title: Understanding Information System Agility -- The Example of Business Intelligence

abstract: To achieve strategic advantage in today's turbulent environments, it is crucial for organizations to draw increasingly faster conclusions out of changing circumstances. As most decisions today are based on data collected by Information Systems (IS), the IS itself must become more adaptable. This is particularly challenging in the domain of Business Intelligence (BI) Systems since the underlying architectural approach of enterprise-wide decision support systems is not build upon agility, but on reliability and robustness over a period of time. In order to determine the agility of BI, a unique understanding of agility and its underlying criteria is required. This paper provides a literature review which aims to summarize the known criteria from IS literature that can be applied to the domain of BI. The results can be interpreted as a framework for understanding the agility of BI systems and form the basis for generating mandatory criteria for assessing agility.


title: A Survey of Parallel A*

abstract: A* is a best-first search algorithm for finding optimal-cost paths in graphs. A* benefits significantly from parallelism because in many applications, A* is limited by memory usage, so distributed memory implementations of A* that use all of the aggregate memory on the cluster enable problems that can not be solved by serial, single-machine implementations to be solved. We survey approaches to parallel A*, focusing on decentralized approaches to A* which partition the state space among processors. We also survey approaches to parallel, limited-memory variants of A* such as parallel IDA*.


title: Phishing website detection framework through web scraping and data mining

abstract: Phishers often exploit users' trust on the appearance of a site by using webpages that are visually similar to an authentic site. In the past, various research studies have tried to identify and classify the factors contributing towards the detection of phishing websites. The focus of this research is to establish a strong relationship between those identified heuristics (content-based) and the legitimacy of a website by analyzing training sets of websites (both phishing and legitimate websites) and in the process analyze new patterns and report findings. Many existing phishing detection tools are often not very accurate as they depend mostly on the old database of previously identified phishing websites. However, there are thousands of new phishing websites appearing every year targeting financial institutions, cloud storage/file hosting sites, government websites, and others. This paper presents a framework called Phishing-Detective that detects phishing websites based on existing and newly found heuristics. For this framework, a web crawler was developed to scrape the contents of phishing and legitimate websites. These contents were analyzed to rate the heuristics and their contribution scale factor towards the illegitimacy of a website. The data set collected from Web Scraper was then analyzed using a data mining tool to find patterns and report findings. A case study shows how this framework can be used to detect a phishing website. This research is still in progress but shows a new way of finding and using heuristics and the sum of their contributing weights to effectively and accurately detect phishing websites. Further development of this framework is discussed at the end of the paper.


title: Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set

abstract: This paper presents a novel multi-chaotic framework, which is used for the parent selection process in Success-History based Adaptive Differential Evolution (SHADE) algorithm. Created variant of the Differential Evolution (DE) algorithm was named MC-SHADE and its performance is tested on the CEC2014 benchmark set in order to participate in the CEC2016 competition on bound constrained single objective numerical optimization — single parameter-operator set based case.


title: Using Genetic Algorithm for Symmetric key Generation in Image Encryption

abstract: Cryptography is essential for protecting information as the importance of security is increasing day by day with the advent of online transaction processing and e commerce. In now a day the security of digital images attracts much attention, especially when these digital images are stored in memory or send through the communication networks. Genetic algorithms are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modelling a simplified version of genetic processes. In this paper, I proposed a method based on Genetic Algorithm which is used to generate key by the help of pseudo random number generator. Random number will be generated on the basis of current time of the system. Using Genetic Algorithm we can keep the strength of the key to be good, still make the whole algorithm good enough. Symmetric key algorithm AES has been proposed for encrypting the image as it is very secure method for symmetric key encryption.


title: Predictable Outcomes with Porcelain Laminate Veneers: A Clinical Report

abstract: This clinical report describes how to achieve predictable outcomes for anterior teeth esthetic restorations with porcelain laminate veneers by associating the digital planning and design of the restoration with interim restorations. The previous digital smile design of the restoration eliminates the communication barrier with the patient and assists the clinician throughout patient treatment. Interim restorations (diagnostic mock-ups) further enhance communication with the patient and prevent unnecessary tooth reduction for conservative tooth preparation. Adequate communication between patient and clinician contributes to successful definitive restorations and patient satisfaction with the final esthetic outcome.


title: Sonographic Depiction of the Fetal Anus and Its Utility in the Diagnosis of Anorectal Malformations

abstract: Objectives ::: The purpose of this study was to describe the sonographic appearance of the fetal anus and its usefulness in diagnosis of anal atresia. ::: ::: Methods ::: An anomaly scan was performed in 13,150 patients over 8 years. Gestational ages ranged from 16 to 38 weeks. A tangential scan of the fetal perineum was performed. The anus was seen as a hypoechoic ring representing the wall of the anal canal with a central echogenic dot representing the lumen. This appearance was seen posterior to the external genitalia. If there was failure to see this appearance, a coronal scan of the fetal pelvis was done to look for the anal canal in contiguity with the rectum and to confirm its extension up to perineum. Failure to see these two features was considered diagnostic of anal atresia. ::: ::: Results ::: On the basis of these criteria, anal atresia was diagnosed in 17 fetuses. Sixteen of these fetuses also had other associated anomalies. The appearance of the anus was useful for ruling out anal atresia in 2 fetuses with a dilated colon containing echogenic meconium balls. ::: ::: Conclusions ::: The anus is visible on a tangential scan of the fetal perineum. It can be seen routinely during an anomaly scan to diagnose or rule out anal atresia.


title: A New Multi-level Inverter with FACTS Capabilities for Wind Applications

abstract: The modular multilevel converter (MMC) is an attractive topology for HVDC/FACTS systems. In this paper a new single-phase MMC-based D-STATCOM inverter for grid connection is proposed. The proposed inverter is designed for grid-connected wind turbines in the small- to mid-sized (10kW-20kW) range using the most advanced multi-level inverter topology. The proposed MMC D-STATCOM inverter controls the DC link voltage as well as the active and reactive power transferred between the renewable energy source, specifically wind turbine, and the grid in order to regulate the power factor (PF) of the grid regardless of the input active power from wind turbine. The goal of this paper is to present a new inverter with D-STATCOM capability in a single unit without any additional cost. The 5-level D-STATCOM inverter is simulated and the results are presented to verify the operation of the proposed system. The simulation studies are carried out in the MATLAB/Simulink environment. To validate the simulation results, an experimental configuration of a 5-Level DSTATCOM inverter has been built and tested.


title: A Comprehensive Survey of Wireless Body Area Networks

abstract: Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.


title: Adaptive Classification for Brain Computer Interfaces

abstract: In this paper we evaluate the performance of a new adaptive classifier for the use within a brain computer-interface (BCI). The classifier can either be adaptive in a completely unsupervised manner or using unsupervised adaptation in conjunction with a neuronal evaluation signal to improve adaptation. The first variant, termed adaptive linear discriminant analysis (ALDA), updates mean values as well as covariances of the class distributions continuously in time. In simulated as well as experimental data ALDA substantially outperforms the non-adaptive LDA. The second variant, termed adaptive linear discriminant analysis with error correction (ALDEC), extends the unsupervised algorithm with an additional independent neuronal evaluation signal. Such a signal could be an error related potential which indicates when the decoder did not classify correctly. When the mean values of the class distributions circle around each other or even cross their way, ALDEC can yield a substantially better adaptation than ALDA depending on the reliability of the error signal. Given the non-stationarity of EEG signals during BCI control our approach might strongly improve the precision and the time needed to gain accurate control in future BCI applications.


