plane separation, parallax position, and depth budgets are missing from early planning due to the 2D nature of existing storyboards. Storeoboard is the first of its kind, allowing filmmakers to explore, experiment and conceptualize ideas in stereo early in the film pipeline, develop new stereo-cinematic constructs and foresee potential difficulties. Storeoboard is the design outcome of interviews and field work with directors, stereographers, and storyboard artists. We present our design guidelines and implementation of a tool combining stereo-sketching, depth manipulations and storyboard features into a coherent and novel workflow. We report on feedback from storyboard artists, industry professionals and the director of a live action, feature film on which Storeoboard was deployed.


title: Real-time implementation of digital relay models using MATLAB/SIMULINK and RTDS

abstract: SUMMARY In this paper, an integrated environment for real-time simulation, analysis and validation of digital relay models, based on MATLAB/SIMULINK and RTDS, is presented. A detailed analysis and discussion for this environment is given and example cases are used to illustrate its implementation. Copyright # 2008 John Wiley & Sons, Ltd.


title: Simulation of actuator dynamics based on geometric model of pneumatic artificial muscle

abstract: Dynamic simulation model of the actuator with two pneumatic artificial muscles in antagonistic connection was designed and built in Matlab Simulink environment. The basis for this simulation model was dynamic model of the pneumatic actuator based on advanced geometric muscle model. The main dynamics characteristics of such actuator were obtained by model simulation, as for example muscle force change, pressure change in muscle, arm position of the actuator. Simulation results will be used in design of control system of such actuator using model reference adaptive controller.


title: Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet

abstract: Assigning a positive or negative score to a word out of context (i.e. a word’s prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-ofthe-art approach in computing words’ prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.


title: A Survey of MAC Protocols for Mission-Critical Applications in Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) are generally designed to support applications in long-term deployments, and thus WSN protocols are primarily designed to be energy efficient. However, the research community has recently explored new WSN applications such as industrial process automation. These mission-critical applications demand not only energy efficient operation but also strict data transport performance. In particular, data must be transported to a sink in a timely and reliable fashion. Both WSN's data transport performance and energy consumption pattern are mainly defined by the employed medium access control (MAC) protocol. Therefore, this survey paper explores to what extent existing MAC protocols for WSNs can serve mission-critical applications. The reviewed protocols are classified according to data transport performance and suitability for mission-critical applications. The survey reveals that the existing solutions have a number of limitations and only a few recently developed MAC protocols are suitable for this application domain.


Coin Flipping Over the Telephone


title: BERT: a tool for behavioral regression testing

abstract: During maintenance, software is modified and evolved to enhance its functionality, eliminate faults, and adapt it to changed or new platforms. In this demo, we present BERT, a tool for helping developers identify regression faults that they may have introduced when modifying their code. BERT is based on the concept of behavioral regression testing: given two versions of a program, BERT identifies behavioral differences between the two versions through dynamic analysis, in three steps. First, it generates a large number of test inputs that focus on the changed parts of the code. Second, it runs the generated test inputs on the old and new versions of the code and identifies differences in the tests' behavior. Third, it analyzes the identified differences and presents them to the developers. By focusing on a subset of the code and leveraging differential behavior, BERT can provide developers with more detailed information than traditional regression testing approaches---approaches that rely exclusively on existing test suites, which may be limited in scope and may not adequately test the changes in a program. BERT is implemented as a plug-in for Eclipse, a popular Integrated Development Environment, and is freely available. This demo presents BERT, its underlying technology, and examples of its usage.


title: EEG-guided meditation: A personalized approach

abstract: Abstract The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical–conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization.


