title: Sharing graphs using differentially private graph models

abstract: Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techniques to improve privacy on graphs only target specific attacks, and have been proven to be vulnerable against powerful de-anonymization attacks. Our work seeks a solution to share meaningful graph datasets while preserving privacy. We observe a clear tension between strength of privacy protection and maintaining structural similarity to the original graph. To navigate the tradeoff, we develop a differentially-private graph model we call Pygmalion. Given a graph G and a desired level of e-differential privacy guarantee, Pygmalion extracts a graph's detailed structure into degree correlation statistics, introduces noise into the resulting dataset, and generates a synthetic graph G'. G' maintains as much structural similarity to G as possible, while introducing enough differences to provide the desired privacy guarantee. We show that simply applying differential privacy to graphs results in the addition of significant noise that may disrupt graph structure, making it unsuitable for experimental study. Instead, we introduce a partitioning approach that provides identical privacy guarantees using much less noise. Applied to real graphs, this technique requires an order of magnitude less noise for the same privacy guarantees. Finally, we apply our graph model to Internet, web, and Facebook social graphs, and show that it produces synthetic graphs that closely match the originals in both graph structure metrics and behavior in application-level tests.


title: Comparison between proton boron fusion therapy (PBFT) and boron neutron capture therapy (BNCT): a Monte Carlo study

abstract: The aim of this study is to compare between proton boron fusion therapy (PBFT) and boron neutron capture therapy (BNCT) and to analyze dose escalation using a Monte Carlo simulation. We simulated a proton beam passing through the water with a boron uptake region (BUR) in MCNPX. To estimate the interaction between neutrons/protons and borons by the alpha particle, the simulation yielded with a variation of the center of the BUR location and proton energies. The variation and influence about the alpha particle were observed from the percent depth dose (PDD) and cross-plane dose profile of both the neutron and proton beams. The peak value of the maximum dose level when the boron particle was accurately labeled at the region was 192.4% among the energies. In all, we confirmed that prompt gamma rays of 478 keV and 719 keV were generated by the nuclear reactions in PBFT and BNCT, respectively. We validated the dramatic effectiveness of the alpha particle, especially in PBFT. The utility of PBFT was verified using the simulation and it has a potential for application in radiotherapy.


title: Extractive Summarization Using Supervised and Semi-Supervised Learning

abstract: It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are categorized as surface, content, relevance and event features. Surface features are related to extrinsic aspects of a sentence. Content features measure a sentence based on content-conveying words. Event features represent sentences by events they contained. Relevance features evaluate a sentence from its relatedness with other sentences. Experiments show that the combined features improved summarization performance significantly. Although the evaluation results are encouraging, supervised learning approach requires much labeled data. Therefore we investigate co-training by combining labeled and unlabeled data. Experiments show that this semi-supervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost.


Semantic Frames as Interlingual Representations for Multilingual Lexical Databases


title: Recent development of temperature compensated SAW Devices

abstract: This paper reviews recent progress of temperature compensated surface acoustic wave (SAW) devices for wireless communications. First, temperature compensation techniques based on the SiO2 deposition and the wafer bonding are explained, and their implementation into real devices are discussed. Finally, we will show how high performances have been realized by the use of these technologies.


title: Evaluating the Productive Efficiency and Performance of U . S . Commercial Banks

abstract: Reviews previous research on the efficiency and performance of financial institutions and uses Siems and Barr’s (1998) data envelopment analysis (DEA) model to evaluate the relative productive efficiency of US commercial banks 1984‐1998. Explains the methodology, discusses the input and output measures used and relates bank performance measures to efficiency. Describes the CAMELS rating system used by bank examiners and regulators; and finds that banks with high efficiency scores also have strong CAMELS ratings. Summarizes the other relationship identified and recommends the use of DEA to help analysts and policy makers understand organizations in greater depth, regulators and examiners to develop monitoring tools and banks to benchmark their processes.


title: IPR: Incremental path reduction algorithm for tree-based routing in low-rate wireless mesh networks

abstract: Tree-based routing protocols in low-rate wireless mesh networks usually have the detour problem in return for the no route discovery overhead. In this paper, we propose a novel algorithm, named Incremental Path Reduction (IPR), which incrementally shortens inefficient detoured path as more data packets are delivered. In IPR, data packets are delivered along the tree route in use by using 1-hop broadcast, enabling neighbor nodes to learn about the data packets' hop count. Using the hop counts, each node estimates their residual hop count to destination. As a result, each forwarder selects next hop node that has small residual hop count. In this way, IPR incrementally shortens the detoured route as more data packets are delivered. To verify our algorithm, we applied IPR to the representative tree routing protocols, and evaluated the path stretch and packet delivery ratio as well as control packet overhead. Simulation results show that IPR significantly enhances the overall routing metrics for any types of tree-based routing protocols.


title: Local stereo matching algorithm with efficient matching cost and adaptive guided image filter

abstract: To make a matching algorithm to satisfy the requirements of high precision and anti-interference, a novel stereo-matching algorithm with efficient matching cost and adaptive guided image filter is proposed. Firstly, we adopt a modified Census transform with a local texture metric to compute the initial cost. It can make full use of the cross-correlation information between pixels. Meanwhile, we incorporate the Census, color and gradient costs as a mixed matching cost algorithm. Then, we aggregate the costs with guided image filter based on adaptive rectangular support window instead of the traditional fixed support window. The variable kernel window is constructed by the local color similarity and spatial distance. In this way, less occluded points will be included in the support region. On this basis, we adopt integral image to further speed up the computation of this step. Finally, the initial disparity of each pixel is selected using winner takes all optimization and the final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm not only achieves an average error rate of 5.22?% on the Middlebury stereo benchmark data set, but can also overcome the influence of illumination distortion in the matching effectively.


title: A probabilistic framework for task-oriented grasp stability assessment

abstract: We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.


title: CANDID: Dynamic candidate evaluations for automatic prevention of SQL injection attacks

abstract: SQL injection attacks are one of the top-most threats for applications written for the Web. These attacks are launched through specially crafted user inputs, on Web applications that use low-level string operations to construct SQL queries. In this work, we exhibit a novel and powerful scheme for automatically transforming Web applications to render them safe against all SQL injection attacks. A characteristic diagnostic feature of SQL injection attacks is that they change the intended structure of queries issued. Our technique for detecting SQL injection is to dynamically mine the programmer-intended query structure on any input, and detect attacks by comparing it against the structure of the actual query issued. We propose a simple and novel mechanism, called Candid, for mining programmer intended queries by dynamically evaluating runs over benign candidate inputs. This mechanism is theoretically well founded and is based on inferring intended queries by considering the symbolic query computed on a program run. Our approach has been implemented in a tool called Candid that retrofits Web applications written in Java to defend them against SQL injection attacks. We have also implemented Candid by modifying a Java Virtual Machine, which safeguards applications without requiring retrofitting. We report extensive experimental results that show that our approach performs remarkably well in practice.


title: Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

abstract: Metro systems play an important role in meeting the demand for urban transportation in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of automated fare collection (AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out time stamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, information necessary to solve our problem. While existing methods work well in some specific situations, they hardly work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large-scale data set collected from the Shenzhen Metro system. The measured results provide us with useful input when building the passenger path choice model.


title: Third-party apps on Facebook: privacy and the illusion of control

abstract: Little research examines the privacy threats associated with the use of third-party apps on Facebook. To address this gap in the literature, we systematically study third-party apps' current practices for privacy notice and consent by: i) collecting data from the 1800 most popular Facebook apps to record their data collection practices concerning users and their friends, and ii) developing our own Facebook app to conduct a number of tests to identify problems that exist in the current design of authentication dialogs for third-party apps on Facebook. To address these problems, we propose two new interface designs for third-party apps' authentication dialogs to: i) increase user control of apps' data access and restrict apps' publishing ability during the process of adding them to users' profiles, and ii) alert users when their global privacy settings on Facebook are violated by apps. This research provides both conceptual and empirical insights in terms of design recommendations to address privacy concerns toward third-party apps on Facebook.