title: Reconfigurable Network Systems and Software-Defined Networking

abstract: Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of software-defined networking (SDN). In this paper, we review the current state of the art in reconfigurable network systems, covering hardware reconfiguration, SDN, and the interplay between them. We take a top–down approach, starting with a tutorial on software-defined networks. We then continue to discuss programming languages as the linking element between different levels of software and hardware in the network. We review electronic switching systems, highlighting programmability and reconfiguration aspects, and describe the trends in reconfigurable network elements. Finally, we describe the state of the art in the integration of photonic transceiver and switching elements with electronic technologies, and consider the implications for SDN and reconfigurable network systems.


title: AmpN: Real-time LOS/NLOS identification with WiFi

abstract: WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.


title: Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder

abstract: Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are common in the brains of the elderly population and may be caused by ischemia or demyelination. Lesions are biomarkers for various neurodegenerative diseases, making accurate quantification of them important for both disease diagnosis and progression. Automatic lesion detection using supervised learning requires manually annotated images, which can often be impractical to acquire. Unsupervised lesion detection, on the other hand, does not require any manual delineation; however, these methods can be challenging to construct due to the variability in lesion load, placement of lesions, and voxel intensities. Here we present a novel approach to address this problem using a convolutional autoencoder, which learns to segment brain lesions as well as the white matter, gray matter, and cerebrospinal fluid by reconstructing FLAIR images as conical combinations of softmax layer outputs generated from the corresponding T1, T2, and FLAIR images. Some of the advantages of this model are that it accurately learns to segment lesions regardless of lesion load, and it can be used to quickly and robustly segment new images that were not in the training set. Comparisons with state-of-the-art segmentation methods evaluated on ground truth manual labels indicate that the proposed method works well for generating accurate lesion segmentations without the need for manual annotations.


title: A 100-GbE reverse gearbox IC in 40nm CMOS for supporting legacy 10- and 40-GbE standards

abstract: This paper presents the industry's first low-power 100-Gigabit Ethernet (GbE) multi-link gearbox (MLG) IC, which facilitates transport of independent 10-GbE and 40-GbE signals to 4×25G physical layers implementing 100GBASE-R. The IC consumes only 1.37-W while implementing complicated reverse gearbox functionality. The measured TX jitter from the 25-Gb/s lane is 1.6-ps rms , and the recovered clock jitter is 0.5-ps rms . The measured RX input sensitivity for a BER 10−12 is 42-mV ppd . The proposed gearbox IC, fabricated in a 40nm CMOS process, occupies 3.7×3.4-mm2. The power consumption of RX and TX in 25G interfaces are 47-mW and 51-mW, respectively, and those of a 10G interfaces are 24-mW and 25-mW, respectively. Gearbox functionalities are verified with embedded self-test logics.


title: EAP as artificial muscles: progress and challenges

abstract: During the last decade and a half new polymers have emerged that respond to electrical stimulation with a significant shape or size change. This capability of electroactive polymer (EAP) materials is attracting the attention of engineers and scientists from many different disciplines. Practitioners in biomimetics are particularly excited about these materials since the artificial muscle aspect of EAPs can be applied to mimic the movements of animals and insects. In the foreseeable future, robotic mechanisms actuated by EAP will enable engineers to create devices previously imaginable only in science fiction. Last year, significant accomplishments were reported including the emergence of the first commercial product and the possibility that an arm can be made with EAP actuators having the potential of winning a wrestling match with a human. As such major accomplishments continue to be reported it is interesting to review the progress and provide a prospective regarding the development since the first EAPAD conference in 1999. This manuscript covers the progress in the field of EAP and the challenges that are being addressed.


title: $Q$- and $A$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes

abstract: In clinical practice, physicians make a series of treatment decisions ::: over the course of a patient�s disease based on his/her baseline and ::: evolving characteristics. A dynamic treatment regime is a set of sequential ::: decision rules that operationalizes this process. Each rule corresponds to a ::: decision point and dictates the next treatment action based on the accrued information. ::: Using existing data, a key goal is estimating the optimal regime, ::: that, if followed by the patient population, would yield the most favorable ::: outcome on average. Q- and A-learning are two main approaches for this ::: purpose. We provide a detailed account of these methods, study their performance, ::: and illustrate them using data from a depression study.


title: Enabling 5G backhaul and access with millimeter-waves

abstract: This paper presents the approach of extending cellular networks with millimeter-wave backhaul and access links. Introducing a logical split between control and user plane will permit full coverage while seamlessly achieving very high data rates in the vicinity of mm-wave small cells.


title: An Ontology-Based Similarity between Sets of Concepts

abstract: To help sharing knowledge in those contexts where documents and services are annotated with semantic information, such as the Semantic Web, defining and implementing the similarity between sets of concepts belonging to a common ontology may prove very useful. In fact, if both the required and the provided pieces of information (be they textual documents, services, images, or whatever) are annotated with sets of concepts taken from a reference ontology O, the evaluation of how good a piece of information P is, w.r.t. the required one R, may be based on the similarity between the two sets of concepts that describe P and R. One of the first applications of the agent technology, aimed at “reducing work and information overload”, was that of retrieving and filtering information in an automatic way. Thus, the possibility to calculate the semantic distance between two sets of concepts finds a natural application in the agent field, in particular for improving those agents that act as “digital butlers” for their human owners, by exploring the Semantic Web and looking for useful documents and/or services. Unfortunately, the metrics for calculating the semantic distance between two sets of concepts that can be found in the literature, are often very simple and do not meet some requirements that, up to us, make the metric closer to the common sense reasoning. For this reason, we have designed and implemented two new algorithms for computing the similarity between sets of concepts belonging to the same ontology.


title: Critical Factors Affecting Supply Chain Management: A Case Study in the US Pallet Industry

abstract: Supply chain management is applied by companies across the globe due to its demonstrated results such as delivery time reduction, improved financial performance, greater customer satisfaction, building trust among suppliers, and others. According to D’Amours, Ronnqvist, and Weintraub (2008), companies resort to supply chain practices to improve their performance. Thus, it is important to first understand how their supply chains work. Figure 1 shows a generalized supply chain in the forest products industry.


title: Transformable topological mechanical metamaterials

abstract: Mechanical metamaterials are engineered materials that gain their remarkable mechanical properties, such as negative Poisson's ratios, negative compressibility, phononic bandgaps, and topological phonon modes, from their structure rather than composition. Here we propose a new design principle, based on a uniform soft deformation of the whole structure, to allow metamaterials to be immediately and reversibly transformed between states with contrasting mechanical and acoustic properties. These properties are protected by the topological structure of the phonon band of the whole structure and are thus highly robust against disorder and noise. We discuss the general classification of all structures that exhibit such soft deformations, and provide specific examples to demonstrate how to utilize soft deformations to transform a system between different regimes such that remarkable changes in their properties, including edge stiffness and speed of sound, can be achieved.


title: Lipotoxicity and the gut-liver axis in NASH pathogenesis.

abstract: The pathogenesis of non-alcoholic fatty liver disease, particularly the mechanisms whereby a minority of patients develop a more severe phenotype characterised by hepatocellular damage, inflammation, and fibrosis is still incompletely understood. Herein, we discuss two pivotal aspects of the pathogenesis of NASH. We first analyse the initial mechanisms responsible for hepatocellular damage and inflammation, which derive from the toxic effects of excess lipids. Accumulating data indicate that the total amount of triglycerides stored in hepatocytes is not the major determinant of lipotoxicity, and that specific lipid classes act as damaging agents on liver cells. In particular, the role of free fatty acids such as palmitic acid, cholesterol, lysophosphatidylcholine and ceramides has recently emerged. These lipotoxic agents affect the cell behaviour via multiple mechanisms, including activation of signalling cascades and death receptors, endoplasmic reticulum stress, modification of mitochondrial function, and oxidative stress. In the second part of this review, the cellular and molecular players involved in the cross-talk between the gut and the liver are considered. These include modifications to the microbiota, which provide signals through the intestine and bacterial products, as well as hormones produced in the bowel that affect metabolism at different levels including the liver. Finally, the activation of nuclear receptors by bile acids is analysed.


