title: CDN Brokering

abstract: Content distribution networks (CDNs) increase the capacity of individual Web sites and attempt to deliver content from caches that are located 'closer' to end-users than the origin servers that provide the content. CDN brokering provides CDNs a way to interoperate by allowing one CDN to intelligently redirect clients dynamically to other CDNs. This paper describes the goals, architecture, and performance of a CDN brokerage system. Our system has been deployed on the Internet on a provisional basis, and our architectural ideas have helped advance the evolution of Internet standards for interoperating CDNs.


title: Determination of UART Receiver Baud Rate Tolerance

abstract: UART is one of the most widely used serial communication protocol in semiconductor domain, and proves to be an integral part of a system irrespective of the industry being used in, extending from Automotive to Industrial to Consumer. A complete system can consist of heterogeneous devices communicating with each other through UART interface, where each such device is capable of generating a different baud rate depending upon its source clock, also known as Baud Clock (which could be derived from PLLs or Oscillator). The Basic Principle of UART Protocol defines that the Transmitter and Receiver should be configured at the same baud rate to communicate correctly. There are some clock dividers implemented within and/or external to the module in order to achieve the same. But it is fairly likely that the baud clocks of two communicating devices are different inspite of having configured the clock dividers properly, leading to a mismatch in the baud rate achieved for both the devices thereby causing Bit Corruption at the receiver. Such a baud rate mismatch can be attributed to the following two factors: 1. Baud Clock inaccuracies 2. Limited clock divider range In this paper, we intend to describe the method of determining the acceptable difference between the transmitter and receiver baud rates, in order to do Error Free Communication through UART.


title: Floating capacitance multiplier using DVCC and CCCIIs

abstract: This article introduces a floating capacitance multiplier employing differential voltage current conveyor (DVCC) and second generation current controlled current conveyors (CCCIIs). The provided capacitor is a floating element synthesized from a grounded capacitor. Its outstanding feature is that the capacitive value can be electronically adjusted by input bias currents of the CCCIIs and is temperature-insensitive. The circuit construction comprises one DVCC and two CCCIIs, cooperating with a grounded capacitor. The circuit performances are depicted through PSPICE simulations, they show good agreement to theoretical anticipation. Capacitance increasing and an application as a fifth-order Chebyshev high-pass filter are included..


title: A primary side feedforward control scheme for low power LED driver compatible with Triac dimmer

abstract: This paper proposes a primary side feedforward control scheme for low power Triac dimmable LED driver. The LED driver is based on the Flyback topology operated in constant frequency (CF) DCM mode. The input power is controlled by the proposed control scheme based on the Triac dimming angle, which is not affected by the AC input voltage variation. Therefore the output power and current can be regulated without any isolated feedback circuit. Also, the input current automatically follows the input voltage due to CF DCM operation. With the proposed control method, high efficiency and high power factor can be achieved by eliminating the isolated feedback circuit and the dummy load required for traditional Triac dimmable LED driver. Furthermore, considering the parameters tolerance existed in the practical application, such as LED forward voltage drop and Flyback transformer magnetizing inductance, the output voltage compensation and primary side inductance compensation method for the proposed control schemes are also presented in the paper, which can further improve the control accuracy. The experimental results from a 27V/0.5A prototype verify the theoretical analysis.


title: R/Kademlia: Recursive and topology-aware overlay routing

abstract: Structured peer-to-peer overlays offer a basis for a broad range of applications, such as distributed storage, application layer multicast, and mobility support. This paper introduces R/Kademlia, a low bandwidth and low latency variant of the popular overlay protocol Kademlia. R/Kademlia utilizes recursive overlay routing instead of iterative lookups of keys, thus a higher routing performance can be achieved in network scenarios with churn rates that are characteristic for public KAD networks. Both Proximity Routing and Proximity Neighbor Selection are effectively applicable, which leads to a further decrease of routing latencies. Similar to the original protocol, nodes in a R/Kademlia network meet other nodes during user-triggered routing procedures, hence no expensive periodic tasks are needed. For the simulative evaluation of R/Kademlia, the Performance vs. Cost framework (PVC) and the overlay framework OverSim were employed, where both routing modes-iterative and recursive-were simulated and compared in different network scenarios with varying churn rates.


title: Food and mood : relationship between food, serotonin and affective disorders

abstract: The relationship of food and eating with affective and other clinical disorders is complex and intriguing. Serotoninergic dysfunction in seasonal affective disorder, atypical depression, premenstrual syndrome, anorexia and bulimia nervosa, and binge eating disorder is reviewed. Patients exhibiting a relationship between food and behaviour are found in various diagnostic categories. This points to a need to shift from nosological to functional thinking in psychiatry. It also means application of psychopharmacological treatments across diagnostic boundaries. The use of phototherapy and psychotropic drugs (MAO inhibitors and selective serotonin reuptake inhibitors like fluoxetine) in these disorders is discussed.


title: A Security Threat Analysis for the Routing Protocol for Low-Power and Lossy Networks (RPLs)

abstract: This document presents a security threat analysis for the Routing ::: Protocol for Low-Power and Lossy Networks (RPLs). The development ::: builds upon previous work on routing security and adapts the ::: assessments to the issues and constraints specific to low-power and ::: lossy networks. A systematic approach is used in defining and ::: evaluating the security threats. Applicable countermeasures are ::: application specific and are addressed in relevant applicability ::: statements.


title: Accurate eye center location and tracking using isophote curvature

abstract: The ubiquitous application of eye tracking is precluded by the requirement of dedicated and expensive hardware, such as infrared high definition cameras. Therefore, systems based solely on appearance (i.e. not involving active infrared illumination) are being proposed in literature. However, although these systems are able to successfully locate eyes, their accuracy is significantly lower than commercial eye tracking devices. Our aim is to perform very accurate eye center location and tracking, using a simple Web cam. By means of a novel relevance mechanism, the proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve rotational invariance and to keep low computational costs. In this paper we test our approach for accurate eye location and robustness to changes in illumination and pose, using the BioIDand the Yale Face B databases, respectively. We demonstrate that our system can achieve a considerable improvement in accuracy over state of the art techniques.


title: Big data compression paradigms for supporting efficient and scalable data-intensive IoT frameworks

abstract: In this paper we focus on big data compression paradigms within reference data-intensive IoT frameworks, which are currently recognized as one of the emerging scientific in a rich interdisciplinary field that comprises service-oriented infrastructures, Cloud computing, big data management and analytics. Basically, big data compression techniques allow to tame the complexity of big data management tasks within such frameworks, hence beneficially influencing all the other activities, perhaps delivered as services in a reference Cloud architecture. Inspired by these considerations, in this paper we provide an overview on noticeable state-of-the-art big data compression techniques, and depict future research directions on the investigated scientific topic to be considered during future years.


title: A planetary perspective on the deep Earth

abstract: Earth's composition, evolution and structure are in part a legacy of provenance (where it happened to form) and chance (the stochastics of that formation).


title: Optimal multimodal fusion for multimedia data analysis

abstract: Considerable research has been devoted to utilizing multimodal features for better understanding multimedia data. However, two core research issues have not yet been adequately addressed. First, given a set of features extracted from multiple media sources (e.g., extracted from the visual, audio, and caption track of videos), how do we determine the best modalities? Second, once a set of modalities has been identified, how do we best fuse them to map to semantics? In this paper, we propose a two-step approach. The first step finds statistically independent modalities from raw features. In the second step, we use super-kernel fusion to determine the optimal combination of individual modalities. We carefully analyze the tradeoffs between three design factors that affect fusion performance: modality independence, curse of dimensionality, and fusion-model complexity. Through analytical and empirical studies, we demonstrate that our two-step approach, which achieves a careful balance of the three design factors, can improve class-prediction accuracy over traditional techniques.


title: Customer churn prediction for retail business

abstract: Customer churn happens when a customer discontinues his or her interaction with a company. In retail business, a customer is treated to be churned once his/her transactions outdate a particular amount of time. Once a customer becomes a churn, the loss incurred by the company is not just the lost revenue due to the lost customer but also the costs involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal of every online business. In this project, we have considered data set from UCI Machine Learning repository. This dataset contains records of transactions that occurred between December 1, 2010 and December 1, 2011. This is recorded from an online retail gift store based in United Kingdom. The customers of this company are mainly wholesalers. This data set is pre-processed by removing NAs, validating numerical values, removing erroneous data points. We then perform aggregations on the data to generate invoice based and customer based data sets. A variable churn is attached to each data point. This churn value is determined based on the customer's transactions. Three algorithms are run on this customer aggregated dataset to predict churn value. They are Random forest, Support vector machines and Extreme gradient boosting. A comparative study is done on these three algorithms.