title: A 1.67-ppm/°C 64-ppm/V curvature-compensated bandgap reference based on a transcendental equation

abstract: This paper proposes a current-mode bandgap reference which employs a novel “coarse” voltage replication to offset the 2nd-order curvature due to base-emitter voltage of the BJT. The coarse replication is based on a process insensitive transcendental equation which regulates the independent CTAT current. The bandgap reference which will be implemented in 0.35µm CMOS process has a low operating current of 0.85µA. Under a 2.6V to 4V supply, its line sensitivity is 64ppm/V even in the worst condition, and its TC is only 1.67ppm/°C in the temperature range of −55°C to 115°C.


title: Metaphor as a Medium for Emotion: An Empirical Study

abstract: It is generally believed that a metaphor tends to have a stronger emotional impact than a literal statement; however, there is no quantitative study establishing the extent to which this is true. Further, the mechanisms through which metaphors convey emotions are not well understood. We present the first data-driven study comparing the emotionality of metaphorical expressions with that of their literal counterparts. Our results indicate that metaphorical usages are, on average, significantly more emotional than literal usages. We also show that this emotional content is not simply transferred from the source domain into the target, but rather is a result of meaning composition and interaction of the two domains in the metaphor.


title: Automatic topic discovery for multi-object tracking

abstract: This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet Process Mixture Model (DPMM). The tracking problem is cast as a topic-discovery task where the video sequence is treated analogously to a document. This formulation addresses tracking issues such as object exclusivity constraints as well as cannot-link constraints which are integrated without the need for heuristic thresholds. The video is temporally segmented into epochs to model the dynamics of word (superpixel) co-occurrences and to model the temporal damping effect. In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm.


title: Towards Analogy-Based Story Generation

abstract: Narrative is one of the oldest creative forms, capable of depicting a wide spectrum of human conditions. However, many existing stories generated by planning-based computational narrative systems are confined to the goal-driven, problem-solving aesthetics. This paper focuses on analogy-based story generation. Informed by narratology and computational analogy, we present an analytical framework to survey this area in order to identify trends and areas that have not received sufficient attention. Finally, we introduce the new developments of the Riu project as a case study for possible new narrative aesthetics supported


title: BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

abstract: We are dealing with the problem of fine-grained vehicle make&model recognition and verification. Our contribution is showing that extracting additional data from the video stream – besides the vehicle image itself – and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26% (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.


title: Pitch synchronous waveform processing techniques for text-to-speech synthesis using diphones

abstract: Abstract We review in a common framework several algorithms that have been proposed recently, in order to improve the voice quality of a text-to-speech synthesis based on acoustical units concatenation (Charpentier and Moulines, 1988; Moulines and Charpentier, 1988; Hamon et al., 1989). These algorithms rely on a pitch-synchronous overlap-add (PSOLA) approach for modifying the speech prosody and concatenating speech waveforms. The modifications of the speech signal are performed either in the frequency domain (FD-PSOLA), using the Fast Fourier Transform, or directly in the time domain (TD-PSOLA), depending on the length of the window used in the synthesis process. The frequency domain approach is capable of a great flexibility in modifying the spectral characteristics of the speech signal, while the time domain approach provides very efficient solutions for the real time implementation of synthesis systems. We also discuss the different kinds of distortions involved in these different algorithms.


title: Real-time Log File Analysis Using the Simple Event Correlator (SEC)

abstract: Originally published at Usenix LISA 2004 conference. November 2004 Atlanta, Georiga, USA. Log analysis is an important way to keep track of computers and networks. The use of automated analysis always results in false reports, however these can be minimized by proper specification of recognition criteria. Current analysis approaches fail to provide sucient support for the recognizing the temporal component of log analysis. Temporal recognition of event sequences fall into distinct patterns that can be used to reduce false alerts and improve the eciency of response to problems. This paper discusses these patterns while describing the rationale behind and implementation of a ruleset created at the CS department of the University of Massachusetts at Boston for SEC - the Simple Event Correlation program.


title: Multiple effects of physical activity on molecular and cognitive signs of brain aging: can exercise slow neurodegeneration and delay Alzheimer’s disease?

abstract: Western countries are experiencing aging populations and increased longevity; thus, the incidence of dementia and Alzheimer’s disease (AD) in these countries is projected to soar. In the absence of a therapeutic drug, non-pharmacological preventative approaches are being investigated. One of these approaches is regular participation in physical activity or exercise. This paper reviews studies that have explored the relationship between physical activity and cognitive function, cognitive decline, AD/dementia risk and AD-associated biomarkers and processes. There is now strong evidence that links regular physical activity or exercise to higher cognitive function, decreased cognitive decline and reduced risk of AD or dementia. Nevertheless, these associations require further investigation, more specifically with interventional studies that include long follow-up periods. In particular, relatively little is known about the underlying mechanism(s) of the associations between physical activity and AD neuropathology; clearly this is an area in need of further research, particularly in human populations. Although benefits of physical activity or exercise are clearly recognised, there is a need to clarify how much physical activity provides the greatest benefit and also whether people of different genotypes require tailored exercise regimes.


title: Real-Time Imaging With a 140 GHz Inverse Synthetic Aperture Radar

abstract: A summary of a 140 GHz inverse synthetic aperture radar (ISAR) is presented. High resolution and real time imaging are two key points in the design. Using the frequency-modulated continuous-wave (FMCW) radar technique with a 5 GHz bandwidth, 3 cm range resolution is achieved with assistance of software calibration compensating for nonlinearities of the LFM waveform. In cross-range region, synthetic aperture technique is adopted to improve imaging resolution. Real time imaging capability is enabled by using a hardware structure constructed with FPGA+CPU+GPU in signal processing unit. The final system was used to get ISAR image of a rotating target at 5 m. The 2-D (range and cross-range) imaging resolution is 3 cm × 3 cm with a real time image frame rate of 5 Hz.


title: Cloud traffic control: Smart traffic-driven streetlight

abstract: The smart cloud traffic light sequencer, focused on optimal control of traffic flows at the crossroads, is proposed within the framework of creating cloud traffic control [1]. A logical architecture of the sequencer free of arithmetic operations, intended for generating control signals, depending on the number of cars on intersecting lines. The ways of increasing the traffic capacity of intersections through the decrease of the green signal downtime in the absence of cars on the permitted traffic lane are shown. Test and real experiments on the transport infrastructure, which confirm the effectiveness of the introduction of a smart cloud traffic light to reduce the time of route execution and save energy resources, are presented.


title: DocFace+: ID Document to Selfie Matching

abstract: Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators. However, this process is slow, labor intensive and unreliable. As such, an automated system for matching ID document photographs to live face images (selfies 1 ) in real time and with high accuracy is required. In this paper, we propose DocFace+ to meet this objective. We first show that gradient-based optimization methods converge slowly (due to the underfitting of classifier weights) when many classes have very few samples, a characteristic of existing ID-selfie datasets. To overcome this shortcoming, we propose a method, called dynamic weight imprinting, to update the classifier weights, which allows faster convergence and more generalizable representations. Next, a pair of sibling networks with partially shared parameters are trained to learn a unified face representation with domain-specific parameters. Cross-validation on an ID-selfie dataset shows that while a publicly available general face matcher (InsightFace) only achieves a true accept rate (TAR) of 88.78 ± 1.30% at a false accept rate of 0.01% on the problem, DocFace+ improves the TAR to 95.95 ± 0.54%. 1 Technically, the word “selfies” refers to self-captured photos from mobile phones. But here, we define “selfies” as any self-captured live face photos, including those from mobile phones and kiosks.