title: Substrate-Integrated Millimeter-Wave and Terahertz Antenna Technology

abstract: Significant advances in the development of millimeter-wave and terahertz (30-10 000 GHz) technologies have been made to cope with the increasing interest in this still not fully explored electromagnetic spectrum. The nature of electromagnetic waves over this frequency range is well suited for the development of high-resolution imaging applications, molecular-sensitive spectroscopic devices, and ultrabroadband wireless communications. In this paper, millimeter-wave and terahertz antenna technologies are overviewed including the conventional and nonconventional planar/nonplanar antenna structures based on different platforms. As a promising technological platform, substrate-integrated circuits (SICs) attract more and more attention. Various substrate-integrated waveguide (SIW) schemes and other synthesized guide techniques have been widely employed in the design of antennas and arrays. Different types of substrate-integrated antennas and beamforming networks are discussed with respect to theoretical and experimental results in connection with electrical and mechanical performances.


title: A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion

abstract: We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information-theoretical methods are used to establish a minimax lower bound under the general sampling model. The minimax upper and lower bounds together yield the optimal rate of convergence for the Frobenius norm loss. Computational algorithms and numerical performance are also discussed.


title: Probabilistic Program Abstractions

abstract: Abstraction is a fundamental tool for reasoning about a complex system. Program abstraction has been utilized to great effect for analyzing deterministic programs. At the heart of a program abstraction is a connection between the abstract program, which is simple to analyze, and the concrete program, which may be extremely complex. Program abstractions, however, are typically not probabilistic. In this thesis I generalize a particular class of non- deterministic program abstractions known as sound over-approximations to the probabilistic context. Sound over-approximations are a family of abstract programs which are guaranteed to contain the original program as a subset of their behavior. This thesis shows that when imbued with a probabilistic semantics, sound over-approximations define a family of probabilistic programs which capture key properties of the original program. It then introduces a mechanism for generating sound probabilistic over-approximations as a generalization of a well-known program abstraction technique known as predicate abstraction. Finally, the problem of inference and learning in the context of probabilistic program abstractions are briefly described.


title: TimeWarp: interactive time travel with a mobile Mixed Reality game

abstract: Mobile location-aware applications have become quite popular across a range of new areas such as pervasive games and mobile edutainment applications. However it is only recently, that approaches have been presented which combine gaming and education with mobile Augmented Reality systems. However they typically lack a close crossmedia integration of the surroundings, and often annotate or extend the environment rather than modifying and altering it. In this paper we present a mobile outdoor mixed reality game for exploring the history of a city in the spatial and the temporal dimension. We introduce the design and concept of the game and present a universal mechanism to define and setup multi-modal user interfaces for the game challenges. Finally we discuss the results of the user tests.


BITCOIN: Cryptography, Economics, and the Future


title: Popularity Growth Patterns of YouTube Videos: A Category-based Study

abstract: Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is important for the sustainable deployment of content distribution systems. A significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, little work has been done that investigates the popularity patterns of YouTube videos based on video object category. In this paper, we perform an in-depth analysis of the popularity pattern of YouTube videos, considering video categories. We find that the time varying popularity of different YouTube categories are different from each other. For some categories, views at early ages can be used to predict future popularity, whereas for some other categories, predicting future popularity is a challenging task and requires more sophisticated techniques (e.g. time-series clustering). The outcomes of these analyses can be instrumental towards designing a reliable workload generator, which can be further used to evaluate different caching policies and distribution mechanism for YouTube and similar sites.


title: The BSD Packet Filter: A New Architecture for User-level Packet Capture

abstract: Many versions of Unix provide facilities for user-level packet capture, making possible the use of general purpose workstations for network monitoring. Because network monitors run as user-level processes, packets must be copied across the kernel/user-space protection boundary. This copying can be minimized by deploying a kernel agent called a packet filter, which discards unwanted packets as early as possible. The original Unix packet filter was designed around a stack-based filter evaluator that performs sub-optimally on current RISC CPUs. The BSD Packet Filter (BPF) uses a new, register-based filter evaluator that is up to 20 times faster than the original design. BPF alson uses a straighforward buffering strategy that makes its overall performance up to 100 times faster than Sun's NIT running on the same hardware.


title: Event-related functional near-infrared spectroscopy (fNIRS): Are the measurements reliable?

abstract: Abstract The purpose of the present study was to investigate the retest reliability of event-related functional near-infrared spectroscopy (fNIRS). Therefore, isolated functional activation was evoked in the occipital cortex by a periodic checkerboard stimulation. During a 52-channel fNIRS recording, 12 subjects underwent 60 trials of visual stimulation in two sessions. The retest interval was set to 3 weeks. Linear correlations of the contrast t values supplemented by scatter plots, channel-wise intraclass correlation coefficients (ICC) as well as reproducibility indices for the quantity of activated channels ( R QUANTITY ) and the location ( R OVERLAP ) of the detected activation were calculated. The results at the group level showed good reliability in terms of the single measure ICCs (up to 0.84) and excellent reproducibility quantified by R QUANTITY and R OVERLAP (up to 96% of the quantity and the location were reproducible), whereas the results at the single subjects' level were mediocre. Furthermore, the reliability assessed by single measurement ICCs improved if regarded at a cluster level.


title: Quintuple-Mode UWB Bandpass Filter With Sharp Roll-Off and Super-Wide Upper Stopband

abstract: This letter presents a quintuple-mode ultra-wideband (UWB) bandpass filter (BPF), using a multiple-stub-loaded resonator, consisting of multiple loaded stubs to a uniform transmission-line resonator, i.e., two asymmetrical stepped-impedance stubs at the center and four uniform-impedance stubs at the symmetrical side locations. Six modes including two odd and four even modes are controllable, while the first five modes are allocated within the UWB band. Two transmission zeros generated by the asymmetrical stepped-impedance stub are used to improve the passband selectivity greatly. The resonant mode and transmission zero chart are given, indicating that the higher modes could be suppressed by the transmission zeros generated by the stubs and the parallel-coupled lines. A compact planar UWB BPF is simulated, fabricated and measured, and the 20 dB stopband performance is extended to 29.7 GHz.


title: 2D SLAM quality evaluation methods

abstract: SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. The choice of the algorithm that might be used in every particular problem requires prior knowledge about advantages and disadvantages of each algorithm. This paper presents the approach for comparison of SLAM algorithms that allows to find the most accurate one. The accent of research is made on 2D SLAM algorithms and the focus of analysis is 2D map that is built after algorithm performance. Three metrics for evaluation of maps are presented in this paper


title: The association between aerobic fitness and language processing in children: Implications for academic achievement

abstract: Event-related brain potentials (ERPs) have been instrumental for discerning the relationship between children’s aerobic fitness and aspects of cognition, yet language processing remains unexplored. ERPs linked to the processing of semantic information (the N400) and the analysis of language structure (the P600) were recorded from higher and lower aerobically fit children as they read normal sentences and those containing semantic or syntactic violations. Results revealed that higher fit children exhibited greater N400 amplitude and shorter latency across all sentence types, and a larger P600 effect for syntactic violations. Such findings suggest that higher fitness may be associated with a richer network of words and their meanings, and a greater ability to detect and/or repair syntactic errors. The current findings extend previous ERP research explicating the cognitive benefits associated with greater aerobic fitness in children and may have important implications for learning and academic performance.


title: The Neural Noisy Channel

abstract: We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use.


