title: Adaptative combination rule and proportional conflict redistribution rule for information fusion

abstract: This paper presents two new promising rules of combination for the fusion of uncertain and potentially highly conflicting sources of evidences in the framework of the theory of belief functions in order to palliate the well-know limitations of Dempster's rule and to work beyond the limits of applicability of the Dempster-Shafer theory. We present both a new class of adaptive combination rules (ACR) and a new efficient Proportional Conflict Redistribution (PCR) rule allowing to deal with highly conflicting sources for static and dynamic fusion applications.


title: Íntegro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs

abstract: Detecting fake accounts in online social networks (OSNs) protects OSN operators and their users from various malicious activities. Most detection mechanisms attempt to predict and classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior. We herein demonstrate that victims, benign users who control real accounts and have befriended fakes, form a distinct classification category that is useful for designing robust detection mechanisms. First, as attackers have no control over victim accounts and cannot alter their activities, a victim account classifier which relies on user-level activities is relatively harder to circumvent. Second, as fakes are directly connected to victims, a fake account detection mechanism that integrates victim prediction into graphlevel structures is more robust against manipulations of the graph. To validate this new approach, we designed Integro, a scalable defense system that helps OSNs detect fake accounts using a meaningful a user ranking scheme. Integro starts by predicting victim accounts from user-level activities. After that, it integrates these predictions into the graph as weights, so that edges incident to predicted victims have much lower weights than others. Finally, Integro ranks user accounts based on a modified random walk that starts from a known real account. Integro guarantees that most real accounts rank higher than fakes so that OSN operators can take actions against low-ranking fake accounts. We implemented Integro using widely-used, open-source distributed computing platforms in which it scaled nearly linearly. We evaluated Integro against SybilRank, the state-of-the-art in fake account detection, using real-world datasets and a largescale deployment at Tuenti, the largest OSN in Spain. We show that Integro significantly outperforms SybilRank in user ranking quality, where the only requirement is to employ a victim classifier is better than random. Moreover, the deployment of Integro at Tuenti resulted in up to an order of magnitude higher precision in fake accounts detection, as compared to SybilRank.


title: On the Limits of Coexisting Coverage and Capacity in Multi-RAT Heterogeneous Networks

abstract: This paper devises a general modeling and analyzing framework for a heterogeneous wireless network (HetNet) in which several wireless subnetworks coexist and use multiple radio access technologies (multi-RATs). The coexisting coverage and network capacity in such a multi-RAT HetNet are hardly investigated in prior works. To characterize the coexisting interactions in a multi-RAT HetNet, in this paper, we consider a HetNet consisting of $K$ tiers of access points (APs) and two different RATs, RAT- $ \mathsf {L}$ and RAT- $ \mathsf {U}$ , are adopted in the HetNet. RAT- $ \mathsf {L}$ is adopted by the APs in the first $K-1$ tiers and APs in the $K$ th tier only use RAT- $ \mathsf {U}$ . Both noncrossing-RAT and crossing-RAT user association scenarios are considered. In each scenario, the void probability and the channel access probability of the APs in each tier are first found and then the tight lower bounds and their lowest limits on the proposed coexisting coverage and network capacity are derived. We show that multi-RAT networks in general can achieve higher link coverage and capacity by using opportunistic carrier sense multiple access with collision avoidance that avoids/alleviates severe interfering between all coexisting APs. Also, crossing-RAT user association is shown to achieve much higher coexisting coverage and network capacity than noncrossing-RAT user association. Finally, numerical simulations for the LTE-U and WiFi networks coexisting in the HetNet validate our findings.


title: Inferring the origin locations of tweets with quantitative confidence

abstract: Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.


title: Satellite remote sensing of surface air quality

abstract: Satellite remote sensing of air quality has evolved dramatically over the last decade. Global observations are now available for a wide range of species including aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, and SO2. Capabilities for satellite remote sensing of these species in the boundary layer are reviewed for current instruments, along with physical processes affecting their accuracy and precision. Applications of satellite observations are discussed for case studies of specific events, for estimates of surface concentrations, and to improve emission inventories of trace gases and aerosols. Aerosol remote sensing at visible wavelengths exhibits high sensitivity to boundary layer concentrations. Although atmo- spheric scattering and surface emission of thermal radiation generally reduce instrument sensitivity to trace gases near the surface, a strong boundary layer signal in NO2 arises from its large boundary layer concentrations relative to the free troposphere. Recommendations are presented including (1) additional dedicated validation activities, especially for tropospheric NO2 and HCHO; (2) improved characterization of geophysical fields that affect remote sensing of trace gases and aerosols; (3) continued development of comprehensive assimilation and inversion capabilities to relate satellite observations to emissions and surface concentrations; (4) development of satellite instruments and algorithms to achieve higher spatial resolution to resolve urban scales, facilitate validation, and reduce cloud contamination that increases remote sensing error; and (5) support for the next generate of satellite instrumentation designed for air quality applications.


title: Change detection in hierarchically structured information

abstract: Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a "minimum-cost edit script" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script. Our algorithms make use of some key domain characteristics to achieve substantially better performance than previous, general-purpose algorithms. We study the performance of our algorithms both analytically and empirically, and we describe the application of our techniques to hierarchically structured documents.


title: Ontology-based information extraction: An introduction and a survey of current approaches

abstract: Information extraction (IE) aims to retrieve certain types of information from natural language text by processing them automatically. For example, an IE system might retrieve information about geopolitical indicators of countries from a set of web pages while ignoring other types of information. Ontology-based information extraction (OBIE) has recently emerged as a subfield of information extraction. Here, ontologies - which provide formal and explicit specifications of conceptualizations - play a crucial role in the IE process. Because of the use of ontologies, this field is related to knowledge representation and has the potential to assist the development of the Semantic Web. In this paper, we provide an introduction to ontology-based information extraction and review the details of different OBIE systems developed so far. We attempt to identify a common architecture among these systems and classify them based on different factors, which leads to a better understanding on their operation. We also discuss the implementation details of these systems including the tools used by them and the metrics used to measure their performance. In addition, we attempt to identify the possible future directions for this field.


title: Multimodal Gesture Recognition via Multiple Hypotheses Rescoring.

abstract: We present a new framework for multimodal gesture recognition that is based on a multiple hypotheses rescoring fusion scheme. We specifically deal with a demanding Kinect-based multimodal data set, introduced in a recent gesture recognition challenge (ChaLearn 2013), where multiple subjects freely perform multimodal gestures. We employ multiple modalities, that is, visual cues, such as skeleton data, color and depth images, as well as audio, and we extract feature descriptors of the hands' movement, handshape, and audio spectral properties. Using a common hidden Markov model framework we build single-stream gesture models based on which we can generate multiple single stream-based hypotheses for an unknown gesture sequence. By multimodally rescoring these hypotheses via constrained decoding and a weighted combination scheme, we end up with a multimodally-selected best hypothesis. This is further refined by means of parallel fusion of the monomodal gesture models applied at a segmental level. In this setup, accurate gesture modeling is proven to be critical and is facilitated by an activity detection system that is also presented. The overall approach achieves 93.3% gesture recognition accuracy in the ChaLearn Kinect-based multimodal data set, significantly outperforming all recently published approaches on the same challenging multimodal gesture recognition task, providing a relative error rate reduction of at least 47.6%.


title: Research on a Wavelet Filtering Method of the Fiber Optic Gyroscope Based on EMD

abstract: Due to the weak nonstationary characteristics of high-precision fiber optic gyro output random drift. In order to reduce the random drift, this paper proposes a new nonlinear, nonstationary filter method—the Empirical mode decomposition (EMD) method. Firstly some key technologies were discussed, and then proposed a Wavelet filter method of the fiber optic gyroscope based on EMD. Using fiber optic gyro data conducted off-line simulation verification. The results indicated that Wavelet filter method based on EMD can effectively remove high frequency noise compared with the ordinary wavelet filter method, and can also effectively suppress other noise. The bias instability of Gyro output from 0.0061 ° / h reduced to 0.001 ° / h. Accuracy is improved obviously.


