title: An extended Doherty amplifier with high efficiency over a wide power range

abstract: An extension of the Doherty amplifier, which maintains high efficiency over a wide range of output power (>6 dB), is presented in this paper. This extended Doherty amplifier is demonstrated experimentally with InGaP/GaAs heterojunction bipolar transistors at 950 MHz. Power-added efficiency (PAE) of 46% is measured at P/sub 1dB/ of 27.5 dBm and 45% is measured at 9 dB backed off from P/sub 1dB/. Additionally, PAE of at least 39% is maintained for over an output power range of 12 dB backed off from P/sub 1dB/. This is an improvement over the classical Doherty amplifier, where high efficiency is typically obtained up to 5-6 dB backed off from P/sub 1dB/. Compared to a single transistor class-B amplifier with similar gain and P/sub 1dB/, the extended Doherty amplifier has PAE 2.6 /spl times/ higher at 10 dB back off and 3 /spl times/ higher at 20 dB back off from P/sub 1dB/. Under different bias and output matching conditions, the amplifier was also evaluated with CDMA signals. At the highest measured power of 25 dBm, the extended Doherty amplifier achieves a PAE of 45% with an adjacent channel power ratio of -42 dBc. Generalized design equations are also derived and the consequences of finite device output impedance on amplifier gain and linearity are explored.


An Analysis of “Attention” in Sequence-to-Sequence Models


title: A Multi-Mode Four-Switch Buck-Boost DC/DC Converter

abstract: Considering that the output voltage of battery will decline during its use, a four-switch buck-boost converter was proposed, which could provide stable output voltage over a wide voltage range of battery so as to extend the use time of battery and enhance its use efficiency. To improve the efficiency, the converter is designed to operate in multiple modes, which controlled by different strategies that depends upon the difference between output and input voltages as well as the different load conditions. As results, the proposed converter could provide a steady output voltage over the entire fluctuating range of battery voltage. The converter chip was designed in 1.5- μm BCD (Bipolar-CMOS-DMOS) technology and the expectations were well achieved.


title: Decadal Patterns of Westerly Winds, Temperatures, Ocean Gyre Circulations and Fish Abundance: A Review

abstract: The purpose of this review is to describe the global scope of the multidecadal climate oscillations that go back at least, through several hundred years. Literature, historic data, satellite data and global circulation model output have been used to provide evidence for the zonal and meridional jet stream patterns. These patterns were predominantly zonal from the 1970s to 1990s and switched since the 1990s to a meridional wind phase, with weakening jet streams forming Rossby waves in the northern and southern hemispheres. A weakened northern jet stream has allowed northerly winds to flow down over the continents in the northern hemisphere during the winter period, causing some harsh winters and slowing anthropogenic climate warming regionally. Wind oscillations impact ocean gyre circulation affecting upwelling strength and pelagic fish abundance with synchronous behavior in sub Arctic gyres during phases of the oscillation and asynchronous behavior in subtropical gyres between the Atlantic and Pacific oceans.


Multi-phase Word Sense Embedding Learning Using a Corpus and a Lexical Ontology.


title: PlanetLab: an overlay testbed for broad-coverage services

abstract: PlanetLab is a global overlay network for developing and accessing broad-coverage network services. Our goal is to grow to 1000 geographically distributed nodes, connected by a disverse collection of links. PlanetLab allows multiple service to run concurrently and continuously, each in its own slice of PlanetLab. This paper discribes our initial implementation of PlanetLab, including the mechanisms used to impelment virtualization, and the collection of core services used to manage PlanetLab.


title: An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events

abstract: With the increasing penetration of renewable energy in recent years, wind power ramp events (WPREs) have started affecting the economic and reliable operation of power grids. In this paper, we develop an optimized swinging door algorithm (OpSDA) to improve the state of the art in WPREs detection. The swinging door algorithm (SDA) is utilized to segregate wind power data through a piecewise linear approximation. A dynamic programming algorithm is performed to optimize the segments by: 1)merging adjacent segments with the same ramp changing direction; 2)handling wind power bumps; and 3)postprocessing insignificant-ramps intervals. Measured wind power data from two case studies are utilized to evaluate the performance of the proposed OpSDA. Results show that the OpSDA provides 1)significantly better performance than the SDA and 2)equal-to-better performance compared to the L1-Ramp Detect with Sliding Window (L1-SW) method with significantly less computational time.


title: Developing clinical skills using a virtual patient simulator in a resource-limited setting.

abstract: to measure the effect of training with a virtual patient (VP) simulation system on clinical skills of healthcare professionals in Africa.


title: Weather Derivatives Pricing : Modeling the Seasonal Residual Variance of an Ornstein-Uhlenbeck Temperature Process with Neural Networks

abstract: In this paper, we use neural networks in order to model the seasonal component of the residual variance of a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. This approach can be easily used for pricing weather derivatives by performing Monte Carlo simulations. Moreover, in synergy with neural networks we use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies. Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. Our results show a significant improvement over more traditional alternatives, regarding the statistical properties of the temperature process. This is important since small misspecifications in the temperature process can lead to large pricing errors.


Neural Relation Extraction with Selective Attention over Instances


title: The Natural Language of Playlists

abstract: We propose a simple, scalable, and objective evaluation procedure for playlist generation algorithms. Drawing on standard techniques for statistical natural language processing, we characterize playlist algorithms as generative models of strings of songs belonging to some unknown language. To demonstrate the procedure, we compare several playlist algorithms derived from content, semantics, and meta-data. We then develop an efficient algorithm to learn an optimal combination of simple playlist algorithms. Experiments on a large collection of naturally occurring playlists demonstrate the efficacy of the evaluation procedure and learning algorithm.


Active Object - An Object Behavioral Pattern for Concurrent Programming


Cross-Lingual Propagation for Deep Sentiment Analysis


title: From presence to consciousness through virtual reality

abstract: Immersive virtual environments can break the deep, everyday connection between where our senses tell us we are and where we are actually located and whom we are with. The concept of 'presence' refers to the phenomenon of behaving and feeling as if we are in the virtual world created by computer displays. In this article, we argue that presence is worthy of study by neuroscientists, and that it might aid the study of perception and consciousness.


title: Word Level Language Identification in Online Multilingual Communication

abstract: Multilingual speakers switch between languages in online and spoken communication. Analyses of large scale multilingual data require automatic language identification at the word level. For our experiments with multilingual online discussions, we first tag the language of individual words using language models and dictionaries. Secondly, we incorporate context to improve the performance. We achieve an accuracy of 98%. Besides word level accuracy, we use two new metrics to evaluate this task.


title: Trusted Cloud Computing with Secure Resources and Data Coloring

abstract: Trust and security have prevented businesses from fully accepting cloud platforms. To protect clouds, providers must first secure virtualized data center resources, uphold user privacy, and preserve data integrity. The authors suggest using a trust-overlay network over multiple data centers to implement a reputation system for establishing trust between service providers and data owners. Data coloring and software watermarking techniques protect shared data objects and massively distributed software modules. These techniques safeguard multi-way authentications, enable single sign-on in the cloud, and tighten access control for sensitive data in both public and private clouds.


title: The design and use of algorithms for permuting large entries to the diagonal of sparse matrices

abstract: We consider techniques for permuting a sparse matrix so that the diagonal of the permuted matrix has entries of large absolute value. We discuss various criteria for this and consider their implementation as computer codes. We then indicate several cases where such a permutation can be useful. These include the solution of sparse equations by a direct method and by an iterative technique. We also consider its use in generating a preconditioner for an iterative method. We see that the effect of these reorderings can be dramatic although the best a priori strategy is by no means clear.


title: Defining control strategies for MicroGrids islanded operation

abstract: This paper describes and evaluates the feasibility of control strategies to be adopted for the operation of a microgrid when it becomes isolated. Normally, the microgrid operates in interconnected mode with the medium voltage network; however, scheduled or forced isolation can take place. In such conditions, the microgrid must have the ability to operate stably and autonomously. An evaluation of the need of storage devices and load shedding strategies is included in this paper.


title: Research Article Unobtrusive Biometric System Based on Electroencephalogram Analysis

abstract: Features extracted from electroencephalogram (EEG) recordings have proved to be unique enough between subjects for biometric applications. We show here that biometry based on these recordings offers a novel way to robustly authenticate or identify subjects. In this paper, we present a rapid and unobtrusive authentication method that only uses 2 frontal electrodes referenced to another one placed at the ear lobe. Moreover, the system makes use of a multistage fusion architecture, which demonstrates to improve the system performance. The performance analysis of the system presented in this paper stems from an experiment with 51 subjects and 36 intruders, where an equal error rate (EER) of 3.4% is obtained, that is, true acceptance rate (TAR) of 96.6% and a false acceptance rate (FAR) of 3.4%. The obtained performance measures improve the results of similar systems presented in earlier work.


title: Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

abstract: Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site - one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.


title: The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter

abstract: Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of socialmedia content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on noncontrolled data.


title: Considering the Role of Affect in Learning: Monitoring Students' Self-Efficacy, Sense of Belonging, and Science Identity

abstract: While emphasis is often placed on assessing students' conceptual knowledge, less has been placed on investigating affective aspects of student biology learning. In this paper, we explore self-efficacy, sense of belonging, and science identity, as well as emerging assessment tools to monitor these dimensions of students' learning.


title: Fast-SSC-Flip Decoding of Polar Codes

abstract: Polar codes are widely considered as one of the most exciting recent discoveries in channel coding. For short to moderate block lengths, their error-correction performance under list decoding can outperform that of other modern error-correcting codes. However, high-speed list-based decoders with moderate complexity are challenging to implement. Successive-cancellation (SC)-flip decoding was shown to be capable of a competitive error-correction performance compared to that of list decoding with a small list size, at a fraction of the complexity, but suffers from a variable execution time and a higher worst-case latency. In this work, we show how to modify the state-of-the-art high-speed SC decoding algorithm to incorporate the SC-flip ideas. The algorithmic improvements are presented as well as average execution-time results tailored to a hardware implementation. The results show that the proposed fast-SSC-flip algorithm has a decoding speed close to an order of magnitude better than the previous works while retaining a comparable error-correction performance.


title: Fast human detection in crowded scenes by contour integration and local shape estimation

abstract: The complexity of human detection increases significantly with a growing density of humans populating a scene. This paper presents a Bayesian detection framework using shape and motion cues to obtain a maximum a posteriori (MAP) solution for human configurations consisting of many, possibly occluded pedestrians viewed by a stationary camera. The paper contains two novel contributions for the human detection task: 1. computationally efficient detection based on shape templates using contour integration by means of integral images which are built by oriented string scans; (2) a non-parametric approach using an approximated version of the shape context descriptor which generates informative object parts and infers the presence of humans despite occlusions. The outputs of the two detectors are used to generate a spatial configuration of hypothesized human body locations. The configuration is iteratively optimized while taking into account the depth ordering and occlusion status of the hypotheses. The method achieves fast computation times even in complex scenarios with a high density of people. Its validity is demonstrated on a substantial amount of image data using the CAVIAR and our own datasets. Evaluation results and comparison with state of the art are presented.


title: Gazing at games: using eye tracking to control virtual characters

abstract: Welcome to the course: Gazing at Games: Using Eye Tracking to Control Virtual Characters. I will start with a short introduction of the course which will give you an idea of its aims and structure. I will also talk a bit about my background and research interests and motivate why I think this work is important.


title: Social Network Analysis and Mining for Business Applications

abstract: Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.


title: Malware images: visualization and automatic classification

abstract: We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture. Motivated by this visual similarity, a classification method using standard image features is proposed. Neither disassembly nor code execution is required for classification. Preliminary experimental results are quite promising with 98% classification accuracy on a malware database of 9,458 samples with 25 different malware families. Our technique also exhibits interesting resilience to popular obfuscation techniques such as section encryption.


title: Mining Subjective Properties on the Web

abstract: Even with the recent developments in Web search of answering queries from structured data, search engines are still limited to queries with an objective answer, such as EUROPEAN CAPITALS or WOODY ALLEN MOVIES. However, many queries are subjective, such as SAFE CITIES, or CUTE ANIMALS. The underlying knowledge bases of search engines do not contain answers to these queries because they do not have a ground truth. We describe the Surveyor system that mines the dominant opinion held by authors of Web content about whether a subjective property applies to a given entity. The evidence on which SURVEYOR relies is statements extracted from Web text that either support the property or claim its negation. The key challenge that SURVEYOR faces is that simply counting the number of positive and negative statements does not suffice, because there are multiple hidden biases with which content tends to be authored on the Web. SURVEYOR employs a probabilistic model of how content is authored on the Web. As one example, this model accounts for correlations between the subjective property and the frequency with which it is mentioned on the Web. The parameters of the model are specialized to each property and entity type. Surveyor was able to process a large Web snapshot within a few hours, resulting in opinions for over 4~billion entity-property combinations. We selected a subset of 500 entity-property combinations and compared our results to the dominant opinion of a large number of Amazon Mechanical Turk (AMT) workers. The predictions of Surveyor match the results from AMT in 77\% of all cases (and 87\% for test cases where inter-worker agreement is high), significantly outperforming competing approaches.


title: Intelligent prognostics tools and e-maintenance

abstract: In today's global competitive marketplace, there is intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. With the advent of Internet and tether-free technologies, companies necessitate dramatic changes in transforming traditional "fail and fix (FAF)" maintenance practices to a "predict and prevent (PAP)" e-maintenance methodology. E-maintenance addresses the fundamental needs of predictive intelligence tools to monitor the degradation rather than detecting the faults in a networked environment and, ultimately to optimize asset utilization in the facility.This paper introduces the emerging field of e-maintenance and its critical elements. Furthermore, performance assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns. Recent advances on intelligent prognostic technologies and tools are discussed. Several case studies are introduced to validate these developed technologies and tools.


title: A multiclass classification method based on deep learning for named entity recognition in electronic medical records

abstract: Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.


Clinical Case-Test Yourself Urogenital Painless palpable scrotal mass


title: Classification of HTTP traffic based on C5.0 Machine Learning Algorithm

abstract: Our previous work demonstrated the possibility of distinguishing several kinds of applications with accuracy of over 99%. Today, most of the traffic is generated by web browsers, which provide different kinds of services based on the HTTP protocol: web browsing, file downloads, audio and voice streaming through third-party plugins, etc. This paper suggests and evaluates two approaches to distinguish various HTTP content: distributed among volunteers' machines and centralized running in the core of the network. We also assess accuracy of the global classifier for both HTTP and non-HTTP traffic. We achieved accuracy of 94%, which supposed to be even higher in real-life usage. Finally, we provided graphical characteristics of different kinds of HTTP traffic.


title: Virtual friend or threat? The effects of facial expression and gaze interaction on psychophysiological responses and emotional experience

abstract: The present study aimed to investigate the impact of facial expression, gaze interaction, and gender on attention allocation, physiological arousal, facial muscle responses, and emotional experience in simulated social interactions. Participants viewed animated virtual characters varying in terms of gender, gaze interaction, and facial expression. We recorded facial EMG, fixation duration, pupil size, and subjective experience. Subject's rapid facial reactions (RFRs) differentiated more clearly between the character's happy and angry expression in the condition of mutual eye-to-eye contact. This finding provides evidence for the idea that RFRs are not simply motor responses, but part of an emotional reaction. Eye movement data showed that fixations were longer in response to both angry and neutral faces than to happy faces, thereby suggesting that attention is preferentially allocated to cues indicating potential threat during social interaction.


title: A framework for guiding and evaluating literature reviews

abstract: Given that the synthesis of cumulated knowledge is an essential condition for any field to grow and develop, we believe that the enhanced role of IS reviews requires that this expository form be given careful scrutiny. Over the past decade, several senior scholars have made calls for more review papers in our field. While the number of IS review papers has substantially increased in recent years, no prior research has attempted to develop a general framework to conduct and evaluate the rigor of standalone reviews. In this paper, we fill this gap. More precisely, we present a set of guidelines for guiding and evaluating IS literature reviews and specify to which review types they apply. To do so, we first distinguish between four broad categories of review papers and then propose a set of guidelines that are grouped according to the generic phases and steps of the review process. We hope our work will serve as a valuable source for those conducting, evaluating, and/or interpreting reviews in our field.


title: A Novel Filter-Bank Multicarrier Scheme to Mitigate the Intrinsic Interference: Application to MIMO Systems

abstract: Filter-bank multicarrier (FBMC) transmission system was proposed as an alternative approach to orthogonal frequency division multiplexing (OFDM) system since it has a higher spectral efficiency. One of the characteristics of FBMC is that the demodulated transmitted symbols are accompanied by interference terms caused by the neighboring transmitted data in time-frequency domain. The presence of this interference is an issue for some multiple-input multiple-output (MIMO) schemes and until today their combination with FBMC remains an open problem. We can cite, among these techniques, the Alamouti scheme and the maximum likelihood detection (MLD) with spatial multiplexing (SM). In this paper, we shall propose a new FBMC scheme and transmission strategy in order to avoid this interference term. This proposed scheme (called FFT-FBMC) transforms the FBMC system into an equivalent system formulated as OFDM regardless of some residual interference. Thus, any OFDM transmission technique can be performed straightforwardly to the proposed FBMC scheme with a corresponding complexity growth compared to the classical FBMC. First, we will develop the FFT-FBMC in the case of single-input single-output (SISO) configuration. Then, we extend its application to SM-MIMO configuration with MLD and Alamouti coding scheme. Simulation results show that FFT-FBMC can almost reach the OFDM performance, but it remains slightly outperformed by OFDM.


title: Morinda citrifolia Linn. (Noni) and Its Potential in Obesity-Related Metabolic Dysfunction

abstract: Cultural and economic shifts in the early 19th century led to the rapid development of companies that made good profits from technologically-produced commodities. In this way, some habits changed in society, such as the overconsumption of processed and micronutrient-poor foods and devices that gave rise to a sedentary lifestyle. These factors influenced host-microbiome interactions which, in turn, mediated the etiopathogenesis of "new-era" disorders and diseases, which are closely related, such as obesity, type 2 diabetes mellitus, non-alcoholic fatty liver disease, hypertension, and inflammatory bowel disease, which are characterized by chronic dysregulation of metabolic and immune processes. These pathological conditions require novel and effective therapeutic approaches. Morindacitrifolia (noni) is well known as a traditional healing plant due to its medicinal properties. Thus, many studies have been conducted to understand its bioactive compounds and their mechanisms of action. However, in obesity and obesity-related metabolic (dysfunction) syndrome, other studies are necessary to better elucidate noni's mechanisms of action, mainly due to the complexity of the pathophysiology of obesity and its metabolic dysfunction. In this review, we summarize not only the clinical effects, but also important cell signaling pathways in in vivo and in vitro assays of potent bioactive compounds present in the noni plant which have been reported in studies of obesity and obesity-associated metabolic dysfunction.


title: A New Heuristic Reduct Algorithm Base on Rough Sets Theory

abstract: Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory provides a mathematical approach to find optimal feature subset, but this approach is time consuming. In this paper, we propose a novel heuristic algorithm based on rough sets theory to find out the feature subset. This algorithm employs appearing frequency of attribute as heuristic information. Experiment results show in most times our algorithm can find out optimal feature subset quickly and efficiently.


title: Supporting ARINC 653-based Dynamic Reconfiguration

abstract: As the software for avionics becomes more complex, the challenge to provide the required reliability and safety mechanisms becomes also more complex. Embracing the ARINC 650 and 653 standards to provide a means to embark several systems into a single hardware cabinet opens the door to the development of even more elaborated software avionics systems, by overcoming the space constraints once found in this kind of system. Nevertheless, ARINC 653 exhibits also some limitations regarding fault redundancy management, especially when handling redundant applications with spares that back up other applications. In this paper, a framework to support fault tolerance and reconfiguration in avionics systems under the umbrella of ARINC 653 standard is described. This paper represents part of the results of the research projects carried out during the last two years by Eurocopter Espana in collaboration with the University of Castilla-La Mancha.


title: Access control to web pages based on user browsing behavior

abstract: Nowadays, access control to Web pages relay mostly on identification and authentication. After successful authentication, however, subsequent access may not necessarily be performed by the same user. Aimed at separating identity authentication and behavior authentication in open network environments, this paper proposes an access control method based on the analysis of user behavior in Web browsing as an additional access control mechanism to traditional identity authentication. The paper provides the definition of user behavior, uses browsing time and navigated path to determine the normality of user behavior based on the result of comparing the value calculated using a proposed algorithm with a threshold, thus modeling user behavior in both temporal and spatial dimensions. The proposed method relies on a database that contains the frequency of previous access to the path by the same user. Experiment shows that the proposed method can detect abnormal behavior while adapting to continuous changes in user behavior and can thus be used to prevent the theft of user accounts to improve network security.


title: Extending Networking into the Virtualization Layer

abstract: The move to virtualization has created a new network access layer residing on hosts that connects the various VMs. Virtualized deployment environments impose requirements on networking for which traditional models are not well suited. They also provide advantages to the networking layer (such as software flexibility and welldefined end host events) that are not present in physical networks. To date, this new virtualization network layer has been largely built around standard Ethernet switching, but this technology neither satisfies these new requirements nor leverages the available advantages. We present Open vSwitch, a network switch specifically built for virtual environments. Open vSwitch differs from traditional approaches in that it exports an external interface for fine-grained control of configuration state and forwarding behavior. We describe how Open vSwitch can be used to tackle problems such as isolation in joint-tenant environments, mobility across subnets, and distributing configuration and visibility across hosts.


title: FLOW-CYTOMETRIC CHARACTERIZATION OF THE CELL-SURFACE GLYCANS OF SYMBIOTIC DINOFLAGELLATES (SYMBIODINIUM SPP.) 1

abstract: Symbiodinium spp. dinoflagellates are common symbionts of marine invertebrates. The cell-surface glycan profile may determine whether a particular Symbiodinium is able to establish and maintain a stable symbiotic relationship. To characterize this profile, eight Symbiodinium cultures were examined using eight glycan-specific fluorescent lectin probes. Confocal imaging and flow-cytometric analysis were used to determine significant levels of binding of each probe to the cell surface. No significant variation in glycan profile was seen within each Symbiodinium culture, either over time or over growth phase. No cladal trends in glycan profile were found, but of note, two different Symbiodinium cultures (from clades A and B) isolated from one host species had very similar profiles, and two other cultures (from clades B and F) from different host species had identical profiles. Two lectin probes were particularly interesting: concanavalin A (ConA) and Griffonia simplicifolia-II (GS-II). The ConA probe showed significant binding to all Symbiodinium cultures, suggesting the widespread presence of cell-surface mannose residues, while the GS-II probe, which is specific for glycans possessing N-acetyl groups, showed significant binding to six of eight Symbiodinium cultures. Other probes showed significant binding to the following percentage of Symbiodinium cultures examined: wheat germ agglutinin (WGA), 37.5%; peanut agglutinin (PNA), 50%; Helix pomatia agglutinin (HPA), 50%; phytohemagglutinin-L (PHAL), 62.5%; soybean agglutinin (SBA), 50%; and Griffonia simplicifolia-IB4 (GS-IB4), 12.5%. This study highlights the complexity of cell-surface glycan assemblages and their potential role in the discrimination of different dinoflagellate symbionts by cnidarian hosts.


title: How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning

abstract: With the proliferation of social media, fashion inspired from celebrities, reputed designers as well as fashion influencers has shortened the cycle of fashion design and manufacturing. However, with the explosion of fashion related content and large number of user generated fashion photos, it is an arduous task for fashion designers to wade through social media photos and create a digest of trending fashion. This necessitates deep parsing of fashion photos on social media to localize and classify multiple fashion items from a given fashion photo. While object detection competitions such as MSCOCO have thousands of samples for each of the object categories, it is quite difficult to get large labeled datasets for fast fashion items. Moreover, state-of-the-art object detectors do not have any functionality to ingest large amount of unlabeled data available on social media in order to fine tune object detectors with labeled datasets. In this work, we show application of a generic object detector, that can be pretrained in an unsupervised manner, on 24 categories from recently released Open Images V4 dataset. We first train the base architecture of the object detector using unsupervisd learning on 60K unlabeled photos from 24 categories gathered from social media, and then subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset. On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K photos while performing 11% to 17% better as compared to the state-of-the-art object detectors. We show that this improvement is due to our choice of architecture that lets us do unsupervised learning and that performs significantly better in identifying small objects.


title: Behavior-based personalization in web search

abstract: Personalized search approaches tailor search results to users' current interests, so as to help improve the likelihood of a user finding relevant documents for their query. Previous work on personalized search focuses on using the content of the user's query and of the documents clicked to model the user's preference. In this paper we focus on a different type of signal: We investigate the use of behavioral information for the purpose of search personalization. That is, we consider clicks and dwell time for reranking an initially retrieved list of documents. In particular, we i investigate the impact of distributions of users and queries on document reranking; ii estimate the relevance of a document for a query at 2 levels, at the query-level and at the word-level, to alleviate the problem of sparseness; and iii perform an experimental evaluation both for users seen during the training period and for users not seen during training. For the latter, we explore the use of information from similar users who have been seen during the training period. We use the dwell time on clicked documents to estimate a document's relevance to a query, and perform Bayesian probabilistic matrix factorization to generate a relevance distribution of a document over queries. Our experiments show that: i for personalized ranking, behavioral information helps to improve retrieval effectiveness; and ii given a query, merging information inferred from behavior of a particular user and from behaviors of other users with a user-dependent adaptive weight outperforms any combination with a fixed weight.


title: State-of-the-art on clustering data streams

abstract: Clustering is a key data mining task. This is the problem of partitioning a set of observations into clusters such that the intra-cluster observations are similar and the inter-cluster observations are dissimilar. The traditional set-up where a static dataset is available in its entirety for random access is not applicable as we do not have the entire dataset at the launch of the learning, the data continue to arrive at a rapid rate, we can not access the data randomly, and we can make only one or at most a small number of passes on the data in order to generate the clustering results. These types of data are referred to as data streams. The data stream clustering problem requires a process capable of partitioning observations continuously while taking into account restrictions of memory and time. In the literature of data stream clustering methods, a large number of algorithms use a two-phase scheme which consists of an online component that processes data stream points and produces summary statistics, and an offline component that uses the summary data to generate the clusters. An alternative class is capable of generating the final clusters without the need of an offline phase. This paper presents a comprehensive survey of the data stream clustering methods and an overview of the most well-known streaming platforms which implement clustering.


title: Approximate Bayesian inference in spatial environments

abstract: We propose to learn a stochastic recurrent model to solve the problem of simultaneous localisation and mapping (SLAM). Our model is a deep variational Bayes filter augmented with a latent global variable---similar to an external memory component---representing the spatially structured environment. Reasoning about the pose of an agent and the map of the environment is then naturally expressed as posterior inference in the resulting generative model. We evaluate the method on a set of randomly generated mazes which are traversed by an agent equipped with laser range finders. Path integration based on an accurate motion model is consistently outperformed, and most importantly, drift practically eliminated. Our approach inherits favourable properties from neural networks, such as differentiability, flexibility and the ability to train components either in isolation or end-to-end.


title: Recognition and Treatment of Central Sensitization in Chronic Pain Patients: Not Limited to Specialized Care

abstract: Modern pain neuroscience has substantially improved our understanding of the (development of) chronic musculoskeletal pain. The time has come for orthopaedic and sports physical therapists to implement modern pain neuroscience in specialized, but definitely also in primary, care settings, including the role of central sensitization (CS) in amplifying and explaining the presence of the pain experience. Central sensitization dominates the clinical picture in a subgroup of the musculoskeletal pain population, ranging from tennis elbow over shoulder pain to osteoarthritis and whiplash. Applying modern pain neuroscience to clinical practice implies (1) recognizing those patients having predominant CS pain, and (2) accounting for CS when designing the treatment plan in those with predominant CS pain. Future work in this area should (1) examine the validity of the proposed clinical classification algorithm for identifying CS pain in patients with orthopaedic and sports injuries, and (2) explore evidence-based treatment options for patients having predominant CS pain. J Orthop Sports Phys Ther 2016;46(12):1024-1028. doi:10.2519/jospt.2016.0612.


title: Going Beyond the Dominant Paradigm for Information Technology Innovation Research: Emerging Concepts and Methods ∗

abstract: Research on information technology (IT) innovation is concerned with identifying the factors that facilitate or hinder the adoption and diffusion of new IT-based processes or products. Most of this research has been conducted within the confines of a dominant paradigm wherein innovations are assumed to be beneficial, and organizations that have greater innovation-related needs and abilities are expected to exhibit a greater amount of innovative activity. This essay suggests that the dominant paradigm may be reaching the point of diminishing returns as a framework for supporting ground-breaking research, and urges researchers to adopt a more innovative approach to the study of IT innovation itself. Toward this end, I present seven opportunities for conducting new kinds of research that go beyond the dominant paradigm.


LFG-Based Machine Translation Engine for English and Filipino


title: An Enhanced Grid Current Compensator for Grid-Connected Distributed Generation Under Nonlinear Loads and Grid Voltage Distortions

abstract: This paper introduces an advanced current control strategy for grid-connected operations of distributed generation (DG), which supports the DG to transfer a sinusoidal current into the utility grid despite the distorted grid voltage and nonlinear local load conditions. The proposed current controller is designed in the synchronous reference frame and composed of a proportional-integral (PI) controller and a repetitive controller (RC). An RC serves as a bank of resonant controllers, which can compensate a large number of harmonic components with a simple delay function. Hence, the control strategy can be greatly simplified. In addition, the proposed control method does not require the local load current measurement or harmonic analysis of the grid voltage. Therefore, the proposed control method can be easily adopted into the traditional DG control system without installation of extra hardware. Despite the reduced number of sensors, the grid current quality is significantly improved compared with the traditional methods with the PI controller. The operation principle of the proposed control method is analyzed in detail, and its effectiveness is validated through simulated and experimental results.


title: A Bayesian approach for predicting the popularity of tweets

abstract: We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or "graph" structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e., less than one tenth) of the retweet path is observed. This translates to good predictions within a few minutes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes, or trends in social networks.


title: Foggy: A Framework for Continuous Automated IoT Application Deployment in Fog Computing

abstract: Traditional Cloud model is not designed to handle latency-sensitive Internet of Things applications. The new trend consists on moving data to be processed close to where it was generated. To this end, Fog Computing paradigm suggests using the compute and storage power of network elements. In such environments, intelligent and scalable orchestration of thousands of heterogeneous devices in complex environments is critical for IoT Service providers. In this vision paper, we present a framework, called Foggy, that facilitates dynamic resource provisioning and automated application deployment in Fog Computing architectures. We analyze several applications and identify their requirements that need to be taken intoconsideration in our design of the Foggy framework. We implemented a proof of concept of a simple IoT application continuous deployment using Raspberry Pi boards.


title: Neural Networks using Genetic Algorithms

abstract: Combining neural network with evolutionary algorithms leads to evolutionary artificial neural network. Evolutionary algorithms like GA to train neural nets choose their structure or design related aspects like the functions of their neurons. Along basic concepts of neural networks and genetic algorithm this paper includes a flexible method for solving travelling salesman problem using genetic algorithm. This offers a solution which includes a genetic algorithm implementation in order to give a maximal approximation of the problem with the reduction of cost.


title: Empirical studies concerning the maintenance of UML diagrams and their use in the maintenance of code: A systematic mapping study

abstract: Abstract Context The Unified Modelling Language (UML) has, after ten years, become established as the de facto standard for the modelling of object-oriented software systems. It is therefore relevant to investigate whether its use is important as regards the costs involved in its implantation in industry being worthwhile. Method We have carried out a systematic mapping study to collect the empirical studies published in order to discover “What is the current existing empirical evidence with regard to the use of UML diagrams in source code maintenance and the maintenance of the UML diagrams themselves? Results We found 38 papers, which contained 63 experiments and 3 case studies. Conclusion Although there is common belief that the use of UML is beneficial for source code maintenance, since the quality of the modifications is greater when UML diagrams are available, only 3 papers concerning this issue have been published. Most research (60 empirical studies) concerns the maintainability and comprehensibility of the UML diagrams themselves which form part of the system’s documentation, since it is assumed that they may influence source code maintainability, although this has not been empirically validated. Moreover, the generalizability of the majority of the experiments is questionable given the material, tasks and subjects used. There is thus a need for more experiments and case studies to be performed in industrial contexts, i.e., with real systems and using maintenance tasks conducted by practitioners under real conditions that truly show the utility of UML diagrams in maintaining code, and that the fact that a diagram is more comprehensible or modifiable influences the maintainability of the code itself. This utility should also be studied from the viewpoint of cost and productivity, and the consistent and simultaneous maintenance of diagrams and code must also be considered in future empirical studies.


title: Speech emotion recognition with acoustic and lexical features

abstract: In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%.


title: Optimal Space and Time Complexity Analysis on the Lattice of Cuboids Using Galois Connections for Data Warehousing

abstract: In this paper, an optimal aggregation and counter-aggregation (drill-down) methodology is proposed on multidimensional data cube. The main idea is to aggregate on smaller cuboids after partitioning those depending on the cardinality of the individual dimensions. Based on the operations to make these partitions, a Galois Connection is identified for formal analysis that allow to guarantee the soundness of optimizations of storage space and time complexity for the abstraction and concretization functions defined on the lattice structure. Our contribution can be seen as an application to OLAP operations on multidimensional data model in the Abstract Interpretation framework.


title: Impact of cleaning and other interventions on the reduction of hospital-acquired Clostridium difficile infections in two hospitals in England assessed using a breakpoint model.

abstract: BACKGROUND ::: Clostridium difficile infection remains a major challenge for hospitals. Although targeted infection control initiatives have been shown to be effective in reducing the incidence of hospital-acquired C. difficile infection, there is little evidence available to assess the effectiveness of specific interventions. ::: ::: ::: AIM ::: To use statistical modelling to detect substantial reductions in the incidence of C. difficile from time series data from two hospitals in England, and relate these time points to infection control interventions. ::: ::: ::: METHODS ::: A statistical breakpoints model was fitted to likely hospital-acquired C. difficile infection incidence data from a teaching hospital (2002-2009) and a district general hospital (2005-2009) in England. Models with increasing complexity (i.e. increasing the number of breakpoints) were tested for an improved fit to the data. Partitions estimated from breakpoint models were tested for individual stability using statistical process control charts. Major infection control interventions from both hospitals during this time were grouped according to their primary target (antibiotics, cleaning, isolation, other) and mapped to the model-suggested breakpoints. ::: ::: ::: FINDINGS ::: For both hospitals, breakpoints coincided with enhancements to cleaning protocols. Statistical models enabled formal assessment of the impact of different interventions, and showed that enhancements to deep cleaning programmes are the interventions that have most likely led to substantial reductions in hospital-acquired C. difficile infections at the two hospitals studied.


title: Lesion analysis of the brain areas involved in language comprehension

abstract: The cortical regions of the brain traditionally associated with the comprehension of language are Wernicke’s area and Broca’s area. However, recent evidence suggests that other brain regions might also be involved in this complex process. This paper describes the opportunity to evaluate a large number of brain-injured patients to determine which lesioned brain areas might affect language comprehension. Sixty-four chronic left hemisphere stroke patients were evaluated on 11 subtests of the Curtiss– Yamada Comprehensive Language Evaluation – Receptive (CYCLE-R; Curtiss, S., & Yamada, J. (1988). Curtiss –Yamada Comprehensive Language Evaluation. Unpublished test, UCLA). Eight right hemisphere stroke patients and 15 neurologically normal older controls also participated. Patients were required to select a single line drawing from an array of three or four choices that best depicted the content of an auditorily-presented sentence. Patients’ lesions obtained from structural neuroimaging were reconstructed onto templates and entered into a voxel-based lesion-symptom mapping (VLSM; Bates, E., Wilson, S., Saygin, A. P., Dick, F., Sereno, M., Knight, R. T., & Dronkers, N. F. (2003). Voxel-based lesion-symptom mapping. Nature Neuroscience, 6(5), 448– 450.) analysis along with the behavioral data. VLSM is a brain – behavior mapping technique that evaluates the relationships between areas of injury and behavioral performance in all patients on a voxel-by-voxel basis, similar to the analysis of functional neuroimaging data. Results indicated that lesions to five left hemisphere brain regions affected performance on the CYCLE-R, including the posterior middle temporal gyrus and underlying white matter, the anterior superior temporal gyrus, the superior temporal sulcus and angular gyrus, mid-frontal cortex in Brodmann’s area 46, and Brodmann’s area 47 of the inferior frontal gyrus. Lesions to Broca’s and Wernicke’s areas were not found to significantly alter language comprehension on this particular measure. Further analysis


title: Autocalibration via Rank-Constrained Estimation of the Absolute Quadric

abstract: We present an autocalibration algorithm for upgrading a projective reconstruction to a metric reconstruction by estimating the absolute dual quadric. The algorithm enforces the rank degeneracy and the positive semidefiniteness of the dual quadric as part of the estimation procedure, rather than as a post-processing step. Furthermore, the method allows the user, if he or she so desires, to enforce conditions on the plane at infinity so that the reconstruction satisfies the chirality constraints. The algorithm works by constructing low degree polynomial optimization problems, which are solved to their global optimum using a series of convex linear matrix inequality relaxations. The algorithm is fast, stable, robust and has time complexity independent of the number of views. We show extensive results on synthetic as well as real datasets to validate our algorithm.


title: SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

abstract: In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.


title: Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

abstract: This paper presents \textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search. Instead of exact matching query and document n-grams, \textttConv-KNRM uses Convolutional Neural Networks to represent n-grams of various lengths and soft matches them in a unified embedding space. The n-gram soft matches are then utilized by the kernel pooling and learning-to-rank layers to generate the final ranking score. \textttConv-KNRM can be learned end-to-end and fully optimized from user feedback. The learned model»s generalizability is investigated by testing how well it performs in a related domain with small amounts of training data. Experiments on English search logs, Chinese search logs, and TREC Web track tasks demonstrated consistent advantages of \textttConv-KNRM over prior neural IR methods and feature-based methods.


title: Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions

abstract: This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic platforms with limited computational resources and training times. For this purpose, we propose the use of decentralized RL, in combination with finite support basis functions as alternatives to Gaussian RBF, in order to alleviate the effects of the curse of dimensionality on the action and state spaces respectively, and to reduce the computation time. As testbed, a RL based controller for the in-walk kick in NAO robots, a challenging and critical problem for soccer robotics, is used. The reported experiments show empirically that our solution saves up to 99.94% of execution time and 98.82% of memory consumption during execution, without diminishing performance compared to classical approaches.


title: An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

abstract: During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.


title: A Decade of Progress in Indexing and Mining Large Time Series Databases

abstract: Time series data is ubiquitous; large volumes of time series data are routinely created in scientific, industrial, entertainment, medical and biological domains. Examples include gene expression data, electrocardiograms, electroencephalograms, gait analysis, stock market quotes, space telemetry etc. Although statisticians have worked with time series for more than a century, many of their techniques hold little utility for researchers working with massive time series databases.A decade ago, a seminal paper by Faloutsos, Ranganathan, Manolopoulos appeared in SIGMOD. The paper, Fast Subsequence Matching in Time-Series Databases, has spawned at least a thousand references and extensions in the database/ data mining and information retrieval communities. This tutorial will summarize the decade of progress since this influential paper appeared.


title: Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency

abstract: This paper reports a real-time monocular visual simultaneous localization and mapping (SLAM) algorithm and results for its application in the area of autonomous underwater ship hull inspection. The proposed algorithm overcomes some of the specific challenges associated with underwater visual SLAM, namely, limited field of view imagery and feature-poor regions. It does so by exploiting our SLAM navigation prior within the image registration pipeline and by being selective about which imagery is considered informative in terms of our visual SLAM map. A novel online bag-of-words measure for intra and interimage saliency are introduced and are shown to be useful for image key-frame selection, information-gain-based link hypothesis, and novelty detection. Results from three real-world hull inspection experiments evaluate the overall approach, including one survey comprising a 3.4-h/2.7-km-long trajectory.


title: Unifying local and non-local signal processing with graph CNNs

abstract: This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.


title: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

abstract: Abstract An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.


title: A Comprehensive Symbolic Analysis of TLS 1.3

abstract: The TLS protocol is intended to enable secure end-to-end communication over insecure networks, including the Internet. Unfortunately, this goal has been thwarted a number of times throughout the protocol's tumultuous lifetime, resulting in the need for a new version of the protocol, namely TLS 1.3. Over the past three years, in an unprecedented joint design effort with the academic community, the TLS Working Group has been working tirelessly to enhance the security of TLS. We further this effort by constructing the most comprehensive, faithful, and modular symbolic model of the TLS~1.3 draft 21 release candidate, and use the TAMARIN prover to verify the claimed TLS~1.3 security requirements, as laid out in draft 21 of the specification. In particular, our model covers all handshake modes of TLS 1.3. Our analysis reveals an unexpected behaviour, which we expect will inhibit strong authentication guarantees in some implementations of the protocol. In contrast to previous models, we provide a novel way of making the relation between the TLS specification and our model explicit: we provide a fully annotated version of the specification that clarifies what protocol elements we modelled, and precisely how we modelled these elements. We anticipate this model artifact to be of great benefit to the academic community and the TLS Working Group alike.


title: Comparing a Scalable SDN Simulation Framework Built on ns-3 and DCE with Existing SDN Simulators and Emulators

abstract: As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.


Minor-order obstructions for the graphs of vertex cover 6


title: Interventions to improve teamwork and communications among healthcare staff

abstract: Background: Concern over the frequency of unintended harm to patients has focused attention on the importance of teamwork and communication in avoiding errors. This has led to experiments with teamwork training programmes for clinical staff, mostly based on aviation models. These are widely assumed to be effective in improving patient safety, but the extent to which this assumption is justified by evidence remains unclear. Methods: A systematic literature review on the effects of teamwork training for clinical staff was performed. Information was sought on outcomes including staff attitudes, teamwork skills, technical performance, efficiency and clinical outcomes. Results: Of 1036 relevant abstracts identified, 14 articles were analysed in detail: four randomized trials and ten non-randomized studies. Overall study quality was poor, with particular problems over blinding, subjective measures and Hawthorne effects. Few studies reported on every outcome category. Most reported improved staff attitudes, and six of eight reported significantly better teamwork after training. Five of eight studies reported improved technical performance, improved efficiency or reduced errors. Three studies reported evidence of clinical benefit, but this was modest or of borderline significance in each case. Studies with a stronger intervention were more likely to report benefits than those providing less training. None of the randomized trials found evidence of technical or clinical benefit. Conclusion: The evidence for technical or clinical benefit from teamwork training in medicine is weak. There is some evidence of benefit from studies with more intensive training programmes, but better quality research and cost-benefit analysis are needed.


title: Screen printed conductive pastes for biomedical electronics

abstract: This paper describes the evaluation of screen printed materials fabricated with an additive manufacturing process for flexible biomedical applications. Five different conductive polymeric thick film pastes, printed on a polyimide substrate have been investigated. For the intended biocompatible applications, the cytotoxicity of the used materials was tested through adherent cell test. Furthermore, the electrical resistance, the printed structure thickness, the surface energy and roughness have been examined. Additionally, the mechanical resilience of the printed materials was tested through a bending test. During the bending the electrical resistance of printed meander structures could be monitored indicating failures. Two out of five materials were qualified as non-toxic, all of the materials are useable for flexible electronics, as they provide good electrical and mechanical properties.


Coming out about breast cancer in lesbian and bisexual women.


title: SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs

abstract: In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously. Until recently, it was not possible to implement a load balancer with PCC in a merchant switching ASIC, because high-performance switching ASICs typically can not maintain per-connection states with PCC. Newer switching ASICs provide resources and primitives to enable PCC at a large scale. In this paper, we explore how to use switching ASICs to build much faster load balancers than have been built before. Our system, called SilkRoad, is defined in a 400 line P4 program and when compiled to a state-of-the-art switching ASIC, we show it can load-balance ten million connections simultaneously at line rate.


A Genetic Optimization Technique for the Strategic Use of Renewable Energy Supply


Geothermal energy pile subjected to thermo-mechanical loading


title: Building enterprise architecture for hospital information system

abstract: Hospital, as one of the most complex public sectors that currently exist, has problems with structuring and organizing its IT capabilities. The applications in hospital information system usually work independently, making clinical data spread heterogeneously in the system. Failure in IT implementation has increased due to the lack of integration and alignment with the organization goal. Enterprise architecture has been considered as a solution for such problems, but there is currently no standardized guidelines to tailor it for hospital. The existing approaches from researches do not provide two core requirements of hospital information system architecture: the capability to handle multiple branches and the exhaustive view of the whole topology, from business to technology architecture. This paper aims to define the suitable approach to build enterprise architecture for hospital information system by defining the architectural needs, analyzing the existing frameworks to fit the requirements, and tailoring the architecture as the proposed solution. The case study to support the research is conducted in one of the biggest hospital group in Indonesia, Permata Group Hospital.


title: Towards automatically generating summary comments for Java methods

abstract: Studies have shown that good comments can help programmers quickly understand what a method does, aiding program comprehension and software maintenance. Unfortunately, few software projects adequately comment the code. One way to overcome the lack of human-written summary comments, and guard against obsolete comments, is to automatically generate them. In this paper, we present a novel technique to automatically generate descriptive summary comments for Java methods. Given the signature and body of a method, our automatic comment generator identifies the content for the summary and generates natural language text that summarizes the method's overall actions. According to programmers who judged our generated comments, the summaries are accurate, do not miss important content, and are reasonably concise.


title: Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict

abstract: Entries in the burgeoning “text-as-data” movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into another analysis (like locating parties in ideological space) or both. We discuss a variety of techniques for selecting words that capture partisan, or other, differences in political speech and for evaluating the relative importance of those words. We introduce and emphasize several new approaches based on Bayesian shrinkage and regularization. We illustrate the relative utility of these approaches with analyses of partisan, gender, and distributive speech in the U.S. Senate.


title: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python

abstract: The fastcluster package is a C++ library for hierarchical, agglomerative clustering. It provides a fast implementation of the most efficient, current algorithms when the input is a dissimilarity index. Moreover, it features memory-saving routines for hierarchical clustering of vector data. It improves both asymptotic time complexity (in most cases) and practical performance (in all cases) compared to the existing implementations in standard software: several R packages, MATLAB, Mathematica, Python with SciPy.


title: CrowdCleaner: Data cleaning for multi-version data on the web via crowdsourcing

abstract: Multi-version data is often one of the most concerned information on the Web since this type of data is usually updated frequently. Even though there exist some Web information integration systems that try to maintain the latest update version, the maintained multi-version data usually includes inaccurate and invalid information due to the data integration or update delay errors. In this demo, we present CrowdCleaner, a smart data cleaning system for cleaning multi-version data on the Web, which utilizes crowdsourcing-based approaches for detecting and repairing errors that usually cannot be solved by traditional data integration and cleaning techniques. In particular, CrowdCleaner blends active and passive crowdsourcing methods together for rectifying errors for multi-version data. We demonstrate the following four facilities provided by CrowdCleaner: (1) an error-monitor to find out which items (e.g., submission date, price of real estate, etc.) are wrong versions according to the reports from the crowds, which belongs to a passive crowdsourcing strategy; (2) a task-manager to allocate the tasks to human workers intelligently; (3) a smart-decision-maker to identify which answer from the crowds is correct with active crowdsourcing methods; and (4) a whom-to-ask-finder to discover which users (or human workers) should be the most credible according to their answer records.


title: Efficient L-shape fitting of laser scanner data for vehicle pose estimation

abstract: In this paper, we propose an efficient algorithm to fit a cluster of laser scan points with an L-shape. The algorithm partitions a cluster into two disjoint sets optimally in the sense of the least square error, and then fits them with two perpendicular lines. By exploiting the characteristics of both the laser scanner sensor and the fitting problem, the algorithm can test all the possible corner points while keeping the complexity as low as 9 times that of fitting a single pair of orthogonal lines, where the 9 times scaling factor is independent of the number of points in the cluster. Specifically, we exploit the property that the scanner data points are ordered either clockwise or counterclockwise, and incrementally construct the L-shape fitting problem rather than from scratch when the corner point is different. We extend our algorithm to provide multiple hypotheses on pose estimation, which are derived from L-shape fitting, to account for the ambiguity on the corner points. The extended algorithm only requires slightly more computation, which is tested and verified with real laser scanner data. The experimental results justify the correctness and efficacy of our algorithm.


title: Interactive object detection

abstract: In recent years, the rise of digital image and video data available has led to an increasing demand for image annotation. In this paper, we propose an interactive object annotation method that incrementally trains an object detector while the user provides annotations. In the design of the system, we have focused on minimizing human annotation time rather than pure algorithm learning performance. To this end, we optimize the detector based on a realistic annotation cost model based on a user study. Since our system gives live feedback to the user by detecting objects on the fly and predicts the potential annotation costs of unseen images, data can be efficiently annotated by a single user without excessive waiting time. In contrast to popular tracking-based methods for video annotation, our method is suitable for both still images and video. We have evaluated our interactive annotation approach on three datasets, ranging from surveillance, television, to cell microscopy.


title: Collaborative Sparse Regression for Hyperspectral Unmixing

abstract: Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.


title: Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks

abstract: While the action recognition task on videos collected from visible spectrum imaging has received much attention, action recognition in infrared (IR) videos is significantly less explored. Our objective is to exploit imaging data in this modality for the action recognition task. In this work, we propose a novel two-stream 3D convolutional neural network architecture by introducing the discriminative code layer and the corresponding discriminative code loss function. The proposed network processes IR images and the IR-based optical flow field sequences. We pretrain the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune it on the Infrared Action Recognition (InfAR) dataset. We conduct an elaborate analysis of different fusion schemes (weighted average, single and double-layer neural nets) applied to different 3D CNN outputs. Experimental results demonstrate that our approach can achieve state-of-the-art average precision performances on the InfAR dataset.


title: OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas

abstract: Recent work on depth estimation up to now has only focused on projective images ignoring 360 content which is now increasingly and more easily produced. We show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training directly on 360 datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated with acquiring high quality 360 datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360 via rendering. This dataset, which is considerably larger than similar projective datasets, is publicly offered to the community to enable future research in this direction. We use this dataset to learn in an end-to-end fashion the task of depth estimation from 360 images. We show promising results in our synthesized data as well as in unseen realistic images.


title: End-to-end Differentiable Proving

abstract: We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.


title: Gender classification with KNN by extraction of Haar wavelet features from canny shape fingerprints

abstract: Fingerprints give a lot of information about various factors related to an individual. The main characteristic is that they are unique from person to person in many ways. The size, shape, pattern are some of the uniqueness factors seen, so they are area of research and study. Forensic science makes use of different evidences obtained out of which fingerprints are the one to be considered. Fingerprints play a vital role in getting details through the exact identification. Gender identification can also be done easily and efficiently through the fingerprints. Forensic anthropology has gender identification from fingerprints as an important part in order to identify the gender of a criminal and minimize the list of suspects search. Identification of fingerprints is studied and researched a lot in past and is continuously increasing day by day. The gender identification from fingerprints is carried in both spatial domain and frequency domain by applying different techniques. This paper studies frequency domain methods applied for gender identification from fingerprints. A survey of techniques show that DWT is widely used and also in combination with SVD and PCA for gender identification from fingerprints. An overall comparison of frequency domain techniques mainly focusing on DWT and its combinations is presented in this paper with a proposed canny edge detector and Haar DWT based fingerprint gender classification technique.


title: Super-resolution using sub-band self-similarity

abstract: A popular approach for single image super-resolution (SR) is to use scaled down versions of the given image to build an internal training dictionary of pairs of low resolution (LR) and high resolution (HR) image patches, which is then used to predict the HR image. This self-similarity approach has the advantage of not requiring a separate external training database. However, due to their limited size, internal dictionaries are often inadequate for finding good matches for patches containing complex structures such as textures. Furthermore, the quality of matches found are quite sensitive to factors like patch size (larger patches contain structures of greater complexity and may be difficult to match), and dimensions of the given image (smaller images yield smaller internal dictionaries). In this paper we propose a self-similarity based SR algorithm that addresses the abovementioned drawbacks. Instead of seeking similar patches directly in the image domain, we use the self-similarity principle independently on each of a set of different sub-band images, obtained using a bank of orientation selective band-pass filters. Therefore, we allow the different directional frequency components of a patch to find matches independently, which may be in different image locations. Essentially, we decompose local image structure into component patches defined by different sub-bands, with the following advantages: (1) The sub-band image patches are simpler and therefore easier to find matches, than for the more complex textural patches from the original image. (2) The size of the dictionary defined by patches from the sub-band images is exponential in the number of sub-bands used, thus increasing the effective size of the internal dictionary. (3) As a result, our algorithm exhibits a greater degree of invariance to parameters like patch size and the dimensions of the LR image. We demonstrate these advantages and show that our results are richer in textural content and appear more natural than several state-of-the-art methods.


title: A Taxonomy of Domain-Specific Aspect Languages

abstract: Domain-Specific Aspect Languages (DSALs) are Domain-Specific Languages (DSLs) designed to express crosscutting concerns. Compared to DSLs, their aspectual nature greatly amplifies the language design space. We structure this space in order to shed light on and compare the different domain-specific approaches to deal with crosscutting concerns. We report on a corpus of 36 DSALs covering the space, discuss a set of design considerations, and provide a taxonomy of DSAL implementation approaches. This work serves as a frame of reference to DSAL and DSL researchers, enabling further advances in the field, and to developers as a guide for DSAL implementations.


title: The role of espoused national cultural values in technology acceptance

abstract: Prior research has examined age, gender, experience, and voluntariness as the main moderators of beliefs on technology acceptance. This paper extends this line of research beyond these demographic and situational variables. Motivated by research that suggests that behavioral models do not universally hold across cultures, the paper identifies espoused national cultural values as an important set of individual difference moderators in technology acceptance. Building on research in psychological anthropology and cultural psychology that assesses cultural traits by personality tests at the individual level of analysis, we argue that individuals espouse national cultural values to differing degrees. These espoused national cultural values of masculinity/femininity, individualism/collectivism, power distance, and uncertainty avoidance are incorporated into an extended model of technology acceptance as moderators. We conducted two studies to test our model. Results indicated that, as hypothesized, social norms are stronger determinants of intended behavior for individuals who espouse feminine and high uncertainty avoidance cultural values. Contrary to expectations, espoused masculinity/femininity values did not moderate the relationship between perceived usefulness and behavioral intention but, as expected, did moderate the relationship between perceived ease of use and behavioral intention.


title: Identifying Analogies Across Domains

abstract: Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.


A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data


title: Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

abstract: Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models' structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. ::: This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks.


title: Deep Learning for Answer Sentence Selection

abstract: Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that---despite its simplicity---our model matches state of the art performance on the answer sentence selection task.


Middleware for Mobile Computing


title: A kW-class AlGaN/GaN HEMT pallet amplifier for S-band high power application

abstract: We developed a kW-class AlGaN/GaN HEMT pallet amplifier operating at S-band. The pallet amplifier consists of an internally partial-matched AlGaN/GaN HEMT optimized for S-band on a copper base with soft PC boards. The developed pallet amplifier showed excellent performance, which is output power of over 800 W, high linear gain of 13.6dB and high efficiency of 52% over the wide frequency range of 2.9-3.3 GHz, operating at 65 V drain voltage with the pulsed condition at a duty of 10% and a pulse width of 200 musec. With 80 V drain voltage operation the peak power reached to 1 kW with 49.5% drain efficiency and 14.1 dB linear gain at 3.2 GHz. To the best of our knowledge, this is the highest power pallet amplifier ever reported for S-band.


title: A Joint QRS Detection and Data Compression Scheme for Wearable Sensors

abstract: This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.


title: Survey on sportsperson positional marking system

abstract: In the modern era game of professional and amateur basketball, automated statistical positional marking, referral in actual time and video footage analysis for the same have become a part and parcel for the game. Every pre-match discussion is dominated by extensive study of the opponent's defensive and offensive formation, plays and metrics. A computerized video analysis is required for this reason as it will provide a concise guide for analysis. In addition there is a serious impact on the game due to dubious call by referees on real time judgemental calls. A video analysis will make real time video referrals a possibility. Thus in a nutshell a player positional marking system can generate statistical data for strategic planning and achieve proper rule enforcement. This research presents survey on available sports system which tries to place the point of sportsperson.


title: The Mount Vernon stroke service: a feasibility study to determine whether it is possible to apply the principles of stroke unit management to patients and their families on general medical wards.

abstract: A feasibility study has shown that it is possible to apply the principles of stroke unit management to patients and their families on general wards. Ninety-one consecutive stroke patients were managed by a multidisciplinary stroke therapy team which was co-ordinated by a medical registrar, met weekly, and developed a family support service. This was achieved within the pre-existing hospital timetable and staffing level, without providing a specialist 'stroke ward'. (Only 3-4 medical hours a week were needed.) The advantages of the service were: speedy referral to therapists, development of a team approach, provision of better information and psychological support for patients and families, provision of more speech therapy (49%) and occupational therapy (80%) than on other medical wards in other studies. Further research is required to evaluate the effect of the service on specific outcomes of stroke.


title: Seven major challenges for e-learning in developing countries: Case study eBIT, Sri Lanka

abstract: By using an extensive framework for e-learning enablers and disablers (including 37 factors) this paper sets out to identify which of these challenges are most salient for an e-learning course in S ...


title: A Few Good Samples: Shape & Tone Depiction for Hermite RBF Implicits

abstract: We present techniques for rendering Hermite Radial Basis Function (HRBF) Implicits in different pen-and-ink styles. HRBF Implicits is a simple and compact representation, providing three fundamental qualities: a small number of point-normal samples as input for surface reconstruction, good projection of points near the surface, and smoothness of the gradient field. Our approach uses these qualities of HRBF implicits to generate a robust distribution of points to position the drawing primitives. The resulting implicit model is then rendered using point-based primitives to depict shape and tone using silhouettes with hidden-line attenuation, drawing directions, and stippling. We present sample renderings obtained for a variety of models.