title: Perspectives to Predict Dropout in University Students with Machine Learning

abstract: This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.


title: Dynamic tolerant skyline operation for decision making

abstract: Skyline operation is typical multicriteria decision making well documented in data engineering. The assumption of skyline operation is settled human preference, which may be subject to huge challenges in practical decision-making applications because it simplifies preference scenarios that are usually dynamic. This study establishes the mathematical formulation of dynamic preference in real settings. A decision approach called tolerant skyline operation (T-skyline) is completely developed, including its conceptual modeling, computation methods, and a skyline maintenance mechanism on a database. The method is established and its computation mechanism is designed, and both are evaluated through an empirical study of personnel selection and evaluation. We also analyze computation efficiency and system stability. The decision targets are fully achieved, the computation results are satisfactory, and the computation efficiency is rational. The effectiveness and advantages of the approach are significant, as illustrated in different real-world settings. Experiments facilitated the examination of the design and development of T-skyline operation by adopting real and public datasets to evaluate players in the National Basketball Association in the United States. The experiment results validate the practical viability of our decision model, which can inspire discussions in sport industries. The methodology used in this study is valuable for further academic research, particularly for the interdisciplinary investigation of decision making and data engineering.


title: A steered response power iterative method for high-accuracy acoustic source localization.

abstract: Source localization using the steered response power (SRP) usually requires a costly grid-search procedure. To address this issue, a modified SRP algorithm was recently introduced, providing improved robustness when using coarser spatial grids. In this letter, an iterative method based on the modified SRP is presented. A coarse spatial grid is initially evaluated with the modified SRP, selecting the point with the highest accumulated value. Then, its corresponding volume is iteratively decomposed by using a finer spatial grid. Experiments have shown that this method provides almost the same accuracy as the fine-grid search with a substantial reduction of functional evaluations.


title: Strategies for global information systems development

abstract: Developing global information systems is a formidable task. Multinational companies operate in regions that are thousands of miles, many time zones, and many cultures away from headquarters. Organizing the activities and aligning the tasks and mindsets of people that are so far apart and to change the way that business is conducted through the use of IS is a major challenge. This study discusses alternative global IS development strategies and the factors that impact their selection. Four systems from a large transportation company are presented as real life examples to demonstrate the viability of these strategies and the accompanying factors.


title: Detecting true lies: Police officers' ability to detect suspects' lies

abstract: Ninety-nine police officers, not identified in previous research as belonging to groups that are superior in lie detection, attempted to detect truths and lies told by suspects during their videotaped police interviews. Accuracy rates were higher than those typically found in deception research and reached levels similar to those obtained by specialized lie detectors in previous research. Accuracy was positively correlated with perceived experience in interviewing suspects and with mentioning cues to detecting deceit that relate to a suspect’s story. Accuracy was negatively correlated with popular stereotypical cues such as gaze aversion and fidgeting. As in previous research, accuracy and confidence were not significantly correlated, but the level of confidence was dependent on whether officers judged actual truths or actual lies and on the method by which confidence was measured. Overview


title: Performance of Directional and Omnidirectional Antennas in Wideband Head Imaging

abstract: Researchers have proposed numerous wideband antennas with directional or omnidirectional radiations to meet the requirements of microwave-based head imaging systems. This letter aims to study the effect of directionality on image quality of those systems. Hence, both directional and omnidirectional wideband antennas are designed and prototyped. Both antennas cover a wide bandwidth (1.25-2.4 GHz) that is typically used in microwave head imaging and attain a boresight average gain of 3.5 dBi. The antennas' near-field radiation patterns are measured and analyzed. The antennas' transient pulse performance in the near-field region along boresight direction is also studied. It is observed that without the effects of multipath inside the head, the directional antenna provides 20% higher impulse fidelity and 4 dB more peak transient response than the omnidirectional antenna. An experimental verification is examined by imaging a realistic artificial head phantom with an emulated brain injury. Quantitative analysis of the reconstructed images demonstrates that directional antenna yields more focused images with low artifacts than omnidirectional antenna.


title: Comparing Business and Household Sector Innovation in Consumer Products: Findings from a Representative Study in the United Kingdom

abstract: In a first survey of its type, we measure development and modification of consumer products by product users in a representative sample of 1,173 UK consumers age 18 and older. We estimate this previously unmeasured type of household sector innovation to be quite large: 6.1% of UK consumers---nearly 2.9 million individuals---have engaged in consumer product innovation during the prior three years. In aggregate, consumers' annual product development expenditures are more than 1.4 times larger than the annual consumer product R&D expenditures of all firms in the United Kingdom combined. Consumers engage in many small projects that seem complementary to the innovation efforts of incumbent producers. Consumer innovators very seldom protect their innovations via intellectual property, and 17% diffuse to others. These results imply that, at the country level, productivity studies yield inflated effect sizes for producer innovation in consumer goods. They also imply that existing companies should reconfigure their product development systems to find and build on prototypes developed by consumers. ::: ::: This paper was accepted by Lee Fleming, entrepreneurship and innovation.


title: Quantitative Languages Defined by Functional Automata

abstract: A weighted automaton is functional if any two accepting runs on the same finite word have the same value. In this paper, we investigate functional weighted automata for four different measures: the sum, the mean, the discounted sum of weights along edges and the ratio between rewards and costs. On the positive side, we show that functionality is decidable for the four measures. Furthermore, the existential and universal threshold problems, the language inclusion problem and the equivalence problem are all decidable when the weighted automata are functional. On the negative side, we also study the quantitative extension of the realizability problem and show that it is undecidable for sum, mean and ratio. We finally show how to decide whether the language associated with a given functional automaton can be defined with a deterministic one, for sum, mean and discounted sum. The results on functionality and determinizability are expressed for the more general class of functional group automata. This allows one to formulate within the same framework new results related to discounted sum automata and known results on sum and mean automata. Ratio automata do not fit within this general scheme and different techniques are required to decide functionality.


title: Large-scale sparse logistic regression

abstract: Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the l1-norm regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving large-scale sparse logistic regression. Specifically, we formulate the problem as the l1-ball constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems.


title: A New Zero-Voltage-Switching Push-Pull Converter

abstract: A soft switching three-transistor push-pull（TTPP）converter is proposed in this paper. The 3rd transistor is inserted in the primary side of a traditional push-pull converter. Two primitive transistors can achieve zero-voltage-switching (ZVS) easily under a wide load range, the 3rd transistor can also realize zero-voltage-switching assisted by leakage inductance. The rated voltage of the 3rd transistor is half of that of the main transistors. The operation theory is explained in detail. The soft-switching realization conditions are derived. An 800 W with 83.3 kHz switching frequency prototype has been built. The experimental result is provided to verify the analysis.


title: Automatic parking path optimization based on Bezier curve fitting

abstract: The autonomous parking system is an intelligent technology to park a car into a small space. More and more studies have addressed this issue. However, the planned path in these studies is not smooth enough. Based on Ackermann steering geometry, the car kinematic model is established in this paper, the collision possibility between car body and obstacles in the parking space are analyzed, and then the bound of start point and collision-free space are presented. Based on this, the parking trajectory is planned and Bezier curve is used to smooth the trajectory which is generated on multi arcs of circumference, finally, the generation of continuous curvature path is realized. In the end, we build the environment of the simulation in MATLAB and the Fuzzy PID control method is used to track the trajectory which is planned in advance. The simulation results show that the car can not only avoid the obstacles effectively, but also move smoothly on turning points for using the Bezier curve. The design can meet the continuity of the parking requirements very well.


