title: Wearable soft artificial skin for hand motion detection with embedded microfluidic strain sensing

abstract: This paper describes the design and manufacturing of soft artificial skin with an array of embedded soft strain sensors for detecting various hand gestures by measuring joint motions of five fingers. The proposed skin was made of a hyperelastic elastomer material with embedded microchannels filled with two different liquid conductors, an ionic liquid and a liquid metal. The ionic liquid microchannels were used to detect the mechanical strain changes of the sensing material, and the liquid metal microchannels were used as flexible and stretchable electrical wires for connecting the sensors to an external control circuit. The two heterogeneous liquid conductors were electrically interfaced through flexible conductive threads to prevent the two liquid from being intermixed. The skin device was connected to a computer through a microcontroller instrumentation circuit for reconstructing the 3-D hand motions graphically. The paper also presents preliminary calibration and experimental results.


Application of Genetic Algorithms in Machine learning


title: Robust Topological Inference: Distance To a Measure and Kernel Distance

abstract: Let P be a distribution with support S. The salient features of S can be quantified with persistent homology, which summarizes topological features of the sublevel sets of the distance function (the distance of any point x to S). Given a sample from P we can infer the persistent homology using an empirical version of the distance function. However, the empirical distance function is highly non-robust to noise and outliers. Even one outlier is deadly. The distance-to-a-measure (DTM), introduced by Chazal et al. (2011), and the kernel distance, introduced by Phillips et al. (2014), are smooth functions that provide useful topological information but are robust to noise and outliers. Chazal et al. (2015) derived concentration bounds for DTM. Building on these results, we derive limiting distributions and confidence sets, and we propose a method for choosing tuning parameters.


title: Cannabinoid-induced effects on the nociceptive system: A neurophysiological study in patients with secondary progressive multiple sclerosis

abstract: Abstract Although clinical studies show that cannabinoids improve central pain in patients with multiple sclerosis (MS) neurophysiological studies are lacking to investigate whether they also suppress these patients’ electrophysiological responses to noxious stimulation. The flexion reflex (FR) in humans is a widely used technique for assessing the pain threshold and for studying spinal and supraspinal pain pathways and the neurotransmitter system involved in pain control. In a randomized, double-blind, placebo-controlled, cross-over study we investigated cannabinoid-induced changes in RIII reflex variables (threshold, latency and area) in a group of 18 patients with secondary progressive MS. To investigate whether cannabinoids act indirectly on the nociceptive reflex by modulating lower motoneuron excitability we also evaluated the H-reflex size after tibial nerve stimulation and calculated the H wave/M wave (H/M) ratio. Of the 18 patients recruited and randomized 17 completed the study. After patients used a commercial delta-9-tetrahydrocannabinol (THC) and cannabidiol mixture as an oromucosal spray the RIII reflex threshold increased and RIII reflex area decreased. The visual analogue scale score for pain also decreased, though not significantly. Conversely, the H/M ratio measured before patients received cannabinoids remained unchanged after therapy. In conclusion, the cannabinoid-induced changes in the RIII reflex threshold and area in patients with MS provide objective neurophysiological evidence that cannabinoids modulate the nociceptive system in patients with MS.


title: Efficacy of a single treatment of head lice with a neem seed extract: an in vivo and in vitro study on nits and motile stages

abstract: An anti-louse shampoo (Licener®) based on a neem seed extract was tested in vivo and in vitro on its efficacy to eliminate head louse infestation by a single treatment. The hair of 12 children being selected from a larger group due to their intense infestation with head lice were incubated for 10 min with the neem seed extract-containing shampoo. It was found that after this short exposition period, none of the lice had survived, when being observed for 22 h. In all cases, more than 50–70 dead lice had been combed down from each head after the shampoo had been washed out with normal tap water. A second group of eight children had been treated for 20 min with identical results. Intense combing of the volunteers 7 days after the treatment did not result in the finding of any motile louse neither in the 10-min treated group nor in the group the hair of which had been treated for 20 min. Other living head lice were in vitro incubated within the undiluted product (being placed inside little baskets the floor of which consisted of a fine net of gauze). It was seen that a total submersion for only 3 min prior to washing 3× for 2 min with tap water was sufficient to kill all motile stages (larvae and adults). The incubation of nits at 30°C into the undiluted product for 3, 10, and 20 min did not show differences. In all cases, there was no eyespot development or hatching larvae within 7–10 days of observation. This and the fact that the hair of treated children (even in the short-time treated group of only 10 min) did not reveal freshly hatched larval stages of lice indicate that there is an ovicidal activity of the product, too.


title: A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market

abstract: Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices of selected listed companies of Colombo Stock Exchange (CSE) based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. Feedforward, Simple Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) architectures were employed in building models. Closing, High and Low prices of past two days were selected as input variables for each company. Feedforward networks produce the highest and lowest forecasting errors. The forecasting accuracy of the best feedforward networks is approximately 99%. SRNN and LSTM networks generally produce lower errors compared with feedforward networks but in some occasions, the error is higher than feed forward networks. Compared to other two networks, GRU networks are having comparatively higher forecasting errors.


title: Creativity, Customization, and Ownership: Game Design in Bootstrap: Algebra

abstract: Game programming projects are concrete and motivational for students, especially when used to teach more abstract concepts such as algebra. These projects must have open-ended elements to allow for creativity, but too much freedom makes it hard to reach specific learning outcomes. How many degrees of freedom do students need to make a game feel like one they genuinely designed? What kinds of personalization do they undertake of their games? And how do these factors correlate with their prior game-playing experience or with their identified gender? This paper studies these questions in the concrete setting of the Bootstrap:Algebra curriculum. In this curriculum, students are only given four parameters they can customize and only a few minutes in which to do so. Our study shows that despite this very limited personalization, students still feel a strong sense of ownership, originality, and pride in their creations. We also find that females find videogame creation just as satisfying as males, which contradicts some prior research but may also reflect the nature of games created in this curriculum and the opportunities it offers for self-expression.


title: An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information

abstract: Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.


title: Deep-learning-based face detection using iterative bounding-box regression

abstract: Multi-view face detection in open environments is a challenging task due to the diverse variations of face appearances and occlusion. In the task of face detection, localization accuracy is one of the key factors. However, many of the existing methods do not pay enough attention to localization. Some of the current methods have applied localization techniques, but they have not fully realized its potential and realized more accurate localization. In this paper, we propose a deep cascaded detection method that iteratively exploits bounding-box regression, a localization technique, to approach the detection of potential faces in images. In addition, we consider the inherent correlation of classification and bounding-box regression and exploit it to further increase overall performance. In particular, our method leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict the existence of faces. Extensive experiments demonstrate the efficiency of our algorithm by comparing it with several popular face-detection algorithms on the widely used AFW and FDDB datasets.


title: The dark side of open access in Google and Google Scholar: the case of Latin-American repositories

abstract: Since repositories are a key tool in making scholarly knowledge open access (OA), determining their web presence and visibility on the Web (both are proxies of web impact) is essential, particularly in Google (search engine par excellence) and Google Scholar (a tool increasingly used by researchers to search for academic information). The few studies conducted so far have been limited to very specific geographic areas (USA), which makes it necessary to find out what is happening in other regions that are not part of mainstream academia, and where repositories play a decisive role in the visibility of scholarly production. The main objective of this study is to ascertain the web presence and visibility of Latin American repositories in Google and Google Scholar through the application of page count and web mention indicators respectively. For a sample of 137 repositories, the results indicate that the indexing ratio is low in Google, and virtually nonexistent in Google Scholar; they also indicate a complete lack of correspondence between the repository records and the data produced by these two search tools. These results are mainly attributable to limitations arising from the use of description schemas that are incompatible with Google Scholar (repository design) and the reliability of web mention indicators (search engines). We conclude that neither Google nor Google Scholar accurately represent the actual size of OA content published by Latin American repositories; this may indicate a non-indexed, hidden side to OA, which could be limiting the dissemination and consumption of OA scholarly literature.