title: Learning a Hierarchical Monitoring System for Detecting and Diagnosing Service Issues

abstract: We propose a machine learning based framework for building a hierarchical monitoring system to detect and diagnose service issues. We demonstrate its use for building a monitoring system for a distributed data storage and computing service consisting of tens of thousands of machines. Our solution has been deployed in production as an end-to-end system, starting from telemetry data collection from individual machines, to a visualization tool for service operators to examine the detection outputs. Evaluation results are presented on detecting 19 customer impacting issues in the past three months.


title: Convolutional neural networks for image processing: an application in robot vision

abstract: Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feed-forward neural networks and adaptive filters. Two dimensional CNNs are formed by one or more layers of two dimensional filters, with possible non-linear activation functions and/or down-sampling. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). We present a description of the convolutional network architecture, and an application to practical image processing on a mobile robot. A CNN is used to detect and characterize cracks on an autonomous sewer inspection robot. The filter sizes used in all cases were 4x4, with non-linear activations between each layer. The number of feature maps used in the three hidden layers was, from input to output, 4, 4, 4. The network was trained using a dataset of 48x48 sub-regions drawn from 30 still image 320x240 pixel frames sampled from a pre-recorded sewer pipe inspection video. 15 frames were used for training and 15 for validation of network performance. Although development of a CNN system for civil use is on-going, the results support the notion that data-based adaptive image processing methods such as CNNs are useful for image processing, or other applications where the input arrays are large, and spatially / temporally distributed. Further refinements of the CNN architecture, such as the implementation of separable filters, or extensions to three dimensional (ie. video) processing, are suggested.


title: Customer Satisfaction and Stock

abstract: Do investments in customer satisfaction lead to excess returns? If so, are these returns associated with higher stock market risk? The empirical evidence presented in this article suggests that the answer to the first question is yes, but equally remarkable, the answer to the second question is no, suggesting that satisfied customers are economic assets with high returns/low risk. Although these results demonstrate stock market imperfections with respect to the time it takes for share prices to adjust, they are consistent with previous studies in marketing in that a firm’s satisfied customers are likely to improve both the level and the stability of net cash flows. The implication, implausible as it may seem in other contexts, is high return/low risk. Specifically, the authors find that customer satisfaction, as measured by the American Customer Satisfaction Index (ACSI), is significantly related to market value of equity. Yet news about ACSI results does not move share prices. This apparent inconsistency is the catalyst for examining whether excess stock returns might be generated as a result. The authors present two stock portfolios: The first is a paper portfolio that is back tested, and the second is an actual case. At low systematic risk, both outperform the market by considerable margins. In other words, it is possible to beat the market consistently by investing in firms that do well on the ACSI.


title: Automatic Variational ABC

abstract: Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw upon recent advances in variance reduction for SV and likelihood-free inference using deterministic simulations to produce low variance gradient estimators of the variational lower-bound. By then exploiting automatic differentiation libraries we can avoid nearly all model-specific derivations. We demonstrate performance on three problems and compare to existing SVI algorithms. Our results demonstrate the correctness and efficiency of our algorithm.


title: Automatic Learning of Context-Free Grammar

abstract: In this paper we study the problem of learning context-free grammar from a corpus. We investigate a technique that is based on the notion of minimum description length of the corpus. A cost as a function of grammar is defined as the sum of the number of bits required for the representation of a grammar and the number of bits required for the derivation of the corpus using that grammar. On the Academia Sinica Balanced Corpus with part-of-speech tags, the overall cost, or description length, reduces by as much as 14% compared to the initial cost. In addition to the presentation of the experimental results, we also include a novel analysis on the costs of two special context-free grammars, where one derives only the set of strings in the corpus and the other derives the set of arbitrary strings from the alphabet.


title: Strategy Selection as Rational Metareasoning

abstract: Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy's performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to arithmetic by capturing the variability of people's strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and 4 new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the 2 poles of the debate about human rationality by integrating heuristics and biases with learning and rationality. (PsycINFO Database Record


title: Rotation-invariant pattern matching using wavelet decomposition

abstract: In this paper, we propose a wavelet decomposition approach for rotation-invariant template matching. In the matching process, we first decompose an input image into different multi-resolution levels in the wavelet-transformed domain, and use only the pixels with high wavelet coefficients in the decomposed detail subimage at a lower resolution level to compute the normalized correlation between two compared patterns. To make the matching invariant to rotation, we further use the ring-projection transform, which is invariant to object orientation, to represent an object pattern in the detail subimage. The proposed method significantly reduces the computational burden of the traditional pixel-by-pixel matching. Experimental results on a variety of real images have shown the efficacy of the proposed method.


title: Classification, Terminology, and Application of the Modular Multilevel Cascade Converter (MMCC)

abstract: This paper presents the modular multilevel cascade converter (MMCC) family based on cascade connection of multiple reversible-chopper cells or multiple single-phase full-bridge cells. This converter family are classified from circuit configuration as follows; single-star bridge-cells (SSBC), singledelta bridge-cells (SDBC), double-star chopper-cells (DSCC), and double-star bridge-cells (DSBC). The term MMCC corresponds to a family name in a person while, for example, the term SSBC corresponds to a given name. Therefore, the term “MMCC-SSBC” can identify the circuit configuration without any confusion. Among the four circuit configurations, SSBC and DSCC are the most practical than the others in terms of cost and performance, although a distinct difference exists in application between SSBC and DSCC. This paper describes applications of SSBC and DSCC to a battery energy storage system and a motor drive, respectively.


section of this paper. Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications


title: Extended Order-Generic Queries

abstract: Abstract We consider relational databases organized over an ordered domain with some additional relations — a typical example is the ordered domain of rational numbers together with the operation of addition. In the focus of our study are the first-order (FO) queries that are invariant under order-preserving “permutations” — such queries are called order-generic. It has recently been discovered that for some domains order-generic FO queries fail to express more than pure order queries. For example, every order-generic FO query over rational numbers with + can be rewritten without +. For some other domains, however, this is not the case. We provide very general conditions on the FO theory of the domain that ensure the collapse of order-generic extended FO queries to pure order queries over this domain: the Pseudo-finite Homogeneity Property and a stronger Isolation Property. We further distinguish one broad class of domains satisfying the Isolation Property, the so-called quasi-o-minimal domains. This class includes all the o-minimal domains, but also the ordered group of integer numbers and the ordered semigroup of natural numbers, and some other domains. An important difference of this paper from the recent series of related papers is that we generalize all the notions to the case of finitely representable database states — as opposed to finite states — and develop a general lifting technique that, essentially, allows us to extend any result of the kind we are interested in, from finite to finitely representable states. We show, however, that these results cannot be transfered to arbitrary infinite states.


title: Options for Control of Reactive Power by Distributed Photovoltaic Generators

abstract: High penetration levels of distributed photovoltaic(PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g. by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e. local). In general, we find that local control schemes are capable for maintaining voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.


title: Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier

abstract: A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classification because of their ability to “learn” from the training dataset to predict or support decision making with relatively high accuracy. However, when the dataset is large, some algorithms might not scale up well. In this paper, we aim to evaluate the scalability of Naive Bayes classifier (NBC) in large datasets. Instead of using a standard library (e.g., Mahout), we implemented NBC to achieve fine-grain control of the analysis procedure. A Big Data analyzing system is also design for this study. The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases. We have demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.


title: A User-Modeling Approach to Build User's Psycho-Physiological Maps of Emotions using Bio-Sensors

abstract: Near to real-time emotion recognition is a promising task for Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI). Using knowledge about the user's emotions depends upon the possibility to extract information about users' emotions during HCI or HRI without explicitely asking users about the feelings they are experiencing. To be able to sense the user's emotions without interrupting the HCI, we present a new method applied to the emotional experience of the user for extracting semantic information from the Autonomic Nervous System (ANS) signals associated with emotions. We use the concepts of 1st person - where the subject consciously (and subjectively) extracts the semantic meaning of a given lived experience, e.g. 'I felt amused') - and 3rd person approach - where the experimenter interprets the semantic meaning of the subject's experience from a set of externally (and objectively) measured variables (e.g. galvanic skin response measures). Based on the 3rd person approach, our technique aims at psychologically interpreting physiological parameters (skin conductance and heart rate), and at producing a continuous extraction of the user's affective state during HCI or HRI. We also combine it with the 1st person approach measure which allows a tailored interpretation of the physiological measure closely related to the user own emotional experience.