title: A Cognitive Science Based Machine Learning Architecture 1

abstract: In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms: 1) perceptual learning, the learning of new objects, categories, relations, etc., 2) episodic learning of events, the what, where, and when, 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence. Relevance of Cognitive Science to AI Dating back to Samuel’s checker player (1959), machine learning is among the oldest of the sub-branches of AI with many practitioners and many successes to its credit. Still, after fifty years of effort there are remaining difficulties. Machine learning often requires large, accurate training sets, shows little awareness of what’s known or not known, integrates new knowledge poorly into old, learns only one task at a time, allows little transfer of learned knowledge to new tasks, and is poor at learning from human teachers. Clearly, machine learning presents a number of hard AI problems. Can cognitive science help? In contrast, human learning has solved many of these problems, and is typically continual, quick, efficient, accurate, robust, flexible, and effortless. As an example consider perceptual learning, the learning of new objects, categories, relations, etc. Traditional machine learning approaches such as object detection, classification, clustering, etc, are highly susceptible to the problems raised above. However, perceptual learning in humans and animals seem to have no such restrictions. Perceptual learning in humans occurs incrementally so there is no need for a large training set. Learning and knowledge extraction are achieved simultaneously through a dynamical system that can adapt to changes in the nature of the stimuli perceived in the environment. Additionally, human like learning is based on reinforcement rather than fitting to a dataset or model. Therefore, in addition to learning, humans can also forget. Initially, many associations are made between entities, the ones that are sufficiently reinforced persist, while the ones that aren’t decay. All this suggests a possible heuristic: If you want smart software, copy it after humans. We’ve done just that. The Learning Intelligent Distribution Agent (LIDA) architecture that we propose here was designed to be consistent with what is known from cognitive science and neuroscience. In addition to being a computational architecture, it’s intended to model human cognition. We’ll go on to describe the LIDA architecture and its human-like learning capabilities.


title: Wearable wireless sensors for chronic respiratory disease monitoring

abstract: We present a wearable sensor system consisting of a wristband and chest patch to enable the correlation of individual environmental exposure to health response for understanding impacts of ozone on chronic asthma conditions. The wrist worn device measures ambient ozone concentration, heart rate via plethysmography (PPG), three-axis acceleration, ambient temperature, and ambient relative humidity. The chest patch measures heart rate via electrocardiography (ECG) and PPG, respiratory rate via PPG, wheezing via a microphone, and three-axis acceleration. The data from each sensor is continually streamed to a peripheral data aggregation device, and is subsequently transferred to a dedicated server for cloud storage. The current generation of the system uses only commercially-off-the-shelf (COTS) components where the entire electronic structure of the wristband has dimensions of 3.1×4.1×1.2 cm3 while the chest patch electronics has a dimensions of 3.3×4.4×1.2 cm3. The power consumptions of the wristband and chest patch are 78 mW and 33 mW respectively where using a 400 mAh lithium polymer battery would operate the wristband for around 15 hours and the chest patch for around 36 hours.


Frog muscle fibber dynamic stiffness determined using nonlinear system identification techniques


title: Retrieval Algorithms Optimized for Human Learning

abstract: While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one involving unnecessarily easy or difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. We address this problem by introducing a novel theoretical framework, algorithms, and empirical analysis of an information retrieval model that is optimized for learning outcomes instead of generic relevance. We do this by formulating an optimization problem that incorporates a cognitive learning model into a retrieval objective, and then give an algorithm for an efficient approximate solution to find the search results that represent the best 'training set' for a human learner. Our model can personalize results for an individual user's learning goals, as well as account for the effort required to achieve those goals for a given set of retrieval results. We investigate the effectiveness and efficiency of our retrieval framework relative to a commercial search engine baseline ('Google') through a crowdsourced user study involving a vocabulary learning task, and demonstrate the effectiveness of personalized results from our model on word learning outcomes.


title: Spectrum and energy efficiency maximization in UAV-enabled mobile relaying

abstract: Wireless communication by leveraging the use of low-altitude unmanned aerial vehicles (UAVs) has received significant interests recently due to its low-cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. This paper studies a UAV-enabled mobile relaying system, where a high-mobility UAV is deployed to assist in the information transmission from a ground source to a ground destination with their direct link blocked. By assuming that the UAV adopts the energy-efficient circular trajectory and employs time-division duplexing (TDD) based decode-and-forward (DF) relaying, we maximize the spectrum efficiency (SE) in bits/second/Hz as well as energy efficiency (EE) in bits/Joule of the considered system by jointly optimizing the time allocations for the UAV's relaying together with its flying speed and trajectory. It is revealed that for UAV-enabled mobile relaying with the UAV propulsion energy consumption taken into account, there exists a trade-off between the maximum achievable SE and EE by exploiting the new degree of freedom of UAV trajectory design.


title: Accounting for Taste: Ranking Curators and Content in Social Networks

abstract: Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation --- users sharing and promoting content in a network --- has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks --- GitHub and Vine --- and demonstrate its efficacy at ranking content and identifying good curators.


title: A simple and efficient algorithm for computing approximate Nash equilibria in two-person zero-sum sequential games with imcomplete information

abstract: We present a simple primal-dual algorithm for computing approximate Nash equilibria in two-person zero-sum sequential games with imcomplete information and perfect recall (like Texas Hold'em Poker). Our algorithm only performs basic iterations (i.e iterations involving no calls to external first-order oracles, etc.) and is applicable to a broad class of two-person zero-sum games including simultaneous games and sequential games with imcomplete information and perfect recall. The applicability to the latter kind of games is thanks to the sequence-form representation which allows us to encode any such game as a matrix game with polyhedral strategy profiles. We prove that the number of iterations needed to produce a Nash equilibrium with a given precision is inversely proportional to the desired precision. We also present experimental results on simulated and real games (Kuhn poker).


title: Reduced hippocampal volumes and memory loss in patients with early- and late-onset depression

abstract: Background Hippocampal volume reduction has been reported inconsistently in people with major depression. ::: ::: Aims To evaluate the interrelationships between hippocampal volumes, memory and key clinical, vascular and genetic risk factors. ::: ::: Method Totals of 66 people with depression and 20 control participants underwent magnetic resonance imaging and clinical assessment. Measures of depression severity, psychomotor retardation, verbal and visual memory and vascular and specific genetic risk factors were collected. ::: ::: Results Reduced hippocampal volumes occurred in older people with depression, those with both early-onset and late-onset disorders and those with the melancholic subtype. Reduced hippocampal volumes were associated with deficits in visual and verbal memory performance. ::: ::: Conclusions Although reduced hippocampal volumes are most pronounced in late-onset depression, older people with early-onset disorders also display volume changes and memory loss. No clear vascular or genetic risk factors explain these findings. Hippocampal volume changes may explain how depression emerges as a risk factor to dementia.


title: As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian

abstract: Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally. They are often used in everyday communication and are an important part of cultural heritage. Having an up-to-date corpus of similes is challenging, as they are constantly coined and/or adapted to the contemporary times. In this paper we present a methodology for semi-automated collection of similes from the world wide web using text mining techniques. We expanded an existing corpus of traditional similes (containing 333 similes) by collecting 446 additional expressions. We, also, explore how crowdsourcing can be used to extract and curate new similes.


title: An Accelerometer-Based Device for Sleep Apnea Screening

abstract: This paper presents a body-fixed-sensor-based approach to assess potential sleep apnea patients. A trial involving 15 patients at a sleep unit was undertaken. Vibration sounds were acquired from an accelerometer sensor fixed with a noninvasive mounting on the suprasternal notch of subjects resting in supine position. Respiratory, cardiac, and snoring components were extracted by means of digital signal processing techniques. Mainly, the following biomedical parameters used in new sleep apnea diagnosis strategies were calculated: heart rate, heart rate variability, sympathetic and parasympathetic activity, respiratory rate, snoring rate, pitch associated with snores, and airflow indirect quantification. These parameters were compared to those obtained by means of polysomnography and an accurate microphone. Results demonstrated the feasibility of implementing an accelerometry-based portable device as a simple and cost-effective solution for contributing to the screening of sleep apnea-hypopnea syndrome and other breathing disorders.