title: Peristomal Skin Complications Are Common, Expensive, and Difficult to Manage: A Population Based Cost Modeling Study

abstract: BACKGROUND ::: Peristomal skin complications (PSCs) are the most common post-operative complications following creation of a stoma. Living with a stoma is a challenge, not only for the patient and their carers, but also for society as a whole. Due to methodological problems of PSC assessment, the associated health-economic burden of medium to longterm complications has been poorly described. ::: ::: ::: AIM ::: The aim of the present study was to create a model to estimate treatment costs of PSCs using the standardized assessment Ostomy Skin Tool as a reference. The resultant model was applied to a real-life global data set of stoma patients (n = 3017) to determine the prevalence and financial burden of PSCs. ::: ::: ::: METHODS ::: Eleven experienced stoma care nurses were interviewed to get a global understanding of a treatment algorithm that formed the basis of the cost analysis. The estimated costs were based on a seven week treatment period. PSC costs were estimated for five underlying diagnostic categories and three levels of severity. The estimated treatment costs of severe cases of PSCs were increased 2-5 fold for the different diagnostic categories of PSCs compared with mild cases. French unit costs were applied to the global data set. ::: ::: ::: RESULTS ::: The estimated total average cost for a seven week treatment period (including appliances and accessories) was 263€ for those with PSCs (n = 1742) compared to 215€ for those without PSCs (n = 1172). A co-variance analysis showed that leakage level had a significant impact on PSC cost from 'rarely/never' to 'always/often' p<0.00001 and from 'rarely/never' to 'sometimes' p = 0.0115. ::: ::: ::: CONCLUSION ::: PSCs are common and troublesome and the consequences are substantial, both for the patient and from a health economic viewpoint. PSCs should be diagnosed and treated at an early stage to prevent long term, debilitating and expensive complications.


title: Understanding password choices: how frequently entered passwords are re-used across websites

abstract: From email to online banking, passwords are an essential component of modern internet use. Yet, users do not always have good password security practices, leaving their accounts vulnerable to attack. We conducted a study which combines self-report survey responses with measures of actual online behavior gathered from 134 participants over the course of six weeks. We find that people do tend to re-use each password on 1.7-3.4 different websites, they reuse passwords that are more complex, and mostly they tend to re-use passwords that they have to enter frequently. We also investigated whether self-report measures are accurate indicators of actual behavior, finding that though people understand password security, their self-reported intentions have only a weak correlation with reality. These findings suggest that users manage the challenge of having many passwords by choosing a complex password on a website where they have to enter it frequently in order to memorize that password, and then re-using that strong password across other websites.


title: Toward a Developmental Epistemology of Computer Programming

abstract: This paper was written as a companion to my keynote address at the 11th Workshop in Primary and Secondary Computing Education (WiPSCE 2016). The paper outlines my own research on how novices learn to program. Any reader whose interest has been piqued may pursue furher detail in the papers cited. I begin by explaining my philosophical position. In making that explanation, I do not claim that it is the only right position; on the contrary I allude to other philosophical positions that I regard as complimentary to my own. The academic warfare between these positions is pointless and counterproductive --- all the established positions have something positive to offer. Having established my position, I then go on to argue that the work of Jean Piaget, and subsequent neo-Piagetians, offers useful insight into how children learn to program computers.


title: Applying weighted PageRank to author citation networks

abstract: This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956–2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures. © 2011 Wiley Periodicals, Inc.


title: Clustering algorithms for content-based publication-subscription systems

abstract: We consider efficient communication schemes based on both network-supported and application-level multicast techniques for content-based publication-subscription systems. We show that the communication costs depend heavily on the network configurations, distribution of publications and subscriptions. We devise new algorithms and adapt existing partitional data clustering algorithms. These algorithms can be used to determine multicast groups with as much commonality as possible, based on the totality of subscribers' interests. They perform well in the context of highly heterogeneous subscriptions, and they also scale well. An efficiency of 60% to 80% with respect to the ideal solution can be achieved with a small number of multicast groups (less than 100 in our experiments). Some of these same concepts can be applied to match publications to subscribers in real-time, and also to determine dynamically whether to unicast, multicast or broadcast information about the events over the network to the matched subscribers. We demonstrate the quality of our algorithms via simulation experiments.


title: Maximum Entropy Deep Inverse Reinforcement Learning

abstract: This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem. We show in this context that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations, which makes it especially well-suited for applications in life-long learning scenarios. Our approach achieves performance commensurate to the state-of-the-art on existing benchmarks while exceeding on an alternative benchmark based on highly varying reward structures. Finally, we extend the basic architecture - which is equivalent to a simplified subclass of Fully Convolutional Neural Networks (FCNNs) with width one - to include larger convolutions in order to eliminate dependency on precomputed spatial features and work on raw input representations.


title: Earth-Moon Communication from a Moving Lunar Rover

abstract: Carnegie Mellon University has proposed an educationaland entertainment-based robotic lunar mission which will last two years and cover 1000km on the moon and revisit several historic sites. With the transmission of live panoramic video, participants will be provided the opportunity for interactively exploring the moon through teleoperation and telepresence. The requirement of panoramic video and telepresence demands high data rates on the order of 7.5 Mbps. This is challenging since the power available for communication is approximately 100W and occupied bandwidth is limited to less than 10 MHz. The tough environment on the moon introduces additional challenges of survivability and reliability. A communication system based on a phased array antenna, Nyquist QPSK modulation and a rate 2/3 Turbo code is presented which can satisfy requirements of continuous high data rate communication at low power and bandwidth reliably over a two year mission duration. Three ground stations with 22m parabolic antennas are required around the world to maintain continuous communication. The transmission will then be relayed via satellite to the current control station location. This paper presents an overview of the mission, and communication requirements and design.


title: High accuracy event detection for Non-Intrusive Load Monitoring

abstract: This paper proposes a new event detection algorithm for the use in Non-Intrusive Load Monitoring (NILM). This latter is a field where the main concern is to break down, in a non-intrusive manner, the global electrical energy consumption into individual appliances consumption. Detecting events is thus of importance for appliance clustering in event-based NILM systems. A simple and fast algorithm that detects the variations of the signal's envelope is proposed in this paper. Its main advantage is the high localization accuracy of the start times of events. Its performance is evaluated using simulated and real data and is compared to one of the recently proposed algorithms in the field. Simulations show that the proposed detection algorithm gives 100 % precision and 97.13 % recall at a Signal-to-Noise Ratio (SNR) of 50 dB.


title: THREAT MODELLING FOR SQL SERVERS Designing a Secure Database in a Web Application

abstract: In this paper we present the results from an analysis focusing on security threats that can arise against an SQL server when included in Web application environments. The approach used is based on the STRIDE classification methodology. The results presented provide also some general guidelines and countermeasures against the different attacks that can exploit the identified vulnerabilities.


title: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection

abstract: Recently Viola et al. have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce and empirically analysis two extensions to their approach: Firstly, a novel set of rotated haar-like features is introduced. These novel features significantly enrich the simple features of [6] and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. Secondly, we present a through analysis of different boosting algorithms (namely Discrete, Real and Gentle Adaboost) and weak classifiers on the detection performance and computational complexity. We will see that Gentle Adaboost with small CART trees as base classifiers outperform Discrete Adaboost and stumps. The complete object detection training and detection system as well as a trained face detector are available in the Open Computer Vision Library at sourceforge.net [8].