title: Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

abstract: In the knowledge age, the use of the information technology (IT) tools including learning management systems (LMS) has became an imperative. The adoption rate of LMS in academic and training institutions is very promising worldwide. Learning Management system includes several tools that provide academic and training institutions efficient and effective means to support distance education and supplement their traditional way of teaching. LMS also provide academic insinuations mechanisms and tools to store, manage, and share its academic resources and knowledge. Instructors' acceptance is essential for the deployment of LMS. The success of LMS in any institution starts by instructors' acceptance, which in turns initiates and promotes learners' utilization of LMS. Consequently, the objective of this paper is to develop a theoretical framework for evaluating instructors' acceptance of LMS based on the Technology Acceptance Model. This framework provides a comprehensive look of the critical factors that influence the instructors' perceived ease of use and perceived usefulness of LMS and consequently the actual use. These critical factors are related to the instructor, organization, and technology. Instructor factors include self efficacy, attitude toward LMS, experience, teaching style and personal innovativeness. Organization factors include motivators, technology alignment, organization support, technical support and training. Technology factors include system quality, information quality and service quality.


title: Lane Detection With Moving Vehicles in the Traffic Scenes

abstract: A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief. First, lane marks are extracted based on color information. The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy. Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks. The mechanism effectively eliminates the influence of passing vehicles when performing lane detection. Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane. The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic. Experimental results show that the proposed method works well on marked roads in various lighting conditions


title: An evaluation framework for reputation management systems. Working chapter for Trust Modeling

abstract: Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, we propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on our framework’s design decisions. Our goal being to build a generalpurpose simulator, we have the opportunity to characterize the breadth of existing RM systems. Further, we demonstrate our tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. Our analysis permits us to make claims about the algorithms’ comparative merits. We conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives.


title: A novel hybrid PSO–GWO approach for unit commitment problem

abstract: Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO---GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA---PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO---GWO is better than classical and novel PSO, but convergence of hybrid PSO---GWO is much slower than NPSO due to sequential computation of PSO and GWO.


title: Aspirin misuse: a case report

abstract: Aspirin-use disorder is an underreported condition. Identification of the signs and symptoms of aspirin misuse are important in light of prevalent non-prescribed medicine/over-the-counter medication (NPM/OTC) misuse. We discuss here the case of a patient with a history of chronic aspirin misuse who presented to the emergency department with salicylate intoxication and described elation secondary to deliberate aspirin consumption. This case highlights the importance of screening for NPM/OTC medication misuse in at-risk populations.


title: A Knowledge Hunting Framework for Common Sense Reasoning

abstract: We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.


title: OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame

abstract: Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle nodes in real-time. Since CAN message is broadcast from a transmitter to the other nodes on a bus, it does not contain information about the source and destination address for validation. Therefore, an attacker can easily inject any message to lead system malfunctions. In this paper, we propose an intrusion detection method based on the analysis of the offset ratio and time interval between request and response messages in CAN. If a remote frame having a particular identifier is transmitted, a receiver node should respond to the remote frame immediately. In attack-free state, each node has a fixed response offset ratio and time interval while these values vary in attack state. Using this property, we can measure the response performance of the existing nodes based on the offset ratio and time interval between request and response messages. As a result, our methodology can detect intrusions by monitoring offset ratio and time interval, and it allows quick intrusion detection with high accuracy.


Low-back pain in adolescents: consider haematocolpometra by imperforate hymen in the case of a premenarchal girl.


title: The Transition to High School as a Developmental Process Among Multiethnic Urban Youth

abstract: The high school transition was examined in an ethnically diverse, urban sample of 1,979 adolescents, followed from 7th to 10th grade (Mage = 14.6, SD = .37 in 7th grade). Twice annually, data were gathered on adolescents’ perceptions of school climate, psychological functioning, and academic behaviors. Piecewise growth modeling results indicate that adolescents were doing well before the transition but experienced transition disruptions in psychological functioning and grades, and many continued to struggle across high school. The immediate experience of the transition appeared to be particularly challenging for African American and Latino students when the numerical representation of their ethnic groups declined significantly from middle to high school. Findings highlight the value of examining the transition in a larger developmental context and the importance of implementing transition support.


School-based intervention for the prevention of HPV among adolescents: a cluster randomised controlled study


title: Rapid and advanced tools for mycotoxin analysis: a review

abstract: The problems associated with mycotoxin contamination of foods and feeds are well established and, in many cases, have been known for a long time. Consequently, the techniques for detecting known mycotoxins are quite advanced and range from methods for directly detecting the toxins themselves, based upon physical characteristics of the toxins, to methods for indirectly detecting the toxins, such as immunoassays. This review focuses on recent technologies that can be used to detect mycotoxins and, as such, is not a comprehensive review of the mycotoxin analytical literature. Rather, the intent is to survey the range of technologies from those that are instrument intensive such as modern chromatographic methods to those that require no instrumentation, such as certain immunoassays and biosensors. In particular, mass spectrometric techniques using ambient ionization offer the intriguing possibility of non-destructive sampling and detection. The potential application of one such technique, desorption electrospray ionization (DESI), is demonstrated for fumonisin B(1) on maize. While methods for detecting mycotoxins are quite advanced, the need remains for assays with increased throughput, for the exploration of novel detection technologies, and for the comprehensive validation of such technologies as they continue to be developed.


Chapter 22 Active Learning : A Survey


title: Green software engineering with agile methods

abstract: The energy consumption of information and communication technology (ICT) is still increasing. Since several concepts regarding hardware solutions for Green IT exist, the contribution of software to Green IT is still not well investigated. This comprises the production and the usage impact of software on energy consumption. In our paper, we discuss this contribution. Especially, we present a model that integrates Green IT aspects into software engineering processes with agile methods in order to produce "greener" software from scratch.


title: The role of parietal cortex in the formation of color and motion based concepts

abstract: Imaging evidence shows that separate subdivisions of parietal cortex, in and around the intraparietal sulcus (IPS), are engaged when stimuli are grouped according to color and to motion (Zeki and Stutters, 2013). Since grouping is an essential step in the formation of concepts, we wanted to learn whether parietal cortex is also engaged in the formation of concepts according to these two attributes. Using functional magnetic resonance imaging (fMRI), and choosing the recognition of concept-based color or motion stimuli as our paradigm, we found that there was strong concept-related activity in and around the IPS, a region whose homolog in the macaque monkey is known to receive direct but segregated anatomical inputs from V4 and V5. Parietal activity related to color concepts was juxtaposed but did not overlap with activity related to motion concepts, thus emphasizing the continuation of the segregation of color and motion into the conceptual system. Concurrent retinotopic mapping experiments showed that within the parietal cortex, concept-related activity increases within later stage IPS areas.


title: Recurrent Neural Networks for Music Computation

abstract: Some researchers in the computational sciences have considered music computation, including music reproduction and generation, as a dynamic system, i.e., a feedback process. The key element is that the state of the musical system depends on a history of past states. Recurrent (neural) networks have been deployed as models for learning musical processes. We first present a tutorial discussion of recurrent networks, covering those that have been used for music learning. Following this, we examine a thread of development of these recurrent networks for music computation that shows how more intricate music has been learned as the state of the art in recurrent networks improves. We present our findings that show that a long short-term memory recurrent network, with new representations that include music knowledge, can learn musical tasks, and can learn to reproduce long songs. Then, given a reharmonization of the chordal structure, it can generate an improvisation.


title: Efficient application of GPGPU for lava flow hazard mapping

abstract: The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe's most active volcano.