title: Transmit power adaptation for multiuser OFDM systems

abstract: In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequency-division multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough.


title: The Information Systems Design Science Research Body Of Knowledge - A Citation Analysis In Recent Top-Journal Publications

abstract: Information systems (IS) design science research (ISDSR) has received particular attention after the seminal MISQ article by Hevner et al. (2004). Since then, several articles on ISDSR have appeared in top IS journals. This paper examines the development of a common body of knowledge in ISDSR. To this end, a citation analysis in recently published ISDSR articles in top IS journals is conducted. According to Kuhn (1970), a common body of knowledge is important for each emerging scientific discipline. For the purposes of delineating ISDSR from other types of IS research, the author develops three criteria. Based upon these criteria, he identifies 50 ISDSR articles published in the journals of the basket of the six from 2007-2010. An analysis of the citations of these articles reveals that Hevner et al. (2004) is cited as a source in 70% of all articles. Moreover, six further specific publications are cited by more than 10% of all articles. The author argues that ISDSR is developing a common body of knowledge and does have a cumulative tradition.


title: Large-scale machine learning at twitter

abstract: The success of data-driven solutions to difficult problems, along with the dropping costs of storing and processing massive amounts of data, has led to growing interest in large-scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles "traditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specifically on supervised classification. In particular, we have identified stochastic gradient descent techniques for online learning and ensemble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature generation, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-defined functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, scheduling, and monitoring in a production environment, as well as access to rich libraries of user-defined functions and the materialized output of other scripts.


title: Iris localization in frontal eye images for less constrained iris recognition systems

abstract: Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.


title: Consensus Recommendations for Optimal Augmentation of the Asian Face with Hyaluronic Acid and Calcium Hydroxylapatite Fillers.

abstract: BACKGROUND ::: Although the use of filling agents for soft-tissue augmentation has increased worldwide, most consensus statements do not distinguish between ethnic populations. There are, however, significant differences between Caucasian and Asian faces, reflecting not only cultural disparities, but also distinctive treatment goals. Unlike aesthetic patients in the West, who usually seek to improve the signs of aging, Asian patients are younger and request a broader range of indications. ::: ::: ::: METHODS ::: Members of the Asia-Pacific Consensus group-comprising specialists from the fields of dermatology, plastic surgery, anatomy, and clinical epidemiology-convened to develop consensus recommendations for Asians based on their own experience using cohesive polydensified matrix, hyaluronic acid, and calcium hydroxylapatite fillers. ::: ::: ::: RESULTS ::: The Asian face demonstrates differences in facial structure and cosmetic ideals. Improving the forward projection of the "T zone" (i.e., forehead, nose, cheeks, and chin) forms the basis of a safe and effective panfacial approach to the Asian face. Successful augmentation may be achieved with both (1) high- and low-viscosity cohesive polydensified matrix/hyaluronic acid and (2) calcium hydroxylapatite for most indications, although some constraints apply. ::: ::: ::: CONCLUSION ::: The Asia-Pacific Consensus recommendations are the first developed specifically for the use of fillers in Asian populations. ::: ::: ::: CLINCIAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, V.


title: Energy harvesting vibration sources for microsystems applications

abstract: This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equations that relate to the three main transduction mechanisms employed to extract energy from the system. These transduction mechanisms are: piezoelectric, electromagnetic and electrostatic. Piezoelectric generators employ active materials that generate a charge when mechanically stressed. A comprehensive review of existing piezoelectric generators is presented, including impact coupled, resonant and human-based devices. Electromagnetic generators employ electromagnetic induction arising from the relative motion between a magnetic flux gradient and a conductor. Electromagnetic generators presented in the literature are reviewed including large scale discrete devices and wafer-scale integrated versions. Electrostatic generators utilize the relative movement between electrically isolated charged capacitor plates to generate energy. The work done against the electrostatic force between the plates provides the harvested energy. Electrostatic-based generators are reviewed under the classifications of in-plane overlap varying, in-plane gap closing and out-of-plane gap closing; the Coulomb force parametric generator and electret-based generators are also covered. The coupling factor of each transduction mechanism is discussed and all the devices presented in the literature are summarized in tables classified by transduction type; conclusions are drawn as to the suitability of the various techniques.


title: Game private networks performance: analytical models for very-large scale simulation

abstract: The WTFast’s Gamers Private Network (GPN R ) is a client/server solution that makes online games faster. GPN connects online video-game players with a common game service across a wide-area network. Online games are interactive competitions by individual players who compete in a virtual environment. Response time, latency and its predictability are keys to GPN success and runs against the vast complexity of internet-wide systems. We have built an experimental network of virtualized GPN components so as to carefully measure the statistics of latency for distributed Minecraft games and to do so in a controlled laboratory environment. This has led to a better understanding of the coupling between parameters such as: the number of players, the subset of players that are idle or active, the volume of packets exchanged, the size of packets, latency to and from the game servers, and time-series for most of those parameters. In this paper we present a mathematical model of those system game network parameters and show how it leads to: (1) realistic simulation of each of those network or game parameters, without relying on the experimental setup; (2) very large-scale numerical simulation of the game setup so as to explore various internet-wide performance scenarios that: (a) are impossible to isolate from internet “noise” in their real environment and; (b) would require vast supercomputing resources if they were to be simulated exhaustively. We motivate all elements of our mathematical model and estimate the savings in computational costs they will bring for very large-scale simulation of the GPN. Such simulations will improve quality of service for GPN systems and their reliability.


title: A Review of Uncertainty in Data Visualization

abstract: Most visualization techniques have been designed on the assumption that the data to be represented are free from uncertainty. Yet this is rarely the case. Recently the visualization community has risen to the challenge of incorporating an indication of uncertainty into visual representations, and in this article we review their work. We place the work in the context of a reference model for data visualization, that sees data pass through a pipeline of processes. This allows us to distinguish the visualization of uncertainty—which considers how we depict uncertainty specified with the data—and the uncertainty of visualization—which considers how much inaccuracy occurs as we process data through the pipeline. It has taken some time for uncertain visualization methods to be developed, and we explore why uncertainty visualization is hard—one explanation is that we typically need to find another display dimension and we may have used these up already! To organize the material we return to a typology developed by one of us in the early days of visualization, and make use of this to present a catalog of visualization techniques describing the research that has been done to extend each method to handle uncertainty. Finally we note the responsibility on us all to incorporate any known uncertainty into a visualization, so that integrity of the discipline is maintained.


title: Visual snow syndrome: what we know so far

abstract: PURPOSE OF REVIEW ::: ::: We provide an overview of the neurological condition known as visual snow syndrome. Patients affected by this chronic disorder suffer with a pan-field visual disturbance described as tiny flickering dots, which resemble the static noise of an untuned television. ::: ::: RECENT FINDINGS ::: ::: The term 'visual snow' has only appeared in the medical literature very recently. The clinical features of the syndrome have now been reasonably described and the pathophysiology has begun to be explored. This review focuses on what is currently known about visual snow. ::: ::: SUMMARY ::: ::: Recent evidence suggests visual snow is a complex neurological syndrome characterized by debilitating visual symptoms. It is becoming better understood as it is systematically studied. Perhaps the most important unmet need for the condition is a sufficient understanding of it to generate and test hypotheses about treatment.


title: Biologically inspired protection of deep networks from adversarial attacks

abstract: Inspired by biophysical principles underlying nonlinear dendritic computation in neural circuits, we develop a scheme to train deep neural networks to make them robust to adversarial attacks. Our scheme generates highly nonlinear, saturated neural networks that achieve state of the art performance on gradient based adversarial examples on MNIST, despite never being exposed to adversarially chosen examples during training. Moreover, these networks exhibit unprecedented robustness to targeted, iterative schemes for generating adversarial examples, including second-order methods. We further identify principles governing how these networks achieve their robustness, drawing on methods from information geometry. We find these networks progressively create highly flat and compressed internal representations that are sensitive to very few input dimensions, while still solving the task. Moreover, they employ highly kurtotic weight distributions, also found in the brain, and we demonstrate how such kurtosis can protect even linear classifiers from adversarial attack.