title: CNN-based single image obstacle avoidance on a quadrotor

abstract: This paper demonstrates the use of a single forward facing camera for obstacle avoidance on a quadrotor. We train a CNN for estimating depth from a single image. The depth map is then fed to a behaviour arbitration based control algorithm that steers the quadrotor away from obstacles. We conduct experiments with simulated and real drones in a variety of environments.


title: Automatic Vectorization of Interleaved Data Revisited

abstract: Automatically exploiting short vector instructions sets (SSE, AVX, NEON) is a critically important task for optimizing compilers. Vector instructions typically work best on data that is contiguous in memory, and operating on non-contiguous data requires additional work to gather and scatter the data. There are several varieties of non-contiguous access, including interleaved data access. An existing approach used by GCC generates extremely efficient code for loops with power-of-2 interleaving factors (strides). In this paper we propose a generalization of this approach that produces similar code for any compile-time constant interleaving factor. In addition, we propose several novel program transformations, which were made possible by our generalized representation of the problem. Experiments show that our approach achieves significant speedups for both power-of-2 and non--power-of-2 interleaving factors. Our vectorization approach results in mean speedups over scalar code of 1.77x on Intel SSE and 2.53x on Intel AVX2 in real-world benchmarking on a selection of BLAS Level 1 routines. On the same benchmark programs, GCC 5.0 achieves mean improvements of 1.43x on Intel SSE and 1.30x on Intel AVX2. In synthetic benchmarking on Intel SSE, our maximum improvement on data movement is over 4x for gathering operations and over 6x for scattering operations versus scalar code.


title: The role of social media in higher education classes (real and virtual) - A literature review

abstract: Abstract The ubiquity of social media (e.g., Facebook, Twitter) is no more apparent than at the university. Social media are increasingly visible in higher education settings as instructors look to technology to mediate and enhance their instruction as well as promote active learning for students. Many scholars argue for the purposeful integration of social media as an educational tool. Empirical evidence, however, has lagged in supporting the claim. Most of the existing research on the utility and effectiveness of social media in the higher education class is limited to self-reported data (e.g., surveys, questionnaires) and content analyses. This paper summarizes the scholarly writings as well as reviews the findings of empirical investigations. Some limitations are discussed, and future areas of research are proposed.


title: Integration of the CORE and EMANE Network Emulators

abstract: Researchers who evaluate networking protocols and applications can benefit significantly from the availability of flexible, open source tools. Network emulation has been one of the tools of choice for conducting experiments on commodity hardware, and for wireless MANET environments, in the absence of a possibly large number of radio devices. We present an easy-to-use system based on a combination of two such open source emulation tools. We describe integrating the Common Open Research Emulator (CORE), providing virtualization controlled by a graphical user interface, with the Extendable Mobile Ad-hoc Network Emulator (EMANE) framework that provides for more detailed radio models and scenarios. We discuss the benefits of integrating the two tools, along with challenges of this approach and how they were addressed. We address the performance experienced when using these more detailed link- and physical-layer models. Finally, we conclude with open issues and future directions.


GermaNER: Free Open German Named Entity Recognition Tool


title: Amplitude-oriented exercise in Parkinson’s disease: a randomized study comparing LSVT-BIG and a short training protocol

abstract: LSVT-BIG is an exercise for patients with Parkinson's disease (PD) comprising of 16 1-h sessions within 4 weeks. LSVT-BIG was compared with a 2-week short protocol (AOT-SP) consisting of 10 sessions with identical exercises in 42 patients with PD. UPDRS-III-score was reduced by -6.6 in LSVT-BIG and -5.7 in AOT-SP at follow-up after 16 weeks (p < 0.001). Measures of motor performance were equally improved by LSVT-BIG and AOT-SP but high-intensity LSVT-BIG was more effective to obtain patient-perceived benefit.


title: Arc-flash in large battery energy storage systems — Hazard calculation and mitigation

abstract: This paper deals with the arc-flash haz ard calculation in battery energy storage systems (BESSs). The lack of international harmonized standards, coupled with a foreseeable increasing use of BESSs, makes this subject very interesting, especially due to the practical involvements related to arc-flash hazard associated with BESS maintenance operation. A Li-ion battery circuit model, based on experimental short-circuit tests conducted by TERNA (the Italian transmission system operator) and a non-linear arc resistance model are used to assess the incident energy related to arc-flash. Efficient and cost-effective solutions, able to reduce incident energy below the safe value 8 cal/cm 2 and based on the installation of fast-acting fuses, are then proposed and discussed.


title: Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation

abstract: This paper presents a calibration procedure for a fish-eye lens (a high-distortion lens) mounted on a CCD TV camera. The method is designed to account for the differences in images acquired via a distortion-free lens camera setup and the images obtained by a fish-eye lens camera. The calibration procedure essentially defines a mapping between points in the world coordinate system and their corresponding point locations in the image plane. This step is important for applications in computer vision which involve quantitative measurements. The objective of this mapping is to estimate the internal parameters of the camera, including the effective focal length, one-pixel width on the image plane, image distortion center, and distortion coefficients. The number of parameters to be calibrated is reduced by using a calibration pattern with equally spaced dots and assuming a pin-hole model camera behavior for the image center, thus assuming negligible distortion at the image distortion center. Our method employs a non-linear transformation between points in the world coordinate system and their corresponding location on the image plane. A Lagrangian minimization method is used to determine the coefficients of the transformation. The validity and effectiveness of our calibration and distortion correction procedure are confirmed by application of this procedure on real images.


title: Balanced echo state networks

abstract: This paper investigates the interaction between the driving output feedback and the internal reservoir dynamics in echo state networks (ESNs). The interplay is studied experimentally on the multiple superimposed oscillators (MSOs) benchmark. The experimental data reveals a dual effect of the output feedback strength on the network dynamics: it drives the dynamic reservoir but it can also block suitable reservoir dynamics. Moreover, the data shows that the reservoir size crucially co-determines the likelihood of generating an effective ESN. We show that dependent on the complexity of the MSO dynamics somewhat smaller networks can yield better performance. Optimizing the output feedback weight range and the network size is thus crucial for generating an effective ESN. With proper parameter choices, we show that it is possible to generate ESNs that approximate MSOs with several orders of magnitude smaller errors than those previously reported. We conclude that there appears to be still much more potential in ESNs than previously thought and sketch-out some promising future research directions.


title: Helix: Accelerating Human-in-the-loop Machine Learning

abstract: Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose Helix, a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. Helix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized -- trading off the cost of materialization for potential future benefits -- to speed up future iterations. Additionally, Helix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of Helix programming interface and the speed and ease of iterative development using Helix. In our evaluations, Helix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.


title: Structure-Immunogenicity Relationships of Therapeutic Proteins

abstract: As more recombinant human proteins become available on the market, the incidence of immunogenicity problems is rising. The antibodies formed against a therapeutic protein can result in serious clinical effects, such as loss of efficacy and neutralization of the endogenous protein with essential biological functions. Here we review the literature on the relations between the immunogenicity of the therapeutic proteins and their structural properties. The mechanisms by which protein therapeutics can induce antibodies as well as the models used to study immunogenicity are discussed. Examples of how the chemical structure (including amino acid sequence, glycosylation, and pegylation) can influence the incidence and level of antibody formation are given. Moreover, it is shown that physical degradation (especially aggregation) of the proteins as well as chemical decomposition (e.g., oxidation) may enhance the immune response. To what extent the presence of degradation products in protein formulations influences their immunogenicity still needs further investigation. Immunization of transgenic animals, tolerant for the human protein, with well-defined, artificially prepared degradation products of therapeutic proteins may shed more light on the structure-immunogenicity relationships of recombinant human proteins.