title: The vulnerability of vote assignments

abstract: In a faulty distributed system, voting is commonly used to achieve mutual exclusion among groups of nodes. Each node is assigned a number of votes, and any group with a majority of votes can perform the critical operations. Vote assignments can have a significant impact on system reliability, and in this paper we study the vote assignment problem. To compare vote assignments we define two deterministic measures, node and edge vulnerability. We present various properties of these measures and discuss how they can be computed. For these measures we discuss the selection of the best assignment and propose heuristics to identify good candidate assignments.


title: Light-weight contexts: an OS abstraction for safety and performance

abstract: We introduce a new OS abstraction--light-weight contexts (lwCs)--that provides independent units of protection, privilege, and execution state within a process. A process may include several lwCs, each with possibly different views of memory, file descriptors, and access capabilities. lwCs can be used to efficiently implement roll-back (process can return to a prior recorded state), isolated address spaces (lwCs within the process may have different views of memory, e.g., isolating sensitive data from network-facing components or isolating different user sessions), and privilege separation (in-process reference monitors can arbitrate and control access). ::: ::: lwCs can be implemented efficiently: the overhead of a lwC is proportional to the amount of memory exclusive to the lwC; switching lwCs is quicker than switching kernel threads within the same process. We describe the lwC abstraction and API, and an implementation of lwCs within the FreeBSD 11.0 kernel. Finally, we present an evaluation of common usage patterns, including fast rollback, session isolation, sensitive data isolation, and inprocess reference monitoring, using Apache, nginx, PHP, and OpenSSL.


title: OWL-S Based Semantic Cloud Service Broker

abstract: Cloud broker is an entity that manages the use, performance and delivery of cloud services and negotiates relationships between cloud providers and cloud consumers. In real life scenarios, automated cloud service brokering is often challenging because the service descriptions may involve complex constraints and require flexible semantic matching. Furthermore, cloud providers often use non-standard formats leading to semantic interoperability issues. In this paper, we formulate cloud service brokering under a service oriented framework, and propose a novel OWL-S based semantic cloud service discovery and selection system. The proposed system supports dynamic semantic matching of cloud services described with complex constraints. We consider a practical cloud service brokering scenario, and show with detailed illustration that our system is promising for real-life applications.


title: Dense xUnit Networks

abstract: Deep net architectures have constantly evolved over the past few years, leading to significant advancements in a wide array of computer vision tasks. However, besides high accuracy, many applications also require a low computational load and limited memory footprint. To date, efficiency has typically been achieved either by architectural choices at the macro level (e.g. using skip connections or pruning techniques) or modifications at the level of the individual layers (e.g. using depth-wise convolutions or channel shuffle operations). Interestingly, much less attention has been devoted to the role of the activation functions in constructing efficient nets. Recently, Kligvasser et al. showed that incorporating spatial connections within the activation functions, enables a significant boost in performance in image restoration tasks, at any given budget of parameters. However, the effectiveness of their xUnit module has only been tested on simple small models, which are not characteristic of those used in high-level vision tasks. In this paper, we adopt and improve the xUnit activation, show how it can be incorporated into the DenseNet architecture, and illustrate its high effectiveness for classification and image restoration tasks alike. While the DenseNet architecture is extremely efficient to begin with, our dense xUnit net (DxNet) can typically achieve the same performance with far fewer parameters. For example, on ImageNet, our DxNet outperforms a ReLU-based DenseNet having 30% more parameters and achieves state-of-the-art results for this budget of parameters. Furthermore, in denoising and super-resolution, DxNet significantly improves upon all existing lightweight solutions, including the xUnit-based nets of Kligvasser et al.


title: Presentation and validation of the Radboud Faces Database

abstract: Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several important, technical aspects. The present article presents the freely available Radboud Faces Database offering such a stimulus set, containing both Caucasian adult and children images. This face database is described both procedurally and in terms of content, and a validation study concerning its most important characteristics is presented. In the validation study, all frontal images were rated with respect to the shown facial expression, intensity of expression, clarity of expression, genuineness of expression, attractiveness, and valence. The results show very high recognition of the intended facial expressions.


title: Text to speech synthesis system for English to Malayalam translation

abstract: Speech recognition and Speech synthesis are the two emerging technologies in the communication field. Speech recognition is a system which generates text for the given speech, while a speech synthesizer is a system that should be able to read any text aloud. Research is conducting all over the world, to develop an efficient text to speech synthesis (TTS) system for minority languages. India has the largest democracy system in the world, also known as land of unity in diversity and has more than 22 official languages. It becomes difficult to understand the English and the other European languages to common people. This works aims to help people to translate English text to their own language and implement a TTS for the minority language, Malayalam. It is achieved by combining both Machine Translation and TTS. When an English text is given, it is translated to Malayalam with the help of a parser, using grammatical rules, applying morphology and a bilingual dictionary. From each of the translated Malayalam text, syllables are separated. A good number of syllables are recorded and stored in the syllable corpus. Syllables are concatenated to generate a synthesized Malayalam speech. Machine translation of English to Malayalam text is tested and achieved 73 percentage accuracy. For the TTS system, accuracy is verified by checking the naturalness and intelligibility. 87 percentages of the sentences are uttered correctly.


Sleep-Wake Regulation and Its Impact on Working Memory Performance: The Role of Adenosine


Maintenance of Chronic Fatigue Syndrome (CFS) in Young CFS Patients Is Associated with the 5-HTTLPR and SNP rs25531 A > G Genotype


title: {Toward Minimax Off-policy Value Estimation}

abstract: This paper studies the off-policy evaluation problem, where one aims to estimate the value of a target policy based on a sample of observations collected by another policy. We first consider the single-state, or multi-armed bandit case, establish a finite-time minimax risk lower bound, and analyze the risk of three standard estimators. For the so-called regression estimator, we show that while it is asymptotically optimal, for small sample sizes it may perform suboptimally compared to an ideal oracle up to a multiplicative factor that depends on the number of actions. We also show that the other two popular estimators can be arbitrarily worse than the optimal, even in the limit of infinitely many data points. The performance of the estimators are studied in synthetic and real problems; illustrating the methods strengths and weaknesses. We also discuss the implications of these results for off-policy evaluation problems in contextual bandits and fixed-horizon Markov decision processes.


title: Information dissemination in self-organizing intervehicle networks

abstract: Intervehicle communication (IVC) is an emerging topic in research and application that is getting increasing attention from all major car manufacturers. In this paper, a novel method for scalable information dissemination in highly mobile ad hoc networks is proposed: segment-oriented data abstraction and dissemination (SODAD). With SODAD, information can be distributed in an information range multiple orders of magnitude larger than the transmission range of the air interface, even if only 1%-3% of all vehicles are equipped with an IVC system, e.g., during market introduction. By restricting the method to the dissemination of map/position-based data, scalability is achieved. In the second half of this paper, an example application for the SODAD method is presented: a self-organizing traffic-information system (SOTIS). In SOTIS, a car is equipped with a satellite navigation receiver, an IVC system, and a digital map. Each individual vehicle collects traffic information for its local area. Using the digital map, the traffic information is analyzed based on road segments. By distributing the information in the ad hoc intervehicle network using the SODAD method, a decentralized traffic information system is created. The performance of the proposed methods is evaluated using network simulation with vehicular mobility models. Simulation results for typical scenarios are presented. Furthermore, a prototype implementation based on commercially available standard hardware demonstrates the feasibility of the proposed approach.


title: Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery

abstract: Context-aware segmentation of laparoscopic and robot assisted surgical video has been shown to improve performance and perioperative workflow efficiency, and can be used for education and time-critical consultation. Modern pressures on productivity preclude manual video analysis, and hospital policies and legacy infrastructure are often prohibitive of recording and storing large amounts of data. In this paper we present a system that automatically generates a video segmentation of laparoscopic and robot-assisted procedures according to their underlying surgical phases using minimal computational resources, and low amounts of training data. Our system uses an SVM and HMM in combination with an augmented feature space that captures the variability of these video streams without requiring analysis of the nonrigid and variable environment. By using the data reduction capabilities of online k-segment coreset algorithms we can efficiently produce results of approximately equal quality, in realtime. We evaluate our system in cross-validation experiments and propose a blueprint for piloting such a system in a real operating room environment with minimal risk factors.