title: Affiliation with antisocial peers, susceptibility to peer influence, and antisocial behavior during the transition to adulthood.

abstract: Developmental theories suggest that affiliation with deviant peers and susceptibility to peer influence are important contributors to adolescent delinquency, but it is unclear how these variables impact antisocial behavior during the transition to adulthood, a period when most delinquent individuals decline in antisocial behavior. Using data from a longitudinal study of 1,354 antisocial youth, the present study examined how individual variation in exposure to deviant peers and resistance to peer influence affect antisocial behavior from middle adolescence into young adulthood (ages 14 to 22 years). Whereas we find evidence that antisocial individuals choose to affiliate with deviant peers, and that affiliating with deviant peers is associated with an individual’s own delinquency, these complementary processes of selection and socialization operate in different developmental periods. In middle adolescence, both selection and socialization serve to make peers similar in antisocial behavior, but from ages 16 to 20 years, only socialization appears to be important. After age 20, the impact of peers on antisocial behavior disappears as individuals become increasingly resistant to peer influence, suggesting that the process of desistance from antisocial behavior may be tied to normative changes in peer relations that occur as individuals mature socially and emotionally.


title: Bizarreness in dreams and fantasies: Implications for the activation-synthesis hypothesis

abstract: Abstract Dreaming is a statistically robust cognitive correlate of REM sleep, but all of its formal features may occur in other states of sleep and even in waking, especially during fantasy. In order to test the hypothesis that the brain basis of such cognitive features as dream bizarreness is to be found in REM sleep neurophysiology, it is critical to quantify bizarreness in dreams and other mental states and to analyze the data with respect to both the magnitude and the kind of bizarreness so measured. Any differences in the cognitive dimensions are candidate correlates of REM sleep neurophysiology. Sixty pairs of home-based dream and fantasy reports were collected from 12 subjects and scored for bizarreness using a two-stage scoring system adapted from Hobson, Hoffman, Helfand, and Kostner (1987) . Our results show that bizarreness was twice as prevalent in dream reports as in wake-state fantasy reports of the same subjects. Further analysis of the reports also showed differences in other features including the number of persona and remoteness of time and place.


title: The Design and Implementation of Multi-player Card Games on Multi-user Interactive Tabletop Surfaces

abstract: We present the design and implementation of a card game architecture for mulit-user interactive tabletop surfaces. Our system is built on the DiamondTouch, a touch-sensitive input surface that allows several users to interact with a program at the same time. We describe the software architecture and present Blackjack as a sample implementation using this framework.


title: Automatic Structuring of IT Problem Ticket Data for Enhanced Problem Resolution

abstract: In this paper we propose a novel technique to automatically structure problem tickets consisting of free form, heterogeneous textual data, so that IT problem isolation and resolution can be performed rapidly. The originality of our technique consists in applying the conditional random fields (CRFs) supervised learning process to automatically identify individual units of information in the raw data. The CRFs have been shown to be effective on real-world tasks in various fields. We apply our technique to identify structural patterns specific to the problem ticket data used in call centers to enhance the problem resolution system used by remote technical assistance personnel. Most of the existing ticketing data is not explicitly structured, is highly noisy, and very heterogeneous in content, making it hard to effectively apply common data mining techniques to analyze and search the raw data. An example of such an analysis is the detection of the units of information containing the steps taken by the technical people to resolve a particular customer issue. We present a study of the accuracy of our results.


title: Face-to-face or Facebook: Can social connectedness be derived online?

abstract: Previous research has identified the importance of social connectedness in facilitating a number of positive outcomes, however, investigation of connectedness in online contexts is relatively novel. This research aimed to investigate for the first time social connectedness derived from the use of Facebook. Study 1 investigated whether offline social connectedness and Facebook connectedness were separate constructs. Participants were Facebook users (N=344) who completed measures of offline social connectedness and Facebook social connectedness. Factor analysis (Maximum Likelihood analysis with Oblimin rotation) revealed Facebook connectedness to be distinct from offline social connectedness. Study 2 examined the relationship between Facebook social connectedness and anxiety, depression, and subjective well-being in a second sample of Facebook users (N=274) in a cross-sectional design. Results suggest that Facebook use may provide the opportunity to develop and maintain social connectedness in the online environment, and that Facebook connectedness is associated with lower depression and anxiety and greater satisfaction with life. Limitations and future directions are considered. It is concluded that Facebook may act as a separate social medium in which to develop and maintain relationships, providing an alternative social outlet associated with a range of positive psychological outcomes.


Provenance for Non-Experts


title: Convolutional Sequence to Sequence Learning

abstract: The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.


title: ProGuard: Detecting Malicious Accounts in Social-Network-Based Online Promotions

abstract: Online social networks (OSNs) gradually integrate financial capabilities by enabling the usage of real and virtual currency. They serve as new platforms to host a variety of business activities, such as online promotion events, where users can possibly get virtual currency as rewards by participating in such events. Both OSNs and business partners are significantly concerned when attackers instrument a set of accounts to collect virtual currency from these events, which make these events ineffective and result in significant financial loss. It becomes of great importance to proactively detecting these malicious accounts before the online promotion activities and subsequently decreases their priority to be rewarded. In this paper, we propose a novel system, namely ProGuard , to accomplish this objective by systematically integrating features that characterize accounts from three perspectives including their general behaviors, their recharging patterns, and the usage of their currency. We have performed extensive experiments based on data collected from the Tencent QQ, a global leading OSN with built-in financial management activities. Experimental results have demonstrated that our system can accomplish a high detection rate of 96.67% at a very low false positive rate of 0.3%.


title: Monocular reconstruction of vehicles: Combining SLAM with shape priors

abstract: Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detection and tracking problem: (1) 3D representations (eg. wireframe models or voxel based or CAD models) for diverse vehicle skeletal structures learnt from data, and (2) classifiers trained to detect vehicles or vehicle parts in single images built on top of a basic feature extraction step. In this paper, we propose to extend current approaches in two ways. First, we extend detection to a multiple view setting. We show that leveraging information given by feature or part detectors in multiple images can lead to more accurate detection results than single image detection. Secondly, we show that given multiple images of a vehicle, we can also leverage 3D information from the scene generated using a unique structure from motion algorithm. This helps us localize the vehicle in 3D, and constrain the parameters of optimization for fitting the 3D model to image data. We show results on the KITTI dataset, and demonstrate superior results compared with recent state-of-the-art methods, with upto 14.64 % improvement in localization error.


title: Backstepping Control for a Quadrotor Helicopter

abstract: This paper presents a nonlinear dynamic model for a quadrotor helicopter in a form suited for backstepping control design. Due to the under-actuated property of quadrotor helicopter, the controller can set the helicopter track three Cartesian positions (x, y, z) and the yaw angle to their desired values and stabilize the pitch and roll angles. The system has been presented into three interconnected subsystems. The first one representing the under-actuated subsystem, gives the dynamic relation of the horizontal positions (x, y) with the pitch and roll angles. The second fully-actuated subsystem gives the dynamics of the vertical position z and the yaw angle. The last subsystem gives the dynamics of the propeller forces. A backstepping control is presented to stabilize the whole system. The design methodology is based on the Lyapunov stability theory. Various simulations of the model show that the control law stabilizes a quadrotor with good tracking.


Wavelets for Computer Graphics: A Primer - Part 1


title: Efficient Energy Harvesting With Electromagnetic Energy Transducers Using Active Low-Voltage Rectification and Maximum Power Point Tracking

abstract: This paper reports on efficient interfacing of typical vibration-driven electromagnetic transducers for micro energy harvesting. For this reason, an adaptive charge pump for dynamic maximum power point tracking is compared with a novel active full-wave rectifier design. For efficient ultra-low voltage rectification, the introduced active diode design uses a common-gate stage in conjunction with supply-independent biasing. While this active rectifier offers low voltage drops, low complexity and ultra-low power consumption, the adaptive charge pump allows dynamic maximum power point tracking with implicit voltage up-conversion. Hence, efficient energy harvesting with high-resistive transducers, e.g., electromagnetic generators, becomes possible even at buffer voltage levels far above actual transducer output voltages. Both interfaces are fully-integrated in a standard 0.35 μm twin-well CMOS process. The designs are optimized for sub-mW transducer power levels and wide supply voltage ranges. Thus, these presented transducer interfaces are particularly suitable for compact micro energy harvesting systems, such as wireless sensor nodes or medical implants. The active diode rectifier achieves efficiencies over 90% at a wide range of input voltage amplitudes of 0.48 V up to 3.3 V. The adaptive charge pump can harvest with a total efficiency of close to 50%, but very independent of the actual buffer voltage. This charge pump starts operating at a supply voltage of 0.8 V, and has an input voltage range of 0.5 V-2.5 V . Finally, results of harvesting from an actual electromagnetic generator prototype are presented.


Characteristics of Massive Open Online Courses (MOOCs):


title: A survey of software reuse libraries

abstract: The study of storage and retrieval methods of software assets in software libraries gives rise to a number of paradoxes: While this subject has been under investigation for nearly two decades, it still remains an active area of research in software reuse and software engineerings this can be explained by the observation that new technologies (such as the internet, the world wide web, objectdoriented programming) keep opening new opportunities for better asset packaging, better library organizations, and larger scale libraries – thereby posing new technical challenges. Also, while many sophisticated solutions have been proposed to this problem, the state of the practice in software reuse is characterized by the use of addhoc, lowdtech methodss this can be explained by the observation that most existing solutions are either too ineffective to be useful or too intractable to be usable. Finally, while it is difficult to imagine a successful software reuse program without a sophisticated, welldtuned, systematic procedure for software component storage and retrieval, it seems many successful software reuse experiments rely on trivial methods of component storage and retrievals this can be explained by the observation that, in the current state of the practice, software libraries are not the bottleneck of the software reuse process. This paper presents a survey of methods of storage and retrieval of software assets in software libraries. In addition to a review of existing research efforts, the paper makes two contributions. First, a definition of (presumably) orthogonal attributes of storage and retrieval methodss these attributes are used, in turn, to classify existing methods into six broad classes. Second, a definition of (presumably) orthogonal assessment criteria, which include technical, managerial and human factorss these criteria afford us an exhaustive and uniform basis for assessing and comparing individual methods and classes of methods.


title: The Effectiveness of a Wireless Modular Bed Absence Sensor Device for Fall Prevention among Older Inpatients

abstract: BACKGROUND ::: Falls and fall-related injuries are increasingly serious issues among elderly inpatients due to population aging. The bed-exit alarm has only previously been evaluated in a handful of studies with mixed results. Therefore, we evaluated the effectiveness of a modular bed absence sensor device (M-BAS) in detecting bed exits among older inpatients in a middle income nation in East Asia. ::: ::: ::: METHODS ::: Patients aged ≥65 years on an acute geriatric ward who were able to mobilize with or without walking aids and physical assistance were recruited to the study. The total number of alarms and the numbers of true and false alarms were recorded by ward nurses. The M-BAS device is placed across the mattress of all consenting participants. Nurses' workload was assessed using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) score, while nurses' perceptions were surveyed. ::: ::: ::: RESULTS ::: The sensitivity of the M-BAS was 100% with a positive predictive value of 68% and a nuisance alarm rate of 31%. There was a significant reduction in total NASA-TLX workload score (mean difference = 14.34 ± 13.96 SD, p < 0.001) at the end of the intervention period. 83% of the nurses found the device useful for falls prevention, 97% found it user friendly, and 87% would use it in future. ::: ::: ::: CONCLUSION ::: The M-BAS was able to accurately detect bed absence episodes among geriatric inpatients and alert nurses accordingly. The use of the device significantly reduced the total workload score, while the acceptability of the device was high among our nurses. A larger, cluster randomized study to measure actual falls outcome associated with the use of the device is now indicated.


title: A survey on compressed domain video analysis techniques

abstract: Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.


title: Topology: A Source of Nonlinearity in the MEMS Thermal Accelerometer

abstract: This article reports the effect of package and constituent geometries on the response linearity of a micromachined single-axis thermal accelerometer. To investigate this phenomenon, a packaged device is filled with a high-density gas inside a sealed chamber on a rotation stage, and its response is measured as its sensitive axis of acceleration is tilted relative to the Earth's gravitational acceleration g 9.81ms2 . Linearity of the response is examined compared to an analytical model describing natural convection between horizontal heated concentric cylinders. In a 360 turn, full-scale nonlinearities of 3.1 and 0.9 are measured in each half cycle. This asymmetrical performance is attributed to a lower level of geometrical symmetry in the packaged device. To compensate for the modulated response, a geometrical correction factor is introduced to the model, improving its performance prediction with a symmetrical full-scale linearity error of 0.5.


title: A Survey on Image Matting Techniques

abstract: With advanced digital camera, the matting techniques are highly used to create an innovative composite and to facilitate other editing tasks that has gained increasing benefits from both professionals as well as consumers. Image Matting techniques are key step of image and video editing, image translation and in film production to track the object in scene. Image matting methods categories in to three types, sampling based, propagation based, and learning based. A hybrid of sampling based and propagation-based matting uses to improve the result of alpha matte. Various image matting techniques and systems have been proposed to efficiently extract high quality mattes from image. In this paper a comprehensive review of existing image matting techniques along with parametric evaluation of these schemes are discussed.


title: Carpal Tunnel Syndrome: Effectiveness of Physical Therapy and Electrophysical Modalities. An Updated Systematic Review of Randomized Controlled Trials.

abstract: OBJECTIVE ::: To review scientific literature studying the effectiveness of physical therapy and electrophysical modalities for carpal tunnel syndrome (CTS). ::: ::: ::: DATA SOURCES ::: The Cochrane Library, PubMed, Embase, CINAHL, and Physiotherapy Evidence Database. ::: ::: ::: STUDY SELECTION ::: Two reviewers independently applied the inclusion criteria to select potential eligible studies. ::: ::: ::: DATA EXTRACTION ::: Two reviewers independently extracted the data and assessed the methodologic quality using the Cochrane Risk of Bias Tool. ::: ::: ::: DATA SYNTHESIS ::: A best-evidence synthesis was performed to summarize the results of the included studies (2 reviews and 22 randomized controlled trials [RCTs]). For physical therapy, moderate evidence was found for myofascial massage therapy versus ischemic compression on latent, or active, trigger points or low-level laser therapy in the short term. For several electrophysical modalities, moderate evidence was found in the short term (ultrasound vs placebo, ultrasound as single intervention vs other nonsurgical interventions, ultrasound vs corticosteroid injection plus a neutral wrist splint, local microwave hyperthermia vs placebo, iontophoresis vs phonophoresis, pulsed radiofrequency added to wrist splint, continuous vs pulsed vs placebo shortwave diathermy, and interferential current vs transcutaneous electrical nerve stimulation vs a night-only wrist splint). In the midterm, moderate evidence was found in favor of radial extracorporeal shockwave therapy (ESWT) added to a neutral wrist splint, in favor of ESWT versus ultrasound, or cryo-ultrasound, and in favor of ultrasound versus placebo. For all other interventions studied, only limited, conflicting, or no evidence was found. No RCTs investigating the long-term effects of physical therapy and electrophysical modalities were found. Because of heterogeneity in the treatment parameters used in the included RCTs, optimal treatment parameters could not be identified. ::: ::: ::: CONCLUSIONS ::: Moderate evidence was found for several physical therapy and electrophysical modalities for CTS in the short term and midterm. Future studies should concentrate on long-term effects and which treatment parameters of physical therapy and electrophysical modalities are most effective for CTS.