title: xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

abstract: A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.


title: Solution of the multiuser downlink beamforming problem with individual SINR constraints

abstract: We address the problem of joint downlink beamforming in a power-controlled network, where independent data streams are to be transmitted from a multiantenna base station to several decentralized single-antenna terminals. The total transmit power is limited and channel information (possibly statistical) is available at the transmitter. The design goal: jointly adjust the beamformers and transmission powers according to individual SINR requirements. In this context, there are two closely related optimization problems. P1: maximize the jointly achievable SINR margin under a total power constraint. P2: minimize the total transmission power while satisfying a set of SINR constraints. In this paper, both problems are solved within a unified analytical framework. Problem P1 is solved by minimizing the maximal eigenvalue of an extended crosstalk matrix. The solution provides a necessary and sufficient condition for the feasibility of the SINR requirements. Problem P2 is a variation of problem P1. An important step in our analysis is to show that the global optimum of the downlink beamforming problem is equivalently obtained from solving a dual uplink problem, which has an easier-to-handle analytical structure. Then, we make use of the special structure of the extended crosstalk matrix to develop a rapidly converging iterative algorithm. The optimality and global convergence of the algorithm is proven and stopping criteria are given.


title: ASTERIX: An Open Source System for "Big Data" Management and Analysis

abstract: At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.


title: FPGA Implementation of a Single-Precision Floating-Point Multiply-Accumulator with Single-Cycle Accumulation

abstract: This paper describes an FPGA implementation of a single-precision floating-point multiply-accumulator (FPMAC) that supports single-cycle accumulation while maintaining high clock frequencies. A non-traditional internal representation reduces the cost of mantissa alignment within the accumulator. The FPMAC is evaluated on an Altera Stratix III FPGA.


title: Clustering of Client-Sites in Three-Tier Database Architectures ∗

abstract: Conventional two-tier databases have shown performance limitations in the presence of many concurrent clients. We propose logical grouping of clients (or clustering) as the means to improve the performance of collaborative networked databases. In this paper, we discuss a three-tier client-server database architecture (3t-CSD) featuring the above partitioning. The proposed clustering is based on the similarity of clients' access patterns. Each cluster is supervised by a designated manager that coordinates data sharing among its members. A number of clients is optimally partitioned if sites in each individual cluster have the maximum common data access probability possible. We initially show that the optimal client clustering problem is NP-complete and then we develop two approximate solutions based on abstraction and filtering of statistics for client access patterns. Our main goal is to compare the performance of the conventional and three-tier client-server database architecture with respect to the transaction turnaround times and object response times. After developing system prototypes that implement both two-tier and 3t-CSDs, we experimentally show that as long as good client-clustering is possible, the 3t-CSD architecture yields sizable gains over its conventional counterpart. We also compare and evaluate the effectiveness of the two proposed techniques used to create client clusters. Finally, we examine the role of several preprocessing schemes used to reduce the volume of the input data supplied to the clustering techniques.


title: Bandwidth-Enhanced Low-Profile Cavity-Backed Slot Antenna by Using Hybrid SIW Cavity Modes

abstract: A bandwidth enhanced method of a low-profile substrate integrated waveguide (SIW) cavity-backed slot antenna is presented in this paper. Bandwidth enhancement is achieved by simultaneously exciting two hybrid modes in the SIW-backed cavity and merging them within the required frequency range. These two hybrid modes, whose dominant fields are located in different half parts of the SIW cavity, are two different combinations of the and resonances. This design method has been validated by experiments. Compared with those of a previously presented SIW cavity-backed slot antenna, fractional impedance bandwidth of the proposed antenna is enhanced from 1.4% to 6.3%, its gain and radiation efficiency are also slightly improved to 6.0 dBi and 90%, and its SIW cavity size is reduced about 30%. The proposed antenna exhibits low cross polarization level and high front to back ratio. It still retains advantages of low-profile, low fabrication cost, and easy integration with planar circuits.


title: Individual and Class Norms Differentially Predict Proactive and Reactive Aggression: A Functional Analysis

abstract: Theory and research using a social-information processing framework indicate that reward-focused (proactive) aggression has different social consequences than defense-focused (reactive) aggression. Students use norms that identify expected and socially approved behaviors as guides to their own actions. Differences in social-cognitive processing characteristics and social status linked to each type of aggression may increase the relevance of some normative sources relative to others. This study fills a gap in the literature by examining the contributions of personal beliefs, classroom beliefs, and classroom rates of aggression to future proactive and reactive aggression. During fall and spring, we observed students’ aggression on school playgrounds using a random subsample (n = 254) of consented students from 35 classrooms (Grades 3–6). We calculated classroom rates of proactive and reactive aggression from fall observations. Classroom means for beliefs endorsing retaliation were calculated from surveys of 536 students. Results of multilevel analyses revealed, as hypothesized, that personal beliefs predicted high rates of students’ proactive aggression, but not reactive aggression. Classroom beliefs predicted high rates of students’ reactive but not proactive aggression. Students in classrooms with high rates of fall proactive aggression showed low spring rates of both types of aggression. In contrast, students in classrooms with high rates of fall reactive aggression displayed high spring rates of proactive and reactive aggression. The latter pattern may represent classrooms in which students continue to struggle against status inequities. The discussion examines how inequities may impact intervention efforts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: RP-HPLC determination of lipophilicity in series of quinoline derivatives

abstract: In the present paper we describe results on the synthesis and lipophilicity determination of a series of biologically active compounds based on their heterocyclic structure. For synthesis of styrylquinoline-based compounds we applied microwave irradiation and solid phase techniques. The correlation between RP-HPLC retention parameter log k (the logarithm of retention factor k) and log P data calculated in various ways is discussed, as well as, the relationships between the lipophilicity and the chemical structure of the studied compounds.


title: The next generation operational data historian for IoT based on informix

abstract: In the era of the Internet of Things (IoT), increasing numbers of applications face the challenge of using current data management systems to manage the massive volume of operational data gener-ated by sensors and devices. Databases based on time series data model, like PI Server, are developed to handle such data with operational technology (OT) characteristics (high volume, long lifecycle, and simple format). However, while achieving excellent write performance, these database systems provide limited query capabilities. In this paper, we present the next-generation Opera-tional Data Historian (ODH) system that is based on the IBM© Informix© system architecture. The system combines the write advantages of the existing time series databases and the ability to run complex queries in SQL. We demonstrate the high efficiency of our system for both writing and querying data with a variety of case studies in the industries of Energy and Utilities and con-nected vehicles. In addition, we present the first benchmark, IoT-X, to evaluate technologies on operational data management for IoT.


title: Reducing web latency: the virtue of gentle aggression

abstract: To serve users quickly, Web service providers build infrastructure closer to clients and use multi-stage transport connections. Although these changes reduce client-perceived round-trip times, TCP's current mechanisms fundamentally limit latency improvements. We performed a measurement study of a large Web service provider and found that, while connections with no loss complete close to the ideal latency of one round-trip time, TCP's timeout-driven recovery causes transfers with loss to take five times longer on average. In this paper, we present the design of novel loss recovery mechanisms for TCP that judiciously use redundant transmissions to minimize timeout-driven recovery. Proactive, Reactive, and Corrective are three qualitatively-different, easily-deployable mechanisms that (1) proactively recover from losses, (2) recover from them as quickly as possible, and (3) reconstruct packets to mask loss. Crucially, the mechanisms are compatible both with middleboxes and with TCP's existing congestion control and loss recovery. Our large-scale experiments on Google's production network that serves billions of flows demonstrate a 23% decrease in the mean and 47% in 99th percentile latency over today's TCP.


title: special article The Spread of Obesity in a Large Social Network over 32 Years

abstract: BACKGROUND ::: The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. ::: ::: ::: METHODS ::: We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. ::: ::: ::: RESULTS ::: Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. ::: ::: ::: CONCLUSIONS ::: Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.


title: Efficient inference in occlusion-aware generative models of images

abstract: We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stochastic gradient variational bayes (SGVB), which optimizes our fully differentiable lower-bound on the log-likelihood, is sufficient to learn an interpretable representation of images. Finally we present experiments demonstrating the effectiveness of the model for inferring foreground and background objects in images.