title: Bringing up OpenSky: a large-scale ADS-B sensor network for research

abstract: Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the key components of the next generation air transportation system. Since ADS-B will become mandatory by 2020 for most airspaces, it is important that aspects such as capacity, applications, and security are investigated by an independent research community. However, large-scale real-world data was previously only accessible to a few closed industrial and governmental groups because it required specialized and expensive equipment. To enable researchers to conduct experimental studies based on real data, we developed OpenSky, a sensor network based on low-cost hardware connected over the Internet. OpenSky is based on off-the-shelf ADS-B sensors distributed to volunteers throughout Central Europe. It covers 720,000 sq km2, is able to capture more than 30% of the commercial air traffic in Europe, and enables researchers to analyze billions of ADS-B messages. In this paper, we report on the challenges we faced during the development and deployment of this participatory network and the insights we gained over the last two years of operations as a service to academic research groups. We go on to provide real-world insights about the possibilities and limitations of such low-cost sensor networks concerning air traffic surveillance and further applications such as multilateration.


title: A Fully-Integrated 16-Element Phased-Array Receiver in SiGe BiCMOS for 60-GHz Communications

abstract: A phased-array transmitter (TX) for multi-Gb/s non-line-of-sight links in the four frequency channels of the IEEE 802.15.3c standard (58.32 to 64.8 GHz) is fully integrated in a 0.12-μm SiGe BiCMOS process. It consists of an up-conversion core followed by a 1:16 power distribution tree, 16 phase-shifting front-ends, and a digital control unit. The TX core is a two-step sliding-IF up-conversion chain with frequency synthesizer that features 40 dB of gain programmability, I/Q balance and LO leakage correction, and a modulator for 802.15.3c common-mode signaling. The tradeoffs involved in the implementation of a 1:16 power distribution network are analyzed and a hybrid passive/active distribution tree architecture is introduced. Each of the 16 front-ends consists of a balanced passive phase shifter and a variable-gain, 3-stage PA that features oP1dB programmability through the bias control of the its final stage. All of the chip features are digitally controllable and individual memory arrays are integrated at each front-end to enable fast beam steering through a high-speed parallel interface. The IC occupies 44 mm and is fully characterized on wafer. The TX delivers 9 to 13.5 dBm oPidB per element at 60.48 GHz with a total power consumption of 3.8 to 6.2 W. Each element attains a phase-shift range >360° with an amplitude variation <;±1 dB across phase settings and adjacent elements. Measurement results from a packaged IC in an antenna chamber are also presented including the demonstration of spatial power combining up to +40 dBm EIRP and 16-element radiation patterns.


title: Design of a Broadband and Multiband Planar Inverted-F Antenna

abstract: A novel planar inverted-F antenna (PIFA) is designed in this paper. Compared to the previous PIFA, the proposed PIFA can enhance bandwidths and achieve multi-band which is loaded with a T-shaped ground plane and etched slots on ground plane and a rectangular patch. It covered 4 service bands, including GSM900, DCS1800, PCS1900 and ISM2450 under the criteria -7 dB return loss for the first band and -10 dB for the last bands. Process of designing and calculation of parameters are presented in detail. The simulation results showed that each band has good characteristics and the bandwidth has been greatly expanded.


title: ℓ1-Based Construction of Polycube Maps from Complex Shapes

abstract: Polycube maps of triangle meshes have proved useful in a wide range of applications, including texture mapping and hexahedral mesh generation. However, constructing either fully automatically or with limited user control a low-distortion polycube from a detailed surface remains challenging in practice. We propose a variational method for deforming an input triangle mesh into a polycube shape through minimization of the e1-norm of the mesh normals, regularized via an as-rigid-as-possible volumetric distortion energy. Unlike previous work, our approach makes no assumption on the orientation, or on the presence of features in the input model. User-guided control over the resulting polycube map is also offered to increase design flexibility. We demonstrate the robustness, efficiency, and controllability of our method on a variety of examples, and explore applications in hexahedral remeshing and quadrangulation.


title: A Statistical Method for Associating Earthquakes with Their Source Faults in Southern California

abstract: We present a new statistical method for associating earthquakes with their source faults in the Southern California Earthquake Center’s 3D Community Fault Models (CFMs; Plesch et al., 2007) in near‐real time and for historical earthquakes. The method uses the hypocenter location, focal mechanism orientation, and earthquake sequencing to produce the probabilities of association between a given earthquake and each fault in the CFM as well as the probability that the event occurred on a fault not represented in the CFM. We used a set of known likely associations (the Known Likely Sets) as training or testing data and demonstrated that our models perform effectively on these examples and should be expected to perform well on other earthquakes with similar characteristics including the full catalog of southern California earthquakes (Hauksson et al., 2012). To produce near‐real‐time associations for future earthquakes, the models have been implemented as an R script and connected to the Southern California Seismic Network data processing system operated by the California Institute of Technology and the U.S. Geological Survey to automatically produce fault associations for earthquakes of M ≥ 3.0 as they occur. To produce historical associations, we apply the method to the most recent CFM version (v.5.2), yielding modeled historical associations for all events of M ≥ 3.0 in the catalog of southern California earthquakes from 1981 to 2016. More than 80% of these events and 99% of moment within the geography covered by the CFM had a primary association with a CFM fault. The models can help identify clusters of small earthquakes that indicate the onset of activity associated with major faults. The method will also assist in communicating objective information about the faults that source earthquakes to the scientific community and general public. In the event of a damaging southern California earthquake, the near‐real‐time association will provide valuable information regarding the similarity of the current event to forecast scenarios, potentially aiding in earthquake response.


title: A Survey of Emerging Biometric Technologies

abstract: Computer Security System / technology have passed through several changes. The trends have been from what you know (e.g. password, PIN, etc) to what you have (ATM card, Driving License, etc) and presently to who you are (Biometry) or combinations of two or more of the trios. This technology (biometry) has come to solve the problems identified with knowledge-based and token-based authentication systems. It is possible to forget your password and what you have can as well be stolen. The security of determining who you are is referred to as BIOMETRIC. Biometric, in a nutshell, is the use of your body as password. This paper explores the various methods of biometric identification that have evolved over the years and the features used for each modality.


title: Odin II - An Open-Source Verilog HDL Synthesis Tool for CAD Research

abstract: In this work, we present Odin II, a framework for Verilog Hardware Description Language (HDL) synthesis that allows researchers to investigate approaches/improvements to different phases of HDL elaboration that have not been previously possible. Odin II’s output can be fed into traditional back-end flows for both FPGAs and ASICs so that these improvements can be better quantified. Whereas the original Odin [1] provided an open source synthesis tool, Odin II’s synthesis framework offers significant improvements such as a unified environment for both front-end parsing and netlist flattening. Odin II also interfaces directly with VPR [2], a common academic FPGA CAD flow, allowing an architectural description of a target FPGA as an input to enable identification and mapping of design features to custom features. Furthermore, Odin II can also read the netlists from downstream CAD stages into its netlist data-structure to facilitate analysis. Odin II can be used for a wide range of experiments; in this paper, we show three specific instances of how Odin II can be used by ASIC and FPGA researchers for more than basic synthesis. Odin II is open source and released under the MIT License.


title: Studying Fake News via Network Analysis: Detection and Mitigation

abstract: Social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Identifying and mitigating fake news also presents unique challenges. To tackle these challenges, many existing research efforts exploit various features of the data, including network features. In essence, a news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.


title: Multi-barrier two-pole line-start synchronous reluctance motor with high saliency for a bore-well submersible pump

abstract: Energy efficiency of low and medium power range electric motors is one of the major concerns in the present day world, since they contribute to a major portion of the energy demand. The motors in this power range are mainly used for domestic, industrial and irrigation water pumps. Usually, either submersible induction motors or permanent magnet (PM) motors are employed. However, the efficiency and cost of these motors, respectively is one of their drawbacks. In this paper, a 2-pole multi-barrier line-start synchronous reluctance motor (LS SyRM) is proposed for a 6-inch bore-well submersible water pump. Further, two new rotor geometries with high saliency are compared and analyzed in terms of their starting and steady-state performance using 2D-finite element analysis. Finally, one rotor model is adjudicated for the bore-well pump application.


title: Delayed Purchase Options in Single-Leg Revenue Management

abstract: Many airline reservation systems offer the commitment option to their potential passengers. This option allows passengers to reserve a seat for a fixed duration before making a final purchase decision. In this study, we develop single-leg revenue management models that consider such contingent commitment decisions. We start with a dynamic programming model of this problem. This model is computationally intractable as it requires storing a multidimensional state space because of bookkeeping of the committed seats. To alleviate this difficulty, we propose an alternate dynamic programming formulation that uses an approximate model of how the contingent commitments behave and we show how to extract a capacity allocation policy from the approximate dynamic programming formulation. In addition, we present a deterministic linear programming model that gives an upper bound on the optimal expected revenue from the intractable dynamic programming model. As the problem size becomes large in terms of flight capacity and the expected number of arrivals, we demonstrate an asymptotic lower bound for the deterministic linear programming model. Our extensive numerical study indicates that offering commitment options can noticeably increase potential revenue even though offering a contingent commitment option may not always be in the best interest of the airline. Also, our results show that the proposed approximate dynamic programming model coordinates capacity allocation and commitment decisions quite well.


