title: Encryption is Not Enough: Inferring User Activities on KakaoTalk with Traffic Analysis

abstract: Many people started being concerned about their privacy in delivering private chats, photographs, contacts and other personal information through mobile instant messaging services. Fortunately, in the majority of mobile instant messaging services, encrypted communication channels e.g., using the SSL/TLS protocols are used by default to protect delivered messages against eavesdropping attacks. In this paper, however, we show that encryption is not enough. For example, in a real world service named KakaoTalk, many users' online activities can effectively be identified with 99.7i¾?% accuracy even though traffic is encrypted. We present a practical traffic analysis attack using a supervised machine learning technique.


title: Weaknesses in the Key Scheduling Algorithm of RC4

abstract: In this paper we present several weaknesses in the key scheduling algorithm of RC4, and describe their cryptanalytic significance. We identify a large number of weak keys, in which knowledge of a small number of key bits suffices to determine many state and output bits with non-negligible probability. We use these weak keys to construct new distinguishers for RC4, and to mount related key attacks with practical complexities. Finally, we show that RC4 is completely insecure in a common mode of operation which is used in the widely deployed Wired Equivalent Privacy protocol (WEP, which is part of the 802.11 standard), in which a fixed secret key is concatenated with known IV modifiers in order to encrypt different messages. Our new passive ciphertext-only attack on this mode can recover an arbitrarily long key in a negligible amount of time which grows only linearly with its size, both for 24 and 128 bit IV modifiers.


title: Bayesian LSTMs in medicine

abstract: The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time series. Four medical time series datasets are used to show the accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we show cherry-picked examples of confident and uncertain classifications of the medical time series. With simple modifications of the common practice for deep learning, significant improvements can be made for the medical practitioner and patient.


title: Bayesian Network Structure Learning from Limited Datasets through Graph Evolution

abstract: Bayesian networks are stochastic models, widely adopted to encode knowledge in several fields. One of the most interesting features of a Bayesian network is the possibility of learning its structure from a set of data, and subsequently use the resulting model to perform new predictions. Structure learning for such models is a NP-hard problem, for which the scientific community developed two main approaches: score-and-search metaheuristics, often evolutionary-based, and dependency-analysis deterministic algorithms, based on stochastic tests. State-of-the-art solutions have been presented in both domains, but all methodologies start from the assumption of having access to large sets of learning data available, often numbering thousands of samples. This is not the case for many real-world applications, especially in the food processing and research industry. This paper proposes an evolutionary approach to the Bayesian structure learning problem, specifically tailored for learning sets of limited size. Falling in the category of score-and-search techniques, the methodology exploits an evolutionary algorithm able to work directly on graph structures, previously used for assembly language generation, and a scoring function based on the Akaike Information Criterion, a well-studied metric of stochastic model performance. Experimental results show that the approach is able to outperform a state-of-the-art dependency-analysis algorithm, providing better models for small datasets.


title: A Serious Game for Clinical Assessment of Cognitive Status: Validation Study

abstract: Background: We propose the use of serious games to screen for abnormal cognitive status in situations where it may be too costly or impractical to use standard cognitive assessments (eg, emergency departments). If validated, serious games in health care could enable broader availability of efficient and engaging cognitive screening. Objective: The objective of this work is to demonstrate the feasibility of a game-based cognitive assessment delivered on tablet technology to a clinical sample and to conduct preliminary validation against standard mental status tools commonly used in elderly populations. Methods: We carried out a feasibility study in a hospital emergency department to evaluate the use of a serious game by elderly adults (N=146; age: mean 80.59, SD 6.00, range 70-94 years). We correlated game performance against a number of standard assessments, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Confusion Assessment Method (CAM). Results: After a series of modifications, the game could be used by a wide range of elderly patients in the emergency department demonstrating its feasibility for use with these users. Of 146 patients, 141 (96.6%) consented to participate and played our serious game. Refusals to play the game were typically due to concerns of family members rather than unwillingness of the patient to play the game. Performance on the serious game correlated significantly with the MoCA (r=–.339, P <.001) and MMSE (r=–.558, P <.001), and correlated (point-biserial correlation) with the CAM (r=.565, P <.001) and with other cognitive assessments. Conclusions: This research demonstrates the feasibility of using serious games in a clinical setting. Further research is required to demonstrate the validity and reliability of game-based assessments for clinical decision making. [JMIR Serious Games 2016;4(1):e7]


title: Automated Curriculum Learning for Neural Networks

abstract: We introduce a method for automatically selecting the path, or syllabus, that a neural network follows through a curriculum so as to maximise learning efficiency. A measure of the amount that the network learns from each data sample is provided as a reward signal to a nonstationary multi-armed bandit algorithm, which then determines a stochastic syllabus. We consider a range of signals derived from two distinct indicators of learning progress: rate of increase in prediction accuracy, and rate of increase in network complexity. Experimental results for LSTM networks on three curricula demonstrate that our approach can significantly accelerate learning, in some cases halving the time required to attain a satisfactory performance level.


title: An efficient scheme for authenticating public keys in sensor networks

abstract: With the advance of technology, Public Key Cryptography (PKC) will sooner or later be widely used in wireless sensor networks. Recently, it has been shown that the performance of some public-key algorithms, such as Elliptic Curve Cryptography (ECC), is already close to being practical on sensor nodes. However, the energy consumption of PKC is still expensive, especially compared to symmetric-key algorithms. To maximize the lifetime of batteries, we should minimize the use of PKC whenever possible in sensor networks.This paper investigates how to replace one of the important PKC operations--the public key authentication--with symmetric key operations that are much more efficient. Public key authentication is to verify the authenticity of another party's public key to make sure that the public key is really owned by the person it is claimed to belong to. In PKC, this operation involves an expensive signature verification on a certificate. We propose an efficient alternative that uses one-way hash function only. Our scheme uses all sensor's public keys to construct a forest of Merkle trees of different heights. By optimally selecting the height of each tree, we can minimize the computation and communication costs. The performance of our scheme is evaluated in the paper.


title: Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion

abstract: We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences. It then recovers the missing scenes from the clean parts using the proposed "TECROMAC" (TEmporally Contiguous RObust MAtrix Completion) objective. The objective function balances temporal smoothness with a low rank solution while staying close to the original observations. The matrix whose the rows are pixels and columnsare days corresponding to the image, has low-rank because the pixels reflect land-types such as vegetation, roads and lakes and there are relatively few variations as a result. We provide efficient optimization algorithms for TECROMAC, so we can exploit images containing millions of pixels. Empirical results on real satellite image sequences, as well as simulated data, demonstrate that our approach is able to recover underlying images from heavily cloud-contaminated observations.


title: Mobile Payments — State of the Art and Open Problems

abstract: We describe electronic payment solutions and the technical problems, which have to be solved in order to make these payments mobile. Mobile payments should be both secure and convenient for the consumer. The problems of implementing mobile payment systems are manifold. The technical capabilities of mobile devices are - at least today - too limited to allow a simple re-use of existing Internet payment protocols. Moreover, a number of different mobile application environments exist today, which differ from a technical perspective as well as from geographical spread. This makes it extremely difficult to define mobile payment protocols, which can be used on a global basis. Therefore, compromise solutions have to be found and standardized, which offer a reasonable level of security based on the existing functions offered in mobile devices and networks.


title: Summarizing sporting events using twitter

abstract: The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.


title: SVMs for Automatic Speech Recognition: A Survey.

abstract: Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact. ::: ::: During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed. ::: ::: These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research.


title: NP-completeness Results for Graph Burning on Geometric Graphs

abstract: Graph burning runs on discrete time steps. The aim of the graph burning problem is to burn all the vertices in a given graph in the least amount of time steps. The least number of required time steps is known to be the burning number of the graph. The spread of social influence, an alarm, or a social contagion can be modeled using graph burning. The less the burning number, the quick the spread. Computationally, graph burning is hard. It has already been proved that burning of path forests, spider graphs, and trees with maximum degree three are NP-Complete. In this work we study graph burning on geometric graphs and show NP-completeness results on several sub classes. More precisely, we show burning problem to be NP-complete on interval graph, permutation graph and disk graph.


title: High power pulse generator based on avalanche transistor Marx circuit

abstract: Marx bank pulse circuit based on avalanche transistors can generate high amplitude, high-repetition-rate and low-jitter ultra-wideband pulses. The rising edge of the pulse is determined mainly by the performance of avalanche transistors. The amplitude of the pulse can be adjusted by changing the number of stage and the value of charging capacitors. In this paper, A 25 stages Marx bank pulse circuit is designed and tested, In order to reduce the jitter of the output pulses, an optimized driver circuit is designed and the charging resistors and capacitors are tuned to achieve a higher amplitude. Finally, 2.7 kV peak voltage of the output pulse is obtained, the FWHM is 1 ns, the maximum repetition rate is 10 kHz.