Local Sparsity Based Online Dictionary Learning for Environment-Adaptive Speech Enhancement with Nonnegative Matrix Factorization.


title: A cancer cell-line titration series for evaluating somatic classification

abstract: BackgroundAccurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies.ResultsCell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease.ConclusionsOur cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium’s Data Access Compliance Office (ICGC DACO).


title: AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes

abstract: We present \textit{AutoExtend}, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The synset/lexeme embeddings obtained live in the same vector space as the word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet as a lexical resource, but AutoExtend can be easily applied to other resources like Freebase. AutoExtend achieves state-of-the-art performance on word similarity and word sense disambiguation tasks.


title: The Internet of Flying-Things: Opportunities and Challenges with Airborne Fog Computing and Mobile Cloud in the Clouds

abstract: This paper focuses on services and applications provided to mobile users using airborne computing infrastructure. We present concepts such as drones-as-a-service and fly-in,fly-out infrastructure, and note data management and system design issues that arise in these scenarios. Issues of Big Data arising from such applications, optimising the configuration of airborne and ground infrastructure to provide the best QoS and QoE, situation-awareness, scalability, reliability, scheduling for efficiency, interaction with users and drones using physical annotations are outlined.


title: Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning

abstract: Social media has led to the democratisation of opinion sharing. A wealth of information about public opinions, current events, and authors' insights into specific topics can be gained by understanding the text written by users. However, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. Crowdsourcing presents an opportunity to interpret the sentiment, or topic, of free-text. However, the subjectivity and bias of human interpreters raise challenges in inferring the semantics expressed by the text. To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. Our model encodes the relationships between labels and text features in documents, such as tweets, web articles, and blog posts, accounting for the varying reliability of human labellers. It allows inference of annotations that scales to arbitrarily large pools of documents. Our evaluation using two challenging crowdsourcing datasets shows that by efficiently exploiting language models learnt from aggregated crowdsourced labels, we can provide up to 25% improved classifications when only a small portion, less than 4% of documents has been labelled. Compared to the six state-of-the-art methods, we reduce by up to 67% the number of crowd responses required to achieve comparable accuracy. Our method was a joint winner of the CrowdFlower - CrowdScale 2013 Shared Task challenge at the conference on Human Computation and Crowdsourcing (HCOMP 2013).


title: Using SVM based method for equipment fault detection in a thermal power plant

abstract: Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance.


title: Wearable Photoplethysmographic Sensors—Past and Present

abstract: Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.


Sase+: An agile language for kleene closure over event streams


title: Reduction of skin stretch induced motion artifacts in electrocardiogram monitoring using adaptive filtering.

abstract: The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts which can cause misdiagnoses, lead to inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The results demonstrate that this device and method can significantly reduce skin strain induced ECG artifacts.


title: A neural network based predictive mechanism for available bandwidth

abstract: Most recent developments of computer sciences, such as Web services, grid, peer-to-peer, and mobile computing, are network-based computing. Their applicability depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. This lack of prediction limits the applicability of network-based computing, especially for grid computing where concurrent remote processing is essential. In this study, we propose an artificial neural network (ANN) based approach for network performance prediction. The ANN mechanism has been tested on classical trace files and compared with the well-known system NWS (network weather service) for performance. Experimental results show the ANN approach always provides an improved prediction over that of NWS. ANN has a real potential in network computing.


title: PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation

abstract: This paper presents a novel method for visual-inertial odometry. The method is based on an information fusion framework employing low-cost IMU sensors and the monocular camera in a standard smartphone. We formulate a sequential inference scheme, where the IMU drives the dynamical model and the camera frames are used in coupling trailing sequences of augmented poses. The novelty in the model is in taking into account all the cross-terms in the updates, thus propagating the inter-connected uncertainties throughout the model. Stronger coupling between the inertial and visual data sources leads to robustness against occlusion and feature-poor environments. We demonstrate results on data collected with an iPhone and provide comparisons against the Tango device and using the EuRoC data set.


title: Mobile Augmented Reality for Cultural Heritage

abstract: This paper introduces an approach of using mobile Augmented Reality (mobile-AR) in cultural organisations, such as museums and archaeological sites, for information provision and enhancing the visiting experience. We demonstrate our approach by presenting a mobile-AR educational game for iPhones that has been developed for the archaeological site and the exhibition area at Sutton Hoo. This pilot aids visitors’ understanding of the site and its history via an engaging and playful game that connects the site with the British Museum where the objects that have been excavated from the site are exhibited. The paper discusses stakeholders’ requirements, the system architecture and concludes with lessons learned and future work.


MODELING OF DIRECTIONAL OVERCURRENT RELAY IN DIGSILENT


title: Author manuscript, published in "" Portable and Efficient Auto-vectorized Bytecode: a Look at the Interaction between Static and JIT Compilers

abstract: Heterogeneity is a confirmed trend of computing systems. Bytecode formats and just-in-time compilers have been proposed to deal with the diversity of the platforms. By hiding architectural details and giving software developers a unified view of the machine, they help improve portability and manage the complexity of large software. Independently, careful exploitation of SIMD instructions has become crucial for the performance of many applications. However, auto-vectorizing compilers need detailed information about the architectural features of the processor to generate efficient code. We propose to reconcile the use of architecture neutral bytecode formats with the need to generate highly efficient vectorized native code. We make three contributions. 1) We show that vectorized bytecode is a viable approach that can deliver portable performance in the presence of SIMD extensions, while incurring only minor penalty when SIMD is not supported. In other words, the information that a loop can be vectorized is the vectorized loop itself. 2) We analyze the interaction between the static and just-in-time compilers and we derive conditions to deliver performance. 3) We add vectorization capabilities to the CLI port of the GCC compiler.


title: A Quantitative Stock Prediction System based on Financial News

abstract: We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText). The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText's predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18% and a Simulated Trading return of 8.50% (compared to 5.62% for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system's trading return of 8.50% outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund.


title: A Non-Monotonic Goal Specification Language for Planning with Preferences

abstract: This paper introduces a default logic based approach to defining goal specification languages that can be non-monotonic and allow for the specification of inconsistencies and priorities among goals. The paper starts by presenting a basic goal specification language for planning with preferences. It then defines goal default theories resp. with priorities by embedding goal formulae into default logic resp. prioritizing default logic. It is possible to show that the new language is general, as it can express several features of previously developed goal specification languages. The paper discusses how several other features can be subsumed by extending the basic goal specification language. Finally, we identify features that might be important in goal specification that cannot be expressed by our language.


title: Fast AdaBoost training using weighted novelty selection

abstract: In this paper, a new AdaBoost learning framework, called WNS-AdaBoost, is proposed for training discriminative models. The proposed approach significantly speeds up the learning process of adaptive boosting (AdaBoost) by reducing the number of data points. For this purpose, we introduce the weighted novelty selection (WNS) sampling strategy and combine it with AdaBoost to obtain an efficient and fast learning algorithm. WNS selects a representative subset of data thereby reducing the number of data points onto which AdaBoost is applied. In addition, WNS associates a weight with each selected data point such that the weighted subset approximates the distribution of all the training data. This ensures that AdaBoost can trained efficiently and with minimal loss of accuracy. The performance of WNS-AdaBoost is first demonstrated in a classification task. Then, WNS is employed in a probabilistic boosting-tree (PBT) structure for image segmentation. Results in these two applications show that the training time using WNS-AdaBoost is greatly reduced at the cost of only a few percent in accuracy.


