title: Prisoner's dilemma game model for e-commerce

abstract: Introduce an agent based model for computing trust and cooperation level.Comparison of cooperation level in different social trust environments.Address the way in which the level of trust in cooperative relations depends on the network structures.We want to see if the coexistence of the trust update rules, in association with co-evolution game, changes the overall cooperative level.Introduce a model of two interconnected networks where a strong network can promotes the cooperation in weak network. This study investigates how the user's exchanges affect people's trust in electronic commerce and empower them to interact with foreign customers. Online exchanges often occur between strangers who cannot rely on past behavior or the prospect of future interactions to establish mutual trust. Game theorists have formalized this problem as a prisoner's dilemma and predict mutual noncooperation. In this paper, we introduce a social network based model to promote the cooperation in the prisoner's dilemma game. For this study, we implemented an agent-based simulation framework, which models different types of behaviors in online auctions. Agents who represent buyers or sellers are located in the nodes of a small world graph. Each link weight between the agent and its neighbor symbolizes how much it trusts this neighbor. A link with trust inferior to some cut-link threshold will be removed from the graph. Our results show that the evolved structure of the graph induces considerable variation in the level of cooperation and the profit in the e-commerce system. This shows that the outcome of our co-evolutionary game, in terms of cooperative behavior, strongly depends on topology but also on the update rule used in the trust between users. Co-evolution of game dynamics in interconnected networks is also studied, where two networks are interconnected, and players have interactions not only with others in the same network, but also with players in the other network. We will demonstrate that the interdependence between networks promotes the cooperation in both networks. But, the degree of promotion changes as a function of interdependence degree.


title: Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly

abstract: Like all higher organisms, plants have evolved in the context of a microbial world, shaping both their evolution and their contemporary ecology. Interactions between plant roots and soil microorganisms are critical for plant fitness in natural environments. Given this co-evolution and the pivotal importance of plant-microbial interactions, it has been hypothesized, and a growing body of literature suggests, that plants may regulate the composition of their rhizosphere to promote the growth of microorganisms that improve plant fitness in a given ecosystem. Here, using a combination of comparative genomics and exometabolomics, we show that pre-programmed developmental processes in plants (Avena barbata) result in consistent patterns in the chemical composition of root exudates. This chemical succession in the rhizosphere interacts with microbial metabolite substrate preferences that are predictable from genome sequences. Specifically, we observed a preference by rhizosphere bacteria for consumption of aromatic organic acids exuded by plants (nicotinic, shikimic, salicylic, cinnamic and indole-3-acetic). The combination of these plant exudation traits and microbial substrate uptake traits interact to yield the patterns of microbial community assembly observed in the rhizosphere of an annual grass. This discovery provides a mechanistic underpinning for the process of rhizosphere microbial community assembly and provides an attractive direction for the manipulation of the rhizosphere microbiome for beneficial outcomes.


Music evolution and neuroscience ☆ 2


title: Substrate Integrated Waveguide (SIW) to Microstrip Transition at X-Band

abstract: Substrate integrated waveguide (SIW) is a new form of transmission line. It facilitates the realization of non- planar (waveguide based) circuits into planar form for easy integration with other planar (microstrip) circuits and systems. This paper describes the design of an SIW to microstrip transition. The transition is broadband covering the frequency range of 8 - 12GHz. The measured in-band insertion loss is below 0.6dB while the return loss is less than 10dB. The circuit is simulated in HFSS and results are measured on vector network analyzer (VNA).


title: EXPLORING SONIFICATION FOR AUGMENTING BRAIN SCAN DATA

abstract: Medical image data has traditionally been analyzed using visual displays and statistical analysis methods. Visual representations are limited due to the nature of the display and the number of dimensions that can be represented visually. This paper describes the use of sonification to represent medical image data of brain scans of patients with AlzheimerOs dementia. The use of sonification is described as an approach to augment traditional diagnosis methods used to diagnose AlzheimerOs dementia.


title: CMOS-based sensors

abstract: This paper provides an overview of CMOS-based sensor technology with specific attention placed on devices made through micromachining of CMOS substrates and thin films. Microstructures may be formed using either pre-CMOS, intra-CMOS and post-CMOS fabrication approaches. To illustrate and motivate monolithic integration, a handful of microsystem examples, including inertial sensors, gravimetric chemical sensors, microphones, and a bone implantable sensor will be highlighted. Design constraints and challenges for CMOS-MEMS devices will be covered


title: Recurrent neural network language modeling for code switching conversational speech

abstract: Code-switching is a very common phenomenon in multilingual communities. In this paper, we investigate language modeling for conversational Mandarin-English code-switching (CS) speech recognition. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags and trigger words. Second, we propose a structure of recurrent neural networks to predict code-switches. We extend the networks by adding POS information to the input layer and by factorizing the output layer into languages. The resulting models are applied to our task of code-switching language modeling. The final performance shows 10.8% relative improvement in perplexity on the SEAME development set which transforms into a 2% relative improvement in terms of Mixed Error Rate and a relative improvement of 16.9% in perplexity on the evaluation set which leads to a 2.7% relative improvement of MER.


title: WakeDroid: A Remote Boot Trigger for Android Devices as IoT Nodes

abstract: WakeDroid is an external power manager which can wake up "completely off-state" smartphones by remote control via a wireless link. Users just need to plug a WakeDroid USB dongle consisting of a rectifier and an antenna to the USB charging port of the smartphone. The WakeDroid USB converts input microwave signals into DC and charges up the internal capacitor. Then the energy is used to kick-start the smartphone. No hardware modification on the smartphone is required. Instead, we take advantage of the Android OS's battery charge management software that starts up automatically when connected to a charger. WakeDroid can boot smartphones within 35 m from a 0.48 W transmitter. WakeDroid drastically widens the activity area of smartphones by giving them "second life" as IoT nodes.


title: Spatial-Aware Object-Level Saliency Prediction by Learning Graphlet Hierarchies

abstract: To fill the semantic gap between the predictive power of computational saliency models and human behavior, this paper proposes to predict where people look at using spatial-aware object-level cues. While object-level saliency has been recently suggested by psychophysics experiments and shown effective with a few computational models, the spatial relationship between the objects has not yet been explored in this context. We in this work for the first time explicitly model such spatial relationship, as well as leveraging semantic information of an image to enhance object-level saliency modeling. The core computational module is a graphlet-based (i.e., graphlets are moderate-sized connected subgraphs) deep architecture, which hierarchically learns a saliency map from raw image pixels to object-level graphlets (oGLs) and further to spatial-level graphlets (sGLs). Eye tracking data are also used to leverage human experience in saliency prediction. Experimental results demonstrate that the proposed oGLs and sGLs well capture object-level and spatial-level cues relating to saliency, and the resulting saliency model performs competitively compared with the state-of-the-art.


AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding


title: Separating reflections from a single image using local features

abstract: When we take a picture through a window, the image we obtain is often a linear superposition of two images: the image of the scene beyond the window plus the image of the scene reflected by the window. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there is an infinite number of valid decompositions. We describe an algorithm that uses an extremely simple form of prior knowledge to perform the decomposition. Given a single image as input, the algorithm searches for a decomposition into two images that minimize the total amount of edges and comers. The search is performed using belief propagation on a patch representation of the image. We show that this simple prior is surprisingly powerful: our algorithm obtains "correct" separations on challenging reflection scenes using only a single image.