title: Timing Analysis for Inferring the Topology of the Bitcoin Peer-to-Peer Network

abstract: Flooding Peer-to-Peer (P2P) networks form the basis of services such as the electronic currency system Bitcoin. The decentralized architecture enables robustness against failure. However, knowledge of the network's topology can allow adversaries to attack specific peers in order to, e.g., isolate certain peers or even partition the network. Knowledge of the topology might be gained by observing the flooding process, which is inherently possible in such networks,, performing a timing analysis on the observations. In this paper we present a timing analysis method that targets flooding P2P networks, show its theoretical, practical feasibility. A validation in the real-world Bitcoin network proves the possibility of inferring network links of actively participating peers with substantial precision, recall (both ~ 40%), potentially enabling attacks on the network. Additionally, we analyze the countermeasure of trickling, quantify the tradeoff between the effectiveness of the countermeasure, the expected performance penalty. The analysis shows that inappropriate parametrization can actually facilitate inference attacks.


title: A novel coarse-to-fine hair segmentation method

abstract: Segmenting hair regions from human images facilitates many tasks like hair synthesis and hair style trends forecast. However, hair segmentation is quite challenging due to hair/background confusion and large hair pattern diversity. To address these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. In our approach, firstly, the recently proposed “Active Segmentation with Fixation” (ASF) is used to coarsely define an enclosed candidate region with high-recall (but possibly low-precision) of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then Graph Cuts (GC) method is applied to the candidate regions to remove additional false positives by incorporating hair-specific information. Specifically, Bayesian method is employed to select some reliable hair and background regions (seeds) among the ones over-segmented by Mean Shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is subsequently fed into GC algorithm. The novelty of the proposed approach lies in three folds: 1) an elaborate design of hair segmentation framework, which utilizes ASF to reduce the candidate hair regions and adopts GC to achieve more accurate hair region contours; 2) the region-based strategy for seed selection; 3) the exploration of the discriminative method, SVM, to predict the probability of each pixel belonging to hair and background regions. Extensive experimental results demonstrate the approach outperforms recently proposed methods.


title: A High-Efficiency Resonant Switched Capacitor Converter With Continuous Conversion Ratio

abstract: A resonant switched capacitor converter with high efficiency over a wide and continuous conversion ratio is introduced. The efficiency of the topology depends primarily on the conduction losses and is decoupled, to a large extent, from the voltage conversion ratio. This is an advantage over classical switched capacitor converters in which the efficiency is strongly related to the conversion ratio. The operation principle applies three zero current switching (ZCS) states to charge, discharge and balance the remaining charge of the flying capacitor. This results in a Gyrator-behaved voltage-dependent current source with a wide voltage conversion ratio (smaller as well as greater than unity) as well as bidirectional power flow capabilities. The analytical expressions for conversion ratio and expected efficiency are provided and validated by simulation and experiments. The experimental verification of the converter demonstrates peak efficiency of 96%, and above 90% efficiency over a wide range of voltage gains and loading conditions. In addition, the system was found to be highly efficient at the extreme cases of both light and heavy loads.


title: Context in Neural Machine Translation: A Review of Models and Evaluations

abstract: This review paper discusses how context has been used in neural machine translation (NMT) in the past two years (2017-2018). Starting with a brief retrospect on the rapid evolution of NMT models, the paper then reviews studies that evaluate NMT output from various perspectives, with emphasis on those analyzing limitations of the translation of contextual phenomena. In a subsequent version, the paper will then present the main methods that were proposed to leverage context for improving translation quality, and distinguishes methods that aim to improve the translation of specific phenomena from those that consider a wider unstructured context.


title: EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings

abstract: As robots come closer to humans, an efficient human-robot-control interface is an utmost necessity. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. A mathematical model is trained to decode upper limb motion from EMG recordings, using a dimensionality-reduction technique that represents muscle synergies and motion primitives. It is shown that a 2-D embedding of muscle activations can be decoded to a continuous profile of arm motion representation in the 3-D Cartesian space, embedded in a 2-D space. The system is used for the continuous control of a robot arm, using only EMG signals from the upper limb. The accuracy of the method is assessed through real-time experiments, including random arm motions.


title: Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters

abstract: Abstract Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m − 2 μm − 1 sr − 1 ) data showed the highest correlation with near-concurrent in situ chlorophyll- a concentration (Chl in mg m − 3 ). For Chl ranging between 0.4 to 4 mg m − 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m − 2 μm − 1 sr − 1 per mg m − 3 chlorophyll (Chl = 1.255 (FLH × 10) 0.86 , r = 0.92, n = 77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3–15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10 4 to 10 5 cells L − 1 ) to high (> 10 5 cells L − 1 ) concentrations of the toxic dinoflagellate Karenia brevis . The FLH imagery also showed that the bloom started in mid-October south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.


title: Optimal base station deployment for small cell networks with energy-efficient power control

abstract: In this paper, how to optimally deploy base station density in a small cell network with energy-efficient power control was investigated. Base stations (BSs) and users form two independent Poisson point processes (PPPs) in the network. Since user-centric cell association may lead to void cells that do not have any users, the power of each BS is controlled in either all-on or on-off mode depending on whether its cell is void or not. The average cell rates for each power control mode are first found and their corresponding energy efficiency is also characterized. The optimal BS density that maximizes the energy efficiency under a given user density is theoretically proved to exist and its value can be found numerically. Both analytical and simulated results indicate that on-off power control is significantly superior to all-on power control in terms of energy efficiency if BSs are deployed based on their optimal energy-efficient density.


title: Deep learning in partially-labeled data streams

abstract: Of the considerable research on data streams, relatively little deals with classification where only some of the instances in the stream are labeled. Most state-of-the-art data-stream algorithms do not have an effective way of dealing with unlabeled instances from the same domain. In this paper we explore deep learning techniques that provide important advantages such as the ability to learn incrementally in constant memory, and from unlabeled examples. We develop two deep learning methods and explore empirically via a series of empirical evaluations the application to several data streams scenarios based on real data. We find that our methods can offer competitive accuracy as compared with existing popular data-stream learners.


title: Taking the next step: cortical contributions to the control of locomotion

abstract: The planning and execution of both discrete voluntary movements and visually guided locomotion depends on the contribution of multiple cortical areas. In this review, we discuss recent experiments that address the contribution of the posterior parietal cortex (PPC) and the motor cortex to the control of locomotion. The results from these experiments show that the PPC contributes to the planning of locomotion by providing an estimate of the position of an animal with respect to objects in its path. In contrast, the motor cortex contributes primarily to the execution of gait modifications by modulating the activity of groups of synergistic muscles active at different times during the gait cycle.


title: An Anisotropic Etching Effect in the Graphene Basal Plane

abstract: A highly controllable, dry, anisotropic etching technique for graphene sheets has been achieved using hydrogen plasma etching. Zigzag edge formation was achieved by starting the etching at edges and defects and depends strongly on crystallographic orientation of the graphene. This dry, anisotropic etching approach combined with the standard lithographic technique is ideal for scalable graphene tailoring because the etching rates can be precisely controlled and the quality of the graphene can be preserved.


title: Parent perceptions of factors influencing after-school physical activity of children with autism spectrum disorders.

abstract: The study assessed parental perceptions of the benefits of physical activity (PA) and the factors that influence participation of children with autism spectrum disorders in PA after school. Data were collected from 103 parents using an online open-ended questionnaire and focus-group interviews. Data were analyzed using a socioecological model. Parents provided 225 responses that were coded as advantages, 106 as disadvantages, 225 as facilitators, and 250 as barriers of PA. The most frequently reported advantages were physical, followed by psychosocial, and cognitive. Disadvantages were psychosocial and physical. The most frequently reported barriers were intrapersonal, followed by interpersonal, physical, community, and institutional. Facilitators were intrapersonal, followed by physical, interpersonal, community, and institutional. Public policy factors were elicited in the interviews.