title: Interpreting the BOLD signal.

abstract: The development of functional magnetic resonance imaging (fMRI) has brought together a broad community of scientists interested in measuring the neural basis of the human mind. Because fMRI signals are an indirect measure of neural activity, interpreting these signals to make deductions about the nervous system requires some understanding of the signaling mechanisms. We describe our current understanding of the causal relationships between neural activity and the blood-oxygen-level-dependent (BOLD) signal, and we review how these analyses have challenged some basic assumptions that have guided neuroscience. We conclude with a discussion of how to use the BOLD signal to make inferences about the neural signal.


title: LIZARD – A Lightweight Stream Cipher for Power-constrained Devices

abstract: Time-memory-data (TMD) tradeoff attacks limit the security level of many classical stream ciphers (like E 0 , A5/1, Trivium, Grain) to 1 / 2 n , where n denotes the inner state length of the underlying keystream generator. In this paper, we present Lizard, a lightweight stream cipher for power-constrained devices like passive RFID tags. Its hardware efficiency results from combining a Grain-like design with the FP (1)-mode, a recently suggested construction principle for the state initialization of stream ciphers, which offers provable 2 / 3 n -security against TMD tradeoff attacks aiming at key recovery. Lizard uses 120-bit keys, 64-bit IVs and has an inner state length of 121 bit. It is supposed to provide 80-bit security against key recovery attacks. Lizard allows to generate up to 2 18 keystream bits per key/IV pair, which would be sufficient for many existing communication scenarios like Bluetooth, WLAN or HTTPS.


title: Review on Control of DC Microgrids and Multiple Microgrid Clusters

abstract: This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors’ point of view, are also provided in the final concluding part.


title: Child Mortality in sub-Saharan Africa: Why Public Health Spending Matters

abstract: Since 2000, child mortality has dramatically decreased in Africa. Based on an econometrical analysis over 45 sub-Saharan African countries, this paper analyses the determinants of such evolution, and shows that urbanization, sanitation improvement and GDP growth per capita played a critical role in this overall improvement over 2000-2011. The increase in public health expenditures proved to be also decisive, though the elasticity with mortality rate is much weaker. Reaching the Abuja target of 15% of public health expenditure in total public expenditures would have decreased the under-5 child mortality rate by 9% over 2001-2011. It could further reduce this rate by 14% over 2012-2021, and allow Africa to save 19.8 million of children lives. It would also help the region to achieve the Millennium Development Goal on child mortality (reduce by two thirds under-5 child mortality over 1990-2015) by 2022-23, while it would not be reached before 2027 otherwise, according to our estimates.


title: Independent comparison of popular DPI tools for traffic classification

abstract: Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classification. However, the actual performance of DPI is still unclear to the research community, since the lack of public datasets prevent the comparison and reproducibility of their results. This paper presents a comprehensive comparison of 6 well-known DPI tools, which are commonly used in the traffic classification literature. Our study includes 2 commercial products (PACE and NBAR) and 4 open-source tools (OpenDPI, L7-filter, nDPI, and Libprotoident). We studied their performance in various scenarios (including packet and flow truncation) and at different classification levels (application protocol, application and web service). We carefully built a labeled dataset with more than 750K flows, which contains traffic from popular applications. We used the Volunteer-Based System (VBS), developed at Aalborg University, to guarantee the correct labeling of the dataset. We released this dataset, including full packet payloads, to the research community. We believe this dataset could become a common benchmark for the comparison and validation of network traffic classifiers. Our results present PACE, a commercial tool, as the most accurate solution. Surprisingly, we find that some open-source tools, such as nDPI and Libprotoident, also achieve very high accuracy.


A PROPOSED VARIANT OF DIFFERENTIAL EVOLUTION FOR OPTIMAL POWER FLOW PROBLEM


title: Semantic word embedding neural network language models for automatic speech recognition

abstract: Semantic word embeddings have become increasingly important in natural language processing tasks over the last few years. This popularity is due to their ability to easily capture rich semantic information through a distributed representation and the availability of fast and scalable algorithms for learning them from large text corpora. State-of-the-art neural network language models (NNLMs) used in automatic speech recognition (ASR) and natural language processing also learn word embeddings optimized to model local N-gram dependencies given training text but are not optimized to capture semantic information. We hypothesize that semantic word embeddings provide diverse information compared to the word embeddings learned by NNLMs. We propose novel feedforward NNLM architectures that incorporate semantic word embeddings. We apply the resulting NNLMs to ASR on broadcast news and show improvements in both perplexity and word error rate.


title: An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms

abstract: In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.


title: Solutions for a cultivated planet

abstract: Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world's future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture's environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing 'yield gaps' on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture.


title: Large-scale pattern growth of graphene films for stretchable transparent electrodes

abstract: High-performance, transparent and stretchable electrodes are in demand for the development of flexible electronic and optoelectronic applications. Graphene is a candidate as the basis material, because of its excellent optical, electrical and mechanical properties. This paper describes a technique to grow centimetre-scale films using chemical vapour deposition on nickel films and a method to pattern and transfer the films to arbitrary substrates. The electrical conductance and optical transparency are as high as those for microscale graphene films.


title: Control Scheme for Reduced Cross-Regulation in Single-Inductor Multiple-Output DC–DC Converters

abstract: Single-inductor multiple-output (SIMO) dc-dc switching regulators are potentially very good replacement to multiple parallel converters in today's power management units for portable applications where multiple supplies are required. The outputs in these converters being coupled, cross-regulation among the outputs plays a major role in deciding the performance of the system. This paper proposes a control scheme that ensures good load and line regulation and stable system dynamics and reduces cross-regulation effect significantly. In designing a control scheme, proper analysis of the system is an important factor, and SIMO class of converters being driven by a ripple in the inductor current, conventional modeling does not hold good. Consequently, a ripple-based modeling approach that accurately judges the system performance is adopted. A cross-derivative state feedback control methodology has been proposed so as to completely decouple the outputs. Finally, a single-inductor dual-output SIMO converter has been built on a printed circuit board using discrete components, and the test results presented validate the modeling technique proposed. The simulation and experimental results show that the proposed control scheme significantly reduces cross-regulation at the outputs.


title: A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

abstract: This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.


title: Algorithms for Mining the Coevolving Relational Motifs in Dynamic Networks

abstract: Computational methods and tools that can efficiently and effectively analyze the temporal changes in dynamic complex relational networks enable us to gain significant insights regarding the entity relations and their evolution. This article introduces a new class of dynamic graph patterns, referred to as coevolving relational motifs (CRMs), which are designed to identify recurring sets of entities whose relations change in a consistent way over time. CRMs can provide evidence to the existence of, possibly unknown, coordination mechanisms by identifying the relational motifs that evolve in a similar and highly conserved fashion. We developed an algorithm to efficiently analyze the frequent relational changes between the entities of the dynamic networks and capture all frequent coevolutions as CRMs. Our algorithm follows a depth-first exploration of the frequent CRM lattice and incorporates canonical labeling for redundancy elimination. Experimental results based on multiple real world dynamic networks show that the method is able to efficiently identify CRMs. In addition, a qualitative analysis of the results shows that the discovered patterns can be used as features to characterize the dynamic network.


title: Designing Highly Reliable Fiducial Markers

abstract: Fiducial markers are artificial landmarks added to a scene to facilitate locating point correspondences between images, or between images and a known model. Reliable fiducials solve the interest point detection and matching problems when adding markers is convenient. The proper design of fiducials and the associated computer vision algorithms to detect them can enable accurate pose detection for applications ranging from augmented reality, input devices for HCI, to robot navigation. Marker systems typically have two stages, hypothesis generation from unique image features and verification/identification. A set of criteria for high robustness and practical use are identified and then optimized to produce the ARTag fiducial marker system. An edge-based method robust to lighting and partial occlusion is used for the hypothesis stage, and a reliable digital coding system is used for the identification and verification stage. Using these design criteria large gains in performance are achieved by ARTag over conventional ad hoc designs.


title: Software developers' perceptions of productivity

abstract: The better the software development community becomes at creating software, the more software the world seems to demand. Although there is a large body of research about measuring and investigating productivity from an organizational point of view, there is a paucity of research about how software developers, those at the front-line of software construction, think about, assess and try to improve their productivity. To investigate software developers' perceptions of software development productivity, we conducted two studies: a survey with 379 professional software developers to help elicit themes and an observational study with 11 professional software developers to investigate emergent themes in more detail. In both studies, we found that developers perceive their days as productive when they complete many or big tasks without significant interruptions or context switches. Yet, the observational data we collected shows our participants performed significant task and activity switching while still feeling productive. We analyze such apparent contradictions in our findings and use the analysis to propose ways to better support software developers in a retrospection and improvement of their productivity through the development of new tools and the sharing of best practices.


abstract: Abstract Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN ( Knowledge-Based Artificial Neural Networks ) is a hybrid learning system built on top of connectionist learning techniques. It maps problem-specific “domain theories”, represented in propositional logic, into neural networks and then refines this reformulated knowledge using backpropagation. KBANN is evaluated by extensive empirical tests on two problems from molecular biology. Among other results, these tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists.


title: PSYCHOLOGICAL SCIENCE Research Article God Is Watching You Priming God Concepts Increases Prosocial Behavior in an Anonymous Economic Game

abstract: We present two studies aimed at resolving experimentally whether religion increases prosocial behavior in the anonymous dictator game. Subjects allocated more money to anonymous strangers when God concepts were implicitly activated than when neutral or no concepts were activated. This effect was at least as large as that obtained when concepts associated with secular moral institutions were primed. A trait measure of self-reported religiosity did not seem to be associated with prosocial behavior. We discuss different possible mechanisms that may underlie this effect, focusing on the hypotheses that the religious prime had an ideomotor effect on generosity or that it activated a felt presence of supernatural watchers. We then discuss implications for theories positing religion as a facilitator of the emergence of early large-scale societies of cooperators.


Improving chemical disease relation extraction with rich features and weakly labeled data


title: Fakability of Different Measurement Methods for Achievement Motivation: Questionnaire, Semi-Projective, and Objective

abstract: Different means can be applied to assess noncognitive personality aspects: projective, semiprojective, self-report, and objective. However, so far little attention has been paid towards the different fakability of these methods. The present study investigated this question with different achievement motivation instruments. The instruments were randomly administered to three student groups: fake bad (n = 41), fake good (n = 37), and control group (n = 41). The faking groups were given specific faking instructions while the control group only received the standard instructions. All instruments were applied computer-assisted. The results show that all tests are fakeable with the exception of the objective measure which could not be faked good as was expected. The effect sizes (d) ranged from .10 to 2.36. Cut-off scores for the detection of faking were computed based on sensitivity as well as specificity. Moreover, they were tested within a second student sample (n = 123). Sensitivity and specificity values are reported. The practical implications for test authors and practitioners are discussed.


title: The power of statistical tests in meta-analysis.

abstract: Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to compute statistical power of fixed- and random-effects tests of the mean effect size, tests for heterogeneity (or variation) of effect size parameters across studies, and tests for contrasts among effect sizes of different studies. Examples are given using 2 published meta-analyses. The examples illustrate that statistical power is not always high in meta-analysis.


title: Agility from First Principles: Reconstructing the Concept of Agility in Information Systems Development

abstract: Awareness and use of agile methods has grown rapidly among the information systems development (ISD) community in recent years. Like most previous methods, the development and promotion of these methods have been almost entirely driven by practitioners and consultants, with little participation from the research community during the early stages of evolution. While these methods are now the focus of more and more research efforts, most studies are still based on XP, Scrum, and other industry-driven foundations, with little or no conceptual studies of ISD agility in existence. As a result, this study proposes that there are a number of significant conceptual shortcomings with agile methods and the associated literature in its current state, including a lack of clarity, theoretical glue, parsimony, limited applicability, and naivety regarding the evolution of the concept of agility in fields outside systems development. Furthermore, this has significant implications for practitioners, rendering agile method comparison and many other activities very difficult, especially in instances such as distributed development and large teams that are not conducive to many of the commercial agile methods. This study develops a definition and formative taxonomy of agility in an ISD context, based on a structured literature review of agility across a number of disciplines, including manufacturing and management where the concept originated, matured, and has been applied and tested thoroughly over time. The application of the texonomy in practice is then demonstrated through a series of thought trials conducted in a large multinational organization. The intention is that the definition and taxonomy can then be used as a starting point to study ISD method agility regardless of whether the method is XP or Scrum, agile or traditional, complete or fragmented, out-of-the-box or in-house, used as is or tailored to suit the project context.


title: Decision Tree for the Weather Forecasting

abstract: Predicting the classification of data in a suitable class is a challenging task. It depends on various factors to predict the dependent variables. Since decision tree evaluation can be quantified and it is simple to use, so a model using decision tree has been proposed by the author to predict the event like fog, rain and thunder by inputting average temperature, humidity and pressure. Which can be used by farmers or by peoples of all walk of life in taking the intelligent decisions. This model can be used in machine learning and further the proposed model has scope for improvement as more and more relevant attributes can be used in predicting the dependent variable. Decision tree(Decision stump) has been implemented in Weka to facilitate the forecasting of weather..


title: Single sample per person face recognition based on deep convolutional neural network

abstract: As for that there is only one training sample in single sample per person(SSPP) face recognition(FR), deep learning(DL) method is very difficult to be used. To overcome this problem, the paper proposes a series of methods to make it possible to be used in SSPP FR. Firstly, an expanding sample method is proposed to increase training sample. Secondly, a learned Deep Convolutional Neural Network(DCNN) model which is trained with amounts of face images and can represent face very well is brought in. Then, these expanding samples are used to fine-tune the DCNN model. Thirdly, the fine-tuned DCNN model is used to perform experiment. Experiments demonstrate that a better performance is obtained by using the fine-tuned DCNN model.


title: Much ado about nothing: The misestimation and overinterpretation of violent video game effects in Eastern and Western nations: Comment on Anderson et al. (2010).

abstract: The issue of violent video game influences on youth violence and aggression remains intensely debated in the scholarly literature and among the general public. Several recent meta-analyses, examining outcome measures most closely related to serious aggressive acts, found little evidence for a relationship between violent video games and aggression or violence. In a new meta-analysis, C. A. Anderson et al. (2010) questioned these findings. However, their analysis has several methodological issues that limit the interpretability of their results. In their analysis, C. A. Anderson et al. included many studies that do not relate well to serious aggression, an apparently biased sample of unpublished studies, and a “best practices” analysis that appears unreliable and does not consider the impact of unstandardized aggression measures on the inflation of effect size estimates. They also focused on bivariate correlations rather than better controlled estimates of effects. Despite a number of methodological flaws that all appear likely to inflate effect size estimates, the final estimate of r .15 is still indicative of only weak effects. Contrasts between the claims of C. A. Anderson et al. (2010) and real-world data on youth violence are discussed.


title: Antibiotics, Antibiotic Resistance Genes, and Bacterial Community Composition in Fresh Water Aquaculture Environment in China

abstract: Environmental antibiotic resistance has drawn increasing attention due to its great threat to human health. In this study, we investigated concentrations of antibiotics (tetracyclines, sulfonamides and (fluoro)quinolones) and abundances of antibiotic resistance genes (ARGs), including tetracycline resistance genes, sulfonamide resistance genes, and plasmid-mediated quinolone resistance genes, and analyzed bacterial community composition in aquaculture environment in Guangdong, China. The concentrations of sulfametoxydiazine, sulfamethazine, sulfamethoxazole, oxytetracycline, chlorotetracycline, doxycycline, ciprofloxacin, norfloxacin, and enrofloxacin were as high as 446 μg kg−1 and 98.6 ng L−1 in sediment and water samples, respectively. The relative abundances (ARG copies/16S ribosomal RNA (rRNA) gene copies) of ARGs (sul1, sul2, sul3, tetM, tetO, tetW, tetS, tetQ, tetX, tetB/P, qepA, oqxA, oqxB, aac(6′)-Ib, and qnrS) were as high as 2.8 × 10−2. The dominant phyla were Proteobacteria, Bacteroidetes, and Firmicutes in sediment samples and Proteobacteria, Actinobacteria and Bacteroidetes in water samples. The genera associated with pathogens were also observed, such as Acinetobacter, Arcobacter, and Clostridium. This study comprehensively investigated antibiotics, ARGs, and bacterial community composition in aquaculture environment in China. The results indicated that fish ponds are reservoirs of ARGs and the presence of potential resistant and pathogen-associated taxonomic groups in fish ponds might imply the potential risk to human health.


title: Bandwidth and Gain Enhancement of Microstrip Patch Antennas Using Reflective Metasurface

abstract: Bandwidth and gain enhancement of microstrip patch antennas (MPAs) is proposed using reflective metasurface (RMS) as a superstrate. Two different types of the RMS, namely- the double split-ring resonator (DSR) and double closed-ring resonator (DCR) are separately investigated. The two antenna prototypes were manufactured, measured and compared. The experimental results confirm that the RMS loaded MPAs achieve high-gain as well as bandwidth improvement. The desinged antenna using the RMS as a superstrate has a high-gain of over 9.0dBi and a wide impedance bandwidth of over 13%. The RMS is also utilized to achieve a thin antenna with a cavity height of 6 mm, which is equivalent to λ/21 at the center frequency of 2.45GHz. At the same time, the cross polarization level and front-to-back ratio of these antennas are also examined.


title: Lifting line method for modelling covering and blanketing effects for yacht fleet race simulation

abstract: An approach is presented that can be used to enhance the realism of yacht fleet race simulations. The wake of an upwind sailing yacht is represented as a single heeled horseshoe vortex (and image) system. At each time step changes in vortex strength are convected into the wake as a pair of vortex line elements. These subsequently move in accordance with the local wind, self-induced velocity and velocity induced by the presence of the wakes of other yachts. An empirical based decay factor is used to eventually remove the far wake. A synthesis of sail yacht wake representations based on detailed 3D Reynolds Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) calculations with wind tunnel test results are used to capture the initial strength of the combined main-jib vortex system and its vertical height. These were based on a typical upwind sail arrangement for a range of heel angles and in-line calculations for a pair of yachts separated by three boat lengths. This paper details the basis of the validated CFD results for a yacht at heel and the analysis of the CFD results to provide an approximate single line vortex method for the yacht. The developed algorithm will eventually run within the Robo-Race which is a real-time yacht race strategy analysis tool based on MATLAB®-Simulink® developed at the University of Southampton.


title: Improving Public Transit Accessibility for Blind Riders by Crowdsourcing Bus Stop Landmark Locations with Google Street View: An Extended Analysis

abstract: Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for an expected shelter, bench, or newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this article, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool, (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV, and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in nonvisual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5p accuracy across 150 bus stop locations (87.3p with simple quality control).


title: Microperforate Hymen and Pyocolpos: A Case Report and Review of the Literature.

abstract: BACKGROUND ::: Microperforate hymen is a rare congenital anomaly characterized by a small pinpoint opening in the hymen; girls with this anomaly are prone to develop ascending pelvic infections and recurrent urinary tract infections. ::: ::: ::: CASE ::: We report the case of a 3-year-old girl who presented with fevers, abdominal pain, recurrent vaginitis, and dysuria. She was found to have a microperforate hymen and pyocolpos. She was treated with intravenous antibiotics and underwent hymenotomy and drainage of 150 cc of purulent fluid. ::: ::: ::: SUMMARY AND CONCLUSION ::: This case highlights the need to perform thorough genital inspection and to consider hymenal anomalies in the differential diagnosis of girls who present with recurrent dysuria, vaginitis, fevers, and abdominal pain. Early intervention might prevent the development of pyocolpos and other sequelae associated with this anomaly.


title: Summarization Evaluation: An Overview

abstract: This paper provides an overview of different methods for evaluating automatic summarization systems. The challenges in evaluating summaries are characterized. Both intrinsic and extrinsic approaches are discussed. Methods for assessing informativeness and coherence are described. The advantages and disadvantages of specific methods are assessed, along with criteria for choosing among them. The paper concludes with some suggestions for future directions.


A Machine Learning Approach for Cryptanalysis


title: Phishing for user security awareness

abstract: User security education and training is one of the most important aspects of an organizations security posture. Using security exercises to reinforce this aspect is frequently done by education and industry alike; however these exercises usually enlist willing participants. We have taken the concept of using an exercise and modified it in application to evaluate a users propensity to respond to email phishing attacks in an unannounced test. This paper describes the considerations in establishing and the process used to create and implement an evaluation of one aspect of our user information assurance education program. The evaluation takes the form of a exercise, where we send out a phishing styled email record the responses.


title: Combinatorial portfolio selection with the ELECTRE III method: Case study of the Stock Exchange of Thailand (SET)

abstract: Various techniques of portfolio selection are applied to interpret the status of the market and predict the market's future trend, but they are not beneficial to small investors because these techniques should be administered by an expert. In addition, these techniques desire accumulation of data about the market and complicated calculations, which is too much effort for individual small investors. Therefore, portfolio selection with two significant financial ratios using the ELECTRE III method is proposed for these investors to make trading decisions. In order to demonstrate the effectiveness of this new method, it is compared to the situation where a fix percentage allocation existed and data was collected from the Stock Exchange of Thailand (SET).


title: Desperately seeking simplicity: how young adults with cognitive disabilities and their families adopt assistive technologies

abstract: A surprisingly high percentage of assistive technology devices (35% or more) are purchased, but not successfully adopted. Through semi-structured interviews with a dozen families, we have come to understand the role technology plays in the lives of families who have a young adult with cognitive disabilities, and how families find, acquire, and use these technologies. This study addresses gaps in existing research and informs future efforts in assistive technology design. Design implications include the importance of simplicity not only in technology function but in configuration, documentation, maintenance, and upgrade or replacement; as well as the need for designers to use methods that consider the multiple individuals and stages involved in the technology adoption process.


title: A control scheme of the VIENNA rectifier with unbalanced grid voltage

abstract: VIENNA rectifier with simple structure, which is widely used in charging systems for electric vehicle, realizes the operation of unit power factor and low harmonic. However, grid voltage unbalance will aggravate the dc-link voltage ripple and input current THD. A novel control scheme is presented to solve the impacts of three unbalanced grid voltage for VIENNA rectifier in this paper. The enhanced phase lock loop (EPLL) not only rapidly separates the positive and negative sequence components from the grid voltage, but also accurately tracks phase angle. Then the dynamic response of proportional resonant (PR) controller guarantees to quickly track the current references with zero-error. Meantime, a simplified carrier-based PWM (CB-PWM) based on the equivalence of SVM is implemented to balance the neutral-point voltage. The validity of the theoretical analysis and feasibility of the proposed control scheme are verified by simulation results.


title: Interchangeable components for hands-on assembly based modelling

abstract: Interchangeable components allow an object to be easily reconfigured, but usually reveal that the object is composed of parts. In this work, we present a computational approach for the design of components which are interchangeable, but also form objects with a coherent appearance which conceals their composition from parts. These components allow a physical realization of Assembly Based Modelling, a popular virtual modelling paradigm in which new models are constructed from the parts of existing ones. Given a collection of 3D models and a segmentation that specifies the component connectivity, our approach generates the components by jointly deforming and partitioning the models. We determine the component boundaries by evolving a set of closed contours on the input models to maximize the contours' geometric similarity. Next, we efficiently deform the input models to enforce both C0 and C1 continuity between components while minimizing deviation from their original appearance. The user can guide our deformation scheme to preserve desired features. We demonstrate our approach on several challenging examples, showing that our components can be physically reconfigured to assemble a large variety of coherent shapes.


title: Design and comparison of various coupled line Tx-filters for a Ku-band block upconverter

abstract: In this contribution common coupled line filter topologies are developed for their application as Tx-filter in a Ku-band block upconverter (BUC) from 13.75 – 14.5 GHz. Tough requirements of Rx-, LO- and second harmonic suppression were achieved by a detailed analysis of the metallic losses as well as equalizing even- and odd-mode velocities of the coupled line section. Four different filters were designed on an alumina substrate resulting in high measured performance values and small size compared to other work. By using a hairpin topology a designed filter achieves a low insertion loss (IL) less than 1.5 dB, a return loss (RL) of better than 20 dB, for a 3 dB fractional bandwidth (FBW) of 14.7% while maintaining a second harmonic suppression of more than 20 dB.


title: Design and dynamic modelling of a coaxial-rotor system

abstract: Coaxial-rotor configuration with the compact structure and vertical take-off ability make itself a good configuration to execute highly difficult task. In this paper, the structural design of coaxial-rotor system is introduced, and we discuss the control principle behind coaxial configuration. Then, the dynamic modeling is presented based on the combination of considering two rotors as dynamics model and the fuselage as the rigid body. Finally, the simulation is conducted to demonstrate the effectiveness of the modeling.


title: Hybrid model predictive flight mode conversion control of unmanned Quad-TiltRotors

abstract: In this paper the autonomous flight mode conversion control scheme for a Quad-TiltRotor Unmanned Aerial Vehicle is presented. This convertible UAV type has the capability for flying both as a helicopter as well as a fixed-wing aircraft type, by adjusting the orientation of its tilt-enabled rotors. Thus, a platform combining the operational advantages of two commonly distinct aircraft types is formed. However, its autonomous mid-flight conversion is an issue of increased complexity. The approach presented is based on an innovative control scheme, developed based on hybrid systems theory. Particularly, a piecewise affine modeling approximation of the complete nonlinear dynamics is derived and serves as the model for control over which a hybrid predictive controller that provides global stabilization, optimality and constraints satisfaction is computed. The effectiveness of the proposed control scheme in handling the mode conversion from helicopter to fixed-wing (and conversely) is demonstrated via a series of simulation studies. The proposed control scheme exceeds the functionality of the aforementioned flight-mode conversion and is also able to handle the transition to intermediate flight-modes with rotors slightly tilted forward in order to provide a forward force component while flying in close to helicopter-mode.


title: Predicting host tropism of influenza A virus proteins using random forest

abstract: Background ::: Majority of influenza A viruses reside and circulate among animal populations, seldom infecting humans due to host range restriction. Yet when some avian strains do acquire the ability to overcome species barrier, they might become adapted to humans, replicating efficiently and causing diseases, leading to potential pandemic. With the huge influenza A virus reservoir in wild birds, it is a cause for concern when a new influenza strain emerges with the ability to cross host species barrier, as shown in light of the recent H7N9 outbreak in China. Several influenza proteins have been shown to be major determinants in host tropism. Further understanding and determining host tropism would be important in identifying zoonotic influenza virus strains capable of crossing species barrier and infecting humans.


title: Spatial HOG based TV logo detection

abstract: Most existing TV logo detection methods use temporal correlation of multiple consecutive frames. However, these methods cannot meet the need of real-time system. This paper considers a robust detection method based on single frame. Our TV logo detecting method takes the spatial information of all matching patches as a whole, and presents a new descriptor SHOG (Spatial HOG) to formulate the spatial distribution. It is compact but informative. The proposed geometry voting method achieves fast locating TV logo with geometry verification in the step of detection, which is also competent for the detection of semi-transparent and hollow TV logos. Experiments show that SHOG is robust to occlusion and small deviation of the key points' position and our proposed method outperforms the state-of-art local feature based template matching method for logo detection as performance and speed concerned.


title: A Comparative Study on Arabic Text Classification.

abstract: This paper focuses on Automatic Arabic classifications. Arabic language is highly inflectional and derivational language which makes text mining a complex task. In classifying Arabic text, there are many published experimental results. Since these results came from different datasets, authors and evaluation metrics, we cannot compare the performance of the experimented classifiers. In this paper, we compared six well known classifiers, which are: Maximum entropy, Naive Bayes, Decision Tree, Artificial Neural Networks, Support Vector Machine ,and k-Nearest Neighbor using the same data sets and the same experimental settings. The recall , precision and fmeasure for the classifiers are computed and compared. Then, the comparison has been done after applying feature selection on Arabic datasest.


title: Field effect transistor and method of manufacturing the same

abstract: A field effect transistor and a method for manufacturing the same are provided to reduce a parasitic resistance of a source.drain and a leakage current by controlling arbitrarily the schottky barrier in the interface of a first semiconductor region and a source.drain electrode. A channel region is formed with a first semiconductor region(12). A gate electrode(16) is formed on the first semiconductor region with interposing a gate dielectric(15) therebetween. A source.drain region(14a,14b) is formed at each side of the first semiconductor region corresponding to the gate electrode. A second semiconductor region is formed between the first semiconductor region and the source.drain region, and the impurity concentration of the second semiconductor region is higher than that of the first semiconductor region. A portion of the second semiconductor region contacting with the channel region is depleted over the entire of longitudinal direction under a condition of no applying voltage.


title: Transition Systems Reduction: Balancing between Precision and Simplicity

abstract: Transition systems are a powerful formalism, which is widely used for process model representation. A number of approaches were proposed in the process mining field to tackle the problem of constructing transition systems from event logs. Existing approaches discover transition systems that are either too large or too small. In this paper we propose an original approach to discover transition systems that perfectly fit event logs and whose size is adjustable depending on the user’s need. The proposed approach allows the ability to achieve a required balance between simple and precise models.


title: A prognosis case study for electrolytic capacitor degradation in DC-DC converters

abstract: This paper proposes a model based approach for prognosis of DC-DC power converters. We briefly review the prognosis process, and present an overview of different approaches that have been developed. We study the effects of capacitor degradation on DC-DC converter performance by developing a combination of a thermal model for ripple current effects and a physics of failure model of the thermal effects on capacitor degradation. The derived degradation model of the capacitor is reintroduced into the DC-DC converter model to study changes in the system performance using Monte Carlo methods. The simulation results observed under different conditions and experimental setups for model verification are discussed. The paper concludes with comments and future work to be done.


title: Using the theory of interpersonal behavior to explain non-work-related personal use of the Internet at work

abstract: Non-work-related personal use of the Internet within organizations has received increased attention from scholars. We increase previous understanding of this phenomenon by proposing a novel model based on the theory of interpersonal behavior (TIB). The TIB includes previous researched constructs (i.e., attitudes, social influence, and intentions) as well as emotional factors, habits, and different sources of social influence. Our results (N=238) suggest that the model well predicts the use of the Internet at work for non-work purposes. Our results shed new light on the influence of habit, affect, role, and self-concept in the use of the Internet.


title: A review of Genetic Algorithms and Parallel Genetic Algorithms on Graphics Processing Unit (GPU)

abstract: Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of the optimization tools used widely in solving problems based on natural selection and genetics. This paper is intended to cover the study of GA and parallel GA and analyses its usage in CPU and GPU. One of the popular ways to speed up the processing time was by running them as parallel. The idea of parallel GAs may refer to an algorithm that works by dividing large problem into smaller tasks. Broad literature review in this paper includes a categorization of the GA operations that involved with some theories and techniques used in GA, presented with the aid of diagrams. This review attempts to study and analyse the behaviour of GA and parallel GA categories to work in GPU depending on the type of genetic algorithm. Parallel GA for GPU covers the architecture of Compute Unified Device Architecture (CUDA).


title: Improving malware detection time by using RLE and N-gram

abstract: Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.


Ensuring Privacy When Integrating Patient-Based Datasets: New Methods and Developments in Record Linkage


Current View on Autoimmune Gastritis


title: Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks

abstract: Unordered feature sets are a nonstandard data structure that traditional neural networks are incapable of addressing in a principled manner. Providing a concatenation of features in an arbitrary order may lead to the learning of spurious patterns or biases that do not actually exist. Another complication is introduced if the number of features varies between each set. We propose convolutional deep averaging networks (CDANs) for classifying and learning representations of datasets whose instances comprise variable-size, unordered feature sets. CDANs are efficient, permutation-invariant, and capable of accepting sets of arbitrary size. We emphasize the importance of nonlinear feature embeddings for obtaining effective CDAN classifiers and illustrate their advantages in experiments versus linear embeddings and alternative permutation-invariant and -equivariant architectures. We also find that pooling via summation has a significant advantage in CDANs over average- or max-pooling


title: Effects of resistance training on endurance capacity and muscle fiber composition in young top-level cyclists

abstract: Equivocal findings exist on the effect of concurrent strength (S) and endurance (E) training on endurance performance and muscle morphology. Further, the influence of concurrent SE training on muscle fiber-type composition, vascularization and endurance capacity remains unknown in top-level endurance athletes. The present study examined the effect of 16 weeks of concurrent SE training on maximal muscle strength (MVC), contractile rate of force development (RFD), muscle fiber morphology and composition, capillarization, aerobic power (VO2max), cycling economy (CE) and long/short-term endurance capacity in young elite competitive cyclists (n=14). MVC and RFD increased 12-20% with SE (P<0.01) but not E. VO2max remained unchanged. CE improved in E to reach values seen in SE. Short-term (5-min) endurance performance increased (3-4%) after SE and E (P<0.05), whereas 45-min endurance capacity increased (8%) with SE only (P<0.05). Type IIA fiber proportions increased and type IIX proportions decreased after SE training (P<0.05) with no change in E. Muscle fiber area and capillarization remained unchanged. In conclusion, concurrent strength/endurance training in young elite competitive cyclists led to an improved 45-min time-trial endurance capacity that was accompanied by an increased proportion of type IIA muscle fibers and gains in MVC and RFD, while capillarization remained unaffected.


Design and Verification of Secure Systems


title: An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems

abstract: Keeping the dialogue state in dialogue systems is a notoriously difficult task. We introduce an ontology-based dialogue manage(OntoDM), a dialogue manager that keeps the state of the conversation, provides a basis for anaphora resolution and drives the conversation via domain ontologies. The banking and finance area promises great potential for disambiguating the context via a rich set of products and specificity of proper nouns, named entities and verbs. We used ontologies both as a knowledge base and a basis for the dialogue manager; the knowledge base component and dialogue manager components coalesce in a sense. Domain knowledge is used to track Entities of Interest, i.e. nodes (classes) of the ontology which happen to be products and services. In this way we also introduced conversation memory and attention in a sense. We finely blended linguistic methods, domain-driven keyword ranking and domain ontologies to create ways of domain-driven conversation. Proposed framework is used in our in-house German language banking and finance chatbots. General challenges of German language processing and finance-banking domain chatbot language models and lexicons are also introduced. This work is still in progress, hence no success metrics have been introduced yet.


title: Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

abstract: We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.


title: The cyberself: the self-ing project goes online, symbolic interaction in the digital age

abstract: Juxtaposing symbolic interactionist and postmodern interpretations of cyberself-ing, I bring data to bear on the tensions between these two theoretical stances. I argue that postmodernist accounts are no longer tenable; such studies were based on multi-user domains (MUDs), but generalized to cyberspace. I examine the evolving internet population, which has reached a critical mass of the American population, to demonstrate that MUD users no longer constitute the majority of users. After substantiating this shift in the user base, I elucidate evidence that corroborates the countervailing thesis of ‘socialized’ online selves. I argue that using a symbolic interactionist perspective to frame the cyberself-ing project allows us to understand the creation of the cyber ‘I,’‘me,’ and digital ‘generalized other,’ as well as the dynamics of interactional cuing online.


Recognition of Printed Amharic Documents


title: Cognitive Consistency Routing Algorithm of Capsule-network

abstract: Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions that rely on a large number of inputs. The Capsule Neural Network [1] is a novel structure of Convolutional Neural Networks (CNN) which simulates the visual processing system of the human brain. In this paper, we introduce a psychological theory which is called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the working pattern of the human brain. Our experiments show that progress had been made compared with the baseline.


title: GeeLytics: Geo-distributed edge analytics for large scale IoT systems based on dynamic topology

abstract: High data rate sensors such as video cameras, audio sensors, and motion sensors are becoming ubiquitous in the Internet of Things (IoT). In large scale IoT systems like smart cities, a large number of sensors are now widely deployed at different locations, generating a huge amount of stream data. Although the generated data provide us great potential to sense our live environments, it still remains a big challenge to efficiently extract real-time results from sensor data to make fast decisions. Existing stream processing platforms, such as Storm, Spark Streaming, and S4, are well designed to process stream data within a cluster in the Cloud, but they are not suitable for highly distributed IoT systems in which data are naturally geo-distributed and low latency analytics results are expected to be shared across users and applications. To tackle this problem, we design an edge analytics platform called GeeLytics, which can perform real-time stream processing both at the network edges and in the Cloud in a dynamic and transparent manner. In this position paper we discuss its use cases, motivation, and preliminary architecture design. As compared with the start of the art, GeeLytics is designed to support dynamic stream processing topologies by taking into account the system characteristics of heterogeneous edge/Cloud nodes and also the current system workload. This shall achieve low latency analytics results while minimizing the edge-to-Cloud bandwidth consumption. In addition, using docker application containers for packaging up deployable tasks and a distributed pub/sub mechanism for inter-task stream data routing, GeeLytics shall provide better resource isolation and system efficiency to support multi-tenancy.


title: Design consideration of recent advanced low-voltage CMOS boost converter for energy harvesting

abstract: With the emergence of nanoscale material-based energy harvesters such as thermoelectric generator and microbial fuel cells, energy-harvesting-assisted self-powered electronics systems are gaining popularity. The state-of-the-art low-voltage CMOS boost converter, a critical voltage converter circuit for low power energy harvesting sources will be reviewed in this paper. Fundamentals of the boost converter circuit startup problem are discussed and recent circuit solutions to solve this problem are compared and analyzed. Necessary design considerations and trade-offs regarding circuit topology, component and CMOS process are also addressed.


Explorer Modeling Local Coherence : An Entity-Based Approach


title: Controller design for Cuk converter using model order reduction

abstract: Cuk converter contain two inductor and two capacitor hence it is fourth order dc-dc converter. It provides output voltage both higher as well as lower than the input voltage. Design of feedback compensator for fourth order system is quite complex. In this paper, model order reduction technique is used for controller design of Cuk converter. First small signal dynamic model for Cuk converter using state space analysis (SSA) is obtained which provides fourth order transfer function. Then this fourth order transfer function is reduced to second order using Pade approximation.


title: A New Multimodal Approach for Password Strength Estimation—Part II: Experimental Evaluation

abstract: A novel multimodal method for the estimation of password strength was presented in Part I of this series of two papers. In this paper, the experimental framework used for the evaluation of the novel approach is described. The method is evaluated following a reproducible protocol, which includes a three-dimensional approach: 1) deterministic assessment; 2) statistical assessment; and 3) third parties assessment (thanks to the availability upon request of an executable application that integrates the multimodal meter). The key experiment of the protocol compares, from a probabilistic point of view, the strength distributions assigned to passwords broken with increasingly complex attacking approaches, following a common strategy in a typical password cracking session. The experimental evaluation is carried out not only for the new meter, but also for other strength estimators from the state of the art, comparing their overall performance. In addition to its consistent results, the proposed method is highly flexible and can be adjusted to specific environments or to a certain password policy. Furthermore, it can also evolve over time in order to naturally adjust to new password selection trends followed by users.


title: Forming interactivity: a tool for rapid prototyping of physical interactive products

abstract: The current practice used in the design of physical interactive products (such as handheld devices), often suffers from a divide between exploration of form and exploration of interactivity. This can be attributed, in part, to the fact that working prototypes are typically expensive, take a long time to manufacture, and require specialized skills and tools not commonly available in design studios.We have designed a prototyping tool that, we believe, can significantly reduce this divide. The tool allows designers to rapidly create functioning, interactive, physical prototypes early in the design process using a collection of wireless input components (buttons, sliders, etc.) and a sketch of form. The input components communicate with Macromedia Director to enable interactivity.We believe that this tool can improve the design practice by: a) Improving the designer's ability to explore both the form and interactivity of the product early in the design process, b) Improving the designer's ability to detect problems that emerge from the combination of the form and the interactivity, c) Improving users' ability to communicate their ideas, needs, frustrations and desires, and d) Improving the client's understanding of the proposed design, resulting in greater involvement and support for the design.


title: An exploration of crowdsourcing citation screening for systematic reviews

abstract: Systematic reviews are increasingly used to inform health care decisions, but are expensive to produce. We explore the use of crowdsourcing (distributing tasks to untrained workers via the web) to reduce the cost of screening citations. We used Amazon Mechanical Turk as our platform and 4 previously conducted systematic reviews as examples. For each citation, workers answered 4 or 5 questions that were equivalent to the eligibility criteria. We aggregated responses from multiple workers into an overall decision to include or exclude the citation using 1 of 9 algorithms and compared the performance of these algorithms to the corresponding decisions of trained experts. The most inclusive algorithm (designating a citation as relevant if any worker did) identified 95% to 99% of the citations that were ultimately included in the reviews while excluding 68% to 82% of irrelevant citations. Other algorithms increased the fraction of irrelevant articles excluded at some cost to the inclusion of relevant studies. Crowdworkers completed screening in 4 to 17 days, costing $460 to $2220, a cost reduction of up to 88% compared to trained experts. Crowdsourcing may represent a useful approach to reducing the cost of identifying literature for systematic reviews.


title: Some computer science issues in ubiquitous computing

abstract: Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Since we started this work at Xerox PARC in 1988, a number of researchers around the world have begun to work in the ubiquitous computing framework. This paper explains what is new and different about the computer science in ubiquitous computing. It starts with a brief overview of ubiquitous computing, and then elaborates through a series of examples drawn from various subdisciplines of computer science: hardware components (e.g. chips), network protocols, interaction substrates (e.g. software for screens and pens), applications, privacy, and computational methods. Ubiquitous computing offers a framework for new and exciting research across the spectrum of computer science.


title: RFSIM: A feature based image quality assessment metric using Riesz transforms

abstract: Image quality assessment (IQA) aims to provide computational models to measure the image quality in a perceptually consistent manner. In this paper, a novel feature based IQA model, namely Riesz-transform based Feature SIMilarity metric (RFSIM), is proposed based on the fact that the human vision system (HVS) perceives an image mainly according to its low-level features. The 1st-order and 2nd-order Riesz transform coefficients of the image are taken as image features, while a feature mask is defined as the edge locations of the image. The similarity index between the reference and distorted images is measured by comparing the two feature maps at key locations marked by the feature mask. Extensive experiments on the comprehensive TID2008 database indicate that the proposed RFSIM metric is more consistent with the subjective evaluation than all the other competing methods evaluated.