A Comparative Study of Agile Methods: Towards a New Model-based Method


title: High-Performance network traffic processing systems using commodity hardware

abstract: The Internet has opened new avenues for information accessing and sharing in a variety of media formats. Such popularity has resulted in an increase of the amount of resources consumed in backbone links, whose capacities have witnessed numerous upgrades to cope with the ever-increasing demand for bandwidth. Consequently, network traffic processing at today's data transmission rates is a very demanding task, which has been traditionally accomplished by means of specialized hardware tailored to specific tasks. However, such approaches lack either of flexibility or extensibility--or both. As an alternative, the research community has pointed to the utilization of commodity hardware, which may provide flexible and extensible cost-aware solutions, ergo entailing large reductions of the operational and capital expenditure investments. In this chapter, we provide a survey-like introduction to high-performance network traffic processing using commodity hardware. We present the required background to understand the different solutions proposed in the literature to achieve high-speed lossless packet capture, which are reviewed and compared.


title: A new method for design and implementation of intelligent traffic control system based on fuzzy logic using FPGA

abstract: In order to manage the traffic at the intersection, the traffic lights are often used. These lights are turned on and off at the predetermined time. Intelligent traffic control systems are designed to dynamically treat the problem of traffic and reduce traffic, pollution and transit time of vehicles at the intersection. In this paper we present a design of intelligent traffic control based on fuzzy logic. The input parameters for intelligent controller are selected with the various modes of intersections to be a true simulation of the intersection environment. In order to facilitate the hardware implementation and increasing the computational speed decision algorithm state machine in this system is written in Verilog language and has capability to implement in the Field Programmable Gate Array (FPGA). The simulation results show that the proposed traffic signal controller system (that has been) designed with fuzzy logic has better performance than other designed systems.


title: Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems

abstract: With recent adoption of wireless sensor-actuator networks (WSANs) in industrial automation, industrial wireless control systems have emerged as a frontier of cyber-physical systems. Despite their success in industrial monitoring applications, existing WSAN technologies face significant challenges in supporting control systems due to their lack of real-time performance and dynamic wireless conditions in industrial plants. This article reviews a series of recent advances in real-time WSANs for industrial control systems: 1) real-time scheduling algorithms and analyses for WSANs; 2) implementation and experimentation of industrial WSAN protocols; 3) cyber-physical codesign of wireless control systems that integrate wireless and control designs; and 4) a wireless cyber-physical simulator for codesign and evaluation of wireless control systems. This article concludes by highlighting research directions in industrial cyber-physical systems.


title: Implicit Negative Sub-Categorization and Sink Diversion for Object Detection

abstract: In this paper, we focus on improving the proposal classification stage in the object detection task and present implicit negative sub-categorization and sink diversion to lift the performance by strengthening loss function in this stage. First, based on the observation that the "background" class is generally very diverse and thus challenging to be handled as a single indiscriminative class in existing state-of-the-art methods, we propose to divide the background category into multiple implicit sub-categories to explicitly differentiate diverse patterns within it. Second, since the ground truth class inevitably has low-value probability scores for certain images, we propose to add a "sink" class and divert the probabilities of wrong classes to this class when necessary, such that the ground truth label will still have a higher probability than other wrong classes even though it has low probability output. Additionally, we propose to use dilated convolution, which is widely used in the semantic segmentation task, for efficient and valuable context information extraction. Extensive experiments on PASCAL VOC 2007 and 2012 data sets show that our proposed methods based on faster R-CNN implementation can achieve state-of-the-art mAPs, i.e., 84.1%, 82.6%, respectively, and obtain 2.5% improvement on ILSVRC DET compared with that of ResNet.


title: A new pedestrian detection method based on combined HOG and LSS features

abstract: Abstract Pedestrian detection is a critical issue in computer vision, with several feature descriptors can be adopted. Since the ability of various kinds of feature descriptor is different in pedestrian detection and there is no basis in feature selection, we analyze the commonly used features in theory and compare them in experiments. It is desired to find a new feature with the strongest description ability from their pair-wise combinations. In experiments, INRIA database and Daimler database are adopted as the training and testing set. By theoretic analysis, we find the HOG–LSS combined feature have more comprehensive description ability. At first, Adaboost is regarded as classifier and the experimental results show that the description ability of the new combination features is improved on the basis of the single feature and HOG–LSS combined feature has the strongest description ability. For further verifying this conclusion, SVM classifier is used in the experiment. The detection performance is evaluated by miss rate, the false positives per window, and the false positives per image. The results of these indicators further prove that description ability of HOG–LSS feature is better than other combination of these features.


title: Advanced Security Testbed Framework for Wearable IoT Devices

abstract: Analyzing the security of Wearable Internet-of-Things (WIoT) devices is considered a complex task due to their heterogeneous nature. In addition, there is currently no mechanism that performs security testing for WIoT devices in different contexts. In this article, we propose an innovative security testbed framework targeted at wearable devices, where a set of security tests are conducted, and a dynamic analysis is performed by realistically simulating environmental conditions in which WIoT devices operate. The architectural design of the proposed testbed and a proof-of-concept, demonstrating a preliminary analysis and the detection of context-based attacks executed by smartwatch devices, are presented.


title: Deep learning for wireless physical layer: Opportunities and challenges

abstract: Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.


title: Online Ensemble Learning

abstract: Ensemble learning methods train combinations of base models, which may be decision trees, neural networks, or others traditionally used in supervised learning. Ensemble methods have gained popularity because many researchers have demonstrated their superior prediction performance relative to single models on a variety of problems especially when the correlations of the errors made by the base models are low (e.g., (Freund & Schapire 1996; Tumer & Oza 1999)). However, these learning methods have largely operated in batch mode—that is, they repeatedly process the entire set of training examples as a whole. These methods typically require at least one pass through the data for each base model in the ensemble. We would instead prefer to learn the entire ensemble in an online fashion, i.e., using only one pass through the entire dataset. This would make ensemble methods practical when data is being generated continuously so that storing data for batch learning is impractical, or in data mining tasks where the datasets are large enough that multiple passes would require a prohibitively large training time. We have so far developed online versions of the popular bagging (Breiman 1994) and boosting (Freund & Schapire 1996) algorithms. We have shown empirically that both online algorithms converge to the same prediction performance as the batch versions and proved this convergence for online bagging (Oza 2000). However, significant empirical and theoretical work remains to be done. There are several traditional ensemble learning issues that remain in our online ensemble learning framework such as the number and types of base models to use, the combining method to use, and how to maintain diversity among the base models. When learning large datasets, we may hope to avoid using all of the training examples and/or input features. We have developed input decimation (Tumer & Oza 1999), a technique that uses different subsets of the input features in different base models. We have shown that this method performs better than combinations of base models that use all the input features because of two characteristics of our base models: they overfit less by using only a small number of highly-relevant input features, and they have lower correlations in their errors because they use different input feature subsets. However, our method of selecting input features


title: A Reciprocal Recommender System for Graduates' Recruitment

abstract: A reciprocal recommender system for graduates' recruitment (RRSGR) is presented in this paper. RRSGR makes full use of the historical information in university about graduates and former graduates. Both collaborative filtering and content-based filtering are incorporated in RRSGR, which help to achieve a win-win situation between graduates and employers. Probabilistic neighborhood selection and priority k-medoids clustering are adopted to improve the diversity of recommendation results. A preliminary evaluation has been conducted based on the real data of Shandong Normal University. Experiments and user survey show that RRSGR is effective to improve the accuracy and diversity of recruitment recommendation.


title: The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

abstract: The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multi-microphone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.


title: Millimeter-wave system-on-package complex waveguide to microstrip line transition integrated with antenna array

abstract: A complex waveguide to microstrip line transition is proposed for system-on-package (SOP) on low temperature cofired ceramic (LTCC). Transition is designed to operate around 60 GHz and is used to feed the 16 elements microstrip antenna array. Transition includes waveguide to stripline transition, stripline to embedded microstrip line transition, and finally embedded microstrip line to microstrip line transition. Return loss characteristics for single transitions are presented. For the assembled complex transition 10-dB return loss bandwidth is from 52 GHz up to 75 GHz. System with antenna array and feed line has gain more then 17 dB. Analysis has been performed using full-wave simulation software.


title: Multi-source Multi-scale Counting in Extremely Dense Crowd Images

abstract: We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.