title: Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks

abstract: In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification. The squared EMD loss uses the predicted probabilities of all classes and penalizes the miss-predictions according to a ground distance matrix that quantifies the dissimilarities between classes. We demonstrate that on datasets with strong inter-class relationships such as an ordering between classes, our exact squared EMD losses yield new state-of-the-art results. Furthermore, we propose a method to automatically learn this matrix using the CNN's own features during training. We show that our method can learn a ground distance matrix efficiently with no inter-class relationship priors and yield the same performance gain. Finally, we show that our method can be generalized to applications that lack strong inter-class relationships and still maintain state-of-the-art performance. Therefore, with limited computational overhead, one can always deploy the proposed loss function on any dataset over the conventional cross-entropy.


title: Anonymous credentials on a standard java card

abstract: Secure identity tokens such as Electronic Identity (eID) cards are emerging everywhere. At the same time user-centric identity management gains acceptance. Anonymous credential schemes are the optimal realization of user-centricity. However, on inexpensive hardware platforms, typically used for eID cards, these schemes could not be made to meet the necessary requirements such as future-proof key lengths and transaction times on the order of 10 seconds. The reasons for this is the need for the hardware platform to be standardized and certified. Therefore an implementation is only possible as a Java Card applet. This results in severe restrictions: little memory (transient and persistent), an 8-bit CPU, and access to hardware acceleration for cryptographic operations only by defined interfaces such as RSA encryption operations. Still, we present the first practical implementation of an anonymous credential system on a Java Card 2.2.1. We achieve transaction times that are orders of magnitudes faster than those of any prior attempt, while raising the bar in terms of key length and trust model. Our system is the first one to act completely autonomously on card and to maintain its properties in the face of an untrusted terminal. In addition, we provide a formal system specification and share our solution strategies and experiences gained and with the Java Card.


title: A Novel ZVT-ZCT-PWM Boost Converter

abstract: In this study, a new boost converter with an active snubber cell is proposed. The active snubber cell provides main switch to turn ON with zero-voltage transition (ZVT) and to turn OFF with zero-current transition (ZCT). The proposed converter incorporating this snubber cell can operate with soft switching at high frequencies. Also, in this converter all semiconductor devices operate with soft switching. There is no additional voltage stress across the main and auxiliary components. The converter has a simple structure, minimum number of components, and ease of control as well. The operation principle and detailed steady-state analysis of the novel ZVT-ZCT-PWM boost converter are given. The presented theoretical analysis is verified exactly by a prototype of 100 kHz and 1 kW converter. Also, the overall efficiency of the new converter has reached a value of 97.8% at nominal output power.


title: Evaluating WAP News Sites: The Webqual/m Approach

abstract: This paper reports on the evaluation of wireless Internet news sites using the WebQual/m instrument. From initial application in the domain of traditional Internet Web sites, the instrument has been adapted for sites delivered using the wireless application protocol (WAP). The WebQual approach is to assess the Web-site quality from the perspective of the 'voice of the customer’, an approach adopted in quality function deployment. The WebQual/m instrument is used to assess customer perceptions of information, site and useroriented qualities. In particular, the qualities of three UK-based WAP news sites are assessed via an online questionnaire. The results are reported and analysed and demonstrate considerable variations in the offerings of the news sites. The findings and their implications for mobile commerce are discussed and some conclusions and directions for further research are provided.


title: A call for the perfect marriage between classical performance evaluation and state-ofthe-art verifi cation techniques

abstract: A call for the perfect marriage between classical performance evaluation and state-of-the-art verification techniques.


Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition


title: Wideband Circularly Polarized Substrate Integrated Waveguide Endfire Antenna System With High Gain

abstract: A K-band endfire substrate integrated waveguide (SIW) circularly polarized (CP) antenna system on a single-layer printed-circuit board is proposed. A high-gain SIW H-plane horn and a Vivaldi antenna are developed to produce two orthogonal polarizations in the plane of the substrate. They are combined with a low-profile SIW 3-dB coupler to provide identical feeding amplitudes with 90° phase difference. The performance of the CP antenna system is demonstrated over the 23-27-GHz frequency range by comparing simulations and measurements in terms of gain, axial ratio, radiation pattern, and return loss. The results show that the proposed antenna system operates with a wideband 3-dB axial ratio from 24.25 to 26.5 GHz and a high and uniform gain of almost 8 dB. Measured results are found in good agreement with simulations.


title: Recruitment of internal oblique and transversus abdominis muscles during the eccentric phase of the curl-up exercise.

abstract: The purpose of this study was to analyze the results of a training method to increase voluntary recruitment of internal oblique and transversus abdominis muscles. Forty volunteers were assigned to either a Control or an Experimental Group. Training consisted of multisensory (auditory, tactile, visual, and kinesthetic) cuing focused on the lower abdominal muscles during the slow curl-back (eccentric) phase of curl-up exercises with the subjects' feet unsupported. We recorded integrated electromyographic (IEMG) values using surface electrodes from internal oblique and transversus abdominis muscles during pretest and posttest exercises. Using an analysis of covariance, posttest IEMG values of the Experimental Group were significantly higher (p less than .001) than the Control Group. Our results reveal that multisensory cuing with the feet unsupported during the curl-back is an effective method of increasing combined recruitment of internal oblique and transversus abdominis muscles. Motor skills developed by this technique are discussed in relation to optimal trunk function and rehabilitation.


title: Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

abstract: This work concentrates on vision processing for Advanced Driver Assistance Systems (ADAS) and intelligent vehicle applications. The authors propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. The authors extension learns color appearance models for road and obstacle classes in an online and self-supervised fashion. The algorithm is tightly integrated within the core of the optimization process of the original Stixel World, allowing for strong fusion of the disparity and color signals. The authors perform an extensive evaluation, including different self-supervised learning strategies and different color models. The authors newly recorded, publicly available data set is intentionally focused on challenging traffic scenes with many low-texture regions, causing numerous disparity artifacts. In this evaluation, the authors increase the F-score of the drivable distance from 0.86 to 0.97, compared to a tuned version of the state-of-the-art baseline method. This clearly shows that the authors color extension increases the robustness of the Stixel World, by reducing the number of falsely detected obstacles while not deteriorating the detection of true obstacles.


title: An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings

abstract: Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.


title: Curation through use: understanding the personal value of social media

abstract: Content generation on social network sites has been considered mainly from the perspective of individuals interacting with social network contacts. Yet research has also pointed to the potential for social media to become a meaningful personal archive over time. The aim of this paper is to consider how social media, over time and across sites, forms part of the wider digital archiving space for individuals. Our findings, from a qualitative study of 14 social media users, highlight how although some sites are more associated with 'keepable' social media than others, even those are not seen as archives in the usual sense of the word. We show how this perception is bound up with five contradictions, which center on social media as curated, as a reliable repository of meaningful content, as readily encountered and as having the potential to present content as a compelling narrative. We conclude by highlighting opportunities for design relating to curation through use and what this implies for personal digital archives, which are known to present difficulties in terms of curation and re-finding.


title: UWB Printed Slot Antenna With Bluetooth and Dual Notch Bands

abstract: A novel technique to add an extra Bluetooth band and dual notch bands simultaneously to a compact ultrawideband (UWB) printed slot antenna is proposed. The UWB slot antenna, covering 3.1-11 GHz, is in the shape of an octagon and is fed by a rectangular patch with a beveled bottom edge. To create an extra band outside the UWB frequency range, centered at the 2.4-GHz Bluetooth band, a quarter-wavelength stub is attached to the high concentrated current area in the ground plane. Two notch bands, centered at 3.5-GHz WiMAX and 5.8-GHz WLAN, are also created by placing two stubs similar to that of the extra band. The proposed antenna has a compact size (23 × 28 mm 2), almost stable radiation pattern, and reflection coefficient of lower than -10 dB across the entire passband. A prototype of the proposed antenna is fabricated, and the measured results are shown to be in good agreement with the simulated results.