title: Biological effects of pollutants in Australian tropical coastal waters: Embryonic malformations and chromosomal aberrations in developing fish eggs

abstract: Abstract This study establishes baseline information on the biological effects of contaminants in the tropical marine environment of Australia, using as indicators the incidence of embryonic malformation and chromosomal aberration in developing fish eggs. Eighteen sites were examined, including nearshore coastal waters with varying degrees of human activity, and offshore locations on the Great Barrier Reef, in order to assess whether there is any present indication of contaminant effects in the region. Natural background levels of abnormality in embryos and chromosomes of fish are both expected to be less than 10%. All sites examined had chromosomal aberrations well within this limit, and as such, could be classified as uncontaminated by toxicants which have mutagenic effects. Mean rates of embryonic malformation varied with site location, and ranged between 6 and 24%. Coastal sites adjacent to areas used for intensive farming, or with major urban centres, had consistently higher levels of embryo deformity than sites on the Great Barrier Reef. Thus, whilst the incidence of embryonic malformation for the region as a whole (mean 12%) was low in comparison with that of polluted waters (30–80% reported), there is some concern that human activities may be having a localized effect in the region.


title: Can't You Hear Me Knocking: Identification of User Actions on Android Apps via Traffic Analysis

abstract: While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". The concern is not only related to an adversary taking physical or remote control of the device, but also to what a passive adversary without the above capabilities can observe from the device communications. Work in this latter direction aimed, for example, at inferring the apps a user has installed on his device, or identifying the presence of a specific user within a network. In this paper, we move a step forward: we investigate to which extent it is feasible to identify the specific actions that a user is doing on mobile apps, by eavesdropping their encrypted network traffic. We design a system that achieves this goal by using advanced machine learning techniques. We did a complete implementation of this system and run a thorough set of experiments, which show that it can achieve accuracy and precision higher than 95% for most of the considered actions.


title: Plasmonic and bolometric terahertz detection by graphene field-effect transistor

abstract: Polarization dependence analysis of back-gated graphene field-effect transistor terahertz responsivity at frequencies ranging from 1.63 to 3.11 THz reveals two independent mechanisms of THz detection by graphene transistor: plasmonic, associated with the transistor nonlinearity, and bolometric, caused by graphene sheet temperature increase due to THz radiation absorption. In the bolometric regime, electron and hole branches demonstrate a very different response to THz radiation, which we link to the asymmetry of the current-voltage characteristics temperature dependence with respect to the Dirac point. Obtained results are important for development of high-efficiency graphene THz detectors.


title: T.S.: Automatic extraction of femur contours from hip x-ray images

abstract: Extraction of bone contours from x-ray images is an important first step in computer analysis of medical images. It is more complex than the segmentation of CT and MR images because the regions delineated by bone contours are highly nonuniform in intensity and texture. Classical segmentation algorithms based on homogeneity criteria are not applicable. This paper presents a model-based approach for automatically extracting femur contours from hip x-ray images. The method works by first detecting prominent features, followed by registration of the model to the x-ray image according to these features. Then the model is refined using active contour algorithm to get the accurate result. Experiments show that this method can extract the contours of femurs with regular shapes, despite variations in size, shape and orientation.


title: Network forensic frameworks: Survey and research challenges

abstract: Network forensics is the science that deals with capture, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper makes an exhaustive survey of various network forensic frameworks proposed till date. A generic process model for network forensics is proposed which is built on various existing models of digital forensics. Definition, categorization and motivation for network forensics are clearly stated. The functionality of various Network Forensic Analysis Tools (NFATs) and network security monitoring tools, available for forensics examiners is discussed. The specific research gaps existing in implementation frameworks, process models and analysis tools are identified and major challenges are highlighted. The significance of this work is that it presents an overview on network forensics covering tools, process models and framework implementations, which will be very much useful for security practitioners and researchers in exploring this upcoming and young discipline.


title: A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model

abstract: In this work a new method for brain tumor detection is developed. For this purpose watershed method is used in combination with edge detection operation. It is a color based brain tumor detection algorithm using color brain MRI images in HSV color space. The RGB image is converted to HSV color image by which the image is separated in three regions hue, saturation, and intensity. After contrast enhancement watershed algorithm is applied to the image for each region. Canny edge detector is applied to the output image. After combining the three images final brain tumor segmented image is obtained. The algorithm has been applied on twenty brain MRI images. The developed algorithm has given promising results.


Rank-2 Matrix Solution for Semidefinite Relaxations of Arbitrary Polynomial Optimization Problems


Measuring 2 . 4 GHz Helix Antennas


title: Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu lexicon

abstract: The paper describes the enrichment of OntoSenseNet - a verb-centric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because native speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Additionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives.


title: Automated inspection of through hole solder joints utilizing X-ray imaging

abstract: Solder joint inspection has traditionally been done by manual inspection. The disadvantage of manual inspection is the large amount of time required and the decrease in efficiency as operator fatigue occurs. This has prompted the development of automated inspection systems to speed up the inspection process and increase efficiency. Automated inspection systems typically use visible light, infrared light or X-rays to illuminate the board. These systems require solder joint position information that is provided either by CAD data or by human entry of the position information. This paper describes a preliminary, automated inspection system that finds the solder joints in an X-ray image and inspects them using an artificial neural network (ANN). The identification of solder joints in the gray-scale image is done using image processing techniques; CAD data or manual registration of the solder joints is not required. The image processing techniques also yield binary maps (i.e., black and white images) showing the locations of ICs and other components, which is useful for other diagnostics. >


title: An Easy Approach to Calculating Estimated Energy Requirements

abstract: The Dietary Reference Intakes (DRIs) define the daily requirement for energy as the Estimated Energy Requirement (EER). The EER is based on calculations that account for an individual's energy intake, energy expenditure, age, sex, weight, height, and physical activity level. Including physical activity level in the calculations makes determining energy expenditure possible and achieving energy balance a more realistic goal. However, physical activity level is often difficult to measure and accurate assessment of energy expenditure not always possible. We provide an easy way to calculate daily EERs for adults based on physical activity level. We use the EER equations of the DRI Committee and provide a spreadsheet template for the calculation of physical activity level. This technique accounts for all factors and measurements to determine physical activity level and energy expended from daily physical activity. It should prove a useful approach in research, clinical, and public health settings.


title: Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

abstract: The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.


title: Optimal Execution with Nonlinear Impact Functions and Trading-enhanced Risk

abstract: Optimal trading strategies are determined for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. The market impact cost per share is taken to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square root law that has been proposed based on market microstructure theory. In analogy to the linear model, a ‘characteristic time’ for optimal trading is defined, which now depends on the initial portfolio size and decreases as execution proceeds. A model is also considered in which uncertainty of the realized price is increased by demanding rapid execution; it is shown that optimal trajectories are described by a ‘critical portfolio size’ above which this effect is dominant and below which it may be neglected.


title: Implementing total organic carbon analysis for cleaning validation

abstract: Total organic carbon (TOC) analysis a fast and effective analytical technique for cleaning validation. Understanding the various types of TOC technologies is essential for choosing the best solution.