title: A High-Efficiency DC–DC Converter Using 2 nH Integrated Inductors

abstract: Historically, buck converters have relied on high-Q inductors on the order of 1 to 100 μH to achieve a high efficiency. Unfortunately, on-chip inductors are physically large and have poor series resistances, which result in low-efficiency converters. To mitigate this problem, on-chip magnetic coupling is exploited in the proposed stacked interleaved topology to enable the use of small (2 nH) on-chip inductors in a high-efficiency buck converter. The dramatic decrease in the inductance value is made possible by the unique bridge timing of the stacked design that causes magnetic coupling to boost the converter's efficiency by reducing the current ripple in each inductor. The magnetic coupling is realized by stacking the two inductors on top of one another, which not only lowers the required inductance, but also reduces the chip area consumed by the two inductors. The measured conversion efficiency for the prototype circuit, implemented in a 130-nm CMOS technology, shows more than a 15% efficiency improvement over a linear converter for low output voltages rising to a peak efficiency of 77.9% for a 0.9 V output. These efficiencies are comparable to converters implemented with higher Q inductors, validating that the proposed techniques enable high-efficiency converters to be realized with small on-chip inductors.


Seq2seq for Morphological Reinflection: When Deep Learning Fails


title: VIEWPOINT Computer Networks As Social Networks

abstract: Computer networks are inherently social networks, linking people, organizations, and knowledge. They are social institutions that should not be studied in isolation but as integrated into everyday lives. The proliferation of computer networks has facilitated a deemphasis on group solidarities at work and in the community and afforded a turn to networked societies that are loosely bounded and sparsely knit. The Internet increases people's social capital, increasing contact with friends and relatives who live nearby and far away. New tools must be developed to help people navigate and find knowledge in complex, fragmented, networked societies.


title: Graph cube: on warehousing and OLAP multidimensional networks

abstract: We consider extending decision support facilities toward large sophisticated networks, upon which multidimensional attributes are associated with network entities, thereby forming the so-called multidimensional networks. Data warehouses and OLAP (Online Analytical Processing) technology have proven to be effective tools for decision support on relational data. However, they are not well-equipped to handle the new yet important multidimensional networks. In this paper, we introduce Graph Cube, a new data warehousing model that supports OLAP queries effectively on large multidimensional networks. By taking account of both attribute aggregation and structure summarization of the networks, Graph Cube goes beyond the traditional data cube model involved solely with numeric value based group-by's, thus resulting in a more insightful and structure-enriched aggregate network within every possible multidimensional space. Besides traditional cuboid queries, a new class of OLAP queries, crossboid, is introduced that is uniquely useful in multidimensional networks and has not been studied before. We implement Graph Cube by combining special characteristics of multidimensional networks with the existing well-studied data cube techniques. We perform extensive experimental studies on a series of real world data sets and Graph Cube is shown to be a powerful and efficient tool for decision support on large multidimensional networks.


title: A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform

abstract: With the advances in pervasive sensor technologies, physiological signals can be captured continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic seizure onset on collected multi-channel electroencephalogram (EEG) has attracted lots of attention recently. Deep learning is a promising method to analyze large-scale unlabeled data. In this paper, we propose a multi-view deep learning model to capture brain abnormality from multi-channel epileptic EEG signals for seizure detection. Specifically, we first generate EEG spectrograms using short-time Fourier transform (STFT) to represent the time-frequency information after signal segmentation. Second, we adopt stacked sparse denoising autoencoders (SSDA) to unsupervisedly learn multiple features by considering both intra and inter correlation of EEG channels, denoted as intra-channel and cross-channel features, respectively. Third, we add an SSDA-based channel selection procedure using proposed response rate to reduce the dimension of intra-channel feature. Finally, we concatenate the learned multi-features and apply a fully-connected SSDA model with softmax classifier to jointly learn the cross-patient seizure detector in a supervised fashion. To evaluate the performance of the proposed model, we carry out experiments on a real world benchmark EEG dataset and compare it with six baselines. Extensive experimental results demonstrate that the proposed learning model is able to extract latent features with meaningful interpretation, and hence is effective in detecting epileptic seizure.


title: Level shifter design for voltage stacking

abstract: As chips increase in complexity with ever increasing power consumption, pressure in efficient power delivery mechanism such as multi-VDD, voltage stacked and DVS continues to rise. The main objective is to reduce the overall current delivered to the chip. For instance, in voltage stacking, if the circuit is stacked in 2 levels and supply voltage is doubled, the current drawn will be reduced by half. Hence, the same amount of power is delivered, but with half the current. With the prevalence of systems using those techniques, level shifters will have to be optimally designed to perform fast with low power. As the number of level shifters grows, area consumption becomes another design factor. This study explores different types of existing level shifters for voltage stacking application, their optimal sizing and energy, delay and area trade-offs. It includes effect the of PVT variation as another design factor and its impact on delay and energy consumption. We will also propose modifications to the best energy-delay level shifter to reduce its area overhead.


title: Schema and ontology matching with COMA++

abstract: We demonstrate the schema and ontology matching tool COMA++. It extends our previous prototype COMA utilizing a composite approach to combine different match algorithms [3]. COMA++ implements significant improvements and offers a comprehensive infrastructure to solve large real-world match problems. It comes with a graphical interface enabling a variety of user interactions. Using a generic data representation, COMA++ uniformly supports schemas and ontologies, e.g. the powerful standard languages W3C XML Schema and OWL. COMA++ includes new approaches for ontology matching, in particular the utilization of shared taxonomies. Furthermore, different match strategies can be applied including various forms of reusing previously determined match results and a so-called fragment-based match approach which decomposes a large match problem into smaller problems. Finally, COMA++ cannot only be used to solve match problems but also to comparatively evaluate the effectiveness of different match algorithms and strategies.


title: High-precision automated reconstruction of neurons with flood-filling networks

abstract: Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.


Partition-Based Trapdoor Ciphers


6.7 MMAV- A MINIATURE UNMANNED AERIAL VEHICLE (MINI-UAV) FOR METEOROLOGICAL PURPOSES


title: Deep Reflectance Maps

abstract: Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constrained nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image editing tasks on real images.


title: Distributed GraphLab: A Framework for Machine Learning in the Cloud

abstract: While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.


title: Social Disconnectedness, Perceived Isolation, and Health among Older Adults

abstract: Previous research has identified a wide range of indicators of social isolation that pose health risks, including living alone, having a small social network, infrequent participation in social activities, and feelings of loneliness. However, multiple forms of isolation are rarely studied together, making it difficult to determine which aspects of isolation are most deleterious for health. Using population-based data from the National Social Life, Health, and Aging Project, we combine multiple indicators of social isolation into scales assessing social disconnectedness (e.g., small social network, infrequent participation in social activities) and perceived isolation (e.g., loneliness, perceived lack of social support). We examine the extent to which social disconnectedness and perceived isolation have distinct associations with physical and mental health among older adults. Results indicate that social disconnectedness and perceived isolation are independently associated with lower levels of self-rated physical health. However, the association between disconnectedness and mental health may operate through the strong relationship between perceived isolation and mental health. We conclude that health researchers need to consider social disconnectedness and perceived isolation simultaneously.