title: Compositionality in rational analysis: Grammar-based induction for concept learning

abstract: Rational analysis attempts to explain aspects of human cognition as an adaptive response to the environment (Marr, 1982; Anderson, 1990; Chater, Tenenbaum, & Yuille, 2006). The dominant approach to rational analysis today takes an ecologically reasonable specification of a problem facing an organism, given in statistical terms, then seeks an optimal solution, usually using Bayesian methods. This approach has proven very successful in cognitive science; it has predicted perceptual phenomena (Geisler & Kersten, 2002; Feldman, 2001), illuminated puzzling effects in reasoning (Chater & Oaksford, 1999; Griffiths & Tenenbaum, 2006), and, especially, explained how human learning can succeed despite sparse input and endemic uncertainty (Tenenbaum, 1999; Tenenbaum & Griffiths, 2001). However, there were earlier notions of the “rational” analysis of cognition that emphasized very different ideas. One of the central ideas behind logical and computational approaches, which previously dominated notions of rationality, is that meaning can be captured in the structure of representations, but that compositional semantics are needed for these representations to provide a coherent account of thought. In this chapter we attempt to reconcile the modern approach to rational analysis with some aspects of this older, logico-computational approach. We do this via a model—offered as an extended example—of human concept learning. In the current chapter we are primarily concerned with formal aspects of this approach; in other work (Goodman, Tenenbaum, Feldman, & Griffiths, in press) we more carefully study a variant of this model as a psychological model of human concept learning. Explaining human cognition was one of the original motivations for the development of formal logic. George Boole, the father of digital logic, developed his symbolic language in order to explicate the rational laws underlying thought: his principal work, An Investigation of the Laws of Thought (Boole, 1854), was written to “investigate the fundamental laws of those operations of the mind by which reasoning is performed,” and arrived at “some probable intimations concerning the nature and constitution of the human mind” (p. 1). Much of mathematical logic since Boole can be regarded as an attempt to capture the coherence of thought in a formal system. This is particularly apparent in the work, by Frege (1892), Tarski (1956) and others, on model-theoretic semantics for logic, which aimed to create formal systems both flexible and systematic enough to capture the complexities of mathematical thought. A central component in this program is compositionality. Consider Frege’s Principle1: each syntactic operation of a formal language should have a corresponding semantic operation. This principle requires syntactic compositionality, that meaningful terms in a formal system are built up by combination operations, as well as compatibility between the syntax and semantics of the system. When Turing, Church, and others suggested that formal systems could be manipulated by mechanical computers it was natural (at least in hindsight) to suggest that cognition operates in a similar way: meaning is manipulated in the mind by computation2. Viewing the mind as a formal computational system in this way suggests that compositionality should also be found in the mind; that is, that mental representations may be combined into new representations, and the meaning of mental representations may be decomposed in terms of the meaning of their components. Two important virtues for a theory of thought result (Fodor, 1975): productivity—the number of representations is unbounded because they may be boundlessly combined— and systematicity—the combination of two representations is meaningful to one who can understand each separately. Despite its importance to the computational theory of mind, compositionality has seldom been captured by modern rational analyses. Yet there are a number of reasons to desire a compositional rational analysis. For instance, productivity of mental representations would provide an explanation of the otherwise puzzling ability of human thought to adapt to novel situations populated by new concepts—even those far beyond the ecological pressures of our evolutionary milieu (such as radiator repairs and the use of fiberglass bottom powerboats). We will show in this chapter that Bayesian statistical methods can be fruitfully combined with compositional representational systems by developing such a model in the well-studied setting of concept learning. This addresses a long running tension in the literature on human concepts: similarity-based statistical learning models have provided a good understanding of how simple concepts can be learned (Medin & Schaffer, 1978; Anderson, 1991; Kruschke, 1992;


title: Memsherlock: an automated debugger for unknown memory corruption vulnerabilities

abstract: Software vulnerabilities have been the main contributing factor to the Internet security problems such as fast spreading worms. Among these software vulnerabilities, memory corruption vulnerabilities such as buffer overflow and format string bugs have been the most common ones exploited by network-based attacks. Many security countermeasures (e.g., patching, automatic signature generation for intrusion detection systems) require vulnerability information to function correctly. However, despite many years of research, automatically identifying unknown software vulnerabilities still remains an open problem. In this paper, we present the development of a security debugging tool named MemSherlock, which can automatically identify unknown memory corruption vulnerabilities upon the detection of malicious payloads that exploit such vulnerabilities. MemSherlock provides critical information for unknown memory corruption vulnerabilities, including (1) the corruption point in the source code (i.e., the statement that allows the exploitation of memory corruption vulnerability), (2) the slice of source code that helps the malicious input to reach the corruption point, and (3) the description of how the malicious input exploits the unknown vulnerability. We evaluate MemSherlock with a set of 11 real-world applications that have buffer overflow, heap overflow, and format string vulnerabilities. The evaluation results indicate that MemSherlock is a useful tool to facilitate the automatic vulnerability analysis process.


title: Representation of Word Sentiment, Idioms and Senses

abstract: Distributional Semantic Models (DSM) that represent words as vectors of weights over a high dimensional feature space have proved very effective in representing semantic or syntactic word similarity. For certain tasks however it is important to represent contrasting aspects such as polarity, different senses or idiomatic use of words. We present two methods for creating embeddings that take into account such characteristics: a feed-forward neural network for learning sentiment specific and a skip-gram model for learning sense specific embeddings. Sense specific embeddings can be used to disambiguate queries and other classification tasks. We present an approach for recognizing idiomatic expressions by means of the embeddings. This can be used to segment queries into meaningful chunks. The implementation is available as a library implemented in Python with core numerical processing written in C++, using a parallel linear algebra library for efficiency and scalability.


title: Progress on retinal image analysis for age related macular degeneration

abstract: Age-related macular degeneration (AMD) is the leading cause of vision loss in those over the age of 50 years in the developed countries. The number is expected to increase by ∼1.5 fold over the next ten years due to an increase in aging population. One of the main measures of AMD severity is the analysis of drusen, pigmentary abnormalities, geographic atrophy (GA) and choroidal neovascularization (CNV) from imaging based on color fundus photograph, optical coherence tomography (OCT) and other imaging modalities. Each of these imaging modalities has strengths and weaknesses for extracting individual AMD pathology and different imaging techniques are used in combination for capturing and/or quantification of different pathologies. Current dry AMD treatments cannot cure or reverse vision loss. However, the Age-Related Eye Disease Study (AREDS) showed that specific anti-oxidant vitamin supplementation reduces the risk of progression from intermediate stages (defined as the presence of either many medium-sized drusen or one or more large drusen) to late AMD which allows for preventative strategies in properly identified patients. Thus identification of people with early stage AMD is important to design and implement preventative strategies for late AMD, and determine their cost-effectiveness. A mass screening facility with teleophthalmology or telemedicine in combination with computer-aided analysis for large rural-based communities may identify more individuals suitable for early stage AMD prevention. ::: ::: In this review, we discuss different imaging modalities that are currently being considered or used for screening AMD. In addition, we look into various automated and semi-automated computer-aided grading systems and related retinal image analysis techniques for drusen, geographic atrophy and choroidal neovascularization detection and/or quantification for measurement of AMD severity using these imaging modalities. We also review the existing telemedicine studies which include diagnosis and management of AMD, and how automated disease grading could benefit telemedicine. As there is no treatment for dry AMD and only early intervention can prevent the late AMD, we emphasize mass screening through a telemedicine platform to enable early detection of AMD. We also provide a comparative study between the imaging modalities and identify potential study areas for further improvement and future research direction in automated AMD grading and screening.


title: Label Correction and Event Detection for Electricity Disaggregation

abstract: Electricity disaggregation focuses on identifying individual appliances from one or more aggregate signals. By reporting detailed appliance usage to consumers, disaggregation has the potential to significantly reduce electrical waste in residential and commercial sectors. However, application of existing methods is limited by two critical shortcomings. First, supervised learning methods implicitly assume error-free labels in training data, an unrealistic expectation for imperfectly-labeled consumer data. Second, supervised and unsupervised learning methods require parameters to be tuned to individual appliances and/or datasets, limiting widespread application. To address these limitations, this paper introduces the implementation of Bayesian changepoint detection (BCD) with necessary adaptations to electricity disaggregation. We introduce an algorithm to effectively apply BCD to automatically correct labels. We then apply BCD to event detection to identify transitions between appliances' on and off states. Performance is evaluated using 3 publicly available datasets containing over 250 appliances across 11 houses. Results show both BCD applications are competitive and in some cases outperform existing state-of-the-art methods without the need for parameter tuning, advancing disaggregation towards widespread, real-world deployment.


title: Soccer Field Localization from a Single Image

abstract: In this work, we propose a novel way of efficiently localizing a soccer field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. In contrast, we formulate this problem as a branch and bound inference in a Markov random field where an energy function is defined in terms of field cues such as grass, lines and circles. Moreover, our approach is fully automatic and depends only on single images from the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to various games and obtain promising results. Finally, we posit that our approach can be applied easily to other sports such as hockey and basketball.