title: Robust principal component analysis with side information

abstract: The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and it is still unclear to what extent could such information help robust PCA. Thus, in this paper, we study the problem of robust PCA with side information, where both prior structure and features of entities are exploited for recovery. We propose a convex problem to incorporate side information in robust PCA and show that the low rank matrix can be exactly recovered via the proposed method under certain conditions. In particular, our guarantee suggests that a substantial amount of low rank matrices, which cannot be recovered by standard robust PCA, become recoverable by our proposed method. The result theoretically justifies the effectiveness of features in robust PCA. In addition, we conduct synthetic experiments as well as a real application on noisy image classification to show that our method also improves the performance in practice by exploiting side information.


title: EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses

abstract: Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware typically adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a line of defense - that has a small and battery-efficient code base - requires a rigorous foundation for evaluating HMDs. ::: To this end, we introduce EMMA - a platform to evaluate the efficacy of HMDs for mobile platforms. EMMA deconstructs malware into atomic, orthogonal actions and introduces a systematic way of pitting different HMDs against a diverse subset of malware hidden inside benign applications. EMMA drives both malware and benign programs with real user-inputs to yield an HMD's effective operating range - i.e., the malware actions a particular HMD is capable of detecting. We show that small atomic actions, such as stealing a Contact or SMS, have surprisingly large hardware footprints, and use this insight to design HMD algorithms that are less intrusive than prior work and yet perform 24.7% better. Finally, EMMA brings up a surprising new result - obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.


title: A 12-bit digitally controlled oscillator with low power consumption

abstract: In this paper, a low power and low jitter 12-bit CMOS digitally controlled oscillator (DCO) design is presented. The CMOS DCO is designed based on a ring oscillator implemented with Schmitt trigger based inverters. Simulations of the proposed DCO using 32 nm CMOS predictive transistor model (PTM) achieves controllable frequency range of 570 MHz~850 MHz with a wide linearity. Monte Carlo simulation demonstrates that the time-period jitter due to random power supply fluctuation is under 75 ps and the power consumption is 2.3 mW at 800 MHz with 0.9 V power supply.


title: Arduino-Based Biosensor Impedance Measurement

abstract: Currently, the cell-based biosensors have to be connected to an expensive impedance analyzer in order to obtain the measurement values. In addition, the impedance analyzer is bulky and not easily portable. Therefore, in this work, a low frequency impedance analyzer is designed by generating a low frequency AC signal that is sent to the sensor in order to measure the impedance of a cell. The prototype design is able to detect the presence of water and a cell media. It consists of a biosensor which requires a low frequency AC input signal and the impedance data of the water and cell media is to be measured and displayed using an Arduino system. The design had successfully shown that the prototype is able to detect and differentiate the two different substances based on their resistance and capacitance.


title: Towards Photo Watercolorization with Artistic Verisimilitude

abstract: We present a novel artistic-verisimilitude driven system for watercolor rendering of images and photos. Our system achieves realistic simulation of a set of important characteristics of watercolor paintings that have not been well implemented before. Specifically, we designed several image filters to achieve: 1) watercolor-specified color transferring; 2) saliency-based level-of-detail drawing; 3) hand tremor effect due to human neural noise; and 4) an artistically controlled wet-in-wet effect in the border regions of different wet pigments. A user study indicates that our method can produce watercolor results of artistic verisimilitude better than previous filter-based or physical-based methods. Furthermore, our algorithm is efficient and can easily be parallelized, making it suitable for interactive image watercolorization.


title: Modeling fashion

abstract: We propose a method to try to model fashionable dresses in this paper. We first discover common visual patterns that appear in dress images using a human-in-the-loop, active clustering approach. A fashionable dress is expected to contain certain visual patterns which make it fashionable. An approach is proposed to jointly identify fashionable visual patterns and learn a discriminative fashion classifier. The results show that interesting fashionable patterns can be discovered on a newly collected dress dataset. Furthermore, our model can also achieve high accuracy on distinguishing fashionable and unfashionable dresses.


title: Wavelet Packets for Speech Emotion Recognition

abstract: Speech Emotion Recognition (SER) is an emerging research area in Human Computer Interaction (HCI) with wide range of applications. This paper proposes the application of Wavelet Packet Features (WPF) for Speech emotion Recognition. Daubechies wavelets are used in this work. An emotional speech database consisting of nine emotions is created for this experiment. Extreme Learning Machine (ELM) is used as the pattern classifier. We could achieve an overall recognition accuracy of 76.66 % from this experiment.


title: Characterizations of High Frequency Planar Transformer With a Novel Comb-Shaped Shield

abstract: This paper presents a newly developed comb-shaped Faraday shield for high frequency (HF) planar transformers. High power density and small size planar transformers are widely used in switching mode power supplies (SMPS) as magnetically coupled devices. However, the intra-winding capacitance couples HF noise from the primary winding to the secondary winding. The coupling capacitance can cause serious common mode problems, and thus the design may not be electromagnetically compatible with the surrounding environment. The tradeoff between the leakage inductance and the coupling capacitance becomes problematic for high frequency applications. The coupling capacitance can be reduced either by increasing the separation distance between the primary and the secondary windings or by inserting a conventional Faraday shield. The former may cause the leakage inductance to increase and further increase the insertion losses, whereas the latter would result in a serious demagnetizing problem in a HF planar transformer. These problems can be resolved by using a novel comb-shaped Faraday shield.


title: Whip: A framework for mashup development with block-based development approach

abstract: The emerging of Web 2.0 and Web 3.0 technologies allows web users not only authoring and customizing web pages and their content but also integrating the contents of several web sites into one web page. This web integration is called web mashup. There are various End Users Programming development approaches in making mashup such as scripting, dataflow, widget, spreadsheet, and Programming By Demonstration (PBD). Whip design, as a tool lets casual users - those without programming skill - do the mashup easily through Block-Based Programming (BBP) approach. The design of Whip is guided by a number of principles that support this goal; ranging from situational application, user's preference, User's skill level to code sharing. In this paper we expound the philosophy of Whip design and explain how the design of Whip supports those principles.


title: Beyond Convexity: Stochastic Quasi-Convex Optimization

abstract: Stochastic convex optimization is a basic and well studied primitive in machine learning. It is well known that convex and Lipschitz functions can be minimized efficiently using Stochastic Gradient Descent (SGD). The Normalized Gradient Descent (NGD) algorithm, is an adaptation of Gradient Descent, which updates according to the direction of the gradients, rather than the gradients themselves. In this paper we analyze a stochastic version of NGD and prove its convergence to a global minimum for a wider class of functions: we require the functions to be quasi-convex and locally-Lipschitz. Quasi-convexity broadens the con- cept of unimodality to multidimensions and allows for certain types of saddle points, which are a known hurdle for first-order optimization methods such as gradient decent. Locally-Lipschitz functions are only required to be Lipschitz in a small region around the optimum. This assumption circumvents gradient explosion, which is another known hurdle for gradient descent variants. Interestingly, unlike the vanilla SGD algorithm, the stochastic normalized gradient descent algorithm provably requires a minimal minibatch size.


title: Venus: verification for untrusted cloud storage

abstract: This paper presents Venus, a service for securing user interaction with untrusted cloud storage. Specifically, Venus guarantees integrity and consistency for applications accessing a key-based object store service, without requiring trusted components or changes to the storage provider. Venus completes all operations optimistically, guaranteeing data integrity. It then verifies operation consistency and notifies the application. Whenever either integrity or consistency is violated, Venus alerts the application. We implemented Venus and evaluated it with Amazon S3 commodity storage service. The evaluation shows that it adds no noticeable overhead to storage operations.


title: Language Model Applications to Spelling with Brain-Computer Interfaces

abstract: Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.