title: Robust Object Recognition with Cortex-Like Mechanisms

abstract: We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex


title: Using i * Models to Enrich User Stories

abstract: In agile methods the user stories are widely used to describe require- ments. However, the user stories are an artifact too narrow to represent and de- tail the requirements. Issues like software context and dependencies between stories are also limited with the use of only this artifact. The lack of documenta- tion in agile development environment is identified as one of the main chal- lenges of the methodology. This work proposes the use of i* model that aims to reduce this lack of existing documentation in agile methods. We propose a set of heuristics to perform the mapping of the requirements presented as user sto- ries in i* models. The i* models are used as a form of documentation in agile environment, thus the user stories can be viewed more broadly and with their proper relationships according to the business environment that they will meet.


title: A synchronous DRAM controller for an H.264/AVC encoder

abstract: In order to use a synchronous dynamic RAM (SDRAM) as the off-chip memory of an H.264/AVC encoder, this paper proposes an efficient SDRAM memory controller with an asynchronous bridge. With the proposed architecture, the SDRAM bandwidth is increased by making the operation frequency of an external SDRAM higher than that of the hardware accelerators of an H.264/AVC encoder. Experimental results show that the encoding speed is increased by 30.5% when the SDRAM clock frequency is increased from 100 MHz to 200 MHz while the H.264/AVC hardware accelerators operate at 100 MHz.


title: A general framework for estimating the relative pathogenicity of human genetic variants

abstract: Our capacity to sequence human genomes has exceeded our ability to interpret genetic variation. Current genomic annotations tend to exploit a single information type (e.g. conservation) and/or are restricted in scope (e.g. to missense changes). Here, we describe Combined Annotation Dependent Depletion (CADD), a framework that objectively integrates many diverse annotations into a single, quantitative score. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human derived alleles from 14.7 million simulated variants. We pre-compute “C-scores” for all 8.6 billion possible human single nucleotide variants and enable scoring of short insertions/deletions. C-scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects, and complex trait associations, and highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious, and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current annotation.


title: A Pneumatic-Driven Haptic Glove with Force and Tactile Feedback

abstract: The advent of Oculus Rift indicates the start of a booming era of virtual reality. In order to increase the immersive feeling of interaction with the virtual world, haptic devices allow us to touch and manipulate virtual objects in an intuitive way. In this paper, we introduce a portable and low-cost haptic glove that provides both force and tactile feedback using a direct-control pneumatic concept. To produce force feedback, two inlet ports of a double acting pneumatic cylinder are opened and closed via solenoid DC valves through Pulse-width modulation (PWM) technique. For tactile feedback, an air bladder is actuated using a diaphragm pump via PWM operated solenoid valve. Experiments on a single finger prototype validated that the glove can provide force and tactile feedback with sufficient moving range of the finger joints. The maximum continuous force is 9 Newton and the response time is less than 400ms. The glove is light weighted and easy to be mounted on the index finger. The proposed glove could be potentially used for virtual reality grasping scenarios and for teleoperation of a robotic hand for handling hazardous objects.


title: Advanced Laser Techniques for Filler-Induced Complications

abstract: BACKGROUND ::: The increasing use of injectable fillers has been increasing the occurrence of disfiguring anaerobic infection or granulomas. This study presents two types of laser-assisted evacuation of filler material and inflammatory and necrotic tissue that were used to treat disfiguring facial nodules after different types of gel fillers. ::: ::: ::: MATERIALS AND METHODS ::: Infectious lesions after hydrogels were drained using a lithium triborate laser at 532 nm, with subsequent removal of infected gel and pus (laser assisted evacuation). Granuloma after gels containing microparticles were treated using an 808-nm diode laser using intralesional laser technique. The latter melted and liquefied the organic and synthetic components of the granulomas, facilitating subsequent evacuation. Both lasers had an easily controllable thin laser beam, which enabled the physician to control tissue damage and minimize discomfort and pain. ::: ::: ::: RESULTS ::: All 20 patients experienced reduction or complete resolution, the latter increasing with repeated treatments. ::: ::: ::: CONCLUSION ::: Laser-assisted treatment offers a successful solution for patients who have been suffering from disfiguring nodules from injected fillers-often for many years. The procedure broadens the range of treatment options in cases of untoward reactions to fillers, in line with surgical removal but with lower morbidity and less cosmetic disfigurement.


title: A risk minimization framework for information retrieval

abstract: This paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models, user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We discuss how this framework can unify existing retrieval models and accommodate systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive retrieval models for subtopic retrieval, which is concerned with retrieving documents to cover many different subtopics of a general query topic. These new models differ from traditional retrieval models in that they relax the traditional assumption of independent relevance of documents.


title: Iterative, MT-based Sentence Alignment of Parallel Texts

abstract: Recent research has shown that MT-based sentence alignment is a robust approach for noisy parallel texts. However, using Machine Translation for sentence alignment causes a chicken-and-egg problem: to train a corpus-based MT system, we need sentence-aligned data, and MT-based sentence alignment depends on an MT system. We describe a bootstrapping approach to sentence alignment that resolves this circular dependency by computing an initial alignment with length-based methods. Our evaluation shows that iterative MT-based sentence alignment significantly outperforms widespread alignment approaches on our evaluation set, without requiring any linguistic resources other than the to-be-aligned bitext.


title: Pilot study on biocompatibility of fluorescent nanodiamond-(NV)-Z~800 particles in rats: safety, pharmacokinetics, and bio-distribution (part III)

abstract: Introduction ::: We hereby report on studies aimed to characterize safety, pharmacokinetics, and bio-distribution of fluorescent nanodiamond particles (NV)-Z~800 (FNDP-(NV)) administered to rats by intravenous infusion in a single high dose. ::: ::: ::: Methods ::: Broad scale biological variables were monitored following acute (90 minutes) and subacute (5 or 14 days) exposure to FNDP-(NV). Primary endpoints included morbidity and mortality, while secondary endpoints focused on hematology and clinical biochemistry biomarkers. Particle distribution (liver, spleen, lung, heart, and kidney) was assessed by whole organ near infrared imaging using an in vivo imaging system. This was validated by the quantification of particles extracted from the same organs and visualized by fluorescent and scanning electron microscopy. FNDP-(NV)-treated rats showed no change in morbidity or mortality and preserved normal motor and sensory function, as assessed by six different tests. ::: ::: ::: Results ::: Blood cell counts and plasma biochemistry remained normal. The particles were principally distributed in the liver and spleen. The liver particle load accounted for 51%, 24%, and 18% at 90 minutes, 5 days, and 14 days, respectively. A pilot study of particle clearance from blood indicated 50% clearance 33 minutes following the end of particle infusion. ::: ::: ::: Conclusion ::: We concluded that systemic exposure of rats to a single high dose of FDNP-(NV)-Z~800 (60 mg/kg) appeared to be safe and well tolerated over at least 2 weeks. These data suggest that FNDP-(NV) should proceed to preclinical development in the near future.


title: Wegener's granulomatosis with severe renal failure: clinical course and results of dialysis and transplantation.

abstract: Nine patients with Wegener's granulomatosis have developed chronic renal failure and undergone chronic hemodialysis and transplantation. These patients are all young (less than 43 years); all except one are male. They present with acute renal failure that resolves with hemodialysis and immunosuppression, but then slowly develop chronic renal failure in spite of adequate immunosuppression. On chronic dialysis no manifestations of Wegener's granulomatosis occur despite low or no immunosuppression in most of them. After transplantation the usual post-transplant immunosuppression regimen (azathioprine and prednisone) not only prevents rejection, but also controls the disease. In only one patient has it been necessary to change from azathioprine to cyclophosphamide because of recurrence.


title: A Concurrent Dynamic Task Graph

abstract: Abstract Task graphs are used for scheduling tasks on shared memory parallel processors when the tasks have dependencies. If the program's task graph is known ahead of time, then the tasks can be statically and optimally allocated to the processors. If the tasks and task dependencies are not known ahead of time (the case in some sparse matrix algorithms), then task scheduling must be performed dynamically. We present a simple algorithm for a concurrent dynamic-task graph. A processor that needs to execute a new task can query the task graph for a new task, and new tasks can be added to the task graph dynamically.


title: Functional reflective polarizer for augmented reality and color vision deficiency

abstract: We report a functional reflective polarizer that can be incorporated into a compact augmented reality system. The design principle of the functional reflective polarizer is explained and two design examples are illustrated. In the first example, with the specially designed functional reflective polarizer, the transmittance of the augment reality system is relatively high as compared to a polarizing beam splitter or a conventional reflective polarizer. Such a functional reflective polarizer can also be used for vehicular displays. For the second example, the functional reflective polarizer is specially tailored to help those people with color vision deficiency.


title: Sparse Nonnegative Matrix Factorization for Clustering

abstract: Properties of Nonnegative Matrix Factorization (NMF) as a clustering method are studied by relating its formulation to other methods such as K-means clustering. We show how interpreting the objective function of K-means as that of a lower rank approximation with special constraints allows comparisons between the constraints of NMF and K-means and provides the insight that some constraints can be relaxed from K-means to achieve NMF formulation. By introducing sparsity constraints on the coefficient matrix factor in NMF objective function, we in term can view NMF as a clustering method. We tested sparse NMF as a clustering method, and our experimental results with synthetic and text data shows that sparse NMF does not simply provide an alternative to K-means, but rather gives much better and consistent solutions to the clustering problem. In addition, the consistency of solutions further explains how NMF can be used to determine the unknown number of clusters from data. We also tested with a recently proposed clustering algorithm, Affinity Propagation, and achieved comparable results. A fast alternating nonnegative least squares algorithm was used to obtain NMF and sparse NMF.


title: Consumer-driven definition of traditional food products and innovation in traditional foods. A qualitative cross-cultural study

abstract: Abstract Traditional food products (TFP) are an important part of European culture, identity, and heritage. In order to maintain and expand the market share of TFP, further improvement in safety, health, or convenience is needed by means of different innovations. The aim of this study was to obtain a consumer-driven definition for the concept of TFP and innovation and to compare these across six European countries (Belgium, France, Italy, Norway, Poland and Spain) by means of semantic and textual statistical analyses. Twelve focus groups were performed, two per country, under similar conditions. The transcriptions obtained were submitted to an ordinary semantic analysis and to a textual statistical analysis using the software ALCESTE. Four main dimensions were identified for the concept of TFP: habit-natural, origin-locality, processing-elaboration and sensory properties. Five dimensions emerged around the concept of innovation: novelty-change, variety, processing-technology, origin-ethnicity and convenience. TFP were similarly perceived in the countries analysed, while some differences were detected for the concept of innovation. Semantic and statistical analyses of the focus groups led to similar results for both concepts. In some cases and according to the consumers’ point of view the application of innovations may damage the traditional character of TFP.


title: MP-Index: A Multi-predicate Publish/Subscribe Mechanism for Internet of Things

abstract: The IoT (Internet of Things) is a network ofphysical devices (i.e. objects like Vehicles, Machines, Buildings, Furniture, and so on) that helps achieve better service andvalue. Large amounts of data is generated while connectingthese devices and communicating between them. Faster capturing, processing and forwarding of this data certainly requiresan efficient technique during each stage of communication. The Publish/Subscribe communication paradigm is a powerfulmedium for IoT devices to connect and communicate with eachother. It offers anonymous, many-to-many and asynchronouscommunication which plays a very significant role in designinghighly scalable distributed systems. Event matching is oneof the key aspects of Publish/Subscribe paradigm where anevent that has occurred is matched against the large volumeof subscriptions to find the appropriate subscribers that needto be notified. The existing event matching solutions are based on the assumptionthat an assignment (group of events) will be generatedand matched against a large number of subscriptions. In thereal world, events do not get generated together and henceconsidering them as an assignment is not the best way to handlethem. We are proposing a solution based on the temporal, causal and spatial properties of events, which overcomes theassumption of assignment generation. Our proposed solution, MP-Index (Multi-Predicate Index) allows the Publish/Subscribesystem to match individual events occurring at different timestamps against a large volume of subscriptions. The proposedsolution was implemented and its results were compared withtwo of the existing algorithms. The results of the comparisonshow that the proposed Multi-Predicate Index algorithmoutperforms the other two algorithms in terms of matchingtime. We have also implemented the proposed solution inMosquitto which is an open source implementation of theMQTT (Message Queuing Telemetry Transport) protocol. Theresult of this implementation clearly confirms the support formulti-predicate subscription match against events occurring atdifferent time stamps.


Approximating linear restrictions of boolean functions


title: Linear and nonlinear dependence in Turkish equity returns and its consequences for financial risk management

abstract: Abstract This paper investigates the dynamic behaviour of daily aggregate returns of one of Europe's largest and fastest growing emerging equity markets, the Istanbul Stock Exchange (ISE). It is found that ISE returns exhibit significant linear and nonlinear dependence. We investigate the nature and source of the nonlinear dependence and find that it is due primarily to linear dependence in the conditional variance of returns, rather than nonlinear dependence in the conditional mean. We analyse the implications of our findings for financial risk management. We show that by exploiting the nonlinear dependence in ISE returns, the average capital required to cover against unexpected portfolio losses can be considerably reduced. In contrast, exploiting the linear dependence in ISE returns has only a negligible impact on capital requirements.


title: Optimized amplitude modulated multiband RF pulse design

abstract: Purpose ::: Multiband pulses are characterized by highly temporally modulated waveforms. Rapid phase or frequency modulation can be extremely demanding on the performance of radiofrequency (RF) pulse generation, which can lead to errors that can be avoided if pulses are restricted to amplitude modulation (AM) only. In this work, three existing multiband pulse design techniques are modified to produce AM waveforms. ::: ::: Theory and Methods ::: Multiband refocusing pulses were designed using phase optimization, time shifting, and root flipping. Each technique was constrained to produce AM pulses by exploiting conjugate symmetry in their respective frequency domain representations. Pulses were designed using the AM and unconstrained techniques for a range of multiband factors (ie, number of simultaneously excited slices), time-bandwidth products, and slice separations. Performance was compared by examining the resulting effective pulse durations. Phantom and in vivo experiments were conducted for validation. ::: ::: Results ::: Acquired data confirmed that AM pulses can produce precise results when unconstrained designs may produce artifacts. The average duration of AM pulses is longer than the unconstrained versions. Averaged across a range of parameters, the duration cost for AM pulses was 26, 38, and 20% for phase optimizing, time shifting and root flipping, respectively. ::: ::: Conclusions ::: Amplitude modulation multiband pulses can be produced for a relatively small increase in pulse duration. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.


title: An Evaluation of the Advantages and Disadvantages of Deterministic Database Systems

abstract: Recent proposals for deterministic database system designs argue that deterministic database systems facilitate replication since the same input can be independently sent to two different replicas without concern for replica divergence. In addition, they argue that determinism yields performance benefits due to (1) the introduction of deadlock avoidance techniques, (2) the reduction (or elimination) of distributed commit protocols, and (3) light-weight locking. However, these performance benefits are not universally applicable, and there exist several disadvantages of determinism, including (1) the additional overhead of processing transactions for which it is not known in advance what data will be accessed, (2) an inability to abort transactions arbitrarily (e.g., in the case of database or partition overload), and (3) the increased latency required by a preprocessing layer that ensures that the same input is sent to every replica. This paper presents a thorough experimental study that carefully investigates both the advantages and disadvantages of determinism, in order to give a database user a more complete understanding of which database to use for a given database workload and cluster configuration.


title: Privacy Design Strategies

abstract: In this paper we define the notion of a privacy design strategy. These strategies help IT architects to support privacy by design early in the software development life cycle, during concept development and analysis. Using current data protection legislation as point of departure we derive the following eight privacy design strategies: minimise, hide, separate, aggregate, inform, control, enforce, and demonstrate. The strategies also provide a useful classification of privacy design patterns and the underlying privacy enhancing technologies. We therefore believe that these privacy design strategies are not only useful when designing privacy friendly systems, but also helpful when evaluating the privacy impact of existing IT systems.


title: Task and domain specific modelling in the Carnegie Mellon Communicator system

abstract: Abstract : The Carnegie Mellon Communicator is a telephone-based dialog system that supports planning in a travel domain. The implementation of such a system requires two complimentary components, an architecture capable of managing interaction and the task, as well as a knowledge base that captures the speech, language and task characteristics specific to the domain. Given a suitable architecture, the principal effort in development is taken up in the acquisition and processing of a domain knowledge base. This paper describes a variety of techniques we have applied to modeling in acoustic, language, task, generation and synthesis components of the system.


title: 3D Hospital: Design and implement quest-based game framework for transitional training

abstract: The Ohio State University Comprehensive Cancer Center - Arthur G. James Cancer Hospital and Richard J. Solove Research Institute's (OSUCCC- James) is a transformational facility that fosters the collaboration and integration of cancer research and clinical cancer care. Unlike regular facilities, transferring between hospitals requires the staff to occupy accurate and specialized knowledge of the spatial layout for the new hospital. In order to have a smooth transition, the hospital staffs are required to orient themselves and navigate in the new facility in a timely manner. We designed a quest-based game framework to examine the scenario. This data driven framework allows the gaming content be easily updated online by demand. 3D Hospital game is developed based on that framework, which aims at training participants with spatial orientation in the OSUCCC-James. The quests are designed based on several key locations and the daily tasks for the hospital staff. We completed floor 15 in time before the transfer. To measure the effectiveness of our game for prepping the staff during the transition, we surveyed 26 participants and obtained an overall positive feedback. At last, we also examine the obstacles that we encountered as well as future direction.


title: A fully automated correction method of EOG artifacts in EEG recordings

abstract: Abstract Objective A fully automated method for reducing EOG artifacts is presented and validated. Methods The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. Results The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data. Conclusions The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing. Significance Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.


title: Hashtag Recommendation Using Attention-Based Convolutional Neural Network

abstract: Along with the increasing requirements, the hashtag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of convolutional neural networks (CNNs) for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem. To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works, we propose a novel architecture with an attention mechanism. The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods. By incorporating trigger words into the consideration, the relative improvement of the proposed method over the state-of-the-art method is around 9.4% in the F1-score.


title: Segmenting Critical Success Factors for ERP Implementation Using an Integrated Fuzzy AHP and Fuzzy DEMATEL Approach

abstract: Enterprise Resource planning (ERP) Systems as standard software packages have been welcomed in most of industries. Although implementation of these systems needs considering issues to achieve the success and planned aspiration levels. In order to identifying and classifying these issues as ERP Critical Success Factors (CSFs), various researches have been done and the importances of these factors were discussed. As considerable area, the degree of influence of factors involved in ERP project is a room for investigation. In this paper, with considering the degree of influence factors on each other, a hybrid model was proposed based on fuzzy analytical hierarchy process analysis and fuzzy DEMATEL to evaluate and assess the critical factors in ERP project implementation. Finding the cause and effect relationship between factors and the influence degree on each other was the main contribution of case study of current research on an Iranian steel company. In the case company the cause and grounds factor were identified as: "clear project plan", "training and education", "project champion", "project team competence" and "organizational culture" by utilizing FAHP-DEMATEL hybrid model.


title: Gait-Watch: A Context-aware Authentication System for Smart Watch Based on Gait Recognition

abstract: With recent advances in mobile computing and sensing technology, smart wearable devices have pervaded our everyday lives. The security of these wearable devices is becoming a hot research topic because they store various private information. Existing approaches either only rely on a secret PIN number or require an explicit user authentication process. In this paper, we present Gait-watch, a context-aware authentication system for smart watch based on gait recognition. We address the problem of recognizing the user under various walking activities (e.g., walking normally, walking with calling the phone), and propose a sparse fusion method to improve recognition accuracy. Extensive evaluations show that Gait-watch improves recognition accuracy by up to 20% by leveraging the activity information, and the proposed sparse fusion method is 10% better than several state-of-the-art gait recognition methods. We also report a user study to demonstrate that Gait-watch can accurately authenticate the user in real world scenarios and require low system cost.


title: An Initial Analysis of Facebook's GraphQL Language

abstract: Facebook’s GraphQL is a recently proposed, and increasingly adopted,conceptual framework for providing a new type of data access interface on theWeb. The framework includes a new graph query langua ...


title: Scheme: An interpreter for extended lambda calculus

abstract: Inspired by ACTORS [7, 17], we have implemented an interpreter for a LISP-like language, SCHEME, based on the lambda calculus [2], but extended for side effects, multiprocessing, and process synchronization. The purpose of this implementation is tutorial. We wish to: 1.alleviate the confusion caused by Micro-PLANNER, CONNIVER, etc., by clarifying the embedding of non-recursive control structures in a recursive host language like LISP. 2.explain how to use these control structures, independent of such issues as pattern matching and data base manipulation. 3.have a simple concrete experimental domain for certain issues of programming semantics and style. This paper is organized into sections. The first section is a short “reference manual” containing specifications for all the unusual features of SCHEME. Next, we present a sequence of programming examples which illustrate various programming styles, and how to use them. This will raise certain issues of semantics which we will try to clarify with lambda calculus in the third section. In the fourth section we will give a general discussion of the issues facing an implementor of an interpreter for a language based on lambda calculus. Finally, we will present a completely annotated interpreter for SCHEME, written in MacLISP [13], to acquaint programmers with the tricks of the trade of implementing non-recursive control structures in a recursive language like LISP. ::: ::: This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory‘s artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.


title: What matters most in commercial success: first-in-class or best-in-class?

abstract: It is common for several companies to be simultaneously pursuing promising new targets, and this analysis investigates the relative importance of market entry order and therapeutic impact on commercial success for drugs in the same class.


title: Fuzzy control based on H/sub /spl infin// output feedback for attitude stabilization of flexible satellite

abstract: This paper focuses on designing a robust fuzzy multivariable controller for the attitude stabilization system of a flexible satellite. Fuzzy control is combined with LMI-based H/sub /spl infin// output feedback methods to suppress the interaction of rigid-elastic coupling modes. The proposed method can afford to reinforce the precise pointing capability of satellites under parameter varying space missions, because its control structure reformulated on fuzzy cells offers the blending of sub-optimal control laws.


title: Emoticon Smoothed Language Models for Twitter Sentiment Analysis

abstract: Twitter sentiment analysis (TSA) has become a hot research topic in recent years. The goal of this task is to discover the attitude or opinion of the tweets, which is typically formulated as a machine learning based text classification problem. Some methods use manually labeled data to train fully supervised models, while others use some noisy labels, such as emoticons and hashtags, for model training. In general, we can only get a limited number of training data for the fully supervised models because it is very labor-intensive and time-consuming to manually label the tweets. As for the models with noisy labels, it is hard for them to achieve satisfactory performance due to the noise in the labels although it is easy to get a large amount of data for training. Hence, the best strategy is to utilize both manually labeled data and noisy labeled data for training. However, how to seamlessly integrate these two different kinds of data into the same learning framework is still a challenge. In this paper, we present a novel model, called emoticon smoothed language model (ESLAM), to handle this challenge. The basic idea is to train a language model based on the manually labeled data, and then use the noisy emoticon data for smoothing. Experiments on real data sets demonstrate that ESLAM can effectively integrate both kinds of data to outperform those methods using only one of them.


A Novel Approach of Detection and Mitigation of DDOS Attack


A MEMS Faraday Rotator for MEMS Polarization Controllers, MEMS Magneto- Optic Spatial Light Modulators and Other Enabled Technologies


title: ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators

abstract: Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high-performance DNN accelerators without compromising classification accuracy even in the presence of high timing error rates. Using post-synthesis timing simulations of a DNN accelerator modeled on the Google TPU, we show that Thundervolt enables between 34%-57% energy savings on state-of-the-art speech and image recognition benchmarks with less than 1% loss in classification accuracy and no performance loss. Further, we show that Thundervolt is synergistic with and can further increase the energy efficiency of commonly used run-time DNN pruning techniques like Zero-Skip.


3 D Point Cloud Object Detection with MultiView Convolutional Neural Network


title: An Extremely Efficient Chess-board Detection for Non-trivial Photos

abstract: We present a set of algorithms that can be used to locate and crop the chess-board/chess-pieces from the picture, including every rectangular grid with any pattern. Our method is non-parametric, and thus does not require the prior knowledge from computer vision and machine learning, which is instead inferred from data. We illustrate the application of our method to a variety of examples, such as chess-board cropping and regular grid-pattern localization. In addition, we present two independent algorithms: PAMG (vertices detector) and FAPL (thermal lines) that can be widely used for other tasks in computer vision.


title: Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data

abstract: Recent advances in fluorescence imaging permit studies of Ca(2+) dynamics in large numbers of cells, in anesthetized and awake behaving animals. However, unlike for electrophysiological signals, standardized algorithms for assigning optically recorded signals to individual cells have not yet emerged. Here, we describe an automated sorting procedure that combines independent component analysis and image segmentation for extracting cells' locations and their dynamics with minimal human supervision. In validation studies using simulated data, automated sorting significantly improved estimation of cellular signals compared to conventional analysis based on image regions of interest. We used automated procedures to analyze data recorded by two-photon Ca(2+) imaging in the cerebellar vermis of awake behaving mice. Our analysis yielded simultaneous Ca(2+) activity traces for up to >100 Purkinje cells and Bergmann glia from single recordings. Using this approach, we found microzones of Purkinje cells that were stable across behavioral states and in which synchronous Ca(2+) spiking rose significantly during locomotion.


title: Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

abstract: In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.


title: Symmetry-based monocular vehicle detection system

abstract: In this paper, we describe the development of a symmetry-based vehicle detection system. The system uses a single forward looking camera to capture the road scene. Vehicles are detected based on their edges and symmetrical characteristics. A method to extract the symmetric regions in the image using a multi-sized window and clustering technique is introduced. We hypothesize the vehicle’s locations in the image from the detected symmetric regions and the regions are then further processed to enhance their symmetrical edges. A bounding box of a vehicle is detected from the projection maps of the enhanced vertical and horizontal edges. The hypothesized vehicles are then verified using a two-class classifier, which consists of an edge oriented histogram (EOH) feature extractor and a support vector machine (SVM). Once a vehicle is verified, a tracking process based on a Kalman filter and a reliability point system is used to track the movement of the vehicle in consecutive video frames. The system was successfully implemented and tested on a standard PC. Experimental results on live video feed and pre-recorded video sequences for various road scenes showed that the system is able to detect multiple vehicles in real time.


title: Smart Operation of Smart Grid: Risk-Limiting Dispatch

abstract: The drastic reduction of carbon emission to combat global climate change cannot be realized without a significant contribution from the electricity sector. Renewable energy resources must take a bigger share in the generation mix, effective demand response must be widely implemented, and high-capacity energy storage systems must be developed. A smart grid is necessary to manage and control the increasingly complex future grid. Certain smart grid elements-renewables, storage, microgrid, consumer choice, and smart appliances-increase uncertainty in both supply and demand of electric power. Other smart gird elements-sensors, smart meters, demand response, and communications-provide more accurate information about the power system and more refined means of control. Simply building hardware for renewable generators and the smart grid, but still using the same operating paradigm of the grid, will not realize the full potential for overall system efficiency and carbon reduction. In this paper, a new operating paradigm, called risk-limiting dispatch, is proposed. It treats generation as a heterogeneous commodity of intermittent or stochastic power and uses information and control to design hedging techniques to manage the risk of uncertainty.


title: Image Steganography: Classification, Application and Algorithms

abstract: Steganography is the technique/science of hiding information inside some innocent looking canvas like images. The growth of internet and communication technology has enabled the demand to send a message as safely and as securely as possible. In the last few years, we have seen that new and powerful steganography techniques reported in the literature. This paper intends to give an overview of image steganography, its applications and techniques. The paper gives the description of various techniques used in steganography and attempts to identify the requirements of a good steganography algorithm.


Comparative study of the antioxidant and antibacterial activities of two guava ( Psidium guajava ) fruit varieties cultivated in Andasa Horticulture Site , Ethiopia


title: Factors Affecting the Progression towards Burnout: A Comparison of Professional and Volunteer Counsellors:

abstract: This study compared professional and volunteer counsellors on a number of key variables in the progression to burnout, namely, stressors, strain, defensive coping, social support, Type A personality, and breakdown. Job stressors unique to each group were identified but there was overwhelming evidence for professionals and volunteers belonging to a homogeneous counselling population as evidenced by similarity across major constructs. Path analysis supported theoretical formulations on burnout by Maslach and Cherniss, and gave support to Lin, Dean, and Ensel's argument that social support may act as a buffer to burnout.


title: Evolutionary optimization of classifiers and features for single-trial EEG Discrimination

abstract: BackgroundState-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were implemented as well by limiting the degree of optimization.ResultsUsing only 10 features and 100 patterns, the non-linear wrapper approach achieved the highest validation classification accuracy (subject mean 75%), closely followed by the linear wrapper method (73.5%). The optimal features differed much between subjects, yet some physiologically plausible patterns were observed.ConclusionHigh degrees of classifier parameter, structure and feature subset tailoring on individual levels substantially increase single-trial EEG classification rates, an important consideration in areas where highly accurate detection rates are essential. Also, the presented method provides insight into the spatial characteristics of finger movement EEG patterns.


title: ABSOLUTE MEASUREMENT OF THE NEUTRON FLUX DENSITY IN THE A.E.R.E. REACTOR "GLEEP"

abstract: Abstract The flux density at the sample position of the A.E.R.E. reactor GLEEP has been calculated from the activation produced in gold and in manganese-nickel foils. The 4πβ-γ coincidence technique is shown to be applicable to the absolute measurement of 56 Mn in foils in spite of the complexity of the decay scheme. The flux density is estimated at 1·663 × 10 8 n/cm 2 /sec per μA of ionization current in the monitor chamber. Efforts are being made to investigate possible variations in the flux/current ratio.


Female genital mutilation/cutting type IV in Cambodia: a case report


title: Dynamic Automatic Differentiation of GPU Broadcast Kernels

abstract: We show how forward-mode automatic differentiation (AD) can be employed within larger reverse-mode computations to dynamically differentiate broadcast operations in a GPU-friendly manner. Our technique fully exploits the broadcast Jacobian's inherent sparsity structure, and unlike a pure reverse-mode approach, this "mixed-mode" approach does not require a backwards pass over the broadcasted operation's subgraph, obviating the need for several reverse-mode-specific programmability restrictions on user-authored broadcast operations. Most notably, this approach allows broadcast fusion in primal code despite the presence of data-dependent control flow. We discuss an experiment in which a Julia implementation of our technique outperformed pure reverse-mode TensorFlow and Julia implementations for differentiating through broadcast operations within an HM-LSTM cell update calculation.


title: Linking knowledge bases to social media profiles

abstract: Social media have become an invaluable source of data for a wide variety of tasks. Unfortunately, this data is hard to gather and process due to low amount of machine readable attributes, API limitations and noisiness. In this paper we propose a system that aligns knowledge base entries of people and organisations to the corresponding social media profiles. The motivation is twofold: (i) on the one hand, we facilitate processing of social media data by allowing the import of rich entity descriptions from knowledge bases; (ii) on the other hand, we are enabling an automatic enrichment of a knowledge base with additional data from the social media. We used this system to create a resource of 893,446 alignments between DBpedia entities and Twitter profiles. This resource allows, effectively, to connect Twitter to the Linked Open Data cloud.