title: Intrinsic dendritic filtering gives low-pass power spectra of local field potentials

abstract: The local field potential (LFP) is among the most important experimental measures when probing neural population activity, but a proper understanding of the link between the underlying neural activity and the LFP signal is still missing. Here we investigate this link by mathematical modeling of contributions to the LFP from a single layer-5 pyramidal neuron and a single layer-4 stellate neuron receiving synaptic input. An intrinsic dendritic low-pass filtering effect of the LFP signal, previously demonstrated for extracellular signatures of action potentials, is seen to strongly affect the LFP power spectra, even for frequencies as low as 10 Hz for the example pyramidal neuron. Further, the LFP signal is found to depend sensitively on both the recording position and the position of the synaptic input: the LFP power spectra recorded close to the active synapse are typically found to be less low-pass filtered than spectra recorded further away. Some recording positions display striking band-pass characteristics of the LFP. The frequency dependence of the properties of the current dipole moment set up by the synaptic input current is found to qualitatively account for several salient features of the observed LFP. Two approximate schemes for calculating the LFP, the dipole approximation and the two-monopole approximation, are tested and found to be potentially useful for translating results from large-scale neural network models into predictions for results from electroencephalographic (EEG) or electrocorticographic (ECoG) recordings.


title: External Computations and Interoperability in the New DLV Grounder

abstract: In this paper we focus on some of the most recent advancements in \(\mathcal I\)-DLV, the new intelligent grounder of DLV; the system has been endowed with means aimed at easing the interoperability and integration with external systems and accommodating external source of computation and value invention within ASP programs. In particular, we describe here the support for external computations via explicit calls to Python scripts, and tools for the interoperability with both relational and graph databases.


title: Intelligibility and Narrating Queer Youth

abstract: William Haver (1998) has written that educators “have very nearly agreed that the pedagogical enterprise is about the production of subjects” (349). Although there is little agreement about what sorts of subjects education aims to produce, he says, “pedagogy is the work of Bildung, a coming to subjectivity as jubilant and relieved self-recognition” (350). Whether educators understand the subject as developing according to a natural ontology or according to culture, the education of subjects is defined by a project to “elaborate the ‘systems of the world,’ to make sense, and to transmit the sense that it makes” (350). This development of subjects entails the production of knowledge and self-knowledge, or what I will call “intelligibility.” As children move through adolescence to adulthood, society expects that they will acquire knowledge of self and other (the world and their place in it will become intelligible to them) and that maturing youth will become intelligible to others, knowable as such and such. In this chapter, I explore this problem of intelligibility—not how we can attain it, but what it attains—as it relates to queer youth. The fraught knowledges that contribute to the construction of queer youth have implications for the interventions adults would create for them and for how queer youth come to know and understand themselves.


title: Anabolic signaling deficits underlie amino acid resistance of wasting, aging muscle.

abstract: The nature of the deficit underlying age-related muscle wasting remains controversial. To test whether it could be due to a poor anabolic response to dietary amino acids, we measured the rates of myofibrillar and sarcoplasmic muscle protein synthesis (MPS) in 44 healthy young and old men, of similar body build, after ingesting different amounts of essential amino acids (EAA). Basal rates of MPS were indistinguishable, but the elderly showed less anabolic sensitivity and responsiveness of MPS to EAA, possibly due to decreased intramuscular expression, and activation (phosphorylation) after EAA, of amino acid sensing/signaling proteins (mammalian target of rapamycin, mTOR; p70 S6 kinase, or p70(S6k); eukaryotic initiation factor [eIF]4BP-1; and eIF2B). The effects were independent of insulin signaling since plasma insulin was clamped at basal values. Associated with the anabolic deficits were marked increases in NFkappaB, the inflammation-associated transcription factor. These results demonstrate first, EAA stimulate MPS independently of increased insulin availability; second, in the elderly, a deficit in MPS in the basal state is unlikely; and third, the decreased sensitivity and responsiveness of MPS to EAA, associated with decrements in the expression and activation of components of anabolic signaling pathways, are probably major contributors to the failure of muscle maintenance in the elderly. Countermeasures to maximize muscle maintenance should target these deficits.


title: An Empirical Approach To Conceptual Case Frame Acquisition

abstract: Conceptual natural language processing systems usually rely on case frame instantiation to recognize events and role objects in text. But generating a good set of case frames for a domain is timeconsuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for acquiring conceptual case frames empirically from unannotated text. Our algorithm builds on previous research on corpus-based methods for acquiring extraction patterns and semantic lexicons. Given extraction patterns and a semantic lexicon for a domain, our algorithm learns semantic preferences for each extraction pattern and merges the syntactically compatible patterns to produce multi-slot case frames with selectional restrictions. The case frames generate more cohesive output and produce fewer false hits than the original extraction patterns. Our system requires only preclassified training texts and a few hours of manual review to filter the dictionaries, demonstrating that conceptual case frames can be acquired from unannotated text without special training resources.


title: Secure in-VM monitoring using hardware virtualization

abstract: Kernel-level attacks or rootkits can compromise the security of an operating system by executing with the privilege of the kernel. Current approaches use virtualization to gain higher privilege over these attacks, and isolate security tools from the untrusted guest VM by moving them out and placing them in a separate trusted VM. Although out-of-VM isolation can help ensure security, the added overhead of world-switches between the guest VMs for each invocation of the monitor makes this approach unsuitable for many applications, especially fine-grained monitoring. In this paper, we present Secure In-VM Monitoring (SIM), a general-purpose framework that enables security monitoring applications to be placed back in the untrusted guest VM for efficiency without sacrificing the security guarantees provided by running them outside of the VM. We utilize contemporary hardware memory protection and hardware virtualization features available in recent processors to create a hypervisor protected address space where a monitor can execute and access data in native speeds and to which execution is transferred in a controlled manner that does not require hypervisor involvement. We have developed a prototype into KVM utilizing Intel VT hardware virtualization technology. We have also developed two representative applications for the Windows OS that monitor system calls and process creations. Our microbenchmarks show at least 10 times performance improvement in invocation of a monitor inside SIM over a monitor residing in another trusted VM. With a systematic security analysis of SIM against a number of possible threats, we show that SIM provides at least the same security guarantees as what can be achieved by out-of-VM monitors.


title: Skin Tone Emoji and Sentiment on Twitter

abstract: In 2015, the Unicode Consortium introduced five skin tone emoji that can be used in combination with emoji representing human figures and body parts. In this study, use of the skin tone emoji is analyzed geographically in a large sample of data from Twitter. It can be shown that values for the skin tone emoji by country correspond approximately to the skin tone of the resident populations, and that a negative correlation exists between tweet sentiment and darker skin tone at the global level. In an era of large-scale migrations and continued sensitivity to questions of skin color and race, understanding how new language elements such as skin tone emoji are used can help frame our understanding of how people represent themselves and others in terms of a salient personal appearance attribute.


title: A Constrained Coupled Matrix-Tensor Factorization for Learning Time-evolving and Emerging Topics

abstract: Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving topic discovery has focused on this notion of time. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints well-motivated for, and, as we demonstrate, are essential for high-quality topic discovery. We qualitatively evaluate our approach using real data from the Physics and also Programming Stack Exchange forum, and we were able to identify topics of varying levels of difficulty which can be linked to external events, such as the announcement of gravitational waves by the LIGO lab in Physics forum. We provide a quantitative evaluation of our method by conducting a user study where experts were asked to judge the coherence and quality of the extracted topics. Finally, our proposed method has implications for automatic curriculum design using the extracted topics, where the notion of the level of difficulty is necessary for the proper modeling of prerequisites and advanced concepts.


title: How To Backdoor Federated Learning

abstract: Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word. We design and evaluate a new model-poisoning methodology based on model replacement. An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training.


title: Modeling Friends and Foes

abstract: How can one detect friendly and adversarial behavior from raw data? Detecting whether an environment is a friend, a foe, or anything in between, remains a poorly understood yet desirable ability for safe and robust agents. This paper proposes a definition of these environmental "attitudes" based on an characterization of the environment's ability to react to the agent's private strategy. We define an objective function for a one-shot game that allows deriving the environment's probability distribution under friendly and adversarial assumptions alongside the agent's optimal strategy. Furthermore, we present an algorithm to compute these equilibrium strategies, and show experimentally that both friendly and adversarial environments possess non-trivial optimal strategies.