title: Evaluating the possible role of 68Ga-citrate PET/CT in the characterization of indeterminate lung lesions

abstract: We sought to determine whether PET/CT imaging with Ga-citrate could be of value in distinguishing benign from malignant lung pathology in a setting with a high prevalence of granulomatous diseases. Methods Thirty-six consecutive patients with indeterminate lung lesions prospectively underwent dual time point (60 and 120 min) Ga-citrate PET/CT study prior to lung biopsy. Qualitative and semi-quantitative measures of tracer uptake in the lung lesions (SUVmax) were compared to the histopathology in order to establish an imaging pattern to distinguish benign from malignant lesions. Results Fourteen patients (38.9 %) were diagnosed with a malignant lesion, 12 (33.3 %) with tuberculosis (TB), and 10 participants (27.8 %) with other benign lung lesions. At 60-min post-injection, patients who were diagnosed with a malignant lesion (n = 14) demonstrated a mean SUVmax of 3.36 ± 1.14, with a median value of 3.04 (min = 1.56, max = 4.65).Those with TB (n = 12) demonstrated a SUVmax of 3.99 ± 2.28, and a median value of 3.71 (pct25 = 2.19, pct75 = 4.95). In patients with other benign lesions (n = 10), the following values were observed: a SUVmax of 2.70 ± 1.31, a median value of 2.50 (pct25 = 1.76, pct75 = 3.59). The mean values of these three types of pathology were not statistically significant (p = 0.1919), and therefore the SUVmax could not be used to accurately distinguish between these lesions using both early and delayed imaging. Conclusion This study, as the first Ga-citrate PET/CT in humans for the in vivo imaging of lung pathology, demonstrated its potential for the detection of both malignancy and TB. However, Ga-citrate seemed incapable of providing a clear distinction between malignant and benign lung lesions in a setting with a high prevalence of granulomatous diseases such as TB.


title: Biased skip lists

abstract: We design a variation of skip lists that performs well for generally biased access sequences. Given n items, each with a positive weight wi, 1 ≤ i ≤ n, the time to access item i is O(1 + log (W/wi)), where W=∑i=1nwi; the data structure is dynamic. We present two instantiations of biased skip lists, one of which achieves this bound in the worst case, the other in the expected case. The structures are nearly identical; the deterministic one simply ensures the balance condition that the randomized one achieves probabilistically. We use the same method to analyze both.


title: Combating catastrophic forgetting with developmental compression

abstract: Generally intelligent agents exhibit successful behavior across problems in several settings. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting: sequential learning corrupts knowledge obtained earlier in the sequence, or tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or to enforce modularity such that minimally overlapping sub-functions contain task specific knowledge. While successful, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data. Here we present a method for addressing catastrophic forgetting called developmental compression. It exploits the mild impacts of developmental mutations to lessen adverse changes to previously-evolved capabilities and `compresses' specialized neural networks into a generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation and suggesting better scalability than existing approaches. We validate this method on a robot control problem and hope to extend this approach to other machine learning domains in the future.


title: KBQA: Learning Question Answering over QA Corpora and Knowledge Bases

abstract: Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of "canned" questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates, over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city's population, we learn templates such as What's the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.


title: Classification and segmentation of brain tumor using Adaboost classifier

abstract: Abnormal or uncontrollable growth of a tissue within the brain is known as a brain tumor. The tumor detection, classification and investigate by experts is a usual technique for brain tumor classification from MRI. Explicit classification of brain MRI and interpretation of brain images organized and various techniques has been proposed. The proposed method for classification of Brain Tumor using Classifier and Neural Network uses segmentation algorithms which include Skull Striping along with morphological operation aiming that preprocessing, Anisotropic Diffusion Filtering uses for noise removal and smoothing with edge detection locating the boundaries anatomically of the image. Secondly, wavelet transforms used in the decomposition and the feature extraction of MRI and combining features of MRI images. Finally, Adaboost uses classification techniques, learn by selecting discriminative features proposed to classify MRI images into tumorous (malignant) and non-tumorous (benign). Experimental results show 100% accuracy for 155 MRI images.


title: Towards Measuring Happiness in Saudi Arabia based on Tweets: A research proposal

abstract: Social media platforms such as Twitter, Facebook and Blogs are growing enormously in terms of the number of users. Due to the rapid growth of the data produced from social media users, these platforms are becoming one of the most valuable data sources. Social media became outlets for people to express their thoughts, opinions, and emotions in a real-time manner. Therefore, Social media data have been utilized in literature for many sentiment analysis research. Although there have been many types of research on the sentimental analysis in English, the amount of Arabic-based sentiment analysis studies and tools are still limited. In this paper, we reviewed selected literature related to sentiment analysis in order to present a staged road map of Arabic tweets sentiment analysis in favor of measuring the happiness levels in Saudi Arabia cities. We reported the methodology to numerically represent the happiness of each city by determining the sentiment of 2000 geo-tagged tweets in Saudi Arabia using machine learning techniques. Expected results would not only rank the happiness levels for the cities in Saudi Arabia but certainly identify activities or circumstances that contribute to citizens' happiness. These expected results later can be used by institutions of related interest such as General Entertainment Authority to help planning new activities and attractions.


title: Connection mechanism capable of genderless coupling for modular manipulator system

abstract: Gender characteristics of connecting side between modules affects the number of configurations after assembly of modular manipulator systems. A modular connection mechanism capable of genderless coupling of mechanical and electrical connection between modules is introduced. The connection can be established between two modules via four types of relative positions of 90° intervals.


title: Design and development of a smart baby monitoring system based on Raspberry Pi and Pi camera

abstract: This project presents a baby monitoring system for busy parents so that they can ensure the proper care and safety of their babies. This system can detect the baby's motion and sound; especially crying and video output of baby's present position can be displayed on a display monitor so that the mother or another responsible person can watch the baby while away from him or her. This baby monitoring system is capable of detecting motion and crying condition of the baby automatically. The Raspberry Pi B+ module is used to make the total control system of the hardware, condenser MIC is used to detect baby's crying, PIR motion sensor is incorporated to detect baby's movement and Pi camera is used to capture the baby's motion. A display is used to have video output of sleeping baby. Finally, the developed hardware is tested to analysis the capability of detecting the motion and crying sound of baby as well as the video output. This proposed system can provide an easier and convenient way for busy parents in terms of taking care of their babies.


title: SWRL Rule Editor - A Web Application as Rich as Desktop Business Rule Editors

abstract: The Semantic Web Rule Language (SWRL) allows the combination of rules and ontology terms, defined using the Web Ontology Language (OWL), to increase the expressiveness of both. However, as rule sets grow, they become difficult to understand and error prone, especially when used and maintained by more than one person. If SWRL is to become a true web standard, it has to be able to handle big rule sets. To find answers to this problem, we first surveyed business rule systems and found the key features and interfaces they used and then, based on our finds, we proposed techniques and tools that use new visual representations to edit rules in a web application. They allow error detection, rule similarity analysis, rule clustering visualization and atom reuse between rules. These tools are implemented in the SWRL Editor, an open source plug-in for Web-Protege (a web-based ontology editor) that leverages Web-Protege’s collaborative tools to allow groups of users to not only view and edit rules but also comment and discuss about them. We evaluated our solution comparing it to the only two SWRL editor implementations openly available and showed that it implements more of the key features present in traditional rule systems.


title: System-Level Test Case Prioritization Using Machine Learning

abstract: Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods for test case prioritization exist, they mostly require access to source code (i.e., white-box). However, in industrial practice, system-level testing is an important task that usually grants no access to source code (i.e., black-box). Hence, for an effective regression testing process, other information has to be employed. In this paper, we introduce a novel technique for test case prioritization for manual system-level regression testing based on supervised machine learning. Our approach considers black-box meta-data, such as test case history, as well as natural language test case descriptions for prioritization. We use the machine learning algorithm SVM Rank to evaluate our approach by means of two subject systems and measure the prioritization quality. Our results imply that our technique improves the failure detection rate significantly compared to a random order. In addition, we are able to outperform a test case order given by a test expert. Moreover, using natural language descriptions improves the failure finding rate.