title: Estimation of respiratory waveform using an accelerometer

abstract: The cardiorespiratory signal is a fundamental vital sign to assess a person's health. Additionally, the cardio-respiratory signal gives a great deal of information to healthcare providers wishing to monitor healthy individuals. This paper proposes a method to detect the respiratory waveform from an accelerometer strapped onto the chest. A system was designed and several experiments were conducted on volunteers. The acquisition is performed in different status: normal, apnea, deep breathing and also in different postures: vertical (sitting, standing) or horizontal (lying down). This method could therefore be suitable for automatic identification of some respiratory malfunction, for example during the obstructive apnea.


title: Reduced complexity iterative decoding of low-density parity check codes based on belief propagation

abstract: Two simplified versions of the belief propagation algorithm for fast iterative decoding of low-density parity check codes on the additive white Gaussian noise channel are proposed. Both versions are implemented with real additions only, which greatly simplifies the decoding complexity of belief propagation in which products of probabilities have to be computed. Also, these two algorithms do not require any knowledge about the channel characteristics. Both algorithms yield a good performance-complexity trade-off and can be efficiently implemented in software as well as in hardware, with possibly quantized received values.


title: Capabilities of a three layer feedforward neural network

abstract: Mapping capabilities of a three-layer feedforward neural network with a finite number of hidden units which have sigmoid functions as their nonlinearities are discussed. It is proved that sigmoid functions of a hidden layer of the network can raise the dimension of the input space up to the number of the hidden units. From this result, it is concluded that a three-layer feedforward neural network with N hidden units can assign arbitrary analog values to N arbitrary input vectors. >


title: Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature

abstract: The role of information systems in the creation and appropriation of economic value has a long tradition of research, within which falls the literature on the sustainability of IT-dependent competitive advantage. In this article, we formally define the notion of IT-dependent strategic initiative and use it to frame a review of the literature on the sustain- ability of competitive advantage rooted in information systems use. We offer a framework that articulates both the dynamic approach to IT-dependent strategic advantage currently receiving attention in the literature and the underlying drivers of sustainability. This framework models how and why the characteristics of the IT-dependent strategic initiative enable sustained competitive advantage, and how the determinants of sustainability are developed and strengthened over time. Such explanation facilitates the pre-implementation analysis of planned initiatives by innovators, as well as the post-implementation evaluation of existing initiatives so as to identify the basis of their sustainability. ::: ::: In carrying out this study, we examined the interdisciplinary literature on strategic information systems. Using a structured methodology, we reviewed the titles and abstracts of 648 articles drawn from information systems, strategic management, and marketing literature. We then examined and individually coded a relevant subset of 117 articles. The literature has identified four barriers to erosion of competitive advantage for IT-dependent strategic initiatives and has surfaced the structural determinants of their magnitude. Previous work has also begun to theorize about the process by which these barriers to erosion evolve over time. Our review reveals that significant exploratory research and theoretical development have occurred in this area, but there is a paucity of research providing rigorous tests of theoretical propositions. ::: ::: Our work makes three principal contributions. First, it formalizes the definition of IT-dependent strategic initiative. Second, it organizes the extant interdisciplinary research around an integrative framework that should prove useful to both research and practice. This framework offers an explanation of how and why IT-dependent strategic initiatives contribute to sustained competitive advantage, and explains the process by which they evolve over time. Finally, our review and analysis of the literature offers the basis for future research directions.


title: Automatic construction of inlining heuristics using machine learning

abstract: Method inlining is considered to be one of the most important optimizations in a compiler. However, a poor inlining heuristic can lead to significant degradation of a program's running time. Therefore, it is important that an inliner has an effective heuristic that controls whether a method is inlined or not. An important component of any inlining heuristic are the features that characterize the inlining decision. These features often correspond to the caller method and the callee methods. However, it is not always apparent what the most important features are for this problem or the relative importance of these features. Compiler writers developing inlining heuristics may exclude critical information that can be obtained during each inlining decision. In this paper, we use a machine learning technique, namely neuro-evolution [18], to automatically induce effective inlining heuristics from a set of features deemed to be useful for inlining. Our learning technique is able to induce novel heuristics that significantly out-perform manually-constructed inlining heuristics. We evaluate the heuristic constructed by our neuro-evolutionary technique within the highly tuned Java HotSpot server compiler and the Maxine VM C1X compiler, and we are able to obtain speedups of up to 89% and 114%, respectively. In addition, we obtain an average speedup of almost 9% and 11% for the Java HotSpot VM and Maxine VM, respectively. However, the output of neuro-evolution, a neural network, is not human readable. We show how to construct more concise and read-able heuristics in the form of decision trees that perform as well as our neuro-evolutionary approach.


title: Model-driven risk analysis of evolving critical infrastructures

abstract: The protection and security of critical infrastructures are important parts of Homeland Defense. Adequate means for analyzing the security risks of such infrastructures is a prerequisite for properly understanding the security needs and for maintaining appropriate incident preparedness. Risk management is coordinated activities to direct and control an organization with regard to risk, and includes the identification, analysis and mitigation of unacceptable risks. For critical infrastructures consisting of interdependent systems, risk analysis and mitigation is challenging because the overall risk picture may be strongly affected by changes in only a few of the systems. In order to continuously manage risks and maintain an adequate level of protection, there is a need to continuously maintain the validity of risk models while systems change and evolve. This paper addresses these challenges by presenting an approach to model-driven security risk analysis of changing and evolving systems. The approach is a tool-supported method with techniques and modeling support for traceability of system changes to risk models, as well as the explicit modeling of the impact of changes on the current risk picture. The presented artifacts are exemplified and validated in the domain of air traffic management.


An Efficient Face Detection Method Using Adaboost and Facial Parts


title: A 32 nm Embedded, Fully-Digital, Phase-Locked Low Dropout Regulator for Fine Grained Power Management in Digital Circuits

abstract: The need for fine-grained power management in digital ICs has led to the design and implementation of compact, scalable low-drop out regulators (LDOs) embedded deep within logic blocks. While analog LDOs have traditionally been used in digital ICs, the need for digitally implementable LDOs embedded in digital functional units for ultrafine grained power management is paramount. This paper presents a fully-digital, phase locked LDO implemented in 32 nm CMOS. The control model of the proposed design has been provided and limits of stability have been shown. Measurement results with a resistive load as well as a digital load exhibit peak current efficiency of 98%.


title: Computer Proficiency Questionnaire: Assessing Low and High Computer Proficient Seniors