title: Survey RPL in a nutshell: A survey

abstract: IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a routing protocol specifically designed for Low power and Lossy Networks (LLN) compliant with the 6LoWPAN protocol. It currently shows up as an RFC proposed by the IETF ROLL working group. However, RPL has gained a lot of maturity and is attracting increasing interest in the research community. The absence of surveys about RPL motivates us to write this paper, with the objective to provide a quick introduction to RPL. In addition, we present the most relevant research efforts made around RPL routing protocol that pertain to its performance evaluation, implementation, experimentation, deployment and improvement. We also present an experimental performance evaluation of RPL for different network settings to understand the impact of the protocol attributes on the network behavior, namely in terms of convergence time, energy, packet loss and packet delay. Finally, we point out open research challenges on the RPL design. We believe that this survey will pave the way for interested researchers to understand its behavior and contributes for further relevant research works.


title: Modeling and optimization of complex building energy systems with deep neural networks

abstract: Modern buildings encompass complex dynamics of multiple electrical, mechanical, and control systems. One of the biggest hurdles in applying conventional model-based optimization and control methods to building energy management is the huge cost and effort of capturing diverse and temporally correlated dynamics. Here we propose an alternative approach which is model-free and data-driven. By utilizing high volume of data coming from advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could accurately represent the operation's temporal dynamics of building complexes. The trained network is then directly fitted into a constrained optimization problem with finite horizons. By reformulating the constrained optimization as an unconstrained optimization problem, we use iterative gradient descents method with momentum to find optimal control inputs. Simulation results demonstrate proposed method's improved performances over model-based approach on both building system modeling and control.


title: Minimum Text Corpus Selection for Limited Domain Speech Synthesis

abstract: This paper concerns limited domain TTS system based on the concatenative method, and presents an algorithm capable to extract the minimal domain-oriented text corpus from the real data of the given domain, while still reaching the maximum coverage of the domain. The proposed approach ensures that the least amount of texts are extracted, containing the most common phrases and (possibly) all the words from the domain. At the same time, it ensures that appropriate phrase overlapping is kept, allowing to find smooth concatenation in the overlapped regions to reach high quality synthesized speech. In addition, several recommendations allowing a speaker to record the corpus more fluently and comfortably are presented and discussed. The corpus building is tested and evaluated on several domains differing in size and nature, and the authors present the results of the algorithm and demonstrate the advantages of using the domain oriented corpus for speech synthesis.


title: Interviewed by Author

abstract: Lately, a new computing paradigm has emerged: “Cloud Computing”. It seems to be promoted as heavily as the “Grid” was a few years ago, causing broad discussions on the differences between Grid and Cloud Computing. The first contribution of this paper is thus a detailed discussion about the different characteristics of Grid Computing and Cloud Computing. This technical classification allows for a well-founded discussion of the business opportunities of the Cloud Computing paradigm. To this end, this paper first presents a business model framework for Clouds. It subsequently reviews and classifies current Cloud offerings in the light of this framework. Finally, this paper discusses challenges that have to be mastered in order to make the Cloud vision come true and points to promising areas for future research.


title: Cerebellar fMRI Activation Increases with Increasing Working Memory Demands

abstract: The aim of the present study was to explore cerebellar contributions to the central executive in n-back working memory tasks using 7-T functional magnetic imaging (fMRI). We hypothesized that cerebellar activation increased with increasing working memory demands. Activations of the cerebellar cortex and dentate nuclei were compared between 0-back (serving as a motor control task), 1-back, and 2-back working memory tasks for both verbal and abstract modalities. A block design was used. Data of 27 participants (mean age 26.6 ± 3.8 years, female/male 12:15) were included in group statistical analysis. We observed that cerebellar cortical activations increased with higher central executive demands in n-back tasks independent of task modality. As confirmed by subtraction analyses, additional bilateral activations following higher executive demands were found primarily in four distinct cerebellar areas: (i) the border region of lobule VI and crus I, (ii) inferior parts of the lateral cerebellum (lobules crus II, VIIb, VIII, IX), (iii) posterior parts of the paravermal cerebellar cortex (lobules VI, crus I, crus II), and (iv) the inferior vermis (lobules VI, VIIb, VIII, IX). Dentate activations were observed for both verbal and abstract modalities. Task-related increases were less robust and detected for the verbal n-back tasks only. These results provide further evidence that the cerebellum participates in an amodal bilateral neuronal network representing the central executive during working memory n-back tasks.


title: Microwave antenna applications of metasurfaces

abstract: This paper reviews the past and current research progress in microwave antenna applications using 2D planar metamaterials or metasurfaces. Firstly, we revisit the definitions of metamaterial and metasurface, and the applications of the ultra-thin metasurfaces for low-profile printed antennas in microwave frequency regime, for the purposes of size reduction and performance enhancement. Then we summarize the different types of metasurfaces and their applications. Finally, we provide the future lookout into microwave antenna and EM related applications using metasurfaces.


title: Design and development of flexible reconfigurable SPI interface between baseband and RF subsystems for wireless radio prototyping

abstract: Prototyping of wireless radio is very important to demonstrate the features of any wireless radio before deployment. The core implementation stages of wireless radio prototype include physical layer digital baseband (DBB) processing, RF transceiver chip configuration and interfacing of baseband and RF. Flexibility and adaptivity are very much required in MIMO-OFDM based realtime prototypes, which is considered a key technology in all modern wireless-access communication systems. Realizing flexible digital interface between DBB and RF transceiver in such prototypes is one of the important challenge. Furthermore, to overcome the disadvantage of non-programmability of discrete Commercial-Off-The-Shelf (COTS) RF components and also to reduce the time to prototype an efficient and flexible interfacing of Integrated RF module is required. These RF ICs are interfaced to digital baseband processor through Serial Peripheral Interface (SPI). This paper aims at design and development of a flexible reconfigurable SPI interface to configure the wide-band RF transceiver. The SPI IP core is implemented in VHDL and ported on Altera Cyclone-V FPGA to meet the timing and to do realtime control of RF transceiver.


title: Prediction of Stock Market Index Movement by Ten Data Mining Techniques

abstract: Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Naive Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.


title: Novel planar wideband omni-directional quasi log-periodic antenna

abstract: In this paper, a novel and simple wideband planar omni-directional antenna is proposed. The simultaneously wideband and omni-directional characteristics of the proposed structure are achieved by adding two mutually-coupled elements to the conventional dipole antenna. This newly proposed structure is able to extend the bandwidth (VSWR/spl les/ 2:1, at UHF band) to 30% compared to simple dipole antennas while maintaining an omni-directional radiation pattern with maximum gain of 2.2dBi.


title: Efficient navigation using slow feature gradients

abstract: A model of hierarchical Slow Feature Analysis (SFA) enables a mobile robot to learn a spatial representation of its environment directly from images captured during a random walk. After the unsupervised learning phase a subset of the resulting representations are orientation invariant and code for the position of the robot. Hence, they change monotonically over space even though the variation of the sensory signals received from the environment might change drastically e.g. during rotation on the spot. Furthermore, the property of spatial smoothness allows us to infer a navigation direction by taking the difference between the measurement at the current location and a measurement at a target location. In our work we investigated the use of slow feature representations, learned for a specific environment, for the purpose of navigation. We present a straightforward method for navigation using gradient descent on the difference between two points specified in slow feature space. Due to its slowness objective, the resulting slow feature representations implicitly encode information about static obstacles, allowing a mobile robot to efficiently circumnavigate them by simply following the steepest gradient in slow feature space.


title: Threat-Based Security Analysis for the Internet of Things

abstract: The Internet of Things (IoT) is an emerging paradigm focusing on the inter-connection of things or devices to each other and to the users. This technology is anticipated to become an integral milestone in the development of smart homes and smart cities. For any technology to be successful and achieve widespread use, it needs to gain the trust of users by providing adequate security and privacy assurance. Despite the growing interest of the research community in IoT, and the emergence of several surveys and papers addressing its architecture and its elements, we are still lacking a thorough analysis of the security and privacy properties that are required for a system where the constituent devices vary in their capabilities. In this paper we provide a threat model based on use-cases of IoT, which can be used to determine where efforts should be invested in order to secure these systems. We conclude by recommending measures that will help in providing security and assuring privacy when using IoT.


title: Integration of multiple speech segmentation cues: a hierarchical framework

abstract: A central question in psycholinguistic research is how listeners isolate words from connected speech despite the paucity of clear word-boundary cues in the signal. A large body of empirical evidence indicates that word segmentation is promoted by both lexical (knowledge-derived) and sublexical (signal-derived) cues. However, an account of how these cues operate in combination or in conflict is lacking. The present study fills this gap by assessing speech segmentation when cues are systematically pitted against each other. The results demonstrate that listeners do not assign the same power to all segmentation cues; rather, cues are hierarchically integrated, with descending weights allocated to lexical, segmental, and prosodic cues. Lower level cues drive segmentation when the interpretive conditions are altered by a lack of contextual and lexical information or by white noise. Taken together. the results call for an integrated, hierarchical, and signal-contingent approach to speech segmentation.


title: Intermodal Freight Transportation

abstract: The 21st century will see a renewed focus on intermodal freight transportation driven by the changing requirements of global supply chains. Each of the transportation modes (air, inland water, ocean, pipeline, rail, and road) has gone through technological evolution and has functioned separately under a modally based regulatory structure for most of the 20th century. With the development of containerization in the mid-1900s, the reorientation toward deregulation near the end of the century, and a new focus on logistics and global supply chain requirements, the stage is set for continued intermodal transportation growth. This paper discusses the status and growth of intermodalism.