title: The Psychology of the Child Tr

abstract: Coatings having a matt finish are made by applying a powder varnish composition to a substrate and thereafter reacting the varnish at a temperature of 100 DEG to 240 DEG C to form the matt finish coating. The powder varnish composition is a finely divided, particulate mixture of I. A 1,2-EPOXY COMPOUND HAVING A MELTING POINT GREATER THAN 40 DEG C and containing at least one 1,2-epoxy group; and II. A SALT OF A POLYCARBOXYLIC ACID (SUCH AS PYROMELLITIC ACID) CONTAINING THREE OR MORE CARBOXYL GROUPS AND A CYCLIC AMIDINE (SUCH AS 2-PHENYLIMIDAZOLINE).


title: Amorphous silicon active pixel sensor readout circuit for digital imaging

abstract: The most widely used architecture in large-area amorphous silicon (a-Si) flat panel imagers is a passive pixel sensor (PPS), which consists of a detector and a readout switch. While the PPS has the advantage of being compact and amenable toward high-resolution imaging, reading small PPS output signals requires external column charge amplifiers that produce additional noise and reduce the minimum readable sensor input signal. This work presents a current-mediated amorphous silicon active pixel readout circuit that performs on-pixel amplification of noise-vulnerable sensor input signals to minimize the effect of external readout noise sources associated with "off-chip" charge amplifiers. Results indicate excellent small-signal linearity along with a high, and programmable, charge gain. In addition, the active pixel circuit shows immunity to shift in threshold voltage that is characteristic of a-Si devices. Preliminary circuit noise results and analysis appear promising for its use in noise-sensitive, large-area, medical diagnostic imaging applications such as digital fluoroscopy.


title: Evaluation of the modern visual SLAM methods

abstract: Simultaneous Localization and Mapping (SLAM) is a challenging task in robotics. Researchers work hard on it, so several novel SLAM algorithms as well as enhancements for the known ones are published every year. We have selected recent (2013–mid. 2015) approaches that in theory can be run on mobile robot and evaluated it. This paper gives brief intuitive description of ORB-SLAM, LSD-SLAM, L-SLAM and OpenRatSLAM algorithms, then compares the algorithms theoretically (based on given description) and evaluates them with TUM RGB-D benchmark.


Impact of Smartphone Position on Sensor Values and Context Discovery


title: Process mining: a two-step approach to balance between underfitting and overfitting

abstract: Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such “overfitting” by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are “overfitting” (allow only for what has actually been observed) while other parts may be “underfitting” (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between “overfitting” and “underfitting”. To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the “theory of regions”, the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.


Ammi visnaga in the treatment of the anginal syndrome.


title: Advances in View-Invariant Human Motion Analysis: A Review

abstract: As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis.


title: CyberPhishing: A Game-Based Platform for Phishing Awareness Testing

abstract: Phishing attacks sap billions of dollars annually from unsuspecting individuals while compromising individual privacy. Companies and privacy advocates seek ways to better educate the populace against such attacks. Current approaches examining phishing include test-based techniques that ask subjects to classify content as phishing or not and inthe- wild techniques that directly observe subject behavior through distribution of faked phishing attacks. Both approaches have issues. Test-based techniques produce less reliable data since subjects may adjust their behavior with the expectation of seeing phishing stimuli, while in-the-wild studies can put subjects at risk through lack of consent or exposure of data. This paper examines a third approach that seeks to incorporate game-based learning techniques to combine the realism of in-thewild approaches with the training features of testing approaches. We propose a three phase experiment to test our approach on our CyberPhishing simulation platform, and present the results of phase one.


title: An adaptive system for detecting malicious queries in web attacks

abstract: Web request query strings (queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system (AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute’s web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50% and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is 3.07 times that by the popular support vector machine adaptive learning (SVM AL) method. The malicious queries obtained can be used to update the web application firewall (WAF) signature library.


Limited-Memory BFGS for Click-Through Rate Prediction 2 . Data Set


title: UltraHaptics: multi-point mid-air haptic feedback for touch surfaces

abstract: We introduce UltraHaptics, a system designed to provide multi-point haptic feedback above an interactive surface. UltraHaptics employs focused ultrasound to project discrete points of haptic feedback through the display and directly on to users' unadorned hands. We investigate the desirable properties of an acoustically transparent display and demonstrate that the system is capable of creating multiple localised points of feedback in mid-air. Through psychophysical experiments we show that feedback points with different tactile properties can be identified at smaller separations. We also show that users are able to distinguish between different vibration frequencies of non-contact points with training. Finally, we explore a number of exciting new interaction possibilities that UltraHaptics provides.


title: Weak leakage-resilient client-side deduplication of encrypted data in cloud storage

abstract: Recently, Halevi et al. (CCS '11) proposed a cryptographic primitive called proofs of ownership (PoW) to enhance security of client-side deduplication in cloud storage. In a proof of ownership scheme, any owner of the same file F can prove to the cloud storage that he/she owns file F in a robust and efficient way, in the bounded leakage setting where a certain amount of efficiently-extractable information about file F is leaked. Following this work, we propose a secure client-side deduplication scheme, with the following advantages: our scheme protects data confidentiality (and some partial information) against both outside adversaries and honest-but-curious cloud storage server, while Halevi et al. trusts cloud storage server in data confidentiality; our scheme is proved secure w.r.t. any distribution with sufficient min-entropy, while Halevi et al. (the last and the most practical construction) is particular to a specific type of distribution (a generalization of "block-fixing" distribution) of input files. The cost of our improvements is that we adopt a weaker leakage setting: We allow a bounded amount one-time leakage of a target file before our scheme starts to execute, while Halevi et al. allows a bounded amount multi-time leakage of the target file before and after their scheme starts to execute. To the best of our knowledge, previous works on client-side deduplication prior Halevi et al. do not consider any leakage setting.


title: The tracker: a threat to statistical database security

abstract: The query programs of certain databases report raw statistics for query sets, which are groups of records specified implicitly by a characteristic formula. The raw statistics include query set size and sums of powers of values in the query set. Many users and designers believe that the individual records will remain confidential as long as query programs refuse to report the statistics of query sets which are too small. It is shown that the compromise of small query sets can in fact almost always be accomplished with the help of characteristic formulas called trackers. Schlorer's individual tracker is reviewed; it is derived from known characteristics of a given individual and permits deducing additional characteristics he may have. The general tracker is introduced: It permits calculating statistics for arbitrary query sets, without requiring preknowledge of anything in the database. General trackers always exist if there are enough distinguishable classes of individuals in the database, in which case the trackers have a simple form. Almost all databases have a general tracker, and general trackers are almost always easy to find. Security is not guaranteed by the lack of a general tracker.


title: Bioremediation of Turbid Surface Water Using Seed Extract from Moringa oleifera Lam. (Drumstick) Tree

abstract: An indigenous water treatment method uses Moringa oleifera seeds in the form of a water-soluble extract in suspension, resulting in an effective natural clarification agent for highly turbid and untreated pathogenic surface water. Efficient reduction (80.0% to 99.5%) of high turbidity produces an aesthetically clear supernatant, concurrently accompanied by 90.00% to 99.99% (1 to 4 log) bacterial reduction. Application of this low-cost Moringa oleifera protocol is recommended for simplified, point-of-use, low-risk water treatment where rural and peri-urban people living in extreme poverty are presentlydrinkinghighlyturbidandmicrobiologically contaminated water.Curr. Protoc. Microbiol. 16:1G.2.1-1G.2.14. C � 2010 by John Wiley & Sons, Inc.