title: A Proximal Dual Consensus ADMM Method for Multi-Agent Constrained Optimization

abstract: This paper studies efficient distributed optimization methods for multi-agent networks. Specifically, we consider a convex optimization problem with a globally coupled linear equality constraint and local polyhedra constraints, and develop distributed optimization methods based on the alternating direction method of multipliers (ADMM). The considered problem has many applications in machine learning and smart grid control problems. Due to the presence of the polyhedra constraints, agents in the existing methods have to deal with polyhedra constrained subproblems at each iteration. One of the key issues is that projection onto a polyhedra constraint is not trivial, which prohibits from closed-form solutions or the use of simple algorithms for solving these subproblems. In this paper, by judiciously integrating the proximal minimization method with ADMM, we propose a new distributed optimization method where the polyhedra constraints are handled softly as penalty terms in the subproblems. This makes the subproblems efficiently solvable and consequently reduces the overall computation time. Furthermore, we propose a randomized counterpart that is robust against randomly ON/OFF agents and imperfect communication links. We analytically show that both the proposed methods have a worst-case $\mathcal{O}(1/k)$ convergence rate, where $k$ is the iteration number. Numerical results show that the proposed methods offer considerably lower computation time than the existing distributed ADMM method.


title: Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures

abstract: Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.


title: Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis

abstract: Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies include both work flow errors and low performance problems. People often use system logs produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting system logs to detect anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Therefore, there is a great demand for automatic anomalies detection techniques based on log analysis. In this paper, we propose an unstructured log analysis technique for anomalies detection. In the technique, we propose a novel algorithm to convert free form text messages in log files to log keys without heavily relying on application specific knowledge. The log keys correspond to the log-print statements in the source code which can provide cues of system execution behavior. After converting log messages to log keys, we learn a Finite State Automaton (FSA) from training log sequences to present the normal work flow for each system component. At the same time, a performance measurement model is learned to characterize the normal execution performance based on the log mes-sages’ timing information. With these learned models, we can automatically detect anomalies in newly input log files. Experiments on Hadoop and SILK show that the technique can effectively detect running anomalies.


title: EDDIE: EM-Based Detection of Deviations in Program Execution

abstract: This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations that are emitted as a side effect of execution on the monitored system, and it relies on spikes in the EM spectrum that are produced as a result of periodic (e.g. loop) activity in the monitored execution. During training, EDDIE characterizes normal execution behavior in terms of peaks in the EM spectrum that are observed at various points in the program execution, but it does not need any characterization of the malware or other code that might later be injected. During monitoring, EDDIE identifies peaks in the observed EM spectrum, and compares these peaks to those learned during training. Since EDDIE requires no resources on the monitored machine and no changes to the monitored software, it is especially well suited for security monitoring of embedded and IoT devices. We evaluate EDDIE on a real IoT system and in a cycle-accurate simulator, and find that even relatively brief injected bursts of activity (a few milliseconds) are detected by EDDIE with high accuracy, and that it also accurately detects when even a few instructions are injected into an existing loop within the application.


title: Guideline for a Simplified Differential-Mode EMI Filter Design

abstract: The design of electromagnetic interference (EMI) input filters, needed for switched power converters to fulfill the regulatory standards, is typically associated with high development effort. This paper presents a guideline for a simplified differential-mode (DM) filter design. First, a procedure to estimate the required filter attenuation based on the total input rms current using only a few equations is given. Second, a volume optimization of the needed DM filter based on the previously calculated filter attenuation and volumetric component parameters is introduced. It is shown that a minimal volume can be found for a certain optimal number of filter stages. The considerations are exemplified for two single-phase power factor correction converters operated in continuous and discontinuous conduction modes, respectively. Finally, EMI measurements done with a 300-W power converter prototype prove the proposed filter design method.


title: Re-identification of Smart Meter data

abstract: The Smart Grid approach enhances the power grid with information technology. Smart Meters are an important part of the Smart Grid. They record the energy consumption of households with a high-resolution and transfer consumption records to the energy provider in real time. Since they allow to infer personal information like the daily routine of the household members, Smart Meters are also a promising source for lifelogging. However, in liberalized energy markets, many different parties have access to these data. This puts the privacy of consumers at risk. In this paper, we analyze to which degree Smart Meter data, as collected by our industry partner, can be linked to its producer, using simple statistical measures. We devise features of the energy consumption, for example, the first peak of demand in the morning, and we describe an analytical framework that quantifies how well these features can identify households. Finally, we conduct a study with 60,480 energy-consumption records from 180 households. Our study shows that 68 % of the records can be re-identified with simple means already. This insight is important for Smart Grids, as it emphasizes the need for research and use of anonymization techniques for the Smart Grid.


title: Reply to the discussion of: Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles

abstract: Alysha De Livera’s discussion focuses on the transformation of the data and the inclusion of ARMA terms for the residuals of the exponential smoothing models. Drawing on the formulation for the HWT model, I implemented all exponential smoothing models with an AR(1) term included for the residual. In my paper, I applied a logarithmic transformation to the NHS Direct data prior to fitting all methods. The BATS model, presented in Alysha De Livera’s discussion, is a generalisation of the implementation of the HWT model in my paper, with a Box-Cox transformation and ARMA terms of different lags for the residual.


title: Large-scale cost function learning for path planning using deep inverse reinforcement learning

abstract: We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning IRL framework which exploits the capacity of fully convolutional neural networks FCNs to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework.


title: Modern hierarchical, agglomerative clustering algorithms

abstract: This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed (2) the output is a"stepwise dendrogram", a data structure which is shared by all implementations in current standard software. We present algorithms (old and new) which perform clustering in this setting efficiently, both in an asymptotic worst-case analysis and from a practical point of view. The main contributions of this paper are: (1) We present a new algorithm which is suitable for any distance update scheme and performs significantly better than the existing algorithms. (2) We prove the correctness of two algorithms by Rohlf and Murtagh, which is necessary in each case for different reasons. (3) We give well-founded recommendations for the best current algorithms for the various agglomerative clustering schemes.


title: IPv6 Addressing Strategies for IoT

abstract: In this paper, we analyze the suitability of different IPv6 addressing strategies for nodes, gateways, and various access network deployment scenarios in the Internet of Things. A vast number of things connected to the Internet need IPv6 addresses, as the IPv4 address space was effectively consumed before the introduction of the Internet of Things. We highlight how the heterogeneity of nodes and network technologies, extreme constraint and miniaturization, renumbering, and multihoming, present serious challenges toward IPv6 address allocation. By considering the topologies of various types of IoT networks, their intended uses as well as the types of IPv6 addresses that need to be deployed, we draw attention to allocation solutions as well as potential pitfalls.


title: Micro bubble fluidics by EWOD and ultrasonic excitation for micro bubble tweezers

abstract: Recently, we envisioned so called micro bubble tweezers where EWOD (electrowetting-on-dielectric) actuated bubbles can manipulate micro objects such as biological cells by pushing or pulling them. Besides, oscillating (shrinking and expanding) bubbles in the presence of ultrasonic wave act as a to deliver drugs and molecules into the cells. In this paper, as a great stride in our quest for micro bubble tweezers, we present (1) full realization of two critical bubble operations (generating of bubbles in an on-chip and on-demand manner and splitting of single bubbles) and (2) two possible applications of mobile bubbles oscillating under acoustic excitation (a mobile vortex generator and micro particle carrier).


pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis