title: Recent Advances in Integrated Photonic Sensors

abstract: Nowadays, optical devices and circuits are becoming fundamental components in several application fields such as medicine, biotechnology, automotive, aerospace, food quality control, chemistry, to name a few. In this context, we propose a complete review on integrated photonic sensors, with specific attention to materials, technologies, architectures and optical sensing principles. To this aim, sensing principles commonly used in optical detection are presented, focusing on sensor performance features such as sensitivity, selectivity and rangeability. Since photonic sensors provide substantial benefits regarding compatibility with CMOS technology and integration on chips characterized by micrometric footprints, design and optimization strategies of photonic devices are widely discussed for sensing applications. In addition, several numerical methods employed in photonic circuits and devices, simulations and design are presented, focusing on their advantages and drawbacks. Finally, recent developments in the field of photonic sensing are reviewed, considering advanced photonic sensor architectures based on linear and non-linear optical effects and to be employed in chemical/biochemical sensing, angular velocity and electric field detection.


title: Constrained spectral clustering through affinity propagation

abstract: Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate them into traditional clustering methods, such as K-means, little progress has been made in combining them with spectral clustering. The major challenge in designing an effective constrained spectral clustering is a sensible combination of the scarce pairwise constraints with the original affinity matrix. We propose to combine the two sources of affinity by propagating the pairwise constraints information over the original affinity matrix. Our method has a Gaussian process interpretation and results in a closed-form expression for the new affinity matrix. Experiments show it outperforms state-of-the-art constrained clustering methods in getting good clusterings with fewer constraints, and yields good image segmentation with user-specified pairwise constraints.


title: Design of a multimedia system for multispecialty medical conferences

abstract: In this paper we present the methods and results of a workflow study of a multi-specialty cardiology conference, preliminary design concepts for a digital cardiac conference room, and a component that is anticipated for a complete implementation. Workflow studies at the University of California, San Francisco (UCSF) Medical Center were performed to understand its traditional catheterization conference work procedures and processes. These studies involved observing and interviewing people that prepare, present, and attend the conference. The workflow investigation gave insight into current drawbacks. Scenarios were then generated that described potential new designs of the cardiac catheterization conference.Knowledge gained from the workflow studies, and feedback from UCSF physicians whose reviewed the digital conference room scenarios led to the final system design. We have prototype one of the components of the design: a software tool for improved presentation of dynamic images. This tool has been implemented in Java and is therefore platform independent.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Learning Task Specifications from Demonstrations

abstract: Real world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the sub-tasks can be safely recombined or limit the types of composition available. Motivated by this deficit, we consider the problem of inferring Boolean non-Markovian rewards (also known as logical trace properties or specifications) from demonstrations provided by an agent operating in an uncertain, stochastic environment. Crucially, specifications admit well-defined composition rules that are typically easy to interpret. In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications. In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.


title: Integration of RFID and WSN technologies in a Smart Parking System

abstract: In this paper, a Smart Parking System (SPS) based on the integration of Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) and IEEE 802.15.4 Wireless Sensor Network (WSN) technologies is presented. The system is able to collect information about the occupancy state of parking spaces, and to direct drivers to the nearest vacant parking spot by using a customized software application. Such application also leverages an NFC-based e-wallet system to allow users to pay for the parking fee. Furthermore, a software application based on RESTful Java and Google Cloud Messaging (GCM) technologies has been installed on a Central Server in order to manage alert events (e.g. improper use of a reserved space or expiration of the purchased time). In such a case, it promptly informs the traffic cops through an Android mobile app, which has been designed ad hoc for the considered scenario. A proof-of-concept has demonstrated that the proposed solution can meet the real requirements of a SPS.


title: Edge detection using steerable filters and CNN

abstract: This paper proposes a new approach for edge detection using steerable filters and cellular neural networks (CNNs) where the former yields the local direction of dominant orientation and the latter, provides iterative filtering. For this purpose steerable filter coefficients are used in CNN as a B template. The results are compared to the results where only CNN or steerable filters are used. As a result of this study, the performance of the system can be improved since iterative filtering property of CNN and the ability of steerable filters for edge detection are used.


title: PIITracker: Automatic Tracking of Personally Identifiable Information in Windows

abstract: Personally Identifiable Information (PII) is information that can be used on its own or with other information to distinguish or trace an individual's identity. To investigate an application for PII tracking, a reverse engineer has to put considerable effort to reverse engineer an application and discover what an application does with PII. To automate this process and save reverse engineers substantial time and effort, we propose PIITracker which is a new and novel tool that can track PII automatically and capture if any processes are sending PII over the network. This is made possible by 1) whole-system dynamic information flow tracking 2) monitoring specific function and system calls. We analyzed 15 popular chat applications and browsers using PIITracker, and determined that 12 of these applications collect some form of PII.


title: Job satisfaction among nurses: a literature review

abstract: Abstract The current nursing shortage and high turnover is of great concern in many countries because of its impact upon the efficiency and effectiveness of any health-care delivery system. Recruitment and retention of nurses are persistent problems associated with job satisfaction. This paper analyses the growing literature relating to job satisfaction among nurses and concludes that more research is required to understand the relative importance of the many identified factors to job satisfaction. It is argued that the absence of a robust causal model incorporating organizational, professional and personal variables is undermining the development of interventions to improve nurse retention.


title: Improving trilateration for indoors localization using BLE beacons

abstract: While the Global Positioning System (GPS) tends to be not useful anymore in terms of precise localization once one gets into a building, Low Energy beacons might come in handy instead. Navigating free of signal reception problems throughout a building when one has never visited that place before is a challenge tackled with indoors localization. Using Bluetooth Low Energy1 (BLE) beacons (either iBeacon or Eddystone formats) is the medium to accomplish that. Indeed, different purpose oriented applications can be designed, developed and shaped towards the needs of any person in the context of a certain building. This work presents a series of post-processing filters to enhance the outcome of the estimated position applying trilateration as the main and straightforward technique to locate someone within a building. A later evaluation tries to give enough evidence around the feasibility of this indoor localization technique. A mobile app should be everything a user would need to have within a building in order to navigate inside.


title: Big Data: Principles and best practices of scalable realtime data systems

abstract: Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusiverather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.


title: Full W-Band microstrip-to-waveguide inline transition

abstract: The design and the simulation results of an inline microstrip-to-waveguide transition covering the full W-Band are presented in this Conference paper. The main advantages of this transition are its simple design, low cost, broadband performance operating in the full W-Band and also its easy alignment and assembly. Moreover, the repeatability is ensured due to soldering parts are not required. For all of these reasons, the proposed transition is suitable for characterization of multiple devices built in planar technology at W-Band.


title: On the Morality of Artificial Agents

abstract: Artificial agents (AAs), particularly but not only those in Cyberspace, extend the class of entities that can be involved in moral situations. For they can be conceived of as moral patients (as entities that can be acted upon for good or evil) and also as moral agents (as entities that can perform actions, again for good or evil). In this paper, we clarify the concept of agent and go on to separate the concerns of morality and responsibility of agents (most interestingly for us, of AAs). We conclude that there is substantial and important scope, particularly in Computer Ethics, for the concept of moral agent not necessarily exhibiting free will, mental states or responsibility. This complements the more traditional approach, common at least since Montaigne and Descartes, which considers whether or not (artificial) agents have mental states, feelings, emotions and so on. By focussing directly on ‘mind-less morality’ we are able to avoid that question and also many of the concerns of Artificial Intelligence. A vital component in our approach is the ‘Method of Abstraction’ for analysing the level of abstraction (LoA) at which an agent is considered to act. The LoA is determined by the way in which one chooses to describe, analyse and discuss a system and its context. The ‘Method of Abstraction’ is explained in terms of an ‘interface’ or set of features or observables at a given ‘LoA’. Agenthood, and in particular moral agenthood, depends on a LoA. Our guidelines for agenthood are: interactivity (response to stimulus by change of state), autonomy (ability to change state without stimulus) and adaptability (ability to change the ‘transition rules’ by which state is changed) at a given LoA. Morality may be thought of as a ‘threshold’ defined on the observables in the interface determining the LoA under consideration. An agent is morally good if its actions all respect that threshold; and it is morally evil if some action violates it. That view is particularly informative when the agent constitutes a software or digital system, and the observables are numerical. Finally we review the consequences for Computer Ethics of our approach. In conclusion, this approach facilitates the discussion of the morality of agents not only in Cyberspace but also in the biosphere, where animals can be considered moral agents without their having to display free will, emotions or mental states, and in social contexts, where systems like organizations can play the role of moral agents. The primary ‘cost’ of this facility is the extension of the class of agents and moral agents to embrace AAs.


Automatic Construction of Morphologically Motivated Translation Models for Highly Inflected , Low-Resource Languages


title: CONSUMER: A Novel Hybrid Intrusion Detection System for Distribution Networks in Smart Grid

abstract: Smart meters have been deployed worldwide in recent years that enable real-time communications and networking capabilities in power distribution systems. Problematically, recent reports have revealed incidents of energy theft in which dishonest customers would lower their electricity bills (aka stealing electricity) by tampering with their meters. The physical attack can be extended to a network attack by means of false data injection (FDI). This paper is thus motivated to investigate the currently-studied FDI attack by introducing the combination sum of energy profiles (CONSUMER) attack in a coordinated manner on a number of customers' smart meters, which results in a lower energy consumption reading for the attacker and a higher reading for the others in a neighborhood. We propose a CONSUMER attack model that is formulated into one type of coin change problems, which minimizes the number of compromised meters subject to the equality of an aggregated load to evade detection. A hybrid detection framework is developed to detect anomalous and malicious activities by incorporating our proposed grid sensor placement algorithm with observability analysis to increase the detection rate. Our simulations have shown that the network observability and detection accuracy can be improved by means of grid-placed sensor deployment.


Cryptocurrencies and Bitcoin : Charting the Research Landscape Full Paper


title: Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model

abstract: We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel principal component analysis (KPCA) based feature extraction and factorial conditional random field (FCRF) based motion modeling. Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions. FCRF models temporal sequences in multiple interacting ways, thus increasing joint accuracy by information sharing, with the ideal advantages of discriminative models over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal, intra-and inter-person variations, but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles.


title: Heuristic Approaches for Generating Local Process Models through Log Projections

abstract: Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create socalled local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.


title: Thompson Sampling is Asymptotically Optimal in General Environments

abstract: We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its value converges to the optimal value in mean and (2) given a recoverability assumption regret is sublinear.


Reducing the cost of sales through marketing support mail


title: Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging

abstract: We present a system to assist users in dietary logging habits, which performs food recognition from pictures snapped on their phone in two different scenarios. In the first scenario, called "Food in context", we exploit the GPS information of a user to determine which restaurant they are having a meal at, therefore restricting the categories to recognize to the set of items in the menu. Such context allows us to also report precise calories information to the user about their meal, since restaurant chains tend to standardize portions and provide the dietary information of each meal. In the second scenario, called "Foods in the wild" we try to recognize a cooked meal from a picture which could be snapped anywhere. We perform extensive experiments on food recognition on both scenarios, demonstrating the feasibility of our approach at scale, on a newly introduced dataset with 105K images for 500 food categories.


title: Expression of testicular androgen receptor in non-obstructive azoospermia and its change after hormonal therapy

abstract: Several trials aimed at improving the sperm retrieval from men with non-obstructive azoospermia (NOA) by optimizing intratesticular testosterone (ITT) have reported partial responses, however, an appropriate level of ITT has not been identified. In this study, we examined the expression of the testicular androgen receptor (AR) in NOA and investigated its correlation with clinical and pathological parameters. Expression of the testicular AR was investigated in 52 men with NOA and 22 men with obstructive azoospermia (OA). Twenty-two patients for whom sperm retrieval failed during microdissection testicular sperm extraction (micro-TESE) were enrolled in hormonal therapy using hCG with or without recombinant human follicle stimulating hormone (rhFSH) prior to a second micro-TESE. Sertoli cells were identified by vimentin immunostaining, and positivity in Sertoli cells was used as the AR index. AR immunostaining was robust in the nuclei of Sertoli cells [Sertoli cell androgen receptor (SCAR)] in both OA and NOA. The mean AR index in NOA was significantly higher than that in OA (p < 0.05). In NOA patients, there was no correlation between the AR index and the clinical parameters, whereas the AR index of early maturation arrest (MA) was significantly lower than that of Sertoli cell only, late MA and hypospermatogenesis (p < 0.05). A significant increase in the AR index after salvage hormonal therapy was shown, particularly when using rhFSH. The AR index in patients from whom spermatozoa could be retrieved at the second micro-TESE increased significantly after hormonal therapy. In human testes, the expression of AR is dominant in Sertoli cells, and the expression of SCAR is upregulated by FSH. Germ cell maturation, especially during spermatogonia to spermatocyte stage, has been shown to be SCAR-dependent. Taken together, the results indicate that SCAR elevation is closely associated with sperm retrieval after hormonal therapy and that FSH-based hormonal therapy is potentially effective in NOA men with MA.


title: Big data technologies and Management

abstract: The era of big data has resulted in the development and applications of technologies and methods aimed at effectively using massive amounts of data to support decision-making and knowledge discovery activities. In this paper, the five Vs of big data, volume, velocity, variety, veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that have emerged to accommodate the needs of big data initiatives. The role of conceptual modeling for big data is then analyzed and suggestions made for effective conceptual modeling efforts with respect to big data.


title: Intensionality was only alleged: On adjective-noun composition in distributional semantics

abstract: We acknowledge the support of Spanish MICINN grant FFI2010-09464-E (McNally, Boleda), the ICREA Foundation (McNally), Catalan AGAUR grant 2010BP-A00070, MICINN grant TIN2009-14715-C04-04, EU grant PASCAL2, FP7-ICT-216886, the DARPA DEFT program under AFRL grant FA8750-13-2-0026 (Boleda) and the ERC under the 2011 Starting Independent Research Grant 283554 to the COMPOSES project (Baroni, Pham). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of DARPA, AFRL or the US government.


title: A contour based image segmentation algorithm using morphological edge detection

abstract: In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, it's always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.


title: Applying database replication to multi-player online games

abstract: Multi-player Online Games (MOGs) have emerged as popular data intensive applications in recent years. Being used by many players simultaneously, they require a high degree of fault tolerance, scalability and performance. In this paper we analyze how database replication can be used in MOGs to achieve these goals. In data replication, clients can read data from any database replica while updates have to be executed at all available replicas. Thus, reads can be distributed among the replicas leading to reduced response time and scalability. Furthermore, the system is fault-tolerant as long as a replica is available. However, we are not aware of any previous study on the application of database replication to MOG. In this paper, we present a system, MiddleSIR, which provides database replication support. We illustrate different replication protocols implemented in the system along an example, explaining how data consistency and fault tolerance can be achieved. From there, we design a small multi-player typing game to demonstrate how to apply database replication to MOG. We will discuss how different replication protocols affect the semantics of the game. Our experiments show that database replication can provide good scalability and performance in both Local Area Networks (LAN) and Wide Area Networks (WAN).


title: System effectiveness, user models, and user utility: a conceptual framework for investigation

abstract: There is great interest in producing effectiveness measures that model user behavior in order to better model the utility of a system to its users. These measures are often formulated as a sum over the product of a discount function of ranks and a gain function mapping relevance assessments to numeric utility values. We develop a conceptual framework for analyzing such effectiveness measures based on classifying members of this broad family of measures into four distinct families, each of which reflects a different notion of system utility. Within this framework we can hypothesize about the properties that such a measure should have and test those hypotheses against user and system data. Along the way we present a collection of novel results about specific measures and relationships between them.


title: A 7-μW clock generator in 0.18-μm CMOS for passive UHF RFID EPC G2 tags

abstract: An ultra-low-power clock generator for passive UHF RFID tag is implemented in 0.18-mum CMOS process. By using the technique of dual-path clock generation with multiple clock rates and multiple supply voltages, clock accuracy is much improved and power consumption is reduced. Under the injection-locked condition, the measured cycle-to-cycle jitter of the clock generator is 23.7 ps and the peak-to-peak value is 164 ps with an input at 900 MHz and an output at 3.5 MHz. The overall power consumption is only 7 muW and the core chip area is 0.02 mm2.


title: Two-Tier HetNets with Cognitive Femtocells: Downlink Performance Modeling and Analysis in a Multichannel Environment

abstract: In a two-tier heterogeneous network (HetNet) where femto access points (FAPs) with lower transmission power coexist with macro base stations (BSs) with higher transmission power, the FAPs may suffer significant performance degradation due to inter-tier interference. Introducing cognition into the FAPs through the spectrum sensing (or carrier sensing) capability helps them avoiding severe interference from the macro BSs and enhance their performance. In this paper, we use stochastic geometry to model and analyze performance of HetNets composed of macro BSs and cognitive FAPs in a multichannel environment. The proposed model explicitly accounts for the spatial distribution of the macro BSs, FAPs, and users in a Rayleigh fading environment. We quantify the performance gain in outage probability obtained by introducing cognition into the femto-tier, provide design guidelines, and show the existence of an optimal spectrum sensing threshold for the cognitive FAPs, which depends on the HetNet parameters. We also show that looking into the overall performance of the HetNets is quite misleading in the scenarios where the majority of users are served by the macro BSs. Therefore, the performance of femto-tier needs to be explicitly accounted for and optimized.


title: Decision Combination in Multiple Classifier Systems

abstract: A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness. >


title: A Search-Based Testing Approach for XML Injection Vulnerabilities in Web Applications

abstract: In most cases, web applications communicate with web services (SOAP and RESTful). The former act as a front-end to the latter, which contain the business logic. A hacker might not have direct access to those web services (e.g., they are not on public networks), but can still provide malicious inputs to the web application, thus potentially compromising related services. Typical examples are XML injection attacks that target SOAP communications. In this paper, we present a novel, search-based approach used to generate test data for a web application in an attempt to deliver malicious XML messages to web services. Our goal is thus to detect XML injection vulnerabilities in web applications. The proposed approach is evaluated on two studies, including an industrial web application with millions of users. Results show that we are able to effectively generate test data (e.g., input values in an HTML form) that detect such vulnerabilities.


title: When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies

abstract: We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user's cookie, and further to the user's real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user's entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.


title: From e-learning to social-learning: Mapping development of studies on social media-supported knowledge management

abstract: Knowledge management (KM) process support organizational learning.We mapped the development of studies on social media-supported KM (SMKM).We identified the hot topics, top institutes, authors and trends.SMKM studies increased rapidly from 2011 and trends will be continued. Social media is bringing great challenges and wonderful opportunities for organizational learning. With support of social media, organizations may facilitate the knowledge management process within firms (e.g., knowledge sharing), then to encourage employees to promote collaborative learning behaviors from e-learning to social learning. There is a significant trend in the recent studies is increasing number of publications on social media supported knowledge management (SMKM). However, previous SMKM studies have not been depicted well by combining work of both researchers in social media study and ones in KM (which supports organizational learning) study. By using CiteSpace, this paper mapped important references that lead trends of SMKM development, authors contributing greatly to this field and hot topics of all the related articles. The way that SMKM study developed was analyzed according to the visualization of references and topics. Furthermore, the two most important groups - topics from SM and those from KM study were studied respectively to compare their development in order to show the fusion, the separation and other relationship. Finally, hottest trends and topics in these years and recent future were discussed to provide help for future work.


title: Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics

abstract: Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis.


title: Compression of depth information for 3D rendering

abstract: This paper presents a novel strategy for the compression of depth maps. The proposed scheme starts with a segmentation step which identifies and extracts edges and main objects, then it introduces an efficient compression strategy for the segmented regions' shape. In the subsequent step a novel algorithm is used to predict the surface shape from the segmented regions and a set of regularly spaced samples. Finally the few prediction residuals are efficiently compressed using standard image compression techniques. Experimental results show that the proposed scheme not only offers a significant gain over JPEG2000 on various types of depth maps but also produces depth maps without edge artifacts particularly suited to 3D warping and free viewpoint video applications.


title: Compact center-fed ridged waveguide slot array for SAR applications

abstract: A new, simple, probe-fed technique for a ridged waveguide slot array is presented. The application is for satellite synthetic aperture radar (SAR). The array is fed in the center by a coaxial probe whose inner conductor is attached to the ceiling of the ridged waveguide. An example design of a 6-element slot array with the probe is evaluated by simulation. The −10 dB return loss is around 4% and the cross polarization is well below −33 dB. The feed structure offers good mechanical stability and low voltage around the feed to prevent electrical breakdown, which are critical factors for a rocket launch and for the space environment respectively.


title: Using Network Knowledge to Improve Workload Performance in Virtualized Data Centers

abstract: The scale and expense of modern data centers motivates running them as efficiently as possible. This paper explores how virtualized data center performance can be improved when network traffic and topology data informs VM placement. Our practical heuristics, tested on network-heavy, scale-out workloads in an 80 server cluster, improve overall performance by up to 70% compared to random placement in a multi-tenant configuration.


title: Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

abstract: Location-Based Social Networks (LBSNs) captureindividuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniformfeature space. However, due to the nature of LBSNs, manyusers have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the networkof friends, when learning the new feature space. We firstanalyze the impact of friends on individuals's mobility, andshow that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) inan online setting. Based on our observation, we propose amixed-membership model that infers global mobility patternsfrom users' check-ins and their network of friends, withoutimpairing the model's complexity. Our proposed model infersglobal patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patternsand compare the quality of the new user representation againstbaseline methods on a social link prediction problem.


title: Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

abstract: A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions.


title: A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

abstract: The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.


title: Acral lentiginous melanoma: A case and literature review

abstract: utaneous foot melanoma is rare; it accounts for approximately 1% of all cutaneous melanoma and can be challenging to manage [1]. There are four main variants of cutaneous malignant melanomas (CMMs), superficial spreading melanoma (SSM), lentigo malignant melanoma (LMM), nodular melanoma (NM) and acral lentiginous melanoma (ALM). Superficial spreading melanoma accounts for approximately 65% of all melanomas, followed by LMM (27%), NM (7%) and ALM (1%) [1]. When narrowing the spectrum to foot and ankle malignant melanomas, it is estimated that approximately 60% are acral lentiginous melanoma, whereas the remaining 40% fall into the superficial spreading melanoma sub-category [1]. ALM occurs at the same rate in all races but has been demonstrated to account for higher incidence in those of Asian, Indian, Middle Eastern and African populations [2].


title: LncRNA HOTAIR controls the expression of Rab22a by sponging miR-373 in ovarian cancer

abstract: Increasing evidence suggests that the long non-coding RNA, HOX transcript antisense intergenic RNA (HOTAIR) is widely involved in the progression and metastasis of cancer. However, the specific role of HOTAIR in ovarian carcinogenesis remains to be fully elucidated. In the present study, the levels of HOTAIR were detected in 30 paired cancer and noncancer tissues using reverse transcription-quantitative polymerase chain reaction analysis. The effect of HOTAIR on the ovarian cancer cells was examined by overexpression or small interfering RNA interference experiments. To examine the competitive endogenous RNA (ceRNAs) mechanism, a luciferase reporter assay was used. In patients with ovarian cancer, HOTAIR was significantly upregulated. Furthermore, the upregulation of HOTAIR increased the proliferation, migration and invasion of ovarian cancer cells. By contrast, the knockdown of HOTAIR repressed cell invasion and viability. HOTAIR functioned as a ceRNA, and acted as a sink for microRNA (miR)‑373, thereby regulating the expression of Rab22a. The upregulation of HOTAIR contributed to the malignant progression of ovarian cancer cells. Therefore, the positive regulation between HOTAIR and Rab22a can be partially attributed to the ceRNA regulatory network through miR-373.


title: Urine trouble: should we think differently about UTI?

abstract: Urinary tract infection (UTI) is clinically important, given that it is one of the most common bacterial infections in adult women. However, the current understanding of UTI remains based on a now disproven concept that the urinary bladder is sterile. Thus, current standards for UTI diagnosis have significant limitations that may reduce the opportunity to improve patient care. Using data from our work and numerous other peer-reviewed studies, we identified four major limitations to the contemporary UTI description: the language of UTI, UTI diagnostic testing, the Escherichia coli-centric view of UTI, and the colony-forming units (CFU) threshold-based diagnosis. Contemporary methods and technology, combined with continued rigorous clinical research can be used to correct these limitations.


title: Android malware family classification based on resource consumption over time

abstract: The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe. With respect to DroidScribe, our approach is easier to reproduce. Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices. The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior. Our approach relies on resource consumption metrics available from the proc file system. Features are extracted through detrended fluctuation analysis and correlation. Finally, a SVM is employed to classify malware into families. We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe. Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.


title: Design and Evaluation of Metaphor Processing Systems

abstract: System design and evaluation methodologies receive significant attention in natural language processing NLP, with the systems typically being evaluated on a common task and against shared data sets. This enables direct system comparison and facilitates progress in the field. However, computational work on metaphor is considerably more fragmented than similar research efforts in other areas of NLP and semantics. Recent years have seen a growing interest in computational modeling of metaphor, with many new statistical techniques opening routes for improving system accuracy and robustness. However, the lack of a common task definition, shared data set, and evaluation strategy makes the methods hard to compare, and thus hampers our progress as a community in this area. The goal of this article is to review the system features and evaluation strategies that have been proposed for the metaphor processing task, and to analyze their benefits and downsides, with the aim of identifying the desired properties of metaphor processing systems and a set of requirements for their evaluation.


title: 19.7 A 79GHz binary phase-modulated continuous-wave radar transceiver with TX-to-RX spillover cancellation in 28nm CMOS

abstract: The demand for inexpensive and ubiquitous accurate motion-detection sensors for road safety, smart homes and robotics justifies the interest in single-chip mm-Wave radars: a high carrier frequency allows for a high angular resolution in a compact multi-antenna system and a wide bandwidth allows fora high depth resolution. With the objective of single-chip radar systems, CMOS is the natural candidate to replace SiGe as a leading technology [1-6].


title: Speed Measurement Error Suppression for PMSM Control System Using Self-Adaption Kalman Observer

abstract: This paper proposes a self-adaption Kalman observer (SAKO) used in a permanent-magnet syn- chronous motor (PMSM) servo system. The proposed SAKO can make up measurement noise of the absolute en- coder with limited resolution ratio and avoid differentiating process and filter delay of the traditional speed measur- ing methods. To be different from the traditional Kalman observer, the proposed observer updates the gain matrix by calculating the measurement noise at the current time. The variable gain matrix is used to estimate and correct the observed position, speed, and load torque to solve the problem that the motor speed calculated by the traditional methods is prone to large speed error and time delay when PMSM runs at low speeds. The state variables observed by the proposed observer are used as the speed feed- back signals and compensation signal of the load torque disturbance in PMSM servo system. The simulations and experiments prove that the SAKO can observe speed and load torque precisely and timely and that the feedforward and feedback control system of PMSM can improve the speed tracking ability.


title: Stochastic Activation Pruning for Robust Adversarial Defense

abstract: Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory and cast the problem as a minimax zero-sum game between the adversary and the model. In general, for such games, the optimal strategy for both players requires a stochastic policy, also known as a mixed strategy. In this light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense. SAP prunes a random subset of activations (preferentially pruning those with smaller magnitude) and scales up the survivors to compensate. We can apply SAP to pretrained networks, including adversarially trained models, without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness against attacks, increasing accuracy and preserving calibration.


title: Studying cooperation and conflict between authors with history flow visualizations

abstract: The Internet has fostered an unconventional and powerful style of collaboration: "wiki" web sites, where every visitor has the power to become an editor. In this paper we investigate the dynamics of Wikipedia, a prominent, thriving wiki. We make three contributions. First, we introduce a new exploratory data analysis tool, the history flow visualization, which is effective in revealing patterns within the wiki context and which we believe will be useful in other collaborative situations as well. Second, we discuss several collaboration patterns highlighted by this visualization tool and corroborate them with statistical analysis. Third, we discuss the implications of these patterns for the design and governance of online collaborative social spaces. We focus on the relevance of authorship, the value of community surveillance in ameliorating antisocial behavior, and how authors with competing perspectives negotiate their differences.


title: Interactive Food and Beverage Marketing: Targeting Adolescents in the Digital Age

abstract: Abstract Because of their avid use of new media and their increased spending power, adolescents have become primary targets of a new "Media and Marketing Ecosystem." Digital media resonate particularly well with many of the fundamental developmental tasks of adolescence by enabling instantaneous and constant contact with peers, providing opportunities for self-expression, identity exploration, and social interaction, and facilitating mobility and independence. Six key features of interactive media—ubiquitous connectivity, personalization, peer-to-peer networking, engagement, immersion, and content creation—are emblematic of the ways in which young people are both shaping and being shaped by this new digital culture. The advertising industry, in many instances led by food and beverage marketers, is purposefully exploiting the special relationship that teenagers have with new media, with online marketing campaigns that create unprecedented intimacies between adolescents and the brands and products that now literally surround them. Major food and beverage companies, including Coca-Cola, McDonald's, Burger King, and Kentucky Fried Chicken (KFC), have incorporated these elements into their interactive marketing strategies, posing particular risks to adolescents, who are not being addressed in the current U.S. policy and self-regulatory regimens. However, recent and emerging neuroscience and psychological research on adolescents suggests a need to revisit the traditional approach to regulation of advertising. Despite the growth of interactive marketing, academic research on the impact of digital advertising on children and youth remains underdeveloped. Additional research and policy initiatives are needed to address the growing health threat facing youth in the digital marketplace.


title: Snort - Lightweight Intrusion Detection for Networks

abstract: Network intrusion detection systems (NIDS) are an important part of any network security architecture. They provide a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected. Commercial NIDS have many differences, but Information Systems departments must face the commonalities that they share such as significant system footprint, complex deployment and high monetary cost. Snort was designed to address these issues.


title: Feature Level Fusion of Face and Fingerprint Biometrics

abstract: Theaimofthispaperistostudythefusion at feature extraction level forfaceandfingerprint biometrics. The proposed approach isbasedonthefusion ofthetwotraits by extracting independent feature pointsets fromthetwomodalities, andmakingthetwopointsets compatible forconcatenation. Moreover, tohandle the'problem ofcurse ofdimensionality', the feature pointsets areproperly reduced indimension. Different feature reduction techniques areimplemented, prior andafter thefeature pointsets fusion, andtheresults aredulyrecorded. Thefused feature pointset forthedatabase andthequeryface andfingerprint images arematchedusing techniques basedon either thepoint pattern matching, ortheDelaunay triangulation. Comparative experiments areconducted onchimeric andreal databases, toassess theactual advantage ofthefusion performed atthefeature extraction level, incomparison tothematching score level. IndexTerms-Face,Featurelevelfusion, Fingerprint, Multimodal Biometrics


From Young Adulthood Through the Middle Ages Ravenna Helson


title: Design and analysis of millimeter wave series fed microstrip patch array for next generation wireless communication systems

abstract: A Millimeter-wave series-fed microstrip patch array antenna that operates over the band 27.6 GHz to 30 GHz is presented in this paper. The designed antenna shows a reflection coefficient less than −10 dB over the proposed band. The antenna has a fan beam radiation pattern with 3 dB beamwidth of 9 degrees in the E-plane and 66 degrees in the H-plane. The calculated gain of the proposed antenna is 15.4 dBi. The pulse dispersion effect is quantified by calculating the pulse fidelity factor (PFF) of a transmitted pulse having 500 MHz bandwidth with a center frequency of 28 GHz. The designed antenna exhibits more than 0.9 fidelity factor value, which makes it suitable for wireless data communication systems.


title: Learning-Based Compressive MRI

abstract: In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method, and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.


title: Interpretable Structure-Evolving LSTM

abstract: This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.


title: Cover Tree Bayesian Reinforcement Learning

abstract: This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it suitable for many reinforcement learning problems in continuous state spaces. We demonstrate this in an experimental comparison with least squares policy iteration.


title: Cleanroom process model

abstract: Cleanroom software engineering is a team-oriented process that makes development more manageable and predictable because it is done under statistical quality control. The philosophy behind cleanroom software engineering is to avoid dependence on costly defect-removal processes by writing code increments right the first time and verifying their correctness before testing. Its process model incorporates the statistical quality certification of code increments as they accumulate into a system. >


title: Act quickly to avoid losing patents

abstract: "Microsoft Patents Ones, Zeros", a headline recently announced. Of course, Microsoft did not invent ones and zeros, and neither Microsoft nor anyone else could get a patent covering their use. Nonetheless, the headline firmly underscores the breadth of software patents being issued today. Only a few years ago, the software industry rallied against the famous Compton's NewMedia multimedia patent because of its undue scope. While most software patents aren't met with such fanfare, the US Patent and Trademark Office continues to issue software patents that are often surprisingly broad. In addition to their sweeping scope, software patents are now being issued in record numbers. Only a trickle a few short years ago, software patents now account for as much as 15 percent of the 120,000 patents issued annually. Still, programmers have historically been slow to file their patent applications. Maybe it is because software inventions have only been openly patentable for a few years. Perhaps it is because programmers are too busy testing, documenting, and adding last-minute features to divert attention to patents. Whatever the reason, software inventors often wait until their software is about to be released, or until after it has been released, before filing patent applications.


title: Be Wary of the Economics of "Serverless" Cloud Computing

abstract: One of the latest developments in cloud computing is usually called "serverless" computing, despite the fact that servers are still where processing takes place. The economic benefits of serverless computing heavily depend on the execution behavior and volumes of the application workload. Serverless has the potential to be a great abstraction offering economic advantages for simple workflows, however it's important to model the economic impact of your architecture and operation choices.


title: Scoliosis brace design: influence of visual aesthetics on user acceptance and compliance.

abstract: Adolescent idiopathic scoliosis is a common condition found in adolescents. A rigid brace is often prescribed as the treatment for this spinal deformity, which negatively affects user compliance due to the discomfort caused by the brace, and the psychological distress resulting from its appearance. However, the latter, which is the impact of visual aesthetics, has not been thoroughly studied for scoliosis braces. Therefore, a qualitative study with in-depth interviews has been carried out with 10 participants who have a Cobb angle of 20°-30° to determine the impact of visual aesthetics on user acceptance and compliance towards the brace. It is found that co-designing with patients on the aesthetic aspects of the surface design of the brace increases the level of user compliance and induces positive user perception. Therefore, aesthetic preferences need to be taken into consideration in the design process of braces. Practitioner Summary: The impact of visual aesthetics on user acceptance and compliance towards a rigid brace for scoliosis is investigated. The findings indicate that an aesthetically pleasing brace and the involvement of patients in the design process of the brace are important for increasing user compliance and addressing psychological issues during treatment.


title: Identification of Various Image Operations Using Residual-Based Features

abstract: Image forensics has attracted wide attention during the past decade. However, most existing works aim at detecting a certain operation, which means that their proposed features usually depend on the investigated image operation and they consider only binary classification. This usually leads to misleading results if irrelevant features and/or classifiers are used. For instance, a JPEG decompressed image would be classified as an original or median filtered image if it was fed into a median filtering detector. Hence, it is important to develop forensic methods and universal features that can simultaneously identify multiple image operations. Based on extensive experiments and analysis, we find that any image operation, including existing anti-forensics operations, will inevitably modify a large number of pixel values in the original images. Thus, some common inherent statistics such as the correlations among adjacent pixels cannot be preserved well. To detect such modifications, we try to analyze the properties of local pixels within the image in the residual domain rather than the spatial domain considering the complexity of the image contents. Inspired by image steganalytic methods, we propose a very compact universal feature set and then design a multiclass classification scheme for identifying many common image operations. In our experiments, we tested the proposed features as well as several existing features on 11 typical image processing operations and four kinds of anti-forensic methods. The experimental results show that the proposed strategy significantly outperforms the existing forensic methods in terms of both effectiveness and universality.


title: A Variable-Flux Hybrid-PM Switched-Flux Memory Machine for EV/HEV Applications

abstract: In this paper, a novel topology of hybrid-permanent-magnet switched-flux memory machine (HPM-SFMM) is proposed, which is characterized by a conventional SFPM machine with embedded “V”-shaped Al-Ni-Co magnet poles. The proposed machine combines the distinct synergies of torque enhancement in neodymium–iron–boron (NdFeB) PM and flux variability in aluminum–nickel–cobalt (AlNiCo) PM. By changing the magnetization directions of Al–Ni–Co magnets, the wide-speed-range high-efficiency operation can be readily achieved, which is highly favorable for automotive applications. The configuration and operating principle of the machine are first described, and the combination of stator/rotor pole numbers is optimized. In addition, the torque density/flux adjusting capability as the functions of dual-magnet dimensions are analytically derived. The magnet hybridizing proportion is optimized in order to achieve a favorite tradeoff between flux adjusting range and torque density improvement. Then, the electromagnetic performance of the proposed HPM-SFMM is investigated. Finally, a 12/14-stator/rotor pole prototype is fabricated to experimentally verify the analysis.


title: A link failure recovery algorithm for Virtual Network Function chaining

abstract: This paper addresses Virtual Network Functions (VNFs) placement and chaining in the presence of physical link failures. A decision tree approach to the NP-Hard VNF placement and chaining problem is used to minimize the penalties induced by service interruptions due to link outages. Formulating the problem as decision tree reduces the complexity significantly and leads to a new reliable algorithm, named R-SFC-MCTS, that builds incrementally the decision tree to efficiently search for good placement and chaining solutions. Execution time is improved thanks to the Monte-Carlo Tree Search strategy. The proposed link failure recovery algorithm selects and assigns reliable paths to prevent and avoid the negative effects of link failures and reactively re-maps impacted virtual links in safer physical paths once an outage occurs in the infrastructure. The performance of R-SFC-MCTS is compared via extensive simulations with a baseline and a reactive solution in terms of: i) acceptance rate, ii) induced penalties iii) provider revenue loss and iv) the final provider's profit.


title: Dimension Reduction by Local Principal Component Analysis

abstract: Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network implementations of nonlinear PCA. We find that both nonlinear techniques can provide more accurate representations than PCA and show that the local linear techniques outperform neural network implementations.


Research Statement


title: R3MC: A Riemannian three-factor algorithm for low-rank matrix completion

abstract: We exploit the versatile framework of Riemannian optimization on quotient manifolds to develop R3MC, a nonlinear conjugate-gradient method for low-rank matrix completion. The underlying search space of fixed-rank matrices is endowed with a novel Riemannian metric that is tailored to the least-squares cost. Numerical comparisons suggest that R3MC robustly outperforms state-of-the-art algorithms across different problem instances, especially those that combine scarcely sampled and ill-conditioned data.


title: Formal Design and Verification of Self-Adaptive Systems with Decentralized Control

abstract: Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded. This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines, that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.


title: Model driven EEG/fMRI fusion of brain oscillations

abstract: This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of for- ward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian compu- tations the practical estimation of very large scale EEG/fMRI models shall soon be possible. Hum Brain Mapp 30:2701-2721, 2009. V C 2008 Wiley-Liss, Inc.


title: Random eye state change detection in real-time using EEG signals

abstract: Abstract Maximum and minimum computed across channels is used to monitor the Electroencephalogram signals for possible change of the eye state. Upon detection of a possible change, the last two seconds of the signal is passed through Multivariate Empirical Mode Decomposition and relevant features are extracted. The features are then fed into Logistic Regression and Artificial Neural Network classifiers to confirm the eye state change. The proposed algorithm detects the eye state change with 88.2% accuracy in less than two seconds. This provides a valuable improvement in comparison to a recent procedure that takes about 20 minutes to classify new instances with 97.3% accuracy. The introduced algorithm is promising in the real-time eye state classification as increasing the training examples would increase its accuracy.


TOWARDS ADAPTIVE GAMIFICATION: A SYNTHESIS OF CURRENT DEVELOPMENTS


title: Heritability of autism spectrum disorders: a meta‐analysis of twin studies

abstract: Background ::: The etiology of Autism Spectrum Disorder (ASD) has been recently debated due to emerging findings on the importance of shared environmental influences. However, two recent twin studies do not support this and instead re-affirm strong genetic effects on the liability to ASD, a finding consistent with previous reports. This study conducts a systematic review and meta-analysis of all twin studies of ASD published to date and explores the etiology along the continuum of a quantitative measure of ASD. ::: ::: Methods ::: A PubMed Central, Science Direct, Google Scholar, Web of Knowledge structured search conducted online, to identify all twin studies on ASD published to date. Thirteen primary twin studies were identified, seven were included in the meta-analysis by meeting Systematic Recruitment criterion; correction for selection and ascertainment strategies, and applied prevalences were assessed for these studies. In addition, a quantile DF extremes analysis was carried out on Childhood Autism Spectrum Test scores measured in a population sample of 6,413 twin pairs including affected twins. ::: ::: Results ::: The meta-analysis correlations for monozygotic twins (MZ) were almost perfect at .98 (95% Confidence Interval, .96–.99). The dizygotic (DZ) correlation, however, was .53 (95% CI .44–.60) when ASD prevalence rate was set at 5% (in line with the Broad Phenotype of ASD) and increased to .67 (95% CI .61–.72) when applying a prevalence rate of 1%. The meta-analytic heritability estimates were substantial: 64–91%. Shared environmental effects became significant as the prevalence rate decreased from 5–1%: 07–35%. The DF analyses show that for the most part, there is no departure from linearity in heritability. ::: ::: Conclusions ::: We demonstrate that: (a) ASD is due to strong genetic effects; (b) shared environmental effects become significant as a function of lower prevalence rate; (c) previously reported significant shared environmental influences are likely a statistical artefact of overinclusion of concordant DZ twins.


title: A NOMA scheme for visible light communications using a single carrier transmission

abstract: We propose a non-orthogonal multiple access (NOMA) scheme for visible light communications (VLC) based on single carrier (SC) transmission and frequency-domain successive interference cancellation. The scheme can achieve a low peak-to-average power ratio (PAPR), a good balance between the throughput and fairness, and a higher system capacity for a larger number of users. We show that the proosed scheme offers improved bit error rate performance compared to the orthogonal frequency domain multiplexing based VLC system.


title: The Clinician’s Guide to Autism

abstract: 1. John W. Harrington, MD* ::: 2. Korrie Allen, PsyD† ::: ::: ::: ::: ::: 1. *General Academic Pediatrics, Children’s Hospital of The King’s Daughters, Department of Pediatrics, Eastern Virginia Medical School, Norfolk, VA. ::: ::: 2. †Department of Pediatrics, Eastern Virginia Medical School, Norfolk, VA. ::: ::: * Abbreviations: ::: AAP: : American Academy of Pediatrics ::: ABA: : applied behavioral analysis ::: ADHD: : attention-deficit/hyperactivity disorder ::: ASD: : autism spectrum disorder ::: CAM: : complementary and alternative medicine ::: DSM-IV: : Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition DSM-5: : Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition M-CHAT: : Modified Checklist for Autism in Toddlers ::: ::: 1. Because 1% to 2% of children will be diagnosed as having an autism spectrum disorder, pediatricians need adequate training in screening and diagnosis, particularly with the changes presented in the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Initiating interventions for autism, both evidence-based and complementary, also requires the pediatrician to be familiar with available treatments. ::: ::: 2. Children with autism are likely to have a co-occurring mental health disorder that pediatricians should be able to recognize so that they can initiate treatment or provide timely referral. ::: ::: 3. Children with autism are less likely to have a medical home when compared with other children with special health care needs. ::: ::: After completing this article, readers should be able to: ::: ::: 1. Describe the signs and symptoms of an autism spectrum disorder and therefore be aware of the changes recently made to the DSM-5. ::: ::: 2. Be aware of appropriate screening devices for autism and how to use them effectively. ::: ::: 3. Consider a differential diagnosis and initiate an evaluation, as well as provide evidenced-based advice to a patient with newly diagnosed autism. ::: ::: 4. Recognize and provide screening devices for co-occurring mental health disorders in children with autism. ::: ::: 5. Become familiar with common complementary and alternative medicines and therapies. ::: ::: 6. Provide a more comprehensive medical home for children and families with autism. ::: ::: Autism spectrum disorder (ASD) includes a continuum of neurodevelopmental disorders characterized by deficits in social communication and interactions, along with restrictive, repetitive patterns of behaviors, interests, and activities. (For this article, ASD and autism are used interchangeably.) Pediatric clinicians can significantly affect the outcome of children with ASD by making an early diagnosis and providing referral for evidenced-based behavioral treatment. By …


title: McPAT: an integrated power, area, and timing modeling framework for multicore and manycore architectures

abstract: This paper introduces McPAT, an integrated power, area, and timing modeling framework that supports comprehensive design space exploration for multicore and manycore processor configurations ranging from 90nm to 22nm and beyond. At the microarchitectural level, McPAT includes models for the fundamental components of a chip multiprocessor, including in-order and out-of-order processor cores, networks-on-chip, shared caches, integrated memory controllers, and multiple-domain clocking. At the circuit and technology levels, McPAT supports critical-path timing modeling, area modeling, and dynamic, short-circuit, and leakage power modeling for each of the device types forecast in the ITRS roadmap including bulk CMOS, SOI, and doublegate transistors. McPAT has a flexible XML interface to facilitate its use with many performance simulators. Combined with a performance simulator, McPAT enables architects to consistently quantify the cost of new ideas and assess tradeoffs of different architectures using new metrics like energy-delay-area2 product (EDA2P) and energy-delay-area product (EDAP). This paper explores the interconnect options of future manycore processors by varying the degree of clustering over generations of process technologies. Clustering will bring interesting tradeoffs between area and performance because the interconnects needed to group cores into clusters incur area overhead, but many applications can make good use of them due to synergies of cache sharing. Combining power, area, and timing results of McPAT with performance simulation of PARSEC benchmarks at the 22nm technology node for both common in-order and out-of-order manycore designs shows that when die cost is not taken into account clustering 8 cores together gives the best energy-delay product, whereas when cost is taken into account configuring clusters with 4 cores gives the best EDA2P and EDAP.


title: A Simple Exoskeleton That Assists Plantarflexion Can Reduce the Metabolic Cost of Human Walking

abstract: Background ::: Even though walking can be sustained for great distances, considerable energy is required for plantarflexion around the instant of opposite leg heel contact. Different groups attempted to reduce metabolic cost with exoskeletons but none could achieve a reduction beyond the level of walking without exoskeleton, possibly because there is no consensus on the optimal actuation timing. The main research question of our study was whether it is possible to obtain a higher reduction in metabolic cost by tuning the actuation timing. ::: ::: ::: Methodology/Principal Findings ::: We measured metabolic cost by means of respiratory gas analysis. Test subjects walked with a simple pneumatic exoskeleton that assists plantarflexion with different actuation timings. We found that the exoskeleton can reduce metabolic cost by 0.18±0.06 W kg−1 or 6±2% (standard error of the mean) (p = 0.019) below the cost of walking without exoskeleton if actuation starts just before opposite leg heel contact. ::: ::: ::: Conclusions/Significance ::: The optimum timing that we found concurs with the prediction from a mathematical model of walking. While the present exoskeleton was not ambulant, measurements of joint kinetics reveal that the required power could be recycled from knee extension deceleration work that occurs naturally during walking. This demonstrates that it is theoretically possible to build future ambulant exoskeletons that reduce metabolic cost, without power supply restrictions.


title: A Digital Phase Demodulation Technique for Resonant MEMS Gyroscopes

abstract: This paper introduces a digital phase demodulation technique for resonant MEMS gyroscopes. The proposed method converts the amplitude-modulated Coriolis signal of the gyroscope into a digital phase-modulated output by utilizing the quadrature component of the sense signal. The rate information is extracted from the digital phase-modulated output using an XOR gate as a digital multiplier. Besides offering more robustness to low-frequency amplitude noise sources due to its phase-based operation, the proposed scheme enables direct time-domain digitization of the gyroscope signal at carrier frequency to avoid additional noise folding by the down-conversion multiplier in conventional amplitude demodulation. In addition, due to its digital nature, the proposed phase-based scheme offers better design scalability for deep-submicrometer CMOS implementations. As a proof of concept, the proposed phase demodulation architecture is interfaced with a low-bias-drift mode-matched tuning fork gyroscope. A scale factor of 240 mV/°/s with sub-0.001°/s detectable rate is measured. The complete system exhibits a low bias instability of 0.55°/h and an angle random walk of 0.12°/√h.


title: Modifying Smartphone User Locking Behavior ABSTRACT

abstract: With an increasing number of organizations allowing personal smart phones onto their networks, considerable security risk is introduced. The security risk is exacerbated by the tremendous heterogeneity of the personal mobile devices and their respective installed pool of applications. Furthermore, by virtue of the devices not being owned by the organization, the ability to authoritatively enforce organizational security polices is challenging. As a result, a critical part of organizational security is the ability to drive user security behavior through either on-device mechanisms or security awareness programs. In this paper, we establish a baseline for user security behavior from a population of over one hundred fifty smart phone users. We then systematically evaluate the ability to drive behavioral change via messaging centered on morality, deterrence, and incentives. Our findings suggest that appeals to morality are most effective over time, whereas deterrence produces the most immediate reaction. Additionally, our findings show that while a significant portion of users are securing their devices without prior intervention, it is difficult to influence change in those who do not.


A Car Parking Monitoring System Using a Line-Topology Wireless Sensor Network


title: Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems

abstract: Distributed generator (DG) resources are small scale electric power generating plants that can provide power to homes, businesses or industrial facilities in distribution systems. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Whale optimization algorithm (WOA), a novel metaheuristic algorithm, is used to determine the optimal DG size. WOA is modeled based on the unique hunting behavior of humpback whales. The WOA is evaluated on IEEE 15, 33, 69 and 85-bus test systems. WOA was compared with different types of DGs and other evolutionary algorithms. When compared with voltage sensitivity index method, WOA and index vector methods gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.


title: Filtering spams using the minimum description length principle

abstract: Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle. The proposed model is fast to construct and incrementally updateable. Additionally, we offer an analysis concerning the measurements usually employed to evaluate the quality of the anti-spam classifiers. In this sense, we present a new measure in order to provide a fairer comparison. Furthermore, we conducted an empirical experiment using six well-known, large and public databases. Finally, the results indicate that our approach outperforms the state-of-the-art spam filters.


title: Discovering Actionable Patterns in Event Data

abstract: Applications such as those for systems management and intrusion detection employ an automated real-time operation system in which sensor data are collected and processed in real time. Although such a system effectively reduces the need for operation staff, it requires constructing and maintaining correlation rules. Currently, rule construction requires experts to identify problem patterns, a process that is time-consuming and error-prone. In this paper, we propose reducing this burden by mining historical data that are readily available. Specifically, we first present efficient algorithms to mine three types of important patterns from historical event data: event bursts, periodic patterns, and mutually dependent patterns. We then discuss a framework for efficiently mining events that have multiple attributes. Last, we present Event Correlation Constructor--a tool that validates and extends correlation knowledge.


title: Extracting Summary Sentences Based on the Document Semantic Graph

abstract: We present a method for extracting sentences from an individual document to serve as a document summary or a pre-cursor to creating a generic document abstract. We apply syntactic analysis of the text that produces a logical form analysis for each sentence. We use subject–object–predicate (SOP) triples from individual sentences to create a semantic graph of the original document and the corresponding human extracted summary. Using the Support Vector Machines learning algorithm, we train a classifier to identify SOP triples from the document semantic graph that belong to the summary. The classifier is then used for automatic extraction of summaries from test documents. Our experiments with the DUC 2002 and CAST datasets show that including semantic properties and topological graph properties of logical triples yields statistically significant improvement of the microaverage F1 measure for both the extraction of SOP triples that correspond to the semantic structure of extracts and the extraction of summary sentences. Evaluation based on ROUGE shows similar results for the extracted summary sentences.


SYNSCAN: Towards Complete TCP/IP Fingerprinting", CanSecWest


title: TSVC: timed efficient and secure vehicular communications with privacy preserving

abstract: In this paper, we propose a timed efficient and secure vehicular communication (TSVC) scheme with privacy preservation, which aims at minimizing the packet overhead in terms of signature overhead and signature verification latency without compromising the security and privacy requirements. Compared with currently existing public key based packet authentication schemes for security and privacy, the communication and computation overhead of TSVC can be significantly reduced due to the short message authentication code (MAC) tag attached in each packet for the packet authentication, by which only a fast hash operation is required to verify each packet. Simulation results demonstrate that TSVC maintains acceptable packet latency with much less packet overhead, while significantly reducing the packet loss ratio compared with that of the existing public key infrastructure (PKI) based schemes, especially when the road traffic is heavy.


title: Privacy or Utility in Data Collection? A Contract Theoretic Approach

abstract: With the growing popularity of data mining, privacy has become an issue of growing importance. Privacy can be seen as a special type of goods, in a sense that it can be traded by the owner for incentives. In this paper, we consider a private data collecting scenario where a data collector buys data from multiple data owners and employs anonymization techniques to protect data owners' privacy. Anonymization causes a decline of data utility; therefore, the data owner can only sell his data at a lower price if his privacy is better protected. Can one pursue higher data utility while maintaining acceptable privacy? How to balance the trade-off between privacy protection and data utility is an important question for big data. Considering that different data owners treat privacy differently, and their privacy preferences are unknown to the collector, we propose a contract theoretic approach for data collector to deal with the trade-off. By designing an optimal contract, the collector can make rational decisions on how to pay the data owners, and more importantly, how he should protect the owners' privacy. We show that when the collector requires a large amount of data, he should ask data owners who care privacy less to provide as much as possible data. We also find that whenever the collector requires higher utility of data or the data becomes less profitable, the collector should provide a stronger protection of the owners' privacy. Performance of the proposed contract is evaluated by both numerical simulations and real data experiments.


title: Ultrawideband and High-Efficiency Linear Polarization Converter Based on Double V-Shaped Metasurface

abstract: In this paper, a double V-shaped metasurface that can efficiently convert linear polarizations of electromagnetic (EM) waves in wideband is proposed. Based on the electric and magnetic resonant features of a single V-shaped particle, four EM resonances are generated in a V-shaped pair, leading to significant bandwidth expansion of cross-polarized reflections. The simulation results show that the proposed metasurface is able to convert linearly polarized waves into cross-polarized waves in ultrawideband from 12.4 to 27.96 GHz, with an average polarization conversion ratio (PCR) of 90%. The experimental results are in good agreement with the numerical simulations. Compared to published designs, the proposed polarization converter has a simple geometry but an ultrawideband and hence can be used in many applications, such as reflector antennas, imaging systems, remote sensors, and radiometers. The method can also be extended to the terahertz band.


title: Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images

abstract: The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SD-OCT scans. Evaluation on annotated OCT images of the retina demonstrates that a residual U-Net allows to segment HRF with high accuracy. As our dataset comprised data from different retinal diseases including age-related macular degeneration, diabetic macular edema and retinal vein occlusion, the algorithm can safely be applied in all of them though different pathophysiological origins are known.


title: Carrier and symbol synchronization in digital receivers using feedback compensation loop and early late gate on FPGA

abstract: This paper deals with the complete design of a digital communication receiver on FPGA. Carrier and timing synchronization problems are covered in implementation. Carrier recovery is done using a feedback compensation loop and timing recovery is done using an early late gate. Xilinx ISE is used for simulation and synthesis and the virtex-6 FPGA board is chosen as the hardware platform. Results obtained are highly accurate with very low bit error rate under noisy environment consisting of additive white Gaussian noise (AWGN) channel and variable delays. FPGA results are also presented.


title: Learning from the Wisdom of Crowds by Minimax Entropy

abstract: An important way to make large training sets is to gather noisy labels from crowds of nonexperts. We propose a minimax entropy principle to improve the quality of these labels. Our method assumes that labels are generated by a probability distribution over workers, items, and labels. By maximizing the entropy of this distribution, the method naturally infers item confusability and worker expertise. We infer the ground truth by minimizing the entropy of this distribution, which we show minimizes the Kullback-Leibler (KL) divergence between the probability distribution and the unknown truth. We show that a simple coordinate descent scheme can optimize minimax entropy. Empirically, our results are substantially better than previously published methods for the same problem.


title: Perceived security and World Wide Web purchase intention

abstract: While Web‐based consumer activity is poised on the brink of dramatic growth, concerns about security have the potential to limit this growth by arousing shopper’s concerns about the Web‐based channel. One key aspect to consumer participation in Web commerce may be the extent to which they perceive risk to their sensitive information. This study describes the creation of a scale to measure perceived Web security. We apply the scale in a causal model to investigate the relative impact of beliefs about Web shopping on intent to purchase products using the World Wide Web. Data from two samples is used to develop the scale and to perform the causal analysis. Our findings indicate the favorable psychometric properties of the scale, and the causal analysis from our sample indicates that security is a greater influence on intent to purchase using the Web than is the ease and utility of purchasing products.


title: Enabling 5G mobile wireless technologies

abstract: Research on 5G mobile wireless technologies has been very active in both academia and industry in the past few years. While there has been certain consensus on the overall requirements of 5G wireless systems (e.g., in data rate, network capacity, delay), various enabling wireless technologies have been considered and studied to achieve these performance targets. It has been quite clear, however, that there would be no single enabling technology that can achieve all diverse and even conflicting 5G requirements. In general, many fundamental changes and innovations to re-engineer the overall network architecture and algorithms in different layers and to exploit new system degrees of freedom would be needed for the future 5G wireless system. In particular, we may need to consider other potential waveform candidates that can overcome limitations of the orthogonal frequency multiple access (OFDM) waveform employed in the current 4G system, develop disruptive technologies to fulfill 5G rate and capacity requirements including network densification, employment of large-scale (massive) multiple input multiple output (MIMO), and exploitation of the millimeter wave (mmWave) spectrum to attain Gigabit communications. In addition, design tools from the computer networking domain including software defined networking, virtualization, and cloud computing are expected to play important roles in defining the more flexible, intelligent, and efficient 5G network architecture. This paper aims at describing key 5G enabling wireless mobile technologies and discussing their potentials and open research challenges. We also present how papers published in our special issue contribute to the developments of these disruptive 5G technologies.


title: Keeping Up with the Tweet-dashians: The Impact of 'Official' Accounts on Online Rumoring

abstract: This paper examines how â officialâ accounts participate in the propagation and correction of online rumors in the context of crisis events. Using an emerging method for interpretive analysis of â bigâ social data, we investigate the spread of online rumors through digital tracesâ in this case, tweets. Our study suggests that official accounts can help to slow the spread of a rumor by posting a denial, andâ supported by reflections from an organization that recently dealt with a rumor-crisisâ offers best practices for organizations around social media strategies and protocols. Based on tweet data and connections to existing literature, we also demonstrate and discuss how mainstream media participate in rumoring, and note the role of a new breed of online media, â breaking newsâ accounts. This analysis offers a complementary perspective to existing studies that use surveys and interviews to characterize the role official accounts play in online rumoring.