title: Content-based Neighbor Models for Cold Start in Recommender Systems

abstract: Cold start remains a prominent problem in recommender systems. While rich content information is often available for both users and items few existing models can fully exploit it for personalization. Slow progress in this area can be partially attributed to the lack of publicly available benchmarks to validate and compare models. This year's ACM Recommender Systems Challenge' 17 aimed to address this gap by providing a standardized framework to benchmark cold start models. The challenge organizer XING released a large scaled data collection of user-job interactions from their career oriented social network. Unlike other competitions, here the participating teams were evaluated in two phases -- offline and online. Models were first evaluated on the held-out offline test set. Top models were then A/B tested in the online phase where new target users and items were released daily and recommendations were pushed into XING's live production system. In this paper we present our approach to this challenge, we used a combination of content and neighbor-based models winning both offline and online phases. Our model produced the most consistent online performance wining four of the five online weeks, and showed excellent generalization in the live A/B setting.


title: Free-space transmission method for the characterization of dielectric and magnetic materials at microwave frequencies

abstract: In this work we will present new method, retrieved results and validation for complex and frequency dependent permittivity and permeability parameter extraction of two composite, homogeneous and isotropic magnetically loaded microwave absorbers (CR Eccosorb). Permittivity and permeability are extracted from free space transmission measurements for frequencies up to 140GHz. For the results validation, reflection measurements (samples with and without metal backing) are performed and are compared with simulations that use extracted models. The same method is applied in complex and frequency dependent permittivitymodel extractionof commercially available epoxies StycastW19andStycast 2850FT.


title: General Video Game AI: Learning from screen capture

abstract: General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm.


title: Rational approximations to rational models: Alternative algorithms for category learning

abstract: Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.


Critical Success Factors for a Smart City Strategy


title: Solving differential equations with Fourier series and Evolution Strategies

abstract: A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.


title: A New SiC Trench MOSFET Structure With Protruded p-Base for Low Oxide Field and Enhanced Switching Performance

abstract: The high OFF-state oxide field in the SiC trench MOSFET is a threat for its long term reliability, and thus hinders the wide acceptance of the SiC trench MOSFETs. In this paper, an SiC trench MOSFET with protruded p-bases (PB-MOS) is proposed, which features protruded p-bases to shield the gate oxide at the trench bottom against the high OFF-state drain voltage. Numerical device simulations based on Sentaurus TCAD verify the benefits of the structure. The OFF-state oxide field ( ${ {E}}_{\text {ox-m}}$ ) in the PB-MOS is 1.7 MV/cm, which is dramatically lower compared to the high ${ {E}}_{\text {ox-m}}$ of 8.6 MV/cm in the conventional trench MOSFET (C-MOS). The above benefit is achieved without sacrificing device performances. The reverse transfer capacitance ( ${ {C}}_{\text{rss}}$ ) of the PB-MOS is around ten times lower than that in the C-MOS. Both the gate charge ( ${ {Q}}_{\text{G}}$ ) and the gate-to-drain charge ( ${ {Q}}_{\text{GD}}$ ) of the PB-MOS are significantly improved compared to the C-MOS. A low specific ON-resistance ( ${ {R}}_{\text{ON}}$ ) is maintained in the PB-MOS by using additional JFET doping to compensate the JFET effect. As a result, the PB-MOS presents much better figures of merit ${ {Q}}_{\text{G}} \cdot { {R}}_{\text{ON}}$ and ${ {Q}}_{\text{GD}} \cdot { {R}}_{\text{ON}}$ than those of the C-MOS. The PB-MOS achieves a much faster switching speed than the C-MOS, and consequently exhibits an appreciable reduction in the switching energy loss.


title: Not just an ego-trip: Exploring backers' motivation for funding in incentive-based crowdfunding

abstract: Incentive-based forms of crowdfunding – such as reward-, equity- and lending-based crowdfunding – are becoming increasingly popular. However, research that studies backers’ motivations for funding in these environments is still in an embryonic state, revealing an inconsistent and narrow picture. The few existing studies are largely guided by the idea that backers are mainly egoistically motivated and do not have prosocial motives. We developed a research model that describes backers’ motivation and conducted an empirical study to examine this model. Results indicate that backers indeed have several self-interest motivations for funding: prospect of a reward; expectation of recognition from others; to lobby a certain project in the hopes of its fruition; and to develop their image. However, some backers are also prosocially motivated in that they develop feelings of liking for a certain venture and/or project team. Furthermore, we found evidence that herding has a significant moderating effect on backers’ reward motivation. Strategic IS researchers as well as crowdfunding practitioners can draw on our findings to systematically design, implement, and evaluate potential incentive systems that respond to reward-, recognition-, lobbying-, image- and liking-motives and thereby attract the crowd more effectively to invest in ventures presented on incentive-based crowdfunding systems.


title: Reining in the web with content security policy

abstract: The last three years have seen a dramatic increase in both awareness and exploitation of Web Application Vulnerabilities. 2008 and 2009 saw dozens of high-profile attacks against websites using Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) for the purposes of information stealing, website defacement, malware planting, clickjacking, etc. While an ideal solution may be to develop web applications free from any exploitable vulnerabilities, real world security is usually provided in layers. We present content restrictions, and a content restrictions enforcement scheme called Content Security Policy (CSP), which intends to be one such layer. Content restrictions allow site designers or server administrators to specify how content interacts on their web sites-a security mechanism desperately needed by the untamed Web. These content restrictions rules are activated and enforced by supporting web browsers when a policy is provided for a site via HTTP, and we show how a system such as CSP can be effective to lock down sites and provide an early alert system for vulnerabilities on a web site. Our scheme is also easily deployed, which is made evident by our prototype implementation in Firefox and on the Mozilla Add-Ons web site.


title: Electroactive polymers as artificial muscles - capabilities, potentials and challenges

abstract: For many years, electroactive polymers (EAP) received relatively little attention due to the small number of available materials and their limited actuation capability. The recent emergence of EAP materials with large displacement response changed the paradigm of these materials and their potential capability. The main attractive characteristic of EAP is their operational similarity to biological muscles, particularly their resilience and ability to induce large actuation strains. Unique robotic components and miniature devices are being explored, where EAP serve as actuators to enable new capabilities. In recognition of the need for international cooperation among the developers, users and potential sponsors, an SPIE Conference was organized for the first time on March 1-2, 1999, in Newport Beach, California. The conference was the largest ever on EAP, and it marked an important milestone, turning the spotlight onto these emerging materials and their potential. Following this success, an MRS conference was initiated to address the fundamental issues related to the material science of EAP. The WW-EAP newsletter was initiated to br ing the worldwide EAP community even closer. A homepage was also created to link worldwide EAP research and development facilities websites. In this paper, the current capabilities and potentials as well as the challenges of state -of-the art EAP will be


title: Binding mechanism of PicoGreen to DNA characterized by magnetic tweezers and fluorescence spectroscopy

abstract: Fluorescent dyes are broadly used in many biotechnological applications to detect and visualize DNA molecules. However, their binding to DNA alters the structural and nanomechanical properties of DNA and, thus, interferes with associated biological processes. In this work we employed magnetic tweezers and fluorescence spectroscopy to investigate the binding of PicoGreen to DNA at room temperature in a concentration-dependent manner. PicoGreen is an ultrasensitive quinolinium nucleic acid stain exhibiting hardly any background signal from unbound dye molecules. By means of stretching and overwinding single, torsionally constrained, nick-free double-stranded DNA molecules, we acquired force-extension and supercoiling curves which allow quantifying DNA contour length, persistence length and other thermodynamical binding parameters, respectively. The results of our magnetic tweezers single-molecule binding study were well supported through analyzing the fluorescent spectra of stained DNA. On the basis of our work, we could identify a concentration-dependent bimodal binding behavior, where, apparently, PicoGreen associates to DNA as an intercalator and minor-groove binder simultaneously.