Modeling Skill Combination Patterns for Deeper Knowledge Tracing


Predictive Control Of Differential Drive Mobile Robot Considering Dynamics And Kinematics


title: Python Interpreter Performance Deconstructed

abstract: The Python programming language is known for performing poorly on many tasks. While to some extent this is to be expected from a dynamic language, it is not clear how much each dynamic feature contributes to the costs of interpreting Python. In this study we attempt to quantify the costs of language features such as dynamic typing, reference counting for memory management, boxing of numbers, and late binding of function calls. We use an experimental compilation framework for Python that can make use of type annotations provided by the user to specialize the program as well as elide unnecessary reference counting operations and function lookups. The compiled programs run within the Python interpreter and use its internal API to implement language semantics. By separately enabling and disabling compiler optimizations, we can thus measure how much each language feature contributes to total execution time in the interpreter. We find that a boxed representation of numbers as heap objects is the single most costly language feature on numeric codes, accounting for up to 43% of total execution time in our benchmark set. On symbolic object-oriented code, late binding of function and method calls costs up to 30%. Redundant reference counting, dynamic type checks, and Python's elaborate function calling convention have comparatively smaller costs.


title: Deep Venous Thrombosis and Pulmonary Embolism Caused by an Intravascular Synovial Sarcoma of the Common Femoral Vein

abstract: Malignant tumors arising in deep veins of the lower extremities are very uncommon. To our best knowledge, this is the seventh case of a primary venous intravascular synovial sarcoma (SS) reported in literature. A 32-year-old woman was admitted with a second episode of deep venous thrombosis of the right lower limb and pulmonary embolism. Physical and radiological examinations showed besides the thrombosis a tumor arising from the right common femoral vein involving the bifurcation of the common femoral artery. At surgery, en block resection of the tumor including the deep femoral vein and arterial bifurcation was done with an arterial reconstruction using a synthetic graft. Histopathological examination revealed an intravascular SS of the common femoral vein. The mainstay of curative therapy is complete surgical resection of all tumor manifestations with negative histological margins.


title: Factors affecting post-operative mortality in malignant biliary tract obstruction.

abstract: BACKGROUND/AIMS: Although malignant obstruction, itself, is a significant risk factor associated with post-operative mortality, factors affecting mortality in the surgery of malignant obstruction have not been thoroughly studied in the literature. METHODOLOGY: In order to identify independent risk factors which might be associated with an increase in post-operative mortality in patients with malignant biliary obstruction, 24 clinical and laboratory parameters in 52 patients undergoing biliary tract surgery were analyzed retrospectively. RESULTS: Simple regression revealed 24 factors with prognostic significance, but multivariate analysis detected only 3 factors with independent significance in predicting mortality (cholangitis, weight loss of 10kg or more and operative or post-operative blood transfusion). The presence of 2 of these risk factors identified a group of patients with an 37% mortality rate. CONCLUSIONS: The results of this study show that the presence of pre-operative cholangitis, marked weight loss, and operative and post-operative blood transfusion are associated with high post-operative mortality. The major challenge is the prevention of cholangitis in high-risk patients, improvement of the nutritional status of the patients, and avoidance of unnecessary blood transfusions.


title: Quantifying the Impact of Human Mobility on Malaria

abstract: Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.


title: A Robust Quadruped Walking Gait for Traversing Rough Terrain

abstract: Legged locomotion excels when terrains become too rough for wheeled systems or open-loop walking pattern generators to succeed, i.e., when accurate foot placement is of primary importance in successfully reaching the task goal. In this paper we address the scenario where the rough terrain is traversed with a static walking gait, and where for every foot placement of a leg, the location of the foot placement was selected irregularly by a planning algorithm. Our goal is to adjust a smooth walking pattern generator with the selection of every foot placement such that the COG of the robot follows a stable trajectory characterized by a stability margin relative to the current support triangle. We propose a novel parameterization of the COG trajectory based on the current position, velocity, and acceleration of the four legs of the robot. This COG trajectory has guaranteed continuous velocity and acceleration profiles, which leads to continuous velocity and acceleration profiles of the leg movement, which is ideally suited for advanced model-based controllers. Pitch, yaw, and ground clearance of the robot are easily adjusted automatically under any terrain situation. We evaluate our gait generation technique on the Little-Dog quadruped robot when traversing complex rocky and sloped terrains.


title: A review of out-of-autoclave prepregs – Material properties, process phenomena, and manufacturing considerations

abstract: Abstract Out-of-autoclave (OoA) prepreg materials and methods have gained acceptance over the past decade because of the ability to produce autoclave-quality components under vacuum-bag-only (VBO) cure. To achieve low porosity and tight dimensional tolerances, VBO prepregs rely on specific microstructural features and processing techniques. Furthermore, successful cure is contingent upon appropriate material property and process parameter selection. In this article, we review the existing literature on VBO prepreg processing to summarize and synthesize knowledge on these issues. First, the context, development, and defining properties of VBO prepregs are presented. The key processing phenomena and the influence on quality are subsequently described. Finally, cost and environmental performance are considered. Throughout, we highlight key considerations for VBO prepreg processing and identify areas where further study is required.


Public-key cryptanalysis


title: Challenges and Success Factors of ERP Systems in Australian SMEs

abstract: Today, great potential is envisaged for ERP systems in small and medium-sized enterprises (SMEs), and software vendors have been repackaging their ERP systems for SMEs with a recent focus on cloud-based systems. While cloud ERP offers the best solution for SMEs without the overheads of the huge investment and management costs that are associated with traditional ERP systems, the SME sector faces many challenges in their adoption. Traditional ERP studies have predominantly focused on large organizations, and gaps in the literature indicate that both vendor and consumer perspectives require more understanding with new technology offerings for SMEs. This paper describes some of the common challenges, such as cost effectiveness, alignment between software and business processes, customized governance and training, which form the major SME constraints for ERP system adoption. Due to the dynamic nature of SME businesses, best practice guidelines for an SME’s ERP implementation could be arrived at through closer investigation of its business requirements in order to avoid misfits. This forms the main objective of the study. We identify key success factors of ERP implementation in an Australian SME as a case study. These target success factors are then compared to the actual outcomes achieved. Factors such as business process alignment with the ERP system, meeting customer and stakeholder needs and reducing recurring and maintenance costs were key to the success of ERP implementation for the Australian SME. In particular, the IT and business strategy alignment with a customer focus and flexible reporting features of ERP systems has resulted in business agility.


Issues in Underwater Acoustic Sensor Networks


title: Implementation of behavior change techniques in mobile applications for physical activity.

abstract: Background Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well. Purpose To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps. Methods Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps. Results Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others’ approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education. Conclusions Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.


title: Introduction to Modern Cryptography

abstract: Preface I. Introduction and Classical Cryptography Introduction Cryptography and Modern Cryptography The Setting of Private-Key Encryption Historical Ciphers and Their Cryptanalysis Principles of Modern Cryptography Principle 1 - Formal Definitions Principle 2 - Precise Assumptions Principle 3 - Proofs of Security Provable Security and Real-World Security References and Additional Reading Exercises Perfectly Secret Encryption Definitions The One-Time Pad Limitations of Perfect Secrecy Shannon's Theorem References and Additional Reading Exercises II. Private-Key (Symmetric) Cryptography Private-Key Encryption Computational Security The Concrete Approach The Asymptotic Approach Defining Computationally Secure Encryption The Basic Definition of Security Semantic Security Constructing Secure Encryption Schemes Pseudorandom Generators and Stream Ciphers Proofs by Reduction A Secure Fixed-Length Encryption Scheme Stronger Security Notions Security for Multiple Encryptions Chosen-Plaintext Attacks and CPA-Security Constructing CPA-Secure Encryption Schemes Pseudorandom Functions and Block Ciphers CPA-Secure Encryption from Pseudorandom Functions Modes of Operation Stream-Cipher Modes of Operation Block-Cipher Modes of Operation Chosen-Ciphertext Attacks Defining CCA-Security Padding-Oracle Attacks References and Additional Reading Exercises Message Authentication Codes Message Integrity Secrecy vs. Integrity Encryption vs. Message Authentication Message Authentication Codes - Definitions Constructing Secure Message Authentication Codes A Fixed-Length MAC Domain Extension for MACs CBC-MAC The Basic Construction Proof of Security Authenticated Encryption Definitions Generic Constructions Secure Communication Sessions CCA-Secure Encryption Information-Theoretic MACs Constructing Information-Theoretic MACs Limitations on Information-Theoretic MACs References and Additional Reading Exercises Hash Functions and Applications Definitions Collision Resistance Weaker Notions of Security Domain Extension: The Merkle-Damgard Transform Message Authentication Using Hash Functions Hash-and-MAC HMAC Generic Attacks on Hash Functions Birthday Attacks for Finding Collisions Small-Space Birthday Attacks Time/Space Tradeoffs for Inverting Functions The Random-Oracle Model The Random-Oracle Model in Detail Is the Random-Oracle Methodology Sound? Additional Applications of Hash Functions Fingerprinting and Deduplication Merkle Trees Password Hashing Key Derivation Commitment Schemes References and Additional Reading Exercises Practical Constructions of Symmetric-Key Primitives Stream Ciphers Linear-Feedback Shift Registers Adding Nonlinearity Trivium RC4 Block Ciphers Substitution-Permutation Networks Feistel Networks DES - The Data Encryption Standard 3DES: Increasing the Key Length of a Block Cipher AES - The Advanced Encryption Standard Differential and Linear Cryptanalysis Hash Functions Hash Functions from Block Ciphers MD5 SHA-0, SHA-1, and SHA-2 SHA-3 (Keccak) References and Additional Reading Exercises Theoretical Constructions of Symmetric-Key Primitives One-Way Functions Definitions Candidate One-Way Functions Hard-Core Predicates From One-Way Functions to Pseudorandomness Hard-Core Predicates from One-Way Functions A Simple Case A More Involved Case The Full Proof Constructing Pseudorandom Generators Pseudorandom Generators with Minimal Expansion Increasing the Expansion Factor Constructing Pseudorandom Functions Constructing (Strong) Pseudorandom Permutations Assumptions for Private-Key Cryptography Computational Indistinguishability References and Additional Reading Exercises III. Public-Key (Asymmetric) Cryptography Number Theory and Cryptographic Hardness Assumptions Preliminaries and Basic Group Theory Primes and Divisibility Modular Arithmetic Groups The Group ZN Isomorphisms and the Chinese Remainder Theorem Primes, Factoring, and RSA Generating Random Primes Primality Testing The Factoring Assumption The RSA Assumption Relating the RSA and Factoring Assumptions Cryptographic Assumptions in Cyclic Groups Cyclic Groups and Generators The Discrete-Logarithm/Diffie-Hellman Assumptions Working in (Subgroups of) Zp Elliptic Curves Cryptographic Applications One-Way Functions and Permutations Constructing Collision-Resistant Hash Functions References and Additional Reading Exercises Algorithms for Factoring and Computing Discrete Logarithms Algorithms for Factoring Pollard's p - 1 Algorithm Pollard's Rho Algorithm The Quadratic Sieve Algorithm Algorithms for Computing Discrete Logarithms The Pohlig-Hellman Algorithm The Baby-Step/Giant-Step Algorithm Discrete Logarithms from Collisions The Index Calculus Algorithm Recommended Key Lengths References and Additional Reading Exercises Key Management and the Public-Key Revolution Key Distribution and Key Management A Partial Solution: Key-Distribution Centers Key Exchange and the Diffie-Hellman Protocol The Public-Key Revolution References and Additional Reading Exercises Public-Key Encryption Public-Key Encryption - An Overview Definitions Security against Chosen-Plaintext Attacks Multiple Encryptions Security against Chosen-Ciphertext Attacks Hybrid Encryption and the KEM/DEM Paradigm CPA-Security CCA-Security CDH/DDH-Based Encryption El Gamal Encryption DDH-Based Key Encapsulation A CDH-Based KEM in the Random-Oracle Model Chosen-Ciphertext Security and DHIES/ECIES RSA Encryption Plain RSA Padded RSA and PKCS #1 v1.5 CPA-Secure Encryption without Random Oracles OAEP and RSA PKCS #1 v A CCA-Secure KEM in the Random-Oracle Model RSA Implementation Issues and Pitfalls References and Additional Reading Exercises Digital Signature Schemes Digital Signatures - An Overview Definitions The Hash-and-Sign Paradigm RSA Signatures Plain RSA RSA-FDH and PKCS #1 v Signatures from the Discrete-Logarithm Problem The Schnorr Signature Scheme DSA and ECDSA Signatures from Hash Functions Lamport's Signature Scheme Chain-Based Signatures Tree-Based Signatures Certificates and Public-Key Infrastructures Putting It All Together - SSL/TLS Signcryption References and Additional Reading Exercises Advanced Topics in Public-Key Encryption Public-Key Encryption from Trapdoor Permutations Trapdoor Permutations Public-Key Encryption from Trapdoor Permutations The Paillier Encryption Scheme The Structure of ZN2 The Paillier Encryption Scheme Homomorphic Encryption Secret Sharing and Threshold Encryption Secret Sharing Verifiable Secret Sharing Threshold Encryption and Electronic Voting The Goldwasser-Micali Encryption Scheme Quadratic Residues Modulo a Prime Quadratic Residues Modulo a Composite The Quadratic Residuosity Assumption The Goldwasser-Micali Encryption Scheme The Rabin Encryption Scheme Computing Modular Square Roots A Trapdoor Permutation Based on Factoring The Rabin Encryption Scheme References and Additional Reading Exercises Index of Common Notation Appendix A: Mathematical Background Identities and Inequalities Asymptotic Notation Basic Probability The "Birthday" Problem Finite Fields Appendix B: Basic Algorithmic Number Theory Integer Arithmetic Basic Operations The Euclidean and Extended Euclidean Algorithms Modular Arithmetic Basic Operations Computing Modular Inverses Modular Exponentiation Montgomery Multiplication Choosing a Uniform Group Element Finding a Generator of a Cyclic Group Group-Theoretic Background Efficient Algorithms References and Additional Reading Exercises References Index


title: Using Models of Cognition in HRI Evaluation and Design

abstract: Abstract : In designing and evaluating human-machine systems, cognitive models can be used to (a) provide design principles and (b) guide the construction of experiments. In this paper, we present an information processing model of cognition that we have used extensively in designing and evaluating interfaces and autonomy modes. This model uses a conventional description of short-term memory, but treats long-term memory as a collection of mental models specific for particular tasks. Working memory includes components of both short term and long term memory; short term memory acts as a "scratch" pad for an activated subset of long-term memory. We review this model and discuss how it has been used in several human-robot systems.


title: Non-academic internet use in the classroom is negatively related to classroom learning regardless of intellectual ability

abstract: Abstract The use of laptops and cell phones in the classroom is increasing but there is little research assessing whether these devices create distraction that diminishes learning. Moreover, the contribution of intellectual ability to the relationship between learning and portable device use has not been thoroughly investigated. To bridge this gap, students in an introductory psychology class were surveyed about the frequency and duration of their use of various portable devices in the classroom. Internet use negatively predicted exam scores and added to the prediction of classroom learning, above a measure of intellectual ability. Furthermore, students discounted the effect of using portable devices on learning over time. Concomitantly, those with higher intellectual ability reported using the internet more in class over time. Thus, higher rates of internet use were associated with lower test grades and students' beliefs about this relationship did not reflect their ability to multi-task effectively.


title: Implementation of multipath network virtualization scheme with SDN and NFV

abstract: Multipath algorithms except Equal-Cost Multi-Path(ECMP) which has been widely used in networks are difficult to apply, because multipath provisioning is more complex at cross layers and multipath routing need to get all nodes' information. To address the dilemma, this paper proposes a multipath network virtualization implementation scheme with Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this scheme, SDN schedules network resources in a global view for selecting multiple paths and computing weight of each path, and NFV provides computing and storage resources to split flow, add tag, recover flow, to name a few. This paper also proposes a multipath algorithm for elephant flow with network virtualization. Besides, we build an experimental platform based on OPNFV and SDN, and conduct experiments under this experimental platform. The results show that our proposed algorithm applied on multipath network virtualization experimental platform has superior performance than ECMP applied in networks without virtualization.


title: 2D-to-3D conversion using optical flow based depth generation and cross-scale hole filling algorithm

abstract: 3D display has become the inevitable trend of display technology. Converting the traditional and classical 2D videos to 3D videos is an important and effective measure to solve the shortage of 3D contents. The major work about 2D-to-3D video conversion is how to extract depth information from the 2D video, and synthesize a new image from the existing viewpoint. We propose a depth extraction method based on dense edge-preserving optical flow from 2D videos, reducing the matching error in textureless regions. Moreover, we use the Gaussian Pyramid and Laplace Pyramid at cross scales to fill the holes in the image at new view point after 3D warping. The experiments show that our results outperform state-of-the-art methods in visual effect and statistics.


title: Modular verification of software components in C

abstract: We present a new methodology for automatic verification of C programs against finite state machine specifications. Our approach is compositional, naturally enabling us to decompose the verification of large software systems into subproblems of manageable complexity. The decomposition reflects the modularity in the software design. We use weak simulation as the notion of conformance between the program and its specification. Following the counterexample guided abstraction refinement (CEGAR) paradigm, our tool MAGIC first extracts a finite model from C source code using predicate abstraction and theorem proving. Subsequently, weak simulation is checked via a reduction to Boolean satisfiability. MAGIC has been interfaced with several publicly available theorem provers and SAT solvers. We report experimental results with procedures from the Linux kernel, the OpenSSL toolkit, and several industrial strength benchmarks.


title: A kinematic thumb model for the ACT hand

abstract: The thumb is essential to the hand's function in grasping and manipulating objects. Previous anthropomorphic robot hands have thumbs that are biologically-inspired but kinematically-simplified. In order to study the biomechanics and neuromuscular control of hand function, an anatomical robotic model of the human thumb is constructed for the anatomically-correct testbed (ACT) hand. This paper presents our ACT thumb kinematic model that unifies a number of studies from biomechanical literature. We also validate the functional consistency (i.e. the nonlinear moment arm values) between the cadaveric data and the ACT thumb. This functional consistency preserves the geometric relationship between muscle length and joint angles, which allows robotic actuators to imitate human muscle functionality


title: Polygon crawling: feature edge extraction from a general polygonal surface for mesh generation

abstract: This article describes a method for extracting feature edges of a polygonal surface for mesh generation. This method can extract feature edges from a polygonal surface typically created by a CAD facet generator in which typical feature edge extraction methods fail due to severe nonuniformity and anisotropy. The method is based on the technique called “polygon crawling,” which samples a sequence of points on the polygonal surface by moving a point along the polygonal surface. Extracting appropriate feature edges is important for creating a coarse mesh without yielding self-intersections. Extensive tests have been performed with various CAD-generated facet models, and this technique has shown good performance in extracting feature edges.


abstract: Kenny, Carolyn & Stige, Brynjulf. (2002). Contemporary voices in music therapy: Communication, culture, and community. Unipub forlag: Oslo, Norway. This book is an excellent and timely anthology that reflects the growing awareness within the profession of music therapy of the critical importance of cultural factors as they directly affect virtually every aspect of music therapy theory and practice. We have long passed the time when national borders implied populations of cultural homogeneity. Rather, due to a variety of factors, societies are becoming more diverse. Incorporating new cultural elements as a result of the influx of increasingly mobile immigrant populations, heterogeneity has become the norm of the 21st century. Just how music therapists can adequately respond to this global mix without holding on to established models of practice that may be confining rather than liberating is the basic theme of this stimulating volume. Part I (Essays On Communication, Culture and Community) features articles by an international group of authors who reflect upon different issues and approaches related to music therapy and multiculturalism. Part II (International Reports On Music Therapy) presents a series of shorter contributions related to the status of music therapy and cultural considerations in eight countries in Africa, Asia, Europe, Oceania and North and South America. Part III (Introducing Voices: A World Forum for Music Therapy) provides an introduction to this online journal as a vital global medium for exchange in the field. All of the chapters in Parts I and II grew out of selected articles from previous issues of Voices. Editors Kenny and Stige begin by defining culture, not an easy task. As they point out, while each of us is shaped by our cultural experiences, at the same time, we may not fully recognize the extent of these influences since cultural assimilation is not a conscious process. To a large degree we may take our own cultural context and its related assumptions for granted even though our individual and group identities are largely defined by it. This does not minimize the impact of the more-often cited role of family and the narrower social context in determining who we become, but familial and other social experiences themselves are also largely shaped by their cultural setting. Finally, from the perspective of music therapy, culture is a significant factor that colors each person's musical preferences as well as ways of responding to illness, attitudes and expectations towards health care services. As the editors point out, many music therapists have stated that they believe they are able to ". . .go beyond culture when they work with music in or as therapy," seeing music simply as a sound stimulus without cultural reference. Certainly cultural concerns in music therapy have been raised in the past by a number of scholars and practitioners including Bright, Bunt, Gaston, Kenny, Moreno, Ruud, Sekeles and Schwabe. However, today a broader community of music therapists is committed to confronting this issue with a greater sense of urgency. The editors suggest that to realize a truly client-centered perspective in music therapy a generic form of empathy is not adequate. Rather, what is needed is for the therapist to develop a more specific kind of cultural empathy which must be consciously cultivated. In comparing music therapy with indigenous healing rituals, Pavlicevic in Chapter 10 raises a question posed to her by an African master drummer and music ethnologist who asked if music therapy isn't some kind of colonial import, how it is different from music-making, and why it is calling itself this in South Africa instead of imbibing African music and healing traditions. These are indeed very pertinent questions, especially when we consider what kind of role music therapy can play in the developing world which is still 80% of the planet. In these parts of the world traditional healing practices are often the norm, whereas modern professional music therapy may be unknown. …


Feature Generation and Selection on the Heterogeneous Graph for Music Recommendation


title: Scalable user selection for MU-MIMO networks

abstract: In a multi-user MIMO (MU-MIMO) network, an AP with M antennas can only serve up to M users out of a large user population. The M users’ rates are inter-coupled and depend on their channel orthogonality. Substantial theoretical studies focused on selecting users to maximize capacity, but they require feedback of channel state information (CSI) from all users. The resulting overhead can easily overwhelm useful data in large scale networks. In this paper, we propose a scalable user selection mechanism called orthogonality probing based user selection (OPUS). OPUS only requires up to M rounds of CSI feedback. In each round, it employs a novel probing mechanism that enables a user to evaluate its orthogonality with existing users, and a distributed contention mechanism that singles out the best user to feedback its CSI. Software-radio based implementation and experimentation shows that OPUS significantly outperforms traditional user selection schemes in both throughput and fairness.


title: Current Ripple Damping Control to Minimize Impedance Network for Single-Phase Quasi-Z Source Inverter System

abstract: The single-phase quasi-Z source inverter (qZSI) topology has recently attracted attention for single-phase grid-tie photovoltaic (PV) applications. However, due to the inherent second-harmonic power flow in single-phase systems, a large qZS network is required to reduce the second-harmonic component of currents and voltages on the dc side. Minimization of the qZS network remains an open issue. This paper proposes a technique that minimizes the qZS capacitance and inductance of the single-phase qZSI topology by employing dc-side low-frequency current ripple damping control. Through analysis of power flow, a second-harmonic power model is derived and the ripple power is analyzed for minimization of the qZS network. A current ripple damping control is proposed to ensure suppression of second-harmonic power flow through the inductors. Simulation and experimental results verify the theoretical analysis, damping control, and the proposed design minimization of the qZS network for the single-phase topology.


title: The RDF Book Mashup: From Web APIs to a Web of Data

abstract: The RDF Book Mashup demonstrates how Web 2.0 data sources like Amazon, Google and Yahoo can be integrated into the Semantic Web. Following the principles of linked data, the RDF Book Mashup makes information about books, their authors, reviews, and online bookstores available on the Semantic Web. This information can be used by RDF browsers and crawlers, and other publishers of Semantic Web data can set links to it.


title: A Proxy Design to Leverage the Interconnection of CoAP Wireless Sensor Networks with Web Applications

abstract: In this paper, we present the design of a Constrained Application Protocol (CoAP) proxy able to interconnect Web applications based on Hypertext Transfer Protocol (HTTP) and WebSocket with CoAP based Wireless Sensor Networks. Sensor networks are commonly used to monitor and control physical objects or environments. Smart Cities represent applications of such a nature. Wireless Sensor Networks gather data from their surroundings and send them to a remote application. This data flow may be short or long lived. The traditional HTTP long-polling used by Web applications may not be adequate in long-term communications. To overcome this problem, we include the WebSocket protocol in the design of the CoAP proxy. We evaluate the performance of the CoAP proxy in terms of latency and memory consumption. The tests consider long and short-lived communications. In both cases, we evaluate the performance obtained by the CoAP proxy according to the use of WebSocket and HTTP long-polling.