title: Battery Choice and Management for New-Generation Electric Vehicles

abstract: Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending on cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a high capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa.


title: UWB elliptically-tapered antipodal Vivaldi antenna for microwave imaging applications

abstract: An elliptically-tapered antipodal Vivaldi antenna with operating frequency range from 1.65 GHz – 18 GHz designed for microwave imaging applications is presented. It includes comb-shaped slits and opening elliptical rate of top and bottom radiators to lower frequency band limitation of conventional antipodal Vivaldi antenna. It is shown that the antenna gain has been increased by comb-shaped slits at lower frequencies up to 9.5 dBi at 2 GHz and at higher frequencies up to 14.2 dBi at 18 GHz. Feasibility of the proposed antenna for the use in microwave imaging technique is also demonstrated.


title: Data-Driven Facial Beauty Analysis: Prediction, Retrieval and Manipulation

abstract: Facial beauty analysis becomes an emerging research area due to many potential applications, such as aesthetic surgery plan, cosmetic industry, photo retouching, and entertainment. In this paper, we propose a data-driven facial beauty analysis framework that contains three application modules: prediction, retrieval, and manipulation. A beauty model is the core of the framework. With carefully designed features, the model can be built for different purposes. For prediction, we combine several low-level face representations and high-level features to form a feature vector and perform feature selection to optimize the feature set. The model built with the optimized feature set outperforms state-of-the-art methods. Then, we discuss two scenarios of beauty-oriented face retrieval: for recommendation and for beautification. Finally, we propose two approaches for facial beauty manipulation. One is an exemplar-based approach that uses the retrieved results. The other is a model-based approach that modifies facial features along the gradient of the beauty model. In this case, the model is built with the shape or appearance feature. Experimental results show that the exemplar-based approach is better for shape beautification; the model-based approach is suitable for texture beautification; and the combination of them can increase the attractiveness of a query face robustly.


title: Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

abstract: The convolutional neural network (CNN) has become a powerful tool for various biomedical image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In this paper, we present a novel algorithm, Respond-weighted Class Activation Mapping (Respond-CAM), for making CNN-based models interpretable by visualizing input regions that are important for predictions, especially for biomedical 3D imaging data inputs. Our method uses the gradients of any target concept (e.g. the score of target class) that flow into a convolutional layer. The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept. We prove a preferable sum-to-score property of the Respond-CAM and verify its significant improvement on 3D images from the current state-of-the-art approach. Our tests on Cellular Electron Cryo-Tomography 3D images show that Respond-CAM achieves superior performance on visualizing the CNNs with 3D biomedical image inputs, and is able to get reasonably good results on visualizing the CNNs with natural image inputs. The Respond-CAM is an efficient and reliable approach for visualizing the CNN machinery, and is applicable to a wide variety of CNN model families and image analysis tasks. Our code is available at: https://github.com/xulabs/projects/tree/master/respond_cam.


title: Technical Issues of Forensic Investigations in Cloud Computing Environments

abstract: Cloud Computing is arguably one of the most discussed information technologies today. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to the cloud. One of their main concerns is Cloud Security and the threat of the unknown. Cloud Service Providers (CSP) encourage this perception by not letting their customers see what is behind their virtual curtain. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. Cloud Forensics constitutes a new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed cloud environments. We contribute by assessing whether it is possible for the customer of cloud computing services to perform a traditional digital investigation from a technical point of view. Furthermore we discuss possible solutions and possible new methodologies helping customers to perform such investigations.


title: Youtube traffic characterization: a view from the edge

abstract: This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time. In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional meta-data that should be exploited to improve the effectiveness of strategies like caching.


title: Em-SLAM: a Fast and Robust Monocular SLAM Method for Embedded Systems

abstract: Simultaneous Localization and Mapping (SLAM) is difficult to deploy in the embedded systems due to its high computation cost and stable input requirements. Building on excellent algorithms of recent years, we present Em-SLAM, a monocular SLAM method which is fast and robust in the embedded system. We present Em-SLAM in three stages comprising initial pose estimation, iterative pose optimization and correspondences, and mapping with nearest frame queue. During the first stage, we perform stable initial pose estimation based on the matched ORB features extracted around the selected key points. Regarding initial pose and corresponding key points as input, the second stage of Em-SLAM iteratively optimizes these inputs values by tracking key points in the new frames. At the last stage, we firstly determine keyframes with the help of the proposed nearest frame queue and then design a greedy search algorithm to find matched ORB features between keyframes, which are adopted for compact and robust map reconstruction. Due to the special designs for the embedded systems, Em-SLAM demonstrates a high accurate and fast performance on the embedded system for all SLAM tasks: tracking, mapping and loop closing. We evaluate Em-SLAM on he most popular datasets by comparing with one latest SLAM method.


W-BAND SIW POWER COMBINER/DIVIDER BASED ON THE ANTIPODAL FIN-LINE SIW-RW TRANSITION AND LONGITUDINAL-SLOT COUPLING TECHNIQUES


Process Reengineering A review of recent literature


title: 3D Face Reconstruction Based on Improved CANDIDE-3 Model

abstract: In this paper, an individual 3D face reconstruction method is proposed. A 3D facial model is constructed from a static or captured image from video camera, by procedures like face region detection, feature points detection, blink detection, feature points matching and 3D facial texture mapping. In order to improve performance of reconstruction, a pattern-based blink detection method is proposed in this paper. During the reconstruction, once blink is detected, further computing will be skipped. CANDIDE-3 model, which is just a parameterization grid, lacks ability to determine blink, blink detection is significant. Compared with blink detection base on Otsu, the blink detection method in this paper has higher performance and stronger flexibility toward various illumination conditions. Experimental results show that there are good results of 3D face reconstruction.


title: Unimpeded permeation of water through helium-leak-tight graphene-based membranes.

abstract: Permeation through nanometer pores is important in the design of materials for filtration and separation techniques and because of unusual fundamental behavior arising at the molecular scale. We found that submicrometer-thick membranes made from graphene oxide can be completely impermeable to liquids, vapors, and gases, including helium, but these membranes allow unimpeded permeation of water (H(2)O permeates through the membranes at least 10(10) times faster than He). We attribute these seemingly incompatible observations to a low-friction flow of a monolayer of water through two-dimensional capillaries formed by closely spaced graphene sheets. Diffusion of other molecules is blocked by reversible narrowing of the capillaries in low humidity and/or by their clogging with water.


title: A Survey of Solutions to the Sybil Attack

abstract: Many security mechanisms are based on specific assumptions of identity and are vulnerable to attacks when these assumptions are violated. For example, impersonation is the well-known consequence when authenticating credentials are stolen by a third party. Another attack on identity occurs when credentials for one identity are purposely shared by multiple individuals, for example to avoid paying twice for a service. Such shared accounts are common in practice: friends exchange iTunes passwords to share purchased music; BugMeNot.com is a community that shares website registration passwords; and network address translation [29] devices allow multiple users to pay for a single IP address which is then shared among them. In this paper, we survey the impact of the Sybil attack [26], an attack against identity in which an individual entity masquerades as multiple simultaneous identities. The Sybil attack is a fundamental problem in many systems, and it has so far resisted a universally applicable solution. Many distributed applications and everyday services assume each participating entity controls exactly one identity. When this assumption is unverifiable or unmet, the sevice is subject to attack and the results of the application are questionable if not incorrect. A concrete example of this would be an online voting system where one person can vote using many online identities. Notably, this problem is currently only solved if a central authority, such as the administrator of a certificate authority, can guarantee that each person has a single identity represented by one key; in practice, this is very difficult to ensure on a large scale and would require costly manual attention.