Brief communication: nevus unis lateris and Klippel-Trenaunay-Weber syndrome with the Sturge Weber anomalady in a consanguineous Puerto Rican family.


title: SD-Squared: On the Association Between Semantic Dementia

abstract: Within the connectionist triangle model of reading aloud, interaction between semantic and phonological representations occurs for all words but is particularly important for correct pronunciation of lower frequency exception words. This framework therefore predicts that (a) semantic dementia, which compromises semantic knowledge, should be accompanied by surface dyslexia, a frequency-modulated deficit in exception word reading, and (b) there should be a significant relationship between the severity of semantic degradation and the severity of surface dyslexia. The authors evaluated these claims with reference to 100 observations of reading data from 51 cases of semantic dementia. Surface dyslexia was rampant, and a simple composite semantic measure accounted for half of the variance in low-frequency exception word reading. Although in 3 cases initial testing revealed a moderate semantic impairment but normal exception word reading, all of these became surface dyslexic as their semantic knowledge deteriorated further. The connectionist account attributes such cases to premorbid individual variation in semantic reliance for accurate exception word reading. These results provide a striking demonstration of the association between semantic dementia and surface dyslexia, a phenomenon that the authors have dubbed SD-squared.


title: Simplified active power and reactive power control with MPPT for single-phase grid-connected photovoltaic inverters

abstract: This paper presents the simplified active power and reactive power control with the maximum power point tracking (MPPT) for single-phase grid-connected photovoltaic (PV) inverters. With the proposed control strategy, the perturbation and observation method is used for the simple MPPT algorithm and the current command on the stationary reference frame is regulated by the PI current controller. In order to guarantee the all maximum power is transferred to the grid, the d-axis current or active power command is calculated from the PV power. Validity of the proposed grid-connected inverters is confirmed by simulation.


title: Accurate 3D reconstruction of dynamic scenes with Fourier transform assisted phase shifting

abstract: Phase shifting is a widely used method for accurate and dense 3D reconstruction. However, at least three images of the same scene are required for each reconstruction, so measurement errors are inevitable in dynamic scenes, even with high-speed hardware. In this paper, we propose a Fourier transform assisted phase shifting method to overcome the motion vulnerability in phase shifting. A new model with motion-related phase shifts is formulated, and the coarse phase measurements obtained by Fourier transform profilemetry are used to estimate the unknown phase shifts. The phase errors caused by motion are greatly reduced in this way. Experimental results show that the proposed method can obtain accurate and dense 3D reconstruction of dynamic scenes, with regard to different kinds of motion.


title: Factors Influencing Vitamin D Status

abstract: The steroid hormone vitamin D is required for normal calcium and phosphorus metabolism and is thus an important contributor to musculoskeletal health. Recent data have linked low vitamin D levels to a wide range of diseases, including cancer, cardiovascular disease, autoimmune disease and infection. Adequate levels of vitamin D are maintained through its cutaneous photosynthesis and oral ingestion. By some estimates, one billion people worldwide have vitamin D deficiency or insufficiency. A number of factors influence the photosynthesis and bioavailability of vitamin D and contribute to risk of impaired vitamin D status. These factors include variation in sun exposure due to latitude, season, time of day, atmospheric components, clothing, sunscreen use and skin pigmentation, as well as age, obesity and the incidence of several chronic illnesses. This review will focus on factors that influence vitamin D status and contribute to the prevalence of low vitamin D levels.


title: Seamless Nudity Censorship: an Image-to-Image Translation Approach based on Adversarial Training

abstract: The easy access and widespread of the Internet makes it easier than ever to reach content of any kind at any moment, and while that poses several advantages, there is also the fact that sensitive audiences may be inadvertently exposed to nudity content they did not ask for. Virtually every work on nudity and pornography censorship focus solely on performing binary classification, where the result is used to decide whether to completely ignore the accessed content or not. Such an approach may compromise user experience because the entire content, either images or frames of a video, has to be removed/blocked. In this paper, we propose a paradigmatic shift in the literature of adult censorship: instead of detecting and excluding the identified content, we propose to automatically filter out only the sensitive regions of an image. For that, we have developed an image-to-image translation approach based on adversarial training that implicitly locates sensitive regions in images and covers them whilst preserving its semantics, i.e., putting appropriate clothing. We test this novel approach on images of nude women, in which we are capable of automatically generating bikinis that cover the sensitive parts without the additional effort of previously annotating body parts. Our results are visually impressive, proving that it is possible to perform seamless nudity censorship with small effort of data collection and annotation.


title: Plant Classification Using Artificial Neural Networks

abstract: Automatic plant species identification is a difficulty challenge and an interesting area of research for both botanical taxonomy and computer science. From the past few years, some attempts towards the development of automatic plant recognition systems have been proposed, but the performance of such systems is not satisfactory in terms of accuracy, and these systems are also task dependent, since they are strongly influenced by the set of characteristics extracted from plant samples, leading to the problem known as data set bias. In this work, we use a Multi-Layer Perceptron (MLP) artificial neural network trained with Backpropagation algorithm to perform automatic plant classification. To avoid data set bias problem, some plant data sets which use different plant features obtained by different feature extraction processes are employed. We compare MLP algorithm with several supervised learning methods from plant recognition literature using a statistical hypothesis test of type Friedman/Nemenyi test. The obtained results show the potential of MLP algorithm to deal with plant classification in a unbiased fashion.


title: A structured hazard analysis and risk assessment method for automotive systems - A descriptive study

abstract: Abstract The 2011 release of the first version of the ISO 26262 standard for automotive systems demand the elicitation of safety goals following a rigorous method for hazard and risk analysis. Companies are struggling with the adoption of the standard due to ambiguities, documentation demands and the alignment of the standards demands to existing processes. We previously proposed a structured engineering method to deal with these problems developed in applying action research together with an OEM. In this work, we evaluate how applicable the method is for junior automotive software engineers by a descriptive study. We provided the method to 8 members of the master course Automotive Software Engineering (ASE) at the Technical University Munich. The participants have each been working in the automotive industry for 1–4 years in parallel to their studies. We investigated their application of our method to an electronic steering column lock system. The participants applied our method in a first round alone and afterwards discussed their results in groups. Our data analysis revealed that the participants could apply the method successfully and the hazard analysis and risk assessment achieved a high precision and productivity. Moreover, the precision could be improved significantly during group discussions.


title: Stock time series pattern matching: Template-based vs. rule-based approaches

abstract: One of the major duties of financial analysts is technical analysis. It is necessary to locate the technical patterns in the stock price movement charts to analyze the market behavior. Indeed, there are two main problems: how to define those preferred patterns (technical patterns) for query and how to match the defined pattern templates in different resolutions. As we can see, defining the similarity between time series (or time series subsequences) is of fundamental importance. By identifying the perceptually important points (PIPs) directly from the time domain, time series and templates of different lengths can be compared. Three ways of distance measure, including Euclidean distance (PIP-ED), perpendicular distance (PIP-PD) and vertical distance (PIP-VD), for PIP identification are compared in this paper. After the PIP identification process, both template- and rule-based pattern-matching approaches are introduced. The proposed methods are distinctive in their intuitiveness, making them particularly user friendly to ordinary data analysts like stock market investors. As demonstrated by the experiments, the template- and the rule-based time series matching and subsequence searching approaches provide different directions to achieve the goal of pattern identification.


title: A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection

abstract: With introduction of online transaction the fraudulent activities through World Wide Web have increased rapidly. It's not only affecting common people but also making them lose huge amount of money. Online transaction basically takes place between merchant and customer, and in this case neither customer nor the card needs to be present at the time of transaction so merchant does not know that whether the customer in the other end is an authorized person or fraudster, so it may lead to an unusual transaction. This kind of online transaction can be easily done using stolen credit card information of a cardholder. To detect status of the current transaction it is imperative to analyze all the previous transactions made by a genuine card holder to know the kind of pattern he/she uses. Based on these patterns new transaction can be categorized as either fraud or legal. There are few data mining techniques which help us to detect a certain pattern on complex and large data sets. In this paper it is proposed to compare Decision Tree, Multi-Layer Perceptron (MLP) and Chebyshev functional link artificial neural network (CFLANN) in terms of their classification accuracy and elapsed time for credit card fraud detection.


title: High-Accurate and Fast Power Model Based on Channel Dependency for Mobile AMOLED Displays

abstract: Recently, active matrix organic light-emitting diode (AMOLED) displays have widely been used for user-interface applications on smartphones. Since frequent interactions between a user and a display which come from such applications tend to incur big display power consumption, the display power efficiency should be managed exactly and fast. For this purpose, a high-accurate and fast power model for AMOLED displays is greatly required for efficient power management. In this paper, we propose a novel high-accurate and fast power model for AMOLED displays, which is developed using a multiple regression technique to account for the dependency between R, G, and B channels and is polished using various performance optimization techniques. Then, the proposed model is applied to five AMOLED display panels of a CHIMEI panel and Galaxy S1, 3, 5, and 7 panels from SAMSUMG. The average error rates are obtained on these panels for four well-known image datasets. The proposed model shows the lowest error rate of 1.29% on Galaxy S7, while the simple model and Park’s model show the error rates of 6.91% and 6.57%, respectively. In addition, in order to guarantee the fast execution time of the proposed power model on smartphones, we implement performance optimizations to the model. For runtime application, the proposed power model is implemented at the operating system level of the Android smartphone equipped with Exynos 4412. The optimized power model shows an average execution time of approximately 7.77 milli-seconds per image displayed on the panel with 720x1280 resolution. Consequently, the proposed power model is shown to be very fast and accurate, and thus it is highly useful in smartphones with AMOLED displays.


title: Solidity Parsing Using SmaCC: Challenges and Irregularities

abstract: Solidity is a language used to implement smart contracts on a blockchain platform. Since its initial conception in 2014, Solidity has evolved into one of the major languages for the Ethereum platform as well as other blockchain technologies. Due to its popularity, there are many tools specifically designed to handle smart contracts written in Solidity. However, there is a lack of tools for Pharo to handle Solidity contracts. Therefore, we implemented a parser using SmaCC to serve as a base for further developing Solidity support in Pharo. In this paper, we describe the parser creation, the irregularities we found in the Solidity grammar specification, and common practices on how to adapt the grammar to an LR type parser. Our experiences with parsing the Solidity language using SmaCC may help other developers trying to convert similar grammars.


title: Visualization of boundaries in volumetric data sets using LH histograms

abstract: A crucial step in volume rendering is the design of transfer functions that highlights those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.


title: Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing

abstract: We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. We study the behavior of networks trained on real data when performing inference on synthetic data: a key factor in determining the equivalence of simulation environments. We also compare the behavior of networks trained on synthetic data and evaluated on real-world data. Additionally, by analyzing pre-trained, existing segmentation and detection models, we illustrate how uncorrelated images along with a detailed set of annotations open up new avenues for analysis of computer vision systems, providing fine-grain information about how a model's performance changes according to factors such as distance, occlusion and relative object orientation.


title: Extracting Chatbot Knowledge from Online Discussion Forums

abstract: This paper presents a novel approach for extracting high-quality 〈thread-title, reply〉 pairs as chat knowledge from online discussion forums so as to efficiently support the construction of a chatbot for a certain domain. Given a forum, the high-quality 〈thread-title, reply〉 pairs are extracted using a cascaded framework. First, the replies logically relevant to the thread title of the root message are extracted with an SVM classifier from all the replies, based on correlations such as structure and content. Then, the extracted 〈thread-title, reply〉 pairs are ranked with a ranking SVM based on their content qualities. Finally, the Top-N 〈thread-title, reply〉 pairs are selected as chatbot knowledge. Results from experiments conducted within a movie forum show the proposed approach is effective.


title: Human Behavior Modeling with Maximum Entropy Inverse Optimal Control

abstract: In our research, we view human behavior as a structured sequence of context-sensitive decisions. We develop a conditional probabilistic model for predicting human decisions given the contextual situation. Our approach employs the principle of maximum entropy within the Markov Decision Process framework. Modeling human behavior is reduced to recovering a context-sensitive utility function that explains demonstrated behavior within the probabilistic model. In this work, we review the development of our probabilistic model (Ziebart et al. 2008a) and the results of its application to modeling the context-sensitive route preferences of drivers (Ziebart et al. 2008b). We additionally expand the approach’s applicability to domains with stochastic dynamics, present preliminary experiments on modeling time-usage, and discuss remaining challenges for applying our approach to other human behavior modeling problems.


title: Scalability and sparsity issues in recommender datasets: a survey

abstract: Recommender systems have been widely used in various domains including movies, news, music with an aim to provide the most relevant proposals to users from a variety of available options. Recommender systems are designed using techniques from many fields, some of which are: machine learning, information retrieval, data mining, linear algebra and artificial intelligence. Though in-memory nearest-neighbor computation is a typical approach for collaborative filtering due to its high recommendation accuracy; its performance on scalability is still poor given a huge user and item base and availability of only few ratings (i.e., data sparsity) in archetypal merchandising applications. In order to alleviate scalability and sparsity issues in recommender systems, several model-based approaches were proposed in the past. However, if research in recommender system is to achieve its potential, there is a need to understand the prominent techniques used directly to build recommender systems or for preprocessing recommender datasets, along with its strengths and weaknesses. In this work, we present an overview of some of the prominent traditional as well as advanced techniques that can effectively handle data dimensionality and data sparsity. The focus of this survey is to present an overview of the applicability of some advanced techniques, particularly clustering, biclustering, matrix factorization, graph-theoretic, and fuzzy techniques in recommender systems. In addition, it highlights the applicability and recent research works done using each technique.


title: Modified Wilkinson Power Dividers for Millimeter-Wave Integrated Circuits

abstract: A modification of the Wilkinson power divider is presented that eases planar implementation while maintaining performance. By adding transmission lines between the resistor and the quarter-wave transformers of the traditional design, a range of valid solutions exists that meet the conditions of being reciprocal, isolated between the output ports, and matched at all ports. The proposed design is particularly useful at millimeter-wave frequencies where reduced physical dimensions make a circuit configuration suitable for low-cost package-level implementation difficult using traditional methods. Two frequency bands are demonstrated. At V-band, the circuit gives 0.3-dB excess insertion loss, 19-dB isolation, and 50% bandwidth. At the W-band, the circuit gives 0.75-dB excess insertion loss, 24-dB isolation, and 39% bandwidth.


title: The Socio-Technical Dynamics of Low-Carbon Transitions

abstract: Summary Effective mitigation of climate change will require far-reaching transformations of electricity, heat, agricultural, transport, and other systems. The energy studies and modeling research that so often dominate academic and policy debates provide valuable insights into these transitions, but remain constrained by their focus on rational decision-making and their neglect of non-linear dynamics and broader social processes. This review describes insights from a complementary socio-technical approach that addresses the interdependent social, political, cultural, and technical processes of transitions. Focusing on the "multi-level perspective", the paper conceptualizes transitions as arising from the alignment of processes within and between three analytical levels: niche innovations, socio-technical regimes, and the socio-technical landscape. This analytical framework is illustrated with a case study of the German electricity transition and is used to appraise low-carbon transitions in several other sectors. We end by articulating four lessons for managing low-carbon transitions.


title: A Framework for Robust Subspace Learning

abstract: Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multi-linear models. These models have been widely used for the representation of shape, appearance, motion, etc., in computer vision applications. Methods for learning linear models can be seen as a special case of subspace fitting. One draw-back of previous learning methods is that they are based on least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. We review previous approaches for making linear learning methods robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Subspace Learning (RSL) for linear models within a continuous optimization framework based on robust M-estimation. The framework applies to a variety of linear learning problems in computer vision including eigen-analysis and structure from motion. Several synthetic and natural examples are used to develop and illustrate the theory and applications of robust subspace learning in computer vision.


title: Posttraumatic stress disorder in the US veteran population: results from the National Health and Resilience in Veterans Study.

abstract: OBJECTIVE ::: To describe the prevalence of posttraumatic stress disorder (PTSD) and comorbid psychiatric disorders and identify correlates of PTSD in a contemporary, nationally representative sample of US veterans. ::: ::: ::: METHOD ::: Data were analyzed from Wave 1 of the National Health and Resilience in Veterans Study, a cross-sectional, retrospective, web-based survey of a population-based sample of 3,157 US veterans conducted between October and December 2011. The main outcome measure was probable lifetime PTSD, which was assessed by using a DSM-IV version of the PTSD Checklist (PCL), the PCL-Specific Stressor version. ::: ::: ::: RESULTS ::: The weighted lifetime and current prevalence of probable PTSD was 8.0% (standard error [SE] = 0.48) and 4.8% (SE = 0.40), respectively. 87.0% of veterans reported exposure to at least 1 potentially traumatic event (PTE); veterans reported a mean of 3.4 (SD = 2.8) different PTE types in their lifetime. Sudden death of a loved one was the most frequently endorsed PTE (61.3%), and sexual abuse in adulthood had the highest conditional probability of PTSD (37.3%). PTSD was associated with increased odds of mood, anxiety, and substance use disorders (odds ratios [ORs] = 2.2-19.1, P values < .001); suicidal ideation (OR = 9.7, P < .001); and suicide attempts (OR = 11.8, P < .001). Psychosocial factors, including resilience, community integration, and secure attachment, were associated with decreased odds of PTSD (ORs = 0.5-0.7, P values < .05). ::: ::: ::: CONCLUSIONS ::: In a nationally representative sample of US veterans, the prevalence of lifetime and current PTSD was 8.0% and 4.8%, respectively, and PTSD was associated with elevated risk for several psychiatric conditions and suicidality. Veterans reported exposure to many PTE types in addition to combat, and conditional risk for PTSD was high for noncombat-related trauma. Prevention and treatment efforts designed to bolster protective psychosocial factors may help mitigate PTSD risk in this population.


title: Analyzing user behavior of the micro-blogging website Sinaweibo during hot social events

abstract: The spread and resonance of users’ opinions on Sina Weibo, the most popular micro-blogging website in China, are tremendously influential, having significantly affected the processes of many real-world hot social events. We select 21 hot events that were widely discussed on Sina Weibo in 2011, and do some statistical analyses. Our main findings are that (i) male users are more likely to be involved, (ii) messages that contain pictures and those posted by verified users are more likely to be reposted, while those with URLs are less likely, (iii) the gender factor, for most events, presents no significant difference in reposting likelihood.


title: Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams

abstract: The multigram model assumes that language can be described as the output of a memoryless source that emits variable-length sequences of words. The estimation of the model parameters can be formulated as a maximum likelihood estimation problem from incomplete data. We show that estimates of the model parameters can be computed through an iterative expectation-maximization algorithm and we describe a forward-backward procedure for its implementation. We report the results of a systematical evaluation of multigrams for language modeling on the ATIS database. The objective performance measure is the test set perplexity. Our results show that multigrams outperform conventional n-grams for this task.


title: Performance optimization for doubly-fed wind power generation systems

abstract: Significant variation of the resource kinetic energy, in the form of wind speed, results in substantially reduced energy capture in a fixed speed wind turbine. In order to increase the wind energy capture in the turbine, variable speed generation (VSG) strategies have been proposed and implemented. However, that requires an expensive AC/AC power converter which increases the capital investment significantly. Consequently doubly-fed systems have been proposed to reduce the size of the power converter and thereby the associated cost. Additionally, in doubly-fed systems, at a fixed operating point (power and speed), power flow can be regulated between the two winding systems on the machine. This feature can be utilized to essentially minimize losses in the machine associated with the given operating point or achieve other desired performance enhancements. In this paper, a brushless doubly-fed machine (BDFM) is utilized to develop a VSG wind power generator. The VSG controller employs a wind speed estimation based maximum power point tracker (MPPT) and a heuristic model based maximum efficiency point tracker (MEPT) to optimize the power output of the system. The controller has been verified for efficacy on a 1.5 kW laboratory VSG wind generator. The strategy is applicable to all doubly-fed configurations, including conventional wound rotor induction machines, Scherbius cascades, brushless doubly fed machines, and doubly-fed reluctance machines.


ESTIMATION QUESTION TYPE ANALYZER FOR MULTI CLOSE DOMAIN INDONESIAN QAS


title: Building Brand Community

abstract: Abstract A brand community from a customer-experiential perspective is a fabric of relationships in which the customer is situated. Crucial relationships include those between the customer and the brand, between the customer and the firm, between the customer and the product in use, and among fellow customers. The authors delve ethnographically into a brand community and test key findings through quantitative methods. Conceptually, the study reveals insights that differ from prior research in four important ways: First, it expands the definition of a brand community to entities and relationships neglected by previous research. Second, it treats vital characteristics of brand communities, such as geotemporal concentrations and the richness of social context, as dynamic rather than static phenomena. Third, it demonstrates that marketers can strengthen brand communities by facilitating shared customer experiences in ways that alter those dynamic characteristics. Fourth, it yields a new and richer conceptuali...


title: Design of an Intelligent Bi-Directional DC- DC Converter with Half Bridge Topology

abstract: 2 Abstract: This paper i ntroduces a novel Bi-directional DC-DC converter wi th art ificial neural network controller (A NN).Bidirectional power fl ow is o btained by t he same p ower c omponents and provides a simple, ef ficient, and gal vanically isolated conv erter. In t he presence of DC mains the converter operates as b uck c onverter and charges t he battery. When the DC main fails, the converter operates a s b oost converter and the battery feeds the load. In both the modes the power switches are controlled by PWM technique and the PWM pulses are generated by application of ANN controller. The proposed converter with controller is simulated using MATLAB and t he design is validated.


title: Scikit-learn: Machine Learning in Python

abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.


title: Sentiment analysis: A literature review

abstract: Sentiment analysis has aroused the interest of many researchers in recent years, since subjective texts are useful for many applications. In particular, sentiment analysis on online reviews has become a hot research field. Studies on sentiment analysis mainly focus on framework and lexicon construction, feature extraction, and polarity determination. This paper presents a survey on the latest development in sentiment analysis, and makes an in-depth introduction of its research and application in business and Blogsphere. The methods used in current research are especially emphasized and the existing problems of those studies are discussed. Finally, some possible future directions of research are pointed out.


title: An Intelligent Framework for Auto-filling Web Forms from Different Web Applications

abstract: End-users compose ad-hoc business processes by integrating web applications to conduct online tasks. Generally, end-users have to enter information into web forms of web applications, and often repetitively type the same information into applications. It could be a tedious job for end-users to fill in web forms with identical information. To save end-users from repetitive typing and increase composition productivity, it is critical to propagate and pre-fill user inputs to web applications. In this paper, we propose an intelligent auto-filling framework collecting and propagating user inputs across web applications, identifying user usage patterns and contexts. The empirical results show that our framework, on average, achieves a precision of 74.5% and a recall of 58% on pre-filling web forms, and a precision of 82.25% and a recall of 68.4% on suggesting values to end-users if the end-users edit the initial pre-filled values.


title: SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking

abstract: This paper presents SERIMI, an automatic approach for solving the interlinking problem over RDF data. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem over RDF data.


title: Occupancy map building through Bayesian exploration

abstract: We propose a novel holistic approach to safe autonomous exploration and map building based on constrained Bayesian optimization. This method finds optimal continuous paths instead of discrete sensi...


Source Code Obfuscation : A Technique for Checkmating Software Reverse Engineering


title: Understanding Text Mining: a Pragmatic Approach

abstract: In order to delineate the state of the art of the main TM applications a two-step strategy has been pursued: first of all, some of the main European and Italian companies offering TM solutions were contacted, in order to collect information on the characteristics of the applications; secondly, a detailed search on the web was made to collect further information about users or developers and applications. On the basis of the material collected, a synthetic grid was built to collocate, from more than 300 cases analysed, the 100 ones that we considered most relevant for the typology of function and sector of activity. The joint analysis of the different case studies has given an adequate picture of TM applications according to the possible types of results that can be obtained, the main specifications of the sectors of applications and the type of functions. Finally it is possible to classify the applications matching the level of customisation (followed in the tools development) and the level of integration (between users and developers). This matching produces four different situations: standardisation, outsourcing, internalisation, synergism.


Tutorial on Support Vector Machine ( SVM )


title: KVFTL: Optimization of storage space utilization for key-value-specific flash storage devices

abstract: The strong momentum of key-value store applications drives the commercialization of key-value-specific hard disk drives. To achieve higher degree of performance, the specific flash-based solid state drives would be also commercialized for key-value store applications in the foreseeable future. However, the existing fixed-sized management strategies of flash-based devices would potentially result in low storage space utilization on managing variable-sized key-value data. This problem inspires this paper to propose a key-value flash translation layer (KVFTL) design to improve the storage space utilization of the key-value-specific solid state drives (KVSSDs). A series of experiments was conducted to evaluated the proposed design, and the experimental results on space utilization and device performance are very encouraging.


title: Design and Implementation of an Intra-domain routing module for an SDN controller for Traffic Engineering in SDN environment

abstract: Software Defined Networking (SDN) provides an environment to test and use custom ideas in networking. One of the areas that needs this flexibility is routing in networking. In this study we design and implement a custom intra-domain routing approach in an SDN environment. In SDN routing can be implemented as part of a controller or as an application on top of a controller. In this study we implemented a module in Floodlight controller v1.1 with OpenFlow 1.3 support. This module interacts with another custom module that monitors active bandwidth use of inter-switch links inside a network. Using the information provided by monitoring module, routing module uses available capacity in inter-switch links to determine widest path between any given two points. We tested and evaluated the developed system to show its efficiency. Newly developed module can be used in traffic engineering with additional control options.


A Machine Learning Approach for SQL Queries Response Time Estimation in the Cloud


title: A hybrid approach for identifying sentiments around aspects

abstract: In this paper, we have discussed the development of a complete solution for identification of Voice of Customer (VOC) at the mobile domain. We have defined 27 possible aspects and 3 possible sentiments in this domain. The task is to identify the aspects a specific reviewer is talking about and the sentiments he conveys. The words (terms) and the types (tags) of aspects are identified using a statistical sequence leveling technique (based on CRF) trained on 950 manually annotated sentences and the sentiments of a sentence are identified using a statistical bag-of-word technique (based on SVM) trained on 5450 manually annotated sentences. The association of sentiments with aspects is carried out using rules. The system identifies sentiment around aspect with 70.08% accuracy. This would help the customers to get the insight of the product he or she is looking for and the company to get the real VOC.


title: Artificial-Life Ecosystems - What are they and what could they become?

abstract: This paper summarises the history of the terms ecology and ecosystem, before examining their application in the early and recent literature of A-Life agent-based software simulation. It investigates trends in A-Life that have led to a predominance of simulations incorporating artificial evolution acting on generic agents, but lacking a level of detail that would allow the emergence of phenomena relating to the transfer and transformation of energy and matter between the virtual abiotic environment and biota. Implications of these characteristics for the relevance of A-Life’s virtual ecosystem models to Ecology are discussed. We argue a position that the inclusion of low-level representations of energetics, matter and evolution, in concert with pattern-oriented modelling techniques from Ecology for model validation, will improve the relevance of A-Life models to Ecology. We also suggest two methods that may allows us to meet this goal: artificial evolution can be employed as a mechanism for automating pattern-oriented ecological modelling from the level of individual species up to that of the ecosystem, or it may be employed to explore general principles of ecosystem behaviour over evolutionary time periods.


title: PCB Embedded Bondwire Inductors With Discrete Thin-Film Magnetic Core for Power Supply in Package

abstract: This paper details the design, assembly, and detailed characterization of printed circuit board (PCB) embedded thin magnetic film inductors for Power Supply in Package applications. Solenoidal inductors were assembled on copper tracks printed on PCB with wire bonds to complete the copper loop. Furthermore, the solenoid inductors have laminated amorphous soft magnetic thin films embedded between the two conductor layers. The devices have chemically thinned Vitrovac films laminated as core material. Three different designs of solenoid inductors with different numbers of turns are assembled and characterized. The assembled PCB inductor measured a highest quality factor of 8.5 for a three-turn device at 10 MHz.


title: IoT architecture proposal for disabled people

abstract: We are living in a new communication age, which will radically transform the way we live in our society. A world where anything will be connected to Internet is being created, generating an entirely new dynamic network - The Internet of Things (IoT) - enabling new means of communication between people, things and environment.


title: Fine-tuning deep CNN models on specific MS COCO categories

abstract: Fine-tuning of a deep convolutional neural network (CNN) is often desired. This paper provides an overview of our publicly available py-faster-rcnn-ft software library that can be used to fine-tune the VGG_CNN_M_1024 model on custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset. For example, we improved the procedure so that the user does not have to look for suitable image files in the dataset by hand which can then be used in the demo program. Our implementation randomly selects images that contain at least one object of the categories on which the model is fine-tuned.


SMART LIBRARY MANAGEMENT ROBOT USING LAB VIEW


title: Consistency of the FastSLAM algorithm

abstract: This paper presents an analysis of FastSLAM - a Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping. It shows that the algorithm degenerates with time, regardless of the number of particles used or the density of landmarks within the environment, and would always produce optimistic estimates of uncertainty in the long-term. In essence, FastSLAM behaves like a non-optimal local search algorithm; in the short-term it may produce consistent uncertainty estimates but, in the long-term, it is unable to adequately explore the state-space to be a reasonable Bayesian estimator. However, the number of particles and landmarks does affect the accuracy of the estimated mean and, given sufficient particles, FastSLAM can produce good non-stochastic estimates in practice. FastSLAM also has several practical advantages, particularly with regard to data association, and would probably work well in combination with other versions of stochastic SLAM, such as EKF-based SLAM


title: Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations

abstract: Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for thousands of hours equivalent real robot time. To address this shortcoming, we present a novel approach to efficiently learning new robotic skills directly on a real robot, based on model-predictive control (MPC) and an algorithm for learning task representations. In short, we show how to reuse the simulation from the pre-training step of sim-to-real methods as a tool for foresight, allowing the sim-to-real policy adapt to unseen tasks. Rather than end-to-end learning policies for single tasks and attempting to transfer them, we first use simulation to simultaneously learn (1) a continuous parameterization (i.e. a skill embedding or latent) of task-appropriate primitive skills, and (2) a single policy for these skills which is conditioned on this representation. We then directly transfer our multi-skill policy to a real robot, and actuate the robot by choosing sequences of skill latents which actuate the policy, with each latent corresponding to a pre-learned primitive skill controller. We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot. We discuss the background and principles of our method, detail its practical implementation, and evaluate its performance by using our method to train a real Sawyer Robot to achieve motion tasks such as drawing and block pushing.


title: Dynamic IoT Device Clustering and Energy Management With Hybrid NOMA Systems

abstract: Fog computing, as a promising technique, is with huge advantages in dealing with large amounts of data and information with low latency and high security. We introduce a promising multiple access technique entitled nonorthogonal multiple access to provide communication service between the fog layer and the Internet of Things (IoT) device layer in fog computing, and propose a dynamic cooperative framework containing two stages. At the first stage, dynamic IoT device clustering is solved to reduce the system complexity and the delay for the IoT devices with better channel conditions. At the second stage, power allocation based energy management is solved using Nash bargaining solution in each cluster to ensure fairness among IoT devices. Simulation results reveal that our proposed scheme can simultaneously achieve higher spectrum efficiency and ensure fairness among IoT devices compared to other schemes.


title: Waveguide septum polarizer shaped with Legendre polynomials

abstract: This paper presents the design and performance of a waveguide septum polarizer where the longitudinal profile of the septum blade is shaped with Legendre polynomials. A performance comparison with a conventional septum polarizer based on a blade with a stepped profile is performed at Ka-band (27.5–30.0 GHz) to show the merit of the proposed profile shaping. Preliminary results indicate an increase in frequency bandwidth of about 20% combined with a length reduction of 5.5%. This compact 3-port waveguide device can be used to generate either dual-circular or dual-linear tilted polarization.


title: A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks

abstract: In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.


title: A demonstration of KOSAR: an elastic, scalable, highly available SQL middleware

abstract: This is a demonstration of KOSAR, a middleware to enhance the performance of SQL applications with workloads that exhibit a high read to write ratios. It show cases the administrative interface of KOSAR using a social networking benchmark named BG. We demonstrate KOSAR with both MySQL and Oracle by turning it on and off to show its many fold performance enhancement of these relational databases management systems. The proposed demo is available online at http://kosarsql.com and one may both examine the features described in this proposal and perform its suggested interactive actions.


title: Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

abstract: With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized MIMO model based on an additive quantization noise model considering a realistic power consumption and loss model to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders perform poorly due to their severe insertion losses. Also, the topology of the phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.


title: Subsampling-Based Approximate Monte Carlo for Discrete Distributions.

abstract: Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling also suffers from high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.


Self-Choreographed Musical Fountain System


title: Brains as naturally emerging turing machines

abstract: It has been shown that a Developmental Network (DN) can learn any Finite Automaton (FA) [29] but FA is not a general purpose automaton by itself. This theoretical paper presents that the controller of any Turing Machine (TM) is equivalent to an FA. It further models a motivation-free brain — excluding motivation e.g., emotions — as a TM inside a grounded DN — DN with the real world. Unlike a traditional TM, the TM-in-DN uses natural encoding of input and output and uses emergent internal representations. In Artificial Intelligence (AI) there are two major schools, symbolism and connectionism. The theoretical result here implies that the connectionist school is at least as powerful as the symbolic school also in terms of the general-purpose nature of TM. Furthermore, any TM simulated by the DN is grounded and uses natural encoding so that the DN autonomously learns any TM directly from natural world without a need for a human to encode its input and output. This opens the door for the DN to fully autonomously learn any TM, from a human teacher, reading a book, or real world events. The motivated version of DN [31] further enables a DN to go beyond action-supervised learning — so as to learn based on pain-avoidance, pleasure seeking, and novelty seeking [31].


title: DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns

abstract: In the recent years, the rapid spread of mobile device has create the vast amount of mobile data. However, some shallow-structure models such as support vector machine (SVM) have difficulty dealing with high dimensional data with the development of mobile network. In this paper, we analyze mobile data to predict human trajectories in order to understand human mobility pattern via a deep-structure model called "DeepSpace". To the best of out knowledge, it is the first time that the deep learning approach is applied to predicting human trajectories. Furthermore, we develop the vanilla convolutional neural network (CNN) to be an online learning system, which can deal with the continuous mobile data stream. In general, "DeepSpace" consists of two different prediction models corresponding to different scales in space (the coarse prediction model and fine prediction models). This two models constitute a hierarchical structure, which enable the whole architecture to be run in parallel. Finally, we test our model based on the data usage detail records (UDRs) from the mobile cellular network in a city of southeastern China, instead of the call detail records (CDRs) which are widely used by others as usual. The experiment results show that "DeepSpace" is promising in human trajectories prediction.


title: A series-parallel switched capacitor step-up DC-DC converter and its gate-control circuits for over the supply rail switches

abstract: An efficient control of the gate voltage of switches that operate outside the supply range is a problem that occurs in circuits such as step-up DC/DC converters and stimulation circuits for implantable devices. This paper proposes solutions to this problem, using as case study a 3x, ultra low-power, step-up DC/DC converter with series-parallel architecture. The proposed gate control strategy results in minimizing the gate swing of the switches and recycling of the gate charge in one of the cases, thus reducing the energy spent in driving the switch. The designed converter achieves an 84% simulated efficiency (without the control circuitry) @Vin = 400mV and 5μA load current and operates down to Vin = 200mV.


title: Mothers’ pupillary responses to infant facial expressions

abstract: Background ::: Human parental care relies heavily on the ability to monitor and respond to a child’s affective states. The current study examined pupil diameter as a potential physiological index of mothers’ affective response to infant facial expressions.


title: Determination of Free Electron Density in Sequentially Doped InxGa1-xAs by Raman Spectroscopy

abstract: The advent and exponential growth of mobile computing has spurred greater emphasis on the adoption of III-V compound semiconductors in device architectures. The introduction of high charge carrier densities within InxGa1-xAs and the development of metrologies to quantitate the extent of doping have thus emerged as an urgent imperative. As an amphoteric dopant, Si begins to occupy anionic sites at high concentrations, thereby limiting the maximum carrier density that can be obtained upon Si doping of III-V semiconductors. Here, we present Raman results on sequentially doped In0.53Ga0.47As wherein sulfur monolayer doping is used to introduce additional carrier density to Si-doped samples. The sequential doping of Si and S allows for high carrier concentrations of up to 1.3 × 10(19) cm(-3) to be achieved without damaging the III-V lattice. The coupling of the plasmon in the doped samples to the longitudinal optic phonons allows Raman spectroscopy to serve as an excellent probe of the extent of dopant activation, charge carrier density, and the surface depletion region. In particular, the energy position of a high-frequency coupled mode (HFCM) that is detected above 400 cm(-1) is used to extract the free electron density in these samples. The extracted free electron densities are well correlated with measured sheet resistance values and the carrier densities deduced from Hall measurements.


title: Indoor Positioning Using Visible LED Lights: A Survey

abstract: Visible light LEDs, due to their numerous advantages, are expected to become the dominant indoor lighting technology. These lights can also be switched ON/OFF at high frequency, enabling their additional use for wireless communication and indoor positioning. In this article, visible LED light--based indoor positioning systems are surveyed and classified into two broad categories based on the receiver structure. The basic principle and architecture of each design category, along with various position computation algorithms, are discussed and compared. Finally, several new research, implementation, commercialization, and standardization challenges are identified and highlighted for this relatively novel and interesting indoor localization technology.


title: Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

abstract: The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches.


title: Skipping Selected Steps of DWT Computation in Lossless JPEG 2000 for Improved Bitrates

abstract: In order to improve bitrates of lossless JPEG 2000, we propose to modify the discrete wavelet transform (DWT) by skipping selected steps of its computation. We employ a heuristic to construct the skipped steps DWT (SS-DWT) in an image-adaptive way and define fixed SS-DWT variants. For a large and diverse set of images, we find that SS-DWT significantly im-proves bitrates of non-photographic images. From a practical standpoint, the most interesting results are obtained by applying entropy estimation of coding effects for selecting among the fixed SS-DWT variants. This way we get the compression scheme that, as opposed to the general SS-DWT case, is compliant with the JPEG 2000 part 2 standard. It provides average bitrate improvement of roughly 5% for the entire test-set and roughly 14% for non-photographic images, whereas the overall compression time becomes only 3% greater than that of the unmodified JPEG 2000. At a significantly increased cost of exploiting a heuristic, selecting the steps to be skipped based on the actual bitrate instead of an estimated one, and by applying reversible denoising and lifting steps to SS-DWT, we have attained greater bitrate improvements of up to about 17.5% for non-photographic images.


title: Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

abstract: We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions.


title: Differential privacy for statistics: What we know and what we want to learn

abstract: We motivate and review the denition privacy, our principal motivating scenario was a statistical database, in which the trusted and trustworthy curator (in our minds, the Census Bureau) gathers sensitive information from a large number of respondents (the sample), with the goal of learning and releasing to the public statistical facts about the underlying population. The diculty, of course, is to release statistical information without compromising the privacy of the individual respondents. We initially only thought in terms of a noninteractive setting, in which the curator computes and publishes some statistics, and the data are not used further. Privacy concerns may aect