title: Bile duct angulation and tumor vascularity are useful radiographic features for differentiating pancreatic head cancer and intrapancreatic bile duct cancer

abstract: BACKGROUND AND PURPOSE ::: To perform radical resection without leaving residual cancer, surgeons must distinguish between pancreatic head cancer (PHC) and intrapancreatic bile duct cancer (IPBDC) preoperatively. The aim of this study was to establish the points of difference between these two cancers, especially on preoperative multi-detector computed tomography (MDCT) images. ::: ::: ::: METHODS ::: The subjects of this study were 28 patients with PHC and proven bile duct invasion who underwent pancreatoduodenectomy (PHC group) and 22 patients with IPBDC and upstream bile duct dilation (IPBDC group). We compared the preoperative clinical and radiographic features, including the bile duct angle, calculated on coronal images of MDCT, and the vascularity of the tumor. ::: ::: ::: RESULTS ::: The optimal cut-off values for the bile duct angle, the CT value ratio of the tumor (late arterial phase/non-enhanced), and the main pancreatic duct (MPD) ratio (diameter of MPD/diameter of parenchyma) were 110°, 3.0, and 0.2, respectively. Multivariate analysis revealed that a bile duct angle < 110°, a CT value ratio of the tumor < 3, and an MPD ratio ≥ 0.2 were independently associated with PHC. ::: ::: ::: CONCLUSIONS ::: A bile duct angle and CT value reflecting the vascularity of the tumor might be useful radiographic features for differentiating PHC and IPBDC, in addition to MPD dilatation.


title: Cancer stem cells in solid tumours: accumulating evidence and unresolved questions

abstract: Solid tumours are an enormous cancer burden and a major therapeutic challenge. The cancer stem cell (CSC) hypothesis provides an attractive cellular mechanism to account for the therapeutic refractoriness and dormant behaviour exhibited by many of these tumours. There is increasing evidence that diverse solid tumours are hierarchically organized and sustained by a distinct subpopulation of CSCs. Direct evidence for the CSC hypothesis has recently emerged from mouse models of epithelial tumorigenesis, although alternative models of heterogeneity also seem to apply. The clinical relevance of CSCs remains a fundamental issue but preliminary findings indicate that specific targeting may be possible.


title: Inference rules for RDF(S) and OWL in N3Logic

abstract: This paper presents inference rules for Resource Description Framework (RDF), RDF Schema (RDFS) and Web Ontology Language (OWL). Our formalization is based on Notation 3 Logic, which extended RDF by logical symbols and created Semantic Web logic for deductive RDF graph stores. We also propose OWL-P that is a lightweight formalism of OWL and supports soft inferences by omitting complex language constructs.


title: Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

abstract: Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of `jumping curves? from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding.


title: AEthereal network on chip: concepts, architectures, and implementations

abstract: The continuous advances in semiconductor technology enable the integration of increasing numbers of IP blocks in a single SoC. Interconnect infrastructures, such as buses, switches, and networks on chips (NoCs), combine the IPs into a working SoC. Moreover, the industry expects platform-based SoC design to evolve to communication-centric design, with NoCs as a central enabling technology. In this article, we introduce the AEthereal NoC. The tenet of the AEthereal NoC is that guaranteed services (GSs) - such as uncorrupted, lossless, ordered data delivery; guaranteed throughput; and bounded latency - are essential for the efficient construction of robust SoCs. To exploit the NoC capacity unused by the GS traffic, we provide best-effort services.


title: Fast image stitching and editing for panorama painting on mobile phones

abstract: We propose a fast image stitching and editing approach for panorama painting and have implemented it on a mobile phone. A seam search using dynamic programming finds a good boundary along which to merge the source images, and a transition smoothing process using instant image cloning removes merging artifacts, yielding highquality panoramic images. A sequential image stitching procedure requires only keeping the current source image in memory during image stitching, which saves memory and is more suitable to mobile phone implementations.


title: A Study on Difficulty Level Recognition of Piano Sheet Music

abstract: Looking for a piano sheet music with proper difficulty for a piano learner is always an important work to his/her teacher. In the paper, we study on a new and challenging issue of recognizing the difficulty level of piano sheet music. To analyze the semantic content of music, we focus on symbolic music, i.e., sheet music or score. Specifically, difficulty level recognition is formulated as a regression problem to predict the difficulty level of piano sheet music. Since the existing symbolic music features are not able to capture the characteristics of difficulty, we propose a set of new features. To improve the performance, a feature selection approach, RReliefF, is used to select relevant features. An extensive performance study is conducted over two real datasets with different characteristics to evaluate the accuracy of the regression approach for predicting difficulty level. The best performance evaluated in terms of the R2 statistics over two datasets reaches 39.9% and 38.8%, respectively.


title: Lifted Inference and Learning in Statistical Relational Models

abstract: Statistical relationalmodels combine aspects of first-order logic andprobabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in themodel, and reasoning about groups of objects as awhole, lifted algorithms dramatically improve the run time of inference and learning. The thesis has five main contributions. First, we propose a new method for logical inference, called first-order knowledge compilation. We show that by compiling relational models into a new circuit language, hard inference problems become tractable to solve. Furthermore, we present an algorithm that compiles relational models into our circuit language. Second, we show how to use first-order knowledge compilation for statistical relational models, leading to a new state-of-the-art lifted probabilistic inference algorithm. Third, we develop a formal framework for exact lifted inference, including a definition in terms of its complexity w.r.t. the number of objects in the world. From this follows a first completeness result, showing that the two-variable class of statistical relational models always supports lifted inference. Fourth, we present an algorithm for


in a Managed-Care Population Risk of Cardiovascular Events and All-Cause Mortality in Patients Treated With


title: VMbuddies: Coordinating Live Migration of Multi-Tier Applications in Cloud Environments

abstract: Enabled by virtualization technologies, various multi-tier applications (such as web applications) are hosted by virtual machines (VMs) in cloud data centers. Live migration of multi-tier applications across geographically distributed data centers is important for load management, power saving, routine server maintenance and quality-of-service. Different from a single-VM migration, VMs in a multi-tier application are closely correlated, which results in a correlated VM migrations problem. Current live migration algorithms for single-VM cause significant application performance degradation because intermediate data exchange between different VMs suffers relatively low bandwidth and high latency across distributed data centers. In this paper, we design and implement a coordination system called VMbuddies for correlated VM migrations in the cloud. Particularly, we propose an adaptive network bandwidth allocation algorithm to minimize the migration cost in terms of migration completion time, network traffic and migration downtime. Experiments using a public benchmark show that VMbuddies significantly reduces the performance degradation and migration cost of multi-tier applications.


title: A 34% PAE, 18.6dBm 42–45GHz stacked power amplifier in 45nm SOI CMOS

abstract: A two-stack 42–45GHz power amplifier is implemented in 45nm SOI CMOS. Transistor stacking allows increased drain biasing to increase output power. Additionally, shunt inter-stage matching is used and improves PAE by more than 6%. This amplifier exhibits 18.6dBm saturated output power, with peak power gain of 9.5dB. It occupies 0.3mm2 including pads while achieving a peak PAE of 34%. The PAE remains above 30% from 42 to 45GHz.


title: Bio Pharmaceutics Classification System (BCS) Class IV Drug Nanoparticles: Quantum Leap to Improve Their Therapeutic Index

abstract: Purpose: Biopharmaceutics classification system (BCS) class IV compounds, exhibits least oral bioavailability, low solubility and intestinal permeability among all pharmaceutical classes of drugs. Thus, these drugs need more compatible and efficient delivery system. Since, their solubility in various medium, remains a limitation so, polymeric nano coacervates based drug loading with modified approach for them may prove to be a solution ahead. Therefore, in present study Chitosan is opted for encapsulating the BCS class IV drug (Hydrochlorothiazide) to attain better stability, enhanced permeability and lower toxicity. Methods: For this study, Hydrochlorothiazide (HCTZ) was opted for formulating chitosan based nano-coacervate system. Results: Optimized HCTZ nanocoacervates exhibited the average particle size of 91.39 ± 0.75 nm with Poly-dispersity index score of 0.159 ± 0.01, indicating homogeneity of colloidal solution. Zeta potential and encapsulation efficiency of HCTZ nanocoacervates were recorded as -18.9 ± 0.8 mV and 76.69 ± 0.82 % respectively. Further, from TEM and SEM evaluation the average particle size for the same were found in conformity (35-50 nm), with almost spherical morphology. Also, the EDX (Electron Dispersive X-ray) spectrometry and FT - IR analysis of optimized formulation indicated the balanced chemical composition and interaction between the polymeric molecules. The HCTZ nano coacervates showed the linear diffusion profile through the dialysis membrane. Conclusion: We can conclude from the present study that the optimized HCTZ nano coacervates may prove to be a suitable potential option for effective delivery of BCS class IV drugs.


title: A saliency-based auditory attention model with applications to unsupervised prominent syllable detection in speech.

abstract: A signal detecting circuit for detecting signals from a signal output circuit having a predetermined number of output terminals of which only one output terminal is to be normally selected during each recurrent cycle of operation, comprising the combination of a first circuit unit which has input terminals respectively connected to the output terminals of the signal output circuit and which has a first operative condition established with only one of the input terminals activated and a second operative condition established with at least two of the input terminals activated, the first circuit unit being operative to produce an output signal which has one voltage level under the first operative condition and another voltage level under the second operative condition, and a second circuit unit which is connected to the first circuit unit and which is operative to discriminate one of the first and second operative conditions from the other on the basis of the output signal from the first circuit unit.


Constraints and Mechanisms in Theories of Anaphor Processing


title: The Ethical Knob: ethically-customisable automated vehicles and the law

abstract: Accidents involving autonomous vehicles (AVs) raise difficult ethical dilemmas and legal issues. It has been argued that self-driving cars should be programmed to kill, that is, they should be equipped with pre-programmed approaches to the choice of what lives to sacrifice when losses are inevitable. Here we shall explore a different approach, namely, giving the user/passenger the task (and burden) of deciding what ethical approach should be taken by AVs in unavoidable accident scenarios. We thus assume that AVs are equipped with what we call an "Ethical Knob", a device enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. Accordingly, AVs would be entrusted with implementing users' ethical choices, while manufacturers/programmers would be tasked with enabling the user's choice and ensuring implementation by the AV.


title: Planning and Control for Collision-Free Cooperative Aerial Transportation

abstract: This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader–follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments. Note to Practitioners —This paper presents a viable approach to autonomous aerial transportation using multiple aerial manipulators equipped with a multidegree-of-freedom robotic arm. Existing approaches for cooperative manipulation based on force decomposition or impedance-based control often require a heavy or expensive force/torque sensor. However, this paper suggests a method without using a heavy or expensive force/torque sensor based on closed-chain dynamics in joint space and rapidly exploring random tree star (RRT*) that generates the desired trajectory of aerial manipulators. Unlike conventional RRT*, in this paper, our method can also avoid an unknown moving obstacle during aerial transportation by exploiting RRT* and dynamic movement primitives. The proposed planning and control synthesis is tested to demonstrate performance in a lab environment with two custom-made aerial manipulators and a common object.


title: A peer-to-peer architecture for media streaming

abstract: Given that the Internet does not widely support Internet protocol multicast while content-distribution-network technologies are costly, the concept of peer-to-peer could be a promising start for enabling large-scale streaming systems. In our so-called Zigzag approach, we propose a method for clustering peers into a hierarchy called the administrative organization for easy management, and a method for building the multicast tree atop this hierarchy for efficient content transmission. In Zigzag, the multicast tree has a height logarithmic with the number of clients, and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottlenecks. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics. Zigzag handles such situations gracefully, requiring a constant amortized worst-case control overhead. Especially, failure recovery is done regionally with impact on, at most, a constant number of existing clients and with mostly no burden on the server.


title: Social Stories: Mechanisms of Effectiveness in Increasing Game Play Skills in Children Diagnosed with Autism Spectrum Disorder Using a Pretest Posttest Repeated Measures Randomized Control Group Design

abstract: An increasing body of literature has indicated that social stories are an effective way to teach individuals diagnosed with autism appropriate social behavior. This study compared two formats of a social story targeting the improvement of social skills during game play using a pretest posttest repeated measures randomized control group design. A total of 45 children diagnosed with Autism Spectrum Disorder (ASD) ages 7–14 were randomly assigned to standard, directive, or control story conditions. Results demonstrated that the standard and directive story formats were equally as effective in eliciting, generalizing and maintaining the targeted social skills in participants who had prior game play experience and Verbal Comprehension Index (VCI) scores from the WISC-IV intelligence test in the borderline range or above.


title: Tax evasion and orders of risk aversion

abstract: Abstract The classic Allingham and Sandmo's (Journal of Public Economics, 1 (1972) 323–338) portfolio choice approach to income tax evasion has been increasingly criticised because it requires an `excess' degree of risk aversion to explain the observed rate of tax compliance. In this paper we argue that there may not necessarily be `excess risk aversion'; and that the evidence can be explained by the distinction between orders of risk aversion, as defined by Segal and Spivak (Journal of Economic Theory, 51 (1990) 111–125) and considered in regard to the same problem of anomalous `excess risk aversion' in financial and insurance markets.


title: Friction compensation of double inverted pendulum on a cart using locally linear neuro-fuzzy model

abstract: Double inverted pendulum on a cart (DIPC) is a highly nonlinear system. Due to its complex dynamics, it is widely used as a test-bed plant for the verification of newly designed controllers. In DIPC, two pendulums are kept upward by linear movements of cart. Because of this linear motions and frequent switching of velocity directions, another nonlinearity caused by friction becomes dominant around the equilibrium point. Friction introduces limit cycles to the system and results in a poor steady-state response. To eliminate these negative effects, the locally linear neuro-fuzzy (LLNF) approach is used to build an inverse model for friction compensation. This model is compared with multilayer perceptron network in order to demonstrate the better performance of LLNF. To stabilize DIPC, a common optimal controller is used, and despite its limited performance, experimental results show that the application of inverse modeling for friction compensation improves the steady-state response outstandingly.


title: Practical Lessons for Job Recommendations in the Cold-Start Scenario

abstract: The 2017 ACM RecSys Challenge focuses on the problem of job recommendations on XING in a cold-start scenario. In this paper we describe our solution as well as some practical lessons learned from the competition. We model this task as a binary classification problem. Negative candidate selection is the first key phase in our solution. We design a negative sampling strategy which performs significantly better than taking users' deleted or unclicked items as negative candidates. We then extract comprehensive features to model the relationship between a user-job candidate, including the direct profile similarity between the user and the job, and the profile similarity between the user's historical interested jobs and the target job. To make the whole pipeline scalable and easy to deploy online, we decide to use a single boosting tree model as the final discriminative model, instead of using a stacking ensemble of multiple models. Overall our model ranked 5th on the challenge leaderboard, and our last model has remained in 2nd place during the last two online weeks. We have open-sourced our implementation on https://github.com/Leavingseason/RecsysChallenge2017.


title: Single input fuzzy logic controller tuning for steering control of autonomous underwater vehicle: Genetic algorithm approach

abstract: Autonomous underwater vehicles (AUV) are robotic devices which perform tasks underwater without operator interference. The paper presents, a simple genetic algorithm (sGA) is employed to tune gains for single input fuzzy logic controller (SIFLC) used for steering control of AUV. SIFLC is a computationally optimized form of conventional fuzzy logic controller (CFLC). In contrast to PD-like CFLC which requires two inputs, error and change in error, SIFLC uses only one input, signed distance. The universe of discourse for the input is tuned using a simple genetic algorithm (GA). GA is an optimization algorithm which mimics biological evolution to find the optimum solution to the problem. Population of likely solution is initialized and fitness of the population is calculated. The fit member of population reproduce while the unfit die out. The reproduction is replicated with help of arithmetic crossover. The population is subjected to mutation, to bring diversity to the population. The elite members of the population are preserved from extinction. The optimum value of universe of discourse are used to simulate yaw control of steering subsystem of NPS AUV II.


title: Nonnegative Matrix Factorization With Regularizations

abstract: Matrix factorization techniques have been frequently applied in many fields. Among them, nonnegative matrix factorization (NMF) has received considerable attention for it aims to find a parts-based, linear representations of nonnegative data. Recently, many researchers propose various manifold learning algorithms to enhance learning performance by considering the local manifold smoothness assumption. However, NMF does not consider the geometrical structure of data and the local manifold smoothness does not directly ensure the representations of the data point with different labels being dissimilar. In order to find a better representation of data, we propose a novel matrix decomposition method, called nonnegative matrix factorization with Regularizations (RNMF), which incorporates three appropriate regularizations: nonnegative matrix factorization, the local manifold smoothness and a rank constraint. The representations of data learned by RNMF tend to be discriminative and sparse. By learning a Mahalanobis distance space based on labeled data, RNMF can also be extended to a semi-supervised algorithm (semi-RNMF) which has an amazing improvement on clustering performance. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.


title: A theoretician’s guide to the experimental analysis of algorithms

abstract: This paper presents an informal discussion of issues that arise when one attempts to analyze algorithms experimentally. It is based on lessons learned by the author over the course of more than a decade of experimentation, survey paper writing, refereeing, and lively discussions with other experimentalists. Although written from the perspective of a theoretical computer scientist, it is intended to be of use to researchers from all fields who want to study algorithms experimentally. It has two goals: first, to provide a useful guide to new experimentalists about how such work can best be performed and written up, and second, to challenge current researchers to think about whether their own work might be improved from a scientific point of view. With the latter purpose in mind, the author hopes that at least a few of his recommendations will be considered controversial.


title: Robust Face Tracking via Collaboration of Generic and Specific Models

abstract: Significant appearance changes of objects under different orientations could cause loss of tracking, ldquodrifting.rdquo In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background.


title: Analysis of the Blockchain Protocol in Asynchronous Networks

abstract: Nakamoto’s famous blockchain protocol enables achieving consensus in a so-called permissionless setting—anyone can join (or leave) the protocol execution, and the protocol instructions do not depend on the identities of the players. His ingenious protocol prevents “sybil attacks” (where an adversary spawns any number of new players) by relying on computational puzzles (a.k.a. “moderately hard functions”) introduced by Dwork and Naor (Crypto’92).


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title: Geometric Image Synthesis

abstract: The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.


title: Enterprise Resource Planning (ERP) Systems: An Empirical Analysis of Benefits

abstract: This paper uses a database, derived from a data repository, in order to do an analysis of enterprise resource planning (ERP) system benefits. ERP benefits are important for a number of reasons, including establishing a match between what ERP systems benefits are—as compared to ERP expectations—setting a benchmark for other firms, and measuring those benefits. ERP benefits also are central to the business case for deciding whether a firm will invest in an ERP system. It is found that some benefits vary across industry, while others seem to be important to firms independent of industry. In particular, tangible benefits are largely industry‐independent, while intangible benefits vary across industry. In addition, when compared to an earlier study by Deloitte Consulting, the results are statistically consistent with their findings, but find substantial additional intangible benefits.


title: CloudBench: Experiment Automation for Cloud Environments

abstract: The growth in the adoption of cloud computing is driven by distinct and clear benefits for both cloud customers and cloud providers. However, the increase in the number of cloud providers as well as in the variety of offerings from each provider has made it harder for customers to choose. At the same time, the number of options to build a cloud infrastructure, from cloud management platforms to different interconnection and storage technologies, also poses a challenge for cloud providers. In this context, cloud experiments are as necessary as they are labor intensive. Cloud Bench [1] is an open-source framework that automates cloud-scale evaluation and benchmarking through the running of controlled experiments, where complex applications are automatically deployed. Experiments are described through experiment plans, containing directives with enough descriptive power to make the experiment descriptions brief while allowing for customizable multi-parameter variation. Experiments can be executed in multiple clouds using a single interface. Cloud Bench is capable of managing experiments spread across multiple regions and for long periods of time. The modular approach adopted allows it to be easily extended to accommodate new cloud infrastructure APIs and benchmark applications, directly by external users. A built-in data collection system collects, aggregates and stores metrics for cloud management activities (such as VM provisioning and VM image capture) and application runtime information. Experiments can be conducted in a highly controllable fashion, in order to assess the stability, scalability and reliability of multiple cloud configurations. We demonstrate Cloud Bench's main characteristics through the evaluation of an Open Stack installation, including experiments with approximately 1200 simultaneous VMs at an arrival rate of up to 400 VMs/hour.