abstract: Computers and the Internet have the potential to help seniors (65+) maintain independence and enrich their daily lives. These technologies can connect them with distant family members, provide information about community and national resources, allow them to shop and conduct banking transactions from home, and support prospective memory as cognitive abilities decline (Charness & Boot, 2009). Technological solutions to prolong independence are of growing relevance as many nations face population aging, a trend in which the proportion of the population composed of older adults (65+) is rising dramatically (Lutz, Sanderson, & Scherbov, 2008). However, despite the potential benefits associated with computer and Internet use, seniors often lag behind in the adoption of technology (Czaja et al., 2006b). For example, recent data from the Pew Internet and American Life Project finds that 47% of individuals surveyed more than the age of 64 still did not use the Internet (compared with 3% of 18–29 year olds; Zickuhr & Madden, 2012), with this lack of uptake being more pronounced among those who are of lower socioeconomic status, less educated, or disabled. ::: ::: Attitudinal and cognitive factors have been identified as important barriers to technology adoption among seniors. To the extent that older adults perceive technology as not being beneficial and difficult to use, they do not adopt it (Melenhorst & Bouwhuis, 2004; Melenhorst, Rogers, & Bouwhuis, 2006; Mitzner et al., 2010). Poor technology design that does not take into account normative age-related changes in ability likely contributes to these perceptions (Fisk, Rogers, Charness, Czaja, & Sharit, 2009). Furthermore, Czaja and colleagues (2006b) determined that cognitive abilities (specifically fluid and crystalized intelligence), as well as computer anxiety and self-efficacy, were strong predictors of technology use. Each of these factors represents a barrier to be overcome or bypassed for seniors to fully reap the benefits of new technology. ::: ::: Lack of adequate training is an additional barrier with respect to technology adoption, with seniors indicating a greater willingness to use technology when training is provided (Rogers, Cabrera, Walker, Gilbert, & Fisk, 1996), and many seniors wishing they had more technology training (Mitzner et al., 2008). Facilitating efficient and effective training is an important goal to ensure that seniors can benefit from technology. Understanding initial levels of technology proficiency is an important prerequisite to achieving this goal. Compared with one-on-one technology training, group or classroom training is often ideal in terms of efficiency (and is the format offered by many senior centers, retirement communities, and assisted living facilities). However, in a series of focus group studies conducted by Mitzner and colleagues (2008), seniors indicated a strong preference for one-on-one or solitary training over group training, partly out of fear that individuals with lower proficiency would require the instructor to cover already known material (resulting in boredom and wasted time). Conversely, a class containing individuals with a range of computer proficiencies might progress too quickly for individuals with extremely low proficiency. In fact recent data from a study evaluating a community-based computer and Internet class designed for older adults indicated that an important area of needed course refinement was a method for matching the initial skill level of the course participants (Czaja, Lee, Branham, & Remis, 2012). ::: ::: Being able to assess initial levels of proficiency quickly and accurately allows classes to be organized with students of similar proficiencies, facilitates interactions among the trainees, and enhances trainee satisfaction with the training experience. Even in the case of individual training, knowing initial proficiency is important to ensure that a solid understanding of basic skills is present before more advanced skills are taught and to ensure that time is not wasted training what is already known. All of these issues are ultimately related to degree of learning success (Czaja & Sharit, 2012). ::: ::: To achieve these goals with respect to computer and Internet training, we created the Computer Proficiency Questionnaire (CPQ) to assess the computer proficiency of seniors with a range of abilities, from noncomputer users to frequent computer and Internet users. The need for a new scale arose from the fact that existing surveys of computer proficiency did not meet our research and training needs, specifically with respect to an ongoing study with a focus on training minimally proficient seniors to use computers. For example, questionnaires initially considered were deemed inappropriate because they were developed with a focus on existing computer users, included relatively technical proficiencies without direct relevance for functional independence (e.g., programming, database management, and computer terminology), or were not specifically developed and validated with an older adult sample (Bradlow, Hoch, & Hutchingston, 2002; Kay, 1993). Others were developed with data from older adults who had substantial (multiple years on average) computer experience (Arning & Ziefle, 2008). Still other instruments designed for older adults focused largely on declarative knowledge rather than ability to perform computer tasks (Sengpiel & Dittberner, 2008), and others focused on frequency of use rather than proficiency (Czaja et al., 2006a). ::: ::: To address these limitations, we developed the CPQ to assess proficiency across six different subscales: (a) Computer basics (6 questions), (b) printing (5 questions), (c) communication (9 questions), (d) Internet (7 questions), (e) scheduling software (3 questions), and (f) multimedia use (3 questions). The CPQ was developed in the context of a randomized clinical trial designed to assess the impact of computer and Internet access and training on the well-being of seniors with minimal computer experience. The computer system deployed in this trial was designed to support domains of computer activities predicted to improve quality of life and social support, and the questions of the CPQ were developed to assess proficiency within these same domains. More specifically, the CPQ was designed to assess proficiency with respect to potential computer-based activities to support social engagement/support (communication), information gathering (Internet), prospective memory (printing and scheduling software), and cognitive stimulation (multimedia use). In addition to these domains, the CPQ assesses basic ability to interact with a computer using various input devices (computer basics). ::: ::: We aimed to create an instrument to provide a detailed measure of proficiency that would identify specific areas of needed training and be easier to administer and take substantially less time compared with having older adults demonstrate proficiency in each of these areas. To this end, the 33 questions of the full CPQ are presented in a matrix format with identical response options/anchors for each question to allow users to complete the questionnaire quickly. Here we present evidence that the CPQ is a reliable and valid measure of proficiency. However, 33 questions may still take too long to administer for some training and research purposes, so we also present a short version of the CPQ (CPQ-12), which has similar properties to the full CPQ. The CPQ and CPQ-12 can be used as a tool to measure proficiency for training and research purposes. It might have other applications as well such as helping individuals decide what kind of software to purchase for a parent or for selecting where to start in training a complex piece of software.


title: SoK: Lessons Learned from SSL/TLS Attacks

abstract: Since its introduction in 1994 the Secure Socket Layer (SSL) protocol (later renamed to Transport Layer Security (TLS)) evolved to the de facto standard for securing the transport layer. SSL/TLS can be used for ensuring data confidentiality, integrity and authenticity during transport. A main feature of the protocol is flexibility: Modes of operation and security aims can easily be configured through different cipher suites. However, during the evolutionary development several flaws were found. This paper presents an overview on theoretical and practical attacks of the last 17 years, in chronological order and four categories: Attacks on the Handshake protocol, on the Record and Application Data Protocols, on the PKI infrastructure and various other attacks. ::: ::: We try to give a short "Lesson(s) Learned" at the end of each paragraph.


title: Data Security, Privacy, Availability and Integrity in Cloud Computing: Issues and Current Solutions

abstract: Cloud computing changed the world around us. Now people are moving their data to the cloud since data is getting bigger and needs to be accessible from many devices. Therefore, storing the data on the cloud becomes a norm. However, there are many issues that counter data stored in the cloud starting from virtual machine which is the mean to share resources in cloud and ending on cloud storage itself issues. In this paper, we present those issues that are preventing people from adopting the cloud and give a survey on solutions that have been done to minimize risks of these issues. For example, the data stored in the cloud needs to be confidential, preserving integrity and available. Moreover, sharing the data stored in the cloud among many users is still an issue since the cloud service provider is untrustworthy to manage authentication and authorization. In this paper, we list issues related to data stored in cloud storage and solutions to those issues which differ from other papers which focus on cloud as general.


title: Learning rotation-invariant binary codes for efficient object detection from remote sensing images

abstract: Object detection is one of the most important research topics for remote sensing image (RSI) analysis. Because of the rapid development of satellite imaging technology, the resolution of RSI has increased dramatically, which brings a great challenge to the efficiency of RSI-based object detection. We present an object detection model, which greatly improves the detection speed by using learning-based hashing techniques. Specifically, first, a selective search method is used to generate many high quality object proposals that may contain objects. Then for each proposal, we learn rotation-invariant binary codes, which can handle the objects with arbitrary orientations in RSI, to quickly eliminate most nonobject proposals in Hamming space. And finally, the object detection task can be achieved by classifying the left (very limited amount of) proposals with more discriminating classification model. Experimental evaluations on a public very high-resolution remote sensing dataset show the superiority of the proposed method. Specifically, the rotation-invariant learning metric has been combined with three popular hashing methods, and performance improvements have been obtained for all the cases. Furthermore, comparisons with deep learning-based detectors show that hash-based methods fail to achieve state-of-the-art detection performance, but when the graphics processing unit is inaccessible, it achieves a good compromise between performance and runtime.


Compact representations and pruning in residual networks


title: Exploratory landscape analysis

abstract: Exploratory Landscape Analysis subsumes a number of techniques employed to obtain knowledge about the properties of an unknown optimization problem, especially insofar as these properties are important for the performance of optimization algorithms. Where in a first attempt, one could rely on high-level features designed by experts, we approach the problem from a different angle here, namely by using relatively cheap low-level computer generated features. Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection.