title: A look into Twitter hashtag discovery and generation

abstract: Twitter is a micro-blogging site that allows users and companies to post brief pieces of information called 'Tweets'. Some of the tweets contain keywords such as 'Hashtags' denoted with a '#', essentially one word summaries of either the topic or emotion of the tweet. The goal of this paper is to examine an approach to perform hashtag discovery on Twitter posts that do not contain user labeled hashtags. The process described in this paper is geared to be as automatic as possible, taking advantage of web information, sentiment analysis, geographic location, basic filtering and classification processes, to generate hashtags for tweets. Hashtags provide users and search queries a fast and simple basis to filter and find information that they are interested in.


title: Defing the VET Policy Instruments for Developing the National Innovation System of Fuel Cell Technologies

abstract: Fuel cells have emerged as one of the possible solutions for resolving the severe pollutions and petroleum consumptions by traditional vehicles. During the past decades, national governments worked aggressively toward the fuel cell innovations by national innovation systems (NISs). Albeit the universities play an important role in the NIS and the vocational education and training (VET) usually serve as an important sub-system, very few scholars tried to discuss how the VET system can be configured through the VET policy instruments so that the NIS can be enabled. Thus, the authors aim to configure the VET policy portfolio by defining a hybrid multiple criteria decision making (MCDM) framework aiming at resolving this problem. Components of the NIS as well as VET policy tools which are suitable for developing the VET system and enabling the NIS were first derived from literature review as criteria and alternatives. Then, a hybrid MADM (Multiple Attribute Decision Making) framework consisting of the modified Delphi method, the DEMATEL and the ANP will be introduced for deriving appropriate VET policy tools for developing the VET system and enable the NIS of the fuel cell technologies. Experts from Taiwanese research and academic institutes were invited for providing opinions for defining the VET policy instruments. Mandates, capacity building and system change were recognized by the experts as the most important VET policy tools for enabling the NIS of the fuel cell technologies from the aspects of firms' R&D organizations, research institutes and research association.


title: A Statistical Approach to Material Classification Using Image Patch Exemplars

abstract: In this paper, we investigate material classification from single images obtained under unknown viewpoint and illumination. It is demonstrated that materials can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3times3 pixels square) and that this can outperform classification using filter banks with large support. It is also shown that the performance of filter banks is inferior to that of image patches with equivalent neighborhoods. We develop novel texton-based representations which are suited to modeling this joint neighborhood distribution for Markov random fields. The representations are learned from training images and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. Three such representations are proposed and their performance is assessed and compared to that of filter banks. The power of the method is demonstrated by classifying 2,806 images of all 61 materials present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank-based classifiers such as Leung and Malik (IJCV 01), Cula and Dana (IJCV 04), and Varma and Zisserman (IJCV 05). We also benchmark performance by classifying all of the textures present in the UIUC, Microsoft Textile, and San Francisco outdoor data sets. We conclude with discussions on why features based on compact neighborhoods can correctly discriminate between textures with large global structure and why the performance of filter banks is not superior to that of the source image patches from which they were derived.


title: Integrating computational and creative thinking to improve learning and performance in CS1

abstract: Our research is based on an innovative approach that integrates computational thinking and creative thinking in CS1 to improve student learning performance. Referencing Epstein's Generativity Theory, we designed and deployed a suite of creative thinking exercises with linkages to concepts in computer science and computational thinking, with the premise that students can leverage their creative thinking skills to "unlock" their understanding of computational thinking. In this paper, we focus on our study on differential impacts of the exercises on different student populations. For all students there was a linear "dosage effect" where completion of each additional exercise increased retention of course content. The impacts on course grades, however, were more nuanced. CS majors had a consistent increase for each exercise, while non-majors benefited more from completing at least three exercises. It was also important for freshmen to complete all four exercises. We did find differences between women and men but cannot draw conclusions.


title: A Review of Fuzzy Cognitive Maps Research During the Last Decade

abstract: This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years.


title: Output-Capacitor-Free Adaptively Biased Low-Dropout Regulator for System-on-Chips

abstract: A high-precision low-voltage adaptively biased (AB) low-dropout regulator (LDR) with extended loop bandwidth is proposed. The multistage output-capacitor-free LDR is stabilized by Miller compensation and Q-reduction techniques to reduce the required minimum load current. Adaptive biasing is achieved by using direct current feedback from a simple current mirror. The dynamics of both the main feedback loop (MFL) and the adaptive biasing loop are thoroughly analyzed. Tradeoffs between the adaptive biasing factor and the MFL stability are discussed. The AB LDR is designed using a standard 0.35- ?m CMOS technology ( Vtn ? 0.52 V and Vtp ? -0.72 V). The output is 1.0 V, which delivers a maximum current of 100 mA. The minimum input voltage is 1.2 V, and the minimum load current required is reduced to 50 ?A . Extensive simulation results verify that the proposed LDR achieves high loop bandwidth, fast line and load transient responses, high power supply rejection, and low output impedance.


title: A comparative analysis of image fusion methods

abstract: There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.


title: Video Analysis in Indoor Soccer using a Quadcopter

abstract: Automatic vision systems are widely used in sports competition to analyze individual and collective performance ::: ::: during the matches. However, the complex implementation based on multiple fixed cameras and the ::: ::: human intervention on the process makes this kind of systems expensive and not suitable for the big majority ::: ::: of the teams. In this paper we propose a low-cost, portable and flexible solution based on the use of Unmanned ::: ::: Air Vehicles to capture images from indoor soccer games. Since these vehicles suffer from vibrations and disturbances, ::: ::: the acquired video is very unstable, presenting a set of unusual problems in this type of applications. ::: ::: We propose a complete video-processing framework, including video stabilization, camera calibration, player ::: ::: detection, and team performance analysis. The results showed that camera calibration was able to correct ::: ::: automatically image-to-world homography; the player detection precision and recall was around 75%; and the ::: ::: high-level data interpretation showed a strong similarity with ground-truth derived results.


title: Declarative Language Design for Interactive Visualization

abstract: We investigate the design of declarative, domain-specific languages for constructing interactive visualizations. By separating specification from execution, declarative languages can simplify development, enable unobtrusive optimization, and support retargeting across platforms. We describe the design of the Protovis specification language and its implementation within an object-oriented, statically-typed programming language (Java). We demonstrate how to support rich visualizations without requiring a toolkit-specific data model and extend Protovis to enable declarative specification of animated transitions. To support cross-platform deployment, we introduce rendering and event-handling infrastructures decoupled from the runtime platform, letting designers retarget visualization specifications (e.g., from desktop to mobile phone) with reduced effort. We also explore optimizations such as runtime compilation of visualization specifications, parallelized execution, and hardware-accelerated rendering. We present benchmark studies measuring the performance gains provided by these optimizations and compare performance to existing Java-based visualization tools, demonstrating scalability improvements exceeding an order of magnitude.


title: ePrivateeye: to the edge and beyond!

abstract: Edge computing offers resource-constrained devices low-latency access to high-performance computing infrastructure. In this paper, we present ePrivateEye, an implementation of PrivateEye that offloads computationally expensive computer-vision processing to an edge server. The original PrivateEye locally processed video frames on a mobile device and delivered approximately 20 fps, whereas ePrivateEye transfers frames to a remote server for processing. We present experimental results that utilize our campus Software-Defined Networking infrastructure to characterize how network-path latency, packet loss, and geographic distance impact offloading to the edge in ePrivateEye. We show that offloading video-frame analysis to an edge server at a metro-scale distance allows ePrivateEye to analyze more frames than PrivateEye's local processing over the same period to achieve realtime performance of 30 fps, with perfect precision and negligible impact on energy efficiency.


title: Inkjet printed antennas for NFC systems

abstract: Inkjet printing is one of the most promising techniques that could potentially revolutionize large area and organic electronics fabrication. At the moment, technology is still under development and some problems remain to be solved. Only few applications have been demonstrated with enough performance to be moved to industrial level. In this paper we present the study of a potentially successful inkjet printing application: a Near Field Communication Antenna. In our work we characterized an inkjet printing process and developed an antenna design to evaluate its potential. Fabricated antenna was tested on NFC systems and final results lead us to conclude that NFC antennas are a potentially successful inkjet printing application.


title: Design of CMOS inverter-based output buffers adapting the cherry-hooper broadbanding technique

abstract: A methodology for designing CMOS inverter-based output buffers considering speed, gain, jitter, and drivabil-ity requirements is presented. In this methodology, the band-broadening technique of the classic Cherry-Hooper amplifier is adapted for CMOS inverters. A buffer designed in this manner offers higher speed than a commonly used simple chain of inverters with exponentially increasing gate widths. The buffer is implemented by making minor modifications to a 4-stage CMOS inverter chain. The proposed design is suitable for output buffers for high-speed CMOS logic circuits.


title: Robust Logistic Regression using Shift Parameters (Long Version)

abstract: Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels. In this paper, we present a robust extension of logistic regression that incorporates the possibility of mislabelling directly into the objective. This model can be trained through nearly the same means as logistic regression, and retains its efficiency on highdimensional datasets. We conduct experiments on named entity recognition data and find that our approach can provide a significant improvement over the standard model when annotation errors are present.


title: An aperture-coupled circularly polarized stacked microstrip antenna for GPS frequency bands L1, L2, and L5

abstract: This paper has investigated the technique of stacking microstrip antennas to achieve dual frequency operation for GPS frequencies L1 and L2/L5. The coupling between the two patches is dependent on the relative size of the patches and also whether the top patch is smaller or larger than the bottom patch. The relative patch sizes must be adjusted for proper impedance match. The design of this antenna was a tedious process and required numerous empirical tunings due to the number of interacting parameters. The designed antenna is currently under fabrication and measured results will be provided during the presentation.


title: Classification of malignant melanoma and Benign Skin Lesion by using back propagation neural network and ABCD rule

abstract: Human Cancer is a standout amongest the most unsafe illnesses which is for the most part brought about by hereditary insecurity of various sub-atomic modifications. Among many types of human disease, skin tumour is the most widely recognized one. To recognize skin tumour at an early stage we will think about and break down them through different methods named as segmentation and feature extraction. Here, we center threatening melanoma skin disease, (because of the high grouping of Melanoma-Hier we offer our skin, in the dermis layer of the skin) location. In this, We utilized our ABCD govern dermoscopy innovation for harmful melanoma skin malignancy location. In this framework distinctive stride for melanoma skin injury portrayal i.e, to begin with, the Image Acquisition Technique, pre-processing, segmentation, characterize a component for skin Feature Selection decides sore portrayal, grouping strategies. In the Feature extraction by advanced picture preparing technique incorporates, Asymmetry recognition, Border Detection, Colour, and Diameter detection and furthermore we utilized LBP for extract the texture based features. Here we proposed the Back Propagation Neural Network to classify the benign or malignant stage.


title: Carbon supports for methanol oxidation catalyst

abstract: Abstract Highly mesoporous carbon was synthesized employing conventional sol–gel technique using resorcinol and formaldehyde. The porous carbon electrodes were characterized by X-ray powder diffraction, N 2 adsorption isotherm, atomic absorption spectroscopy (AAS). Platinum was anchored on support by the incipient wetness method and reduced to its metallic form using sodium formate as a reducing agent. The electrocatalysis for methanol oxidation on carbon supported Pt in acid and alkaline solutions were investigated. It was found that the activity of Pt for methanol oxidation was higher in alkaline than in acid medium. High mesopore surface area of carbon can significantly increase the metal dispersion and affect particle size, which favoured the progress of the electrochemical processes occurring during methanol oxidation.


title: Bluespec System Verilog: efficient, correct RTL from high level specifications

abstract: Bluespec System Verilog is an EDL toolset for ASIC and FPGA design offering significantly higher productivity via a radically different approach to high-level synthesis. Many other attempts at high-level synthesis have tried to move the design language towards a more software-like specification of the behavior of the intended hardware. By means of code samples, demonstrations and measured results, we illustrate how Bluespec System Verilog, in an environment familiar to hardware designers, can significantly improve productivity without compromising generated hardware quality.


title: Subverting historical cause & effect: generation of mythic biographies in Caves of Qud

abstract: Procedurally generating history is a daunting task. The process of proceduralization requires codifying relationships into rules, and real-life histories are tangled networks of people, places, and events whose complexities obscure their relational mechanics. The task is further complicated by the fact that history serves a rhetorical function, i.e., historical accounts are used to promote certain cultural narratives that can belie the facts they purport to narrativize (this function is routinely neglected in video games' treatment of history). This paper describes a novel approach for procedurally generating coherent biographical narratives for historical figures, as implemented in our far-future roguelike game Caves of Qud. Using a state machine and replacement grammar as procedural tools, we construct a system that subverts the logic of cause and effect in favor of a process that first generates historical events and rationalizes them ex post facto.


title: Digital Phased Arrays: Challenges and Opportunities

abstract: Digital beamforming (DBF) has long been heralded as the next frontier in phased array technology, and not without reason. The digitization of transmit and receive signals at the element level opens the door to new processing and beamforming schemes and promises to deliver maximum flexibility and unprecedented dynamic range in large systems. However, it is not without inherent technological risks and practical challenges associated with the amount of data to process and the use of less sophisticated transceivers. This paper provides broad overviews of several interrelated aspects of the resulting DBF trade spaces for these systems. In particular, emerging concepts are highlighted for the roles and interconnection of distributed beamforming/processing for fixed and adaptive beamforming. These are then related to digital array calibration mechanisms that can potentially reduce the need for data- and processing-intensive beamforming algorithms.


title: SmartMusicKIOSK: music listening station with chorus-search function

abstract: This paper describes a new music-playback interface for trial listening, SmartMusicKIOSK. In music stores, short trial listening of CD music is not usually a passive experience -- customers often search out the chorus or "hook" of a song using the fast-forward button. Listening of this type, however, has not been traditionally supported. This research achieves a function for jumping to the chorus section and other key parts of a song plus a function for visualizing song structure. These functions make it easier for a listener to find desired parts of a song and thereby facilitate an active listening experience. The proposed functions are achieved by an automatic chorus-section detecting method, and the results of implementing them as a listening station have demonstrated their usefulness.


Bluetooth Indoor Positioning


C.Waghmare: ”Intrusion Detection System Using Data Mining Technique: Support Vector


title: Development of Optimal Photosensors Based Heart Pulse Detector

abstract: The development of heart pulse instruments rapidly fast in market since 21st century. However, the heart pulse detector is expensive due to the complicated system and it is used widely only in hospitals and clinics. The project is targeting to develop a significant photosensor to the medical fields that is easy to use and monitor their health by the user everywhere. The other target is to develop a comfortable instrument, reliable, accurate result to develop of heart pulse using low cost photosensors. This project involved both hardware and software with related to signal processing, mathematical, computational, formalisms, modeling techniques for transforming, transmitting and also for analogue or digital signal. This project also used Peripheral Interface Controller (PIC) 16F877A microcontroller as the main function to control other elements. Result showed this project functioned smoothly and successfully with overall objectives were achieved. Apart from that, this project give good services for people to monitor their heart condition form time to time. In the future, wireless connection e.g. Global System for Mobile Communications (GSM) and Zigbee would be developed to make the system more reliable to the current world. Furthermore, the system should be compatible to various environments such as Android based OS so that it can be controlled away from the original location.


title: Music-evoked nostalgia: Affect, memory, and personality.

abstract: Participants listened to randomly selected excerpts of popular music and rated how nostalgic each song made them feel. Nostalgia was stronger to the extent that a song was autobiographically salient, arousing, familiar, and elicited a greater number of positive, negative, and mixed emotions. These effects were moderated by individual differences (nostalgia proneness, mood state, dimensions of the Affective Neurosciences Personality Scale, and factors of the Big Five Inventory). Nostalgia proneness predicted stronger nostalgic experiences, even after controlling for other individual difference measures. Nostalgia proneness was predicted by the Sadness dimension of the Affective Neurosciences Personality Scale and Neuroticism of the Big Five Inventory. Nostalgia was associated with both joy and sadness, whereas nonnostalgic and nonautobiographical experiences were associated with irritation.


title: B2B E-commerce Website Customer Satisfaction: A Formula and Scale

abstract: It is the trend that Business to Business (B2B) has been becoming the mainstream of e-commerce. With more and more concentration on this field, however, recent research has not defined what the B2B website user satisfaction is and how to evaluate it clearly. Also, traditional scales, such as User Information Satisfaction (UIS), End- User Computer Satisfaction (EUCS) and Customer Information Satisfaction (CIS), are just focus on the Information System (IS) environment, but not fit to the B2B e-commerce environment very well. This article establishes "a Scale of B2B E-commerce Website Customer Satisfaction" (B2B-ECWCS) with five-dimension, explores the impact factors of B2B-ECWCS, proposes formula for calculating B2B-ECWCS, and finally tests Alibaba’s customer satisfaction.


title: Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach

abstract: Energy efficient routing in wireless sensor networks (WSNs) require non-conventional paradigm for design and development of power aware protocols. Swarm intelligence (SI) based metaheuristic can be applied for optimal routing of data, in an energy constraint WSNs environment. In this paper, we present BeeSwarm, a SI based energy-efficient hierarchical routing protocol for WSNs. Our protocol consists of three phases: (1) Set-up phase-BeeCluster, (2) Route discovery phase-BeeSearch and (3) Data transmission phase-BeeCarrier. Integration of three phases for clustering, data routing and transmission, is the key aspect of our proposed protocol, which ultimately contributes to its robustness. Evaluation of simulation results show that BeeSwarm perform better in terms of packet delivery, energy consumption and throughput with increased network life compared to other SI based hierarchical routing protocols.


title: Stylometric Authorship Attribution of Collaborative Documents

abstract: Stylometry is the study of writing style based on linguistic features and is typically applied to authorship attribution problems. In this work, we apply stylometry to a novel dataset of multi-authored documents collected from Wikia using both relaxed classification with a support vector machine (SVM) and multi-label classification techniques. We define five possible scenarios and show that one, the case where labeled and unlabeled collaborative documents by the same authors are available, yields high accuracy on our dataset while the other, more restrictive cases yield lower accuracies. Based on the results of these experiments and knowledge of the multi-label classifiers used, we propose a hypothesis to explain this overall poor performance. Additionally, we perform authorship attribution of pre-segmented text from the Wikia dataset, and show that while this performs better than multi-label learning it requires large amounts of data to be successful.


title: Tactical Decision Making for Lane Changing with Deep Reinforcement Learning

abstract: In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. Our framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. We accomplish this with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints, and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and data efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.


title: Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent

abstract: Nonnegative matrix factorization (NMF) has become a popular data-representation method and has been widely used in image processing and pattern-recognition problems. This is because the learned bases can be interpreted as a natural parts-based representation of data and this interpretation is consistent with the psychological intuition of combining parts to form a whole. For practical classification tasks, however, NMF ignores both the local geometry of data and the discriminative information of different classes. In addition, existing research results show that the learned basis is unnecessarily parts-based because there is neither explicit nor implicit constraint to ensure the representation parts-based. In this paper, we introduce the manifold regularization and the margin maximization to NMF and obtain the manifold regularized discriminative NMF (MD-NMF) to overcome the aforementioned problems. The multiplicative update rule (MUR) can be applied to optimizing MD-NMF, but it converges slowly. In this paper, we propose a fast gradient descent (FGD) to optimize MD-NMF. FGD contains a Newton method that searches the optimal step length, and thus, FGD converges much faster than MUR. In addition, FGD includes MUR as a special case and can be applied to optimizing NMF and its variants. For a problem with 165 samples in R(1600), FGD converges in 28 s, while MUR requires 282 s. We also apply FGD in a variant of MD-NMF and experimental results confirm its efficiency. Experimental results on several face image datasets suggest the effectiveness of MD-NMF.


title: Dimension Reduction and Data Visualization Using Neural Networks

abstract: The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon's mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men's health.


TOURISM WEB SITES AND VALUE-ADDED SERVICES: THE GAP BETWEEN CUSTOMER PREFERENCES AND WEB SITES ’ OFFERINGS


Double Exponential Smoothing: An Alternative to Kalman Filter-Based Predictive Tracking


title: Causal impact for app store analysis

abstract: App developers naturally want to know which of their releases are successful and which are unsuccessful. Such information can help with release planning and requirements prioritisation and elicitation. To address this problem, I performed causal analysis on 52 weeks of popular app releases from Google Play and Windows Phone Store. The results reveal properties of successful releases in multiple app stores, and showcase causal analysis as a useful technique for developers seeking to know more about their software releases.


title: Panorama weaving: fast and flexible seam processing

abstract: A fundamental step in stitching several pictures to form a larger mosaic is the computation of boundary seams that minimize the visual artifacts in the transition between images. Current seam computation algorithms use optimization methods that may be slow, sequential, memory intensive, and prone to finding suboptimal solutions related to local minima of the chosen energy function. Moreover, even when these techniques perform well, their solution may not be perceptually ideal (or even good). Such an inflexible approach does not allow the possibility of user-based improvement. This paper introduces the Panorama Weaving technique for seam creation and editing in an image mosaic. First, Panorama Weaving provides a procedure to create boundaries for panoramas that is fast, has low memory requirements and is easy to parallelize. This technique often produces seams with lower energy than the competing global technique. Second, it provides the first interactive technique for the exploration of the seam solution space. This powerful editing capability allows the user to automatically extract energy minimizing seams given a sparse set of constraints. With a variety of empirical results, we show how Panorama Weaving allows the computation and editing of a wide range of digital panoramas including unstructured configurations.


title: VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images

abstract: The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retrieve multi-scale features from reflective and texture-less regions for accurate disparity estimation. Meanwhile, our network has achieved similar or better performance in other regions for both synthetic light field images and real-world data compared to the state-of-the-art algorithms. Currently, we achieve the best score for mean squared error (MSE) on HCI 4D Light Field Benchmark.


title: A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations

abstract: We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the prediction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the traditional view of discourse, as we induce types of discourse relations and associated discourse cues relevant to the considered opinion analysis task; consequently, the induced discourse relations play the role of opinion and aspect shifters. The quantitative analysis that we conducted indicated that the integration of a discourse model increased the prediction accuracy results with respect to the discourse-agnostic approach and the qualitative analysis suggests that the induced representations encode a meaningful discourse structure.


title: Towards Virtual Machine Migration in Fog Computing

abstract: Handoff mechanisms allow mobile users to move across multiple wireless access points while maintaining their voice and/or data sessions. A traditional handoff process is concerned with smoothly transferring a mobile device session from its current access point (or cell) to a target access point (or cell). These handoff characteristics are sufficient for voice calls and background data transfers, however nowadays many mobile applications are heavily based on data and processing capabilities from the cloud. Such applications, especially those that require greater interactivity, often demand not only a smooth session transfer, but also the maintenance of quality of service requirements that impact a user's experience. In this context, the Fog Computing paradigm arises to overcome delays encountered when applications need low latency to access data or offload processing to the cloud. Fog computing introduces a distributed cloud layer, composed of cloudlets (i.e., "small clouds" with lower computational capacity), between the user and the cloud. Cloudlets allow low latency access to data or processing capabilities, which can be accomplished by offering a VM to the user. An overview of Fog computing is first providing, relating it to general concepts in Cloud-based systems, followed by a general architecture to support virtual machine migration in this emerging paradigm -- discussing both the benefits and challenges associated with such migration.


title: A longitudinal view of HTTP video streaming performance

abstract: This paper investigates HTTP streaming traffic from an ISP perspective. As streaming traffic now represents nearly half of the residential Internet traffic, understanding its characteristics is important. We focus on two major video sharing sites, YouTube and DailyMotion. We use ten packet traces from a residential ISP network, five for ADSL and five for FTTH customers, captured between 2008 and 2011. Covering a time span of four years allows us to identify changes in the service infrastructure of some providers. From the packet traces, we infer for each streaming flow the video characteristics, such as duration and encoding rate, as well as TCP flow characteristics. Using additional information from the BGP routing tables allows us to identify the originating Autonomous System (AS). With this data, we can uncover: the server side distribution policy, the impact of the serving AS on the flow characteristics and the impact of the reception quality on user behavior. A unique aspect of our work is how to measure the reception quality of the video and its impact on the viewing behavior. We see that not even half of the videos are fully downloaded. For short videos of 3 minutes or less, users stop downloading at any point, while for videos longer than 3 minutes, users either stop downloading early on or fully download the video. When the reception quality deteriorates, fewer videos are fully downloaded, and the decision to interrupt download is taken earlier. We conclude that (i) the video sharing sites have a major control over the delivery of the video and its reception quality through DNS resolution and server side streaming policy, and (ii) that only half of the videos are fully downloaded and that this fraction dramatically drops when the video reception quality is bad.


title: Applying QR code in augmented reality applications

abstract: In this paper we present an augmented reality (AR) application based on the QR Code. The system can extract the information embedded in a QR Code and show the information in an extended 3D form with the QR Code being the traditional AR marker. Traditional AR systems often use a particularly designed pattern (the marker) to recover the 3D scene structure and identify the object to be displayed on the scene. In these systems, the marker is used only for tracking and identification. They do not convey any other information. Consequently, the applications of these systems are limited and often a registration procedure is required for a new marker. QR Code has the advantage of large information capacity and is similar to an AR marker in appearance. Thus, more interesting and useful applications can be developed by combining the QR Code with the traditional AR system. In this paper, we combine these two techniques to develop a product demo system. A QR Code is pasted on the package of a product and then a 3D virtual object is displayed on the QR Code. This system allows the customer to visualize the product via a more direct and interactive way. Our system demonstrates the success of using QR Code as the AR marker to a particular application and we believe it can bring more convenient to our life in the future.


title: Wasserstein Auto-Encoders

abstract: We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE). This regularizer encourages the encoded training distribution to match the prior. We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE). Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality, as measured by the FID score.


Applications of ANNs in Stock Market Prediction: A Survey


title: Embodiment in attitudes, social perception, and emotion

abstract: Findings in the social psychology literatures on attitudes, social perception, and emotion demonstrate that social information processing involves embodiment, where embodiment refers both to actual bodily states and to simulations of experience in the brain’s modality-specific systems for perception, action, and introspection. We show that embodiment underlies social information processing when the perceiver interacts with actual social objects (online cognition) and when the perceiver represents social objects in their absence (offline cognition). Although many empirical demonstrations of social embodiment exist, no particularly compelling account of them has been offered. We propose that theories of embodied cognition, such as the Perceptual Symbol Systems (PSS) account (Barsalou, 1999), explain and integrate these findings, and that they also suggest exciting new directions for research. We compare the PSS account to a variety of related proposals and show how it addresses criticisms that have previously posed problems for the general embodiment approach.


title: A 1kW-class S-band compact waveguide combiner unit with GaN HPAs for WPT and space communication

abstract: Using a GaN high power amplifier and a double 4-inputport circular waveguide combiner, a 1kW S-band compact waveguide combiner unit operating at 2.1 GHz was assembled. The maximum power of 200W was realized with a Peltier device for heat sink. The high power amplifier unit shows the PAE of more than 50% with an AB-class operation under the temperature around 40 degrees C. The four GaN high power amplifiers were combined with the 4-inputports circular waveguide combiner, having an output power of 540W. Further the 1kW output power connecting two 500W-class power combiners was achieved. The combining efficiency of the 1kW-class power combiner unit was achieved to about 90 %.


title: Interactive Classification for Deep Learning Interpretation

abstract: We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers. Using modern web technologies to run in-browser inference, users can remove image features using inpainting algorithms and obtain new classifications in real time, which allows them to ask a variety of"what if"questions by experimentally modifying images and seeing how the model reacts. Our system allows users to compare and contrast what image regions humans and machine learning models use for classification, revealing a wide range of surprising results ranging from spectacular failures (e.g., a"water bottle"image becomes a"concert"when removing a person) to impressive resilience (e.g., a"baseball player"image remains correctly classified even without a glove or base). We demonstrate our system at The 2018 Conference on Computer Vision and Pattern Recognition (CVPR) for the audience to try it live. Our system is open-sourced at https://github.com/poloclub/interactive-classification. A video demo is available at https://youtu.be/llub5GcOF6w.


title: Current-mode capacitance multiplier with reduced parasitic elements

abstract: This paper presents a method of implementing a large virtual capacitor using an area-efficient capacitance multiplier circuit. The multiplier solves the major issue of large area consumption in integrated circuits needing high capacitance values. The proposed architecture improves other important parameters, such as the quality factor and the operating signal range. An analytical equivalent model is derived. The circuit is completely characterized in terms of model parameters and voltage and frequency operating ranges. Simulations are run to confirm feasibility and performance. An experimental version is implemented using discrete devices. A comparison between the theoretical, simulated, and experimental results is made.


title: Mechanical design and implementation of a soft inflatable robot arm for safe human-robot interaction

abstract: In this paper, a novel soft inflatable arm is proposed for telepresence robots. It is capable of imitating human arms to realize remote interaction. The new proposed arm using a very common and low cost inflatable material, and it is very light, which weight is only about 50 grams, but can well realize agile movement by driving three tiny cables installed in shoulder joint and elbow joint, respectively. Meanwhile, the proposed cable driven mechanism also allows connecting numbers of joints easily. The soft inflatable can work just by pumping air with very low pressure (7.32 ± 3.45 kPa), and allows human directly and safely contact without any external sensors. Moreover, to solve the challenge problems of soft joint deformation, the kinematic modeling of the joint with deformation compensation is also developed. Experimental results show that the soft inflatable arm can agilely move for remote interaction. The workspace and velocity are also close to an adult's arm movement space and normal motion speed.


title: A proposed semantic recommendation system for e-learning: A rule and ontology based e-learning recommendation system

abstract: With proliferation of learning contents on the web, finding suitable ones has become a very difficult and complicated task for online learners, to achieve better performance. Nevertheless, recommender systems can be a solution to the problem. However, recommendation systems haven't been sufficiently used in e-learning, in comparison with other fields (i.e. commerce, medicine and so on). In this paper, we propose a semantic recommender system for e-learning by means of which, learners will be able to find and choose the right learning materials suitable to their field of interest. The proposed web based recommendation system comprises ontology and web ontology language (OWL) rules. Rule filtering will be used as recommendation technique. Our proposed recommendation system architecture consists of two subsystems; Semantic Based System and Rule Based System. Modules for either subsystem are; Observer, Learner profile, Recommendation storage and User interface.


title: Fit-sphere unwrapping and performance analysis of 3D fingerprints.

abstract: To solve problems associated with conventional 2D fingerprint acquisition processes including skin deformations and print smearing, we developed a noncontact 3D fingerprint scanner employing structured light illumination that, in order to be backwards compatible with existing 2D fingerprint recognition systems, requires a method of unwrapping the 3D scans into 2D equivalent prints. For the latter purpose of virtually flattening a 3D print, this paper introduces a fit-sphere unwrapping algorithm. Taking advantage of detailed 3D information, the proposed method defuses the unwrapping distortion by controlling the distances between neighboring points. Experimental results will demonstrate the high quality and recognition performance of the 3D unwrapped prints versus traditionally collected 2D prints. Furthermore, by classifying the 3D database into high- and low-quality data sets, we demonstrate that the relationship between quality and recognition performance holding for conventional 2D prints is achieved for 3D unwrapped fingerprints.


title: Approximate Multipliers Based on New Approximate Compressors

abstract: Approximate computing is an emerging trend in digital design that trades off the requirement of exact computation for improved speed and power performance. This paper proposes novel approximate compressors and an algorithm to exploit them for the design of efficient approximate multipliers. By using the proposed approach, we have synthesized approximate multipliers for several operand lengths using a 40-nm library. Comparison with previously presented approximated multipliers shows that the proposed circuits provide better power or speed for a target precision. Applications to image filtering and to adaptive least mean squares filtering are also presented in the paper.


title: Lexical access during sentence comprehension: (Re)consideration of context effects

abstract: The effects of prior semantic context upon lexical access during sentence comprehension were examined in two experiments. In both studies, subjects comprehended auditorily presented sentences containing lexical ambiguities and simultaneously performed a lexical decision task upon visually presented letter strings. Lexical decisions for visual words related to each of the meanings of the ambiguity were facilitated when these words were presented simultaneous with the end of the ambiguity (Experiment 1). This effect held even when a strong biasing context was present. When presented four syllables following the ambiguity, only lexical decisions for visual words related to the contextually appropriate meaning of the ambiguity were facilitated (Experiment 2). Arguments are made for autonomy of the lexical access process of a model of semantic context effects is offered.


title: Application of knowledge-based approaches in software architecture: A systematic mapping study

abstract: Context: Knowledge management technologies have been employed across software engineering activities for more than two decades. Knowledge-based approaches can be used to facilitate software architecting activities (e.g., architectural evaluation). However, there is no comprehensive understanding on how various knowledge-based approaches (e.g., knowledge reuse) are employed in software architecture. Objective: This work aims to collect studies on the application of knowledge-based approaches in software architecture and make a classification and thematic analysis on these studies, in order to identify the gaps in the existing application of knowledge-based approaches to various architecting activities, and promising research directions. Method: A systematic mapping study is conducted for identifying and analyzing the application of knowledge-based approaches in software architecture, covering the papers from major databases, journals, conferences, and workshops, published between January 2000 and March 2011. Results: Fifty-five studies were selected and classified according to the architecting activities they contribute to and the knowledge-based approaches employed. Knowledge capture and representation (e.g., using an ontology to describe architectural elements and their relationships) is the most popular approach employed in architecting activities. Knowledge recovery (e.g., documenting past architectural design decisions) is an ignored approach that is seldom used in software architecture. Knowledge-based approaches are mostly used in architectural evaluation, while receive the least attention in architecture impact analysis and architectural implementation. Conclusions: The study results show an increased interest in the application of knowledge-based approaches in software architecture in recent years. A number of knowledge-based approaches, including knowledge capture and representation, reuse, sharing, recovery, and reasoning, have been employed in a spectrum of architecting activities. Knowledge-based approaches have been applied to a wide range of application domains, among which ''Embedded software'' has received the most attention.


title: Universal sex differences in the desire for sexual variety: Tests from 52 nations, 6 continents, and 13 islands

abstract: Evolutionary psychologists have hypothesized that men and women possess both long-term and short-term mating strategies, with men's short-term strategy differentially rooted in the desire for sexual variety. In this article, findings from a cross-cultural survey of 16,288 people across 10 major world regions (including North America, South America, Western Europe, Eastern Europe, Southern Europe, Middle East, Africa, Oceania, South/Southeast Asia, and East Asia) demonstrate that sex differences in the desire for sexual variety are culturally universal throughout these world regions. Sex differences were evident regardless of whether mean, median, distributional, or categorical indexes of sexual differentiation were evaluated. Sex differences were evident regardless of the measures used to evaluate them. Among contemporary theories of human mating, pluralistic approaches that hypothesize sex differences in the evolved design of short-term mating provide the most compelling account of these robust empirical findings.


title: Bridging OpenCL and CUDA: a comparative analysis and translation

abstract: Heterogeneous systems are widening their user-base, and heterogeneous computing is becoming popular in supercomputing. Among others, OpenCL and CUDA are the most popular programming models for heterogeneous systems. Although OpenCL inherited many features from CUDA and they have almost the same platform model, they are not compatible with each other. In this paper, we present similarities and differences between them and propose an automatic translation framework for both OpenCL to CUDA and CUDA to OpenCL. We describe features that make it difficult to translate from one to the other and provide our solution. We show that our translator achieves comparable performance between the original and target applications in both directions. Since each programming model separately has a wide user-base and large code-base, our translation framework is useful to extend the code-base for each programming model and unifies the efforts to develop applications for heterogeneous systems.


title: Interior Plants May Improve Worker Productivity and Reduce Stress in a Windowless Environment

abstract: Abstract This study documents some of the benefits of adding plants to a windowless work place—a college computer lab. Participants' blood pressure and emotions were monitored while completing a simple, timed computer task in the presence or absence of plants. When plants were added to this interior space, the participants were more productive (12% quicker reaction time on the computer task) and less stressed (systolic blood pressure readings lowered by one to four units). Immediately after completing the task, participants in the room with plants present reported feeling more attentive (an increase of 0.5 on a self-reported scale from one to five) than people in the room with no plants.


title: Discovering Simulation Models

abstract: Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a colored Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models.


title: Osmosis in Pocket Switched Networks

abstract: The increase in the variety and capability of mobile communications devices carried by people today has made it possible to envision a new class of networks called pocket switched networks (PSNs). In a PSN, because the nodes are constantly moving and their communication abilities are limited, the design of networking protocols and applications is challenging. This paper presents a new communications scheme for PSN, called Osmosis, based on the biological phenomenon. We show how this scheme can be applied to file sharing. This scheme uses epidemic routing to perform file lookup and achieves controlled flooding for file transfer. The flooding requires very little state information, which is collected during the lookup. This paper analyzes the performance of Osmosis in simulation studies based on real mobility traces.


title: Subspace search and visualization to make sense of alternative clusterings in high-dimensional data

abstract: In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive methods aim to provide effective visual mappings to show, relate, and navigate HD data. Furthermore, almost all of these methods conduct the analysis from a singular perspective, meaning that they consider the data in either the original HD data space, or a reduced version thereof. Additionally, HD data spaces often consist of combined features that measure different properties, in which case the particular relationships between the various properties may not be clear to the analysts a priori since it can only be revealed if appropriate feature combinations (subspaces) of the data are taken into consideration. Considering just a single subspace is, however, often not sufficient since different subspaces may show complementary, conjointly, or contradicting relations between data items. Useful information may consequently remain embedded in sets of subspaces of a given HD input data space. Relying on the notion of subspaces, we propose a novel method for the visual analysis of HD data in which we employ an interestingness-guided subspace search algorithm to detect a candidate set of subspaces. Based on appropriately defined subspace similarity functions, we visualize the subspaces and provide navigation facilities to interactively explore large sets of subspaces. Our approach allows users to effectively compare and relate subspaces with respect to involved dimensions and clusters of objects. We apply our approach to synthetic and real data sets. We thereby demonstrate its support for understanding HD data from different perspectives, effectively yielding a more complete view on HD data.


title: Human Acute Stress Detection via Integration of Physiological Signals and Thermal Imaging

abstract: Daily pressure, work load, and family responsibilities among other factors impose increasing levels of stress on different individuals. Hence, detecting stress as early as possible can potentially reduce the severe consequences and risks that someone may experience. In this paper, we develop a novel dataset to detect acute stress using 50 subjects. We additionally analyze different features extracted automatically from the thermal and physiological modalities. Furthermore, we develop a system that integrates both thermal and physiological features for improved stress detection rates. Our system achieves promising results exceeding 75% accuracy and has the potential to be further improved by adding additional modalities, which can provide a useful and reliable approach in early detection of stress.


title: Privacy for Children and Teenagers on Social Networks from a Usability Perspective: A Case Study on Facebook

abstract: Children and teenagers comprise a user group particularly susceptible to privacy infringement risks in Online Social Networks (OSNs). In this sense, the usability of privacy controls should be appropriate to such users. This study aims to characterize, through a case study on Facebook, how children and teenagers deal with privacy features on OSNs and whether the available privacy controls help this user group to manage the privacy levels of their personal data. The case study considered children and teenagers in Brazil, the top-3 country on the number of Facebook users. The results indicated that there are usability violations that hinder this group from adjusting privacy settings, compromising its privacy and security on OSNs. These results are relevant and not limited to the context of Brazil, because they warn us about the vulnerability of children and teenagers on OSNs, as well as they evince the role of usability in privacy controls.


title: Anterior Cingulate Cortex, Error Detection, and the Online Monitoring of Performance

abstract: An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.


title: A low-voltage low-power sigma-delta modulator for broadband analog-to-digital conversion

abstract: A cascade of sigma-delta modulator stages that employ a feedforward architecture to reduce the signal ranges required at the integrator inputs and outputs has been used to implement a broadband, high-resolution oversampling CMOS analog-to-digital converter capable of operating from low-supply voltages. An experimental prototype of the proposed architecture has been integrated in a 0.25-/spl mu/m CMOS technology and operates from an analog supply of only 1.2 V. At a sampling rate of 40 MSamples/sec, it achieves a dynamic range of 96 dB for a 1.25-MHz signal bandwidth. The analog power dissipation is 44 mW.


title: Joint 2D-3D-Semantic Data for Indoor Scene Understanding

abstract: We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000m2 and contains over 70,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360° equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces. The dataset is available here: this http URL


title: Transactional storage for geo-replicated systems

abstract: We describe the design and implementation of Walter, a key-value store that supports transactions and replicates data across distant sites. A key feature behind Walter is a new property called Parallel Snapshot Isolation (PSI). PSI allows Walter to replicate data asynchronously, while providing strong guarantees within each site. PSI precludes write-write conflicts, so that developers need not worry about conflict-resolution logic. To prevent write-write conflicts and implement PSI, Walter uses two new and simple techniques: preferred sites and counting sets. We use Walter to build a social networking application and port a Twitter-like application.


title: Shift-Reduce CCG Parsing

abstract: CCGs are directly compatible with binary-branching bottom-up parsing algorithms, in particular CKY and shift-reduce algorithms. While the chart-based approach has been the dominant approach for CCG, the shift-reduce method has been little explored. In this paper, we develop a shift-reduce CCG parser using a discriminative model and beam search, and compare its strengths and weaknesses with the chart-based C&C parser. We study different errors made by the two parsers, and show that the shift-reduce parser gives competitive accuracies compared to C&C. Considering our use of a small beam, and given the high ambiguity levels in an automatically-extracted grammar and the amount of information in the CCG lexical categories which form the shift actions, this is a surprising result.


title: The clinical use of mindfulness meditation for the self-regulation of chronic pain

abstract: Ninety chronic pain patients were trained in mindfulness meditation in a 10-week Stress Reduction and Relaxation Program. Statistically significant reductions were observed in measures of present-moment pain, negative body image, inhibition of activity by pain, symptoms, mood disturbance, and psychological symptomatology, including anxiety and depression. Pain-related drug utilization decreased and activity levels and feelings of self-esteem increased. Improvement appeared to be independent of gender, source of referral, and type of pain. A comparison group of pain patients did not show significant improvement on these measures after traditional treatment protocols. At follow-up, the improvements observed during the meditation training were maintained up to 15 months post-meditation training for all measures except present-moment pain. The majority of subjects reported continued high compliance with the meditation practice as part of their daily lives. The relationship of mindfulness meditation to other psychological methods for chronic pain control is discussed.


Enriching Word Embeddings with Domain Knowledge for Readability Assessment


title: A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data

abstract: We address statistical classifier design given a mixed training set consisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is required to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to improve performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data subsets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data. The classifier, based on a joint probability model for features and labels, is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training. The scope of application for the new method is greatly extended by the observation that test data, or any new data to classify, is in fact additional, unlabelled data - thus, a combined learning/classification operation - much akin to what is done in image segmentation - can be invoked whenever there is new data to classify. Experiments with data sets from the UC Irvine database demonstrate that the new learning algorithms and structure achieve substantial performance gains over alternative approaches.


title: Millimeter-wave integrated waveguides on silicon

abstract: An integrated waveguide realized on (high resistivity) silicon using an IC-compatible process is reported in this work. The manufacturing process flow is based on substrate transfer technique and KOH etching, giving the resulting waveguide a trapezoidal cross-section. For this structure, the analysis of the propagating modes described here shows that it is possible to define a TE 10 -like mode, whose field distribution strongly resembles the classical TE 10 mode of a rectangular waveguide. The cut-off frequency of the first higher order mode is also analyzed for different waveguide aspect ratios (width over height). In the fundamental mode operation, extremely low losses smaller than 0.12 dB/mm up to 50 GHz and 0.3 dB/mm up to 100 GHz are obtained respectively for a waveguide with cut-off 35 GHz and 77 GHz.


title: On Model Discovery For Hosted Data Science Projects

abstract: Alongside developing systems for scalable machine learning and collaborative data science activities, there is an increasing trend toward publicly shared data science projects, hosted in general or dedicated hosting services, such as GitHub and DataHub. The artifacts of the hosted projects are rich and include not only text files, but also versioned datasets, trained models, project documents, etc. Under the fast pace and expectation of data science activities, model discovery, i.e., finding relevant data science projects to reuse, is an important task in the context of data management for end-to-end machine learning. In this paper, we study the task and present the ongoing work on ModelHub Discovery, a system for finding relevant models in hosted data science projects. Instead of prescribing a structured data model for data science projects, we take an information retrieval approach by decomposing the discovery task into three major steps: project query and matching, model comparison and ranking, and processing and building ensembles with returned models. We describe the motivation and desiderata, propose techniques, and present opportunities and challenges for model discovery for hosted data science projects.


title: Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion

abstract: The ability to cognitively regulate emotional responses to aversive events is important for mental and physical health. Little is known, however, about neural bases of the cognitive control of emotion. The present study employed functional magnetic resonance imaging to examine the neural systems used to reappraise highly negative scenes in unemotional terms. Reappraisal of highly negative scenes reduced subjective experience of negative affect. Neural correlates of reappraisal were increased activation of the lateral and medial prefrontal regions and decreased activation of the amygdala and medial orbito-frontal cortex. These findings support the hypothesis that prefrontal cortex is involved in constructing reappraisal strategies that can modulate activity in multiple emotion-processing systems.


title: 1.575 GHz dual-polarization textile antenna (DPTA) for GPS application

abstract: This paper presents a novel dual-polarization textile antenna (DPTA) for Global Positioning System (GPS) applications. In this design, four sector radiating elements are centrally-fed by a coaxial probe. A novel antenna structure is successfully achieved by integrating electronic components into a wearable textile antenna. Silver loaded epoxy adhesive is used in embedding four PIN diode switches on ShieldIt super textile of the proposed antenna. The activation of certain PIN diode switches configuration determines the polarization of DPTA. Dimension-wise this antenna is compact and small where it consists of 40mm radius. The proposed DPTA design successfully generated dual polarization omni-directional radiation pattern with maximum gain 1.81 dBi at angle of 0°/3600, 90°, 180° and 270°. Its small size, flexible and wearable features enables this DPTA to be easily integrated onto safety jacket and rain coat for tracking, search and rescue applications.


title: Early detection of promoted campaigns on social media

abstract: Social media expose millions of users every day to information campaigns - some emerging organically from grassroots activity, others sustained by advertising or other coordinated efforts. These campaigns contribute to the shaping of collective opinions. While most information campaigns are benign, some may be deployed for nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. It is therefore important to be able to detect whether a meme is being artificially promoted at the very moment it becomes wildly popular. This problem has important social implications and poses numerous technical challenges. As a first step, here we focus on discriminating between trending memes that are either organic or promoted by means of advertisement. The classification is not trivial: ads cause bursts of attention that can be easily mistaken for those of organic trends. We designed a machine learning framework to classify memes that have been labeled as trending on Twitter. After trending, we can rely on a large volume of activity data. Early detection, occurring immediately at trending time, is a more challenging problem due to the minimal volume of activity data that is available prior to trending. Our supervised learning framework exploits hundreds of time-varying features to capture changing network and diffusion patterns, content and sentiment information, timing signals, and user meta-data. We explore different methods for encoding feature time series. Using millions of tweets containing trending hashtags, we achieve 75% AUC score for early detection, increasing to above 95% after trending. We evaluate the robustness of the algorithms by introducing random temporal shifts on the trend time series. Feature selection analysis reveals that content cues provide consistently useful signals; user features are more informative for early detection, while network and timing features are more helpful once more data is available.


title: Trust in inter-organizational exchanges: a case study in business to business electronic commerce

abstract: Given the uncertainties of online transactions, e-commerce encourages the creation of institutional structures for online exchange relationships. Building upon the notion of institutional trust, this research examines how institutional structures develop through governance mechanisms leading to technology trust derived from structural assurances for online dyadic relationships. Technology trust is defined as the subjective probability by which organizations believe that the underlying technology infrastructure is capable of facilitating transactions according to their confident expectations. Using a cognitive process framework, we discuss four perspectives of trust, namely technological, economic, behavioral, and organizational perspectives, to show how technology trust evolves into relationship trust. We examine the impact of technology trust within an inter-organizational dyad between Cisco and Compaq. The findings posit that technology trust contributes and evolves into relationship trust. The study provides evidence on how specific structural assurances in Cisco Connection Online help to build initial online technology trust in B2B marketplaces that in turn promotes relationship trust and builds trustworthy e-commerce relationships. The paper discusses theoretical and managerial implications of this study, while proposing suggestions for future research.


title: Design of a Hypermedia Interface Translating between Associative and Formal Representations

abstract: It is argued that in order to efficiently tackle complex problems, user and support system should intimately interact, complementing each other's weaknesses. Strengths and limitations of human intelligence, respectively computer intelligence, can be derived from the mechanism of associative, respectively “chunk-based”, memory. A good interactive interface should hence aloow one to translate between associative (context-dependent) and chunk-based (formal) representations. Associative knowledge can be expressed more explicitly through hypermedia, consisting of a network of linked chunks. Different tools for supporting the creation of networks are reviewed: check-lists, outlining, graphic representations, search functions, … Knowledge can be automatically structured by looking for “closed” subnetworks, which define higher-order chunks and link types that can be used to guide inferences. A prototype implementation of an interactive interface, the CONCEPTORGANIZER, is sketched, and some potential applications in the areas of idea processing, knowledge elicitation, decision support, and CSCW are outlined.


Attacking the Diebold Signature Variant – RSA Signatures with Unverified High-order Padding


title: A Technique for Improving P&O MPPT Performances of Double-Stage Grid-Connected Photovoltaic Systems

abstract: In double-stage grid-connected photovoltaic (PV) inverters, the dynamic interactions among the DC/DC and DC/AC stages and the maximum power point tracking (MPPT) controller may reduce the system performances. In this paper, the detrimental effects, particularly in terms of system efficiency and MPPT performances, of the oscillations of the PV array voltage, taking place at the second harmonic of the grid frequency are evidenced. The use of a proper compensation network acting on the error signal between a reference signal provided by the MPPT controller and a signal that is proportional to the PV array voltage is proposed. The guidelines for the proper joint design of the compensation network (which is able to cancel out the PV voltage oscillations) and of the main MPPT parameters are provided in this paper. Simulation results and experimental measurements confirm the effectiveness of the proposed approach.


title: All-hex meshing using singularity-restricted field

abstract: Decomposing a volume into high-quality hexahedral cells is a challenging task in geometric modeling and computational geometry. Inspired by the use of cross field in quad meshing and the CubeCover approach in hex meshing, we present a complete all-hex meshing framework based on singularity-restricted field that is essential to induce a valid all-hex structure. Given a volume represented by a tetrahedral mesh, we first compute a boundary-aligned 3D frame field inside it, then convert the frame field to be singularity-restricted by our effective topological operations. In our all-hex meshing framework, we apply the CubeCover method to achieve the volume parametrization. For reducing degenerate elements appearing in the volume parametrization, we also propose novel tetrahedral split operations to preprocess singularity-restricted frame fields. Experimental results show that our algorithm generates high-quality all-hex meshes from a variety of 3D volumes robustly and efficiently.


title: Towards the Systematic Testing of Aspect-Oriented Programs

abstract: The instant invention relates to the production of composite glass articles involving the bringing together of two or more glasses, while in the molten state, into an integral unit. More particularly, the present invention is concerned with the manufacture of such articles wherein a reaction occurs at the interface between the laminae of molten glass such that an opal phase is spontaneously produced in situ thereat.


title: Internet of Things: A study on security and privacy threats

abstract: Today, the world is influenced by new emerging technologies. As a result we are surrounded by a number of smart devices. These smart devices make our life easy and convenient. On the contrary, we are exposed to a number of threats and cyber attacks. There is always a threat to our privacy. In this paper, we discuss the different applications of IOT and the security threats involved.


title: Indian Sign Language Interpreter with Android Implementation

abstract: Sign language is used as a communication medium among deaf and dumb people to convey the message with each other. A person who can talk and hear properly (normal person) cannot communicate with deaf and dumb person unless he/she is familiar with sign language. Same case is applicable when a deaf and dumb person wants to communicate with a normal person or blind person. In order to bridge the gap in communication among deaf and dumb community and normal community, lot of research work has been carried out to automate the process of sign language interpretation with the help of image processing and pattern recognition techniques. This paper proposes optimized approaches of implementing the famous Viola Jones algorithm with LBP features for hand gesture recognition which will recognize Indian sign language gestures in a real time environment. The performance analysis of the proposed approaches is presented along with the experimental results. An optimized algorithm has been implemented in the form of an android application and tested with real time data.


title: Seeing Mt. Rainier: Lucky imaging for multi-image denoising, sharpening, and haze removal

abstract: Photographing distant objects is challenging for a number of reasons. Even on a clear day, atmospheric haze often represents the majority of light received by a camera. Unfortunately, dehazing alone cannot create a clean image. The combination of shot noise and quantization noise is exacerbated when the contrast is expanded after haze removal. Dust on the sensor that may be unnoticeable in the original images creates serious artifacts. Multiple images can be averaged to overcome the noise, but the combination of long lenses and small camera motion as well as time varying atmospheric refraction results in large global and local shifts of the images on the sensor. An iconic example of a distant object is Mount Rainier, when viewed from Seattle, which is 90 kilometers away. This paper demonstrates a methodology to pull out a clean image of Mount Rainier from a series of images. Rigid and non-rigid alignment steps brings individual pixels into alignment. A novel local weighted averaging method based on ideas from “lucky imaging” minimizes blur, resampling and alignment errors, as well as effects of sensor dust, to maintain the sharpness of the original pixel grid. Finally, dehazing and contrast expansion results in a sharp clean image.


title: A Combined Algorithm for Malaria Detection from Thick Smear Blood Slides

abstract: Thick blood smear examination is a necessary part for rapid screening of malaria parasite. Primary diagnosis of malaria by thick smear examination is cheap and highly sensitive, advocated by the World Health Organization (WHO). For the examination of thick blood smear, manpower and time can be reduced by using automated computational techniques. These techniques would facilitate such detection that are usually based on morphology and in few cases on analysis of colors. In this work, we propose a combined algorithm consisting of morphological operations and color based pixel discrimination technique to identify malaria parasites from thick smear images of Plasmodium vivax. Using morphological operation segmentation of cells from thick blood smear image is done and color based pixel discriminator distinguished malaria cells from segmented image. Evaluation of percentage of detection and False Positive Rate (FPR) shows that our proposed algorithm has significantly higher predictive rate, and lower FPR as compared to any existing methodology when tested on same input slides. Importantly the reported method does not need any training set and assumes unsupervised methodology. This makes the portability of the approach at various scales as discussed in the paper. ::: The experiment has been carried out using JSB (Jaswant Singh Battacharya) stained thick blood smear images. It is assuming that our algorithm can identify malaria in less perfect dirty slides. This makes the algorithm more powerful and robust.


title: Antenna-in-Package and Transmit–Receive Switch for Single-Chip Radio Transceivers of Differential Architecture

abstract: A fully differential architecture from the antenna to the integrated circuit is proposed for radio transceivers in this paper. The physical implementation of the architecture into truly single-chip radio transceivers is described for the first time. Two key building blocks, the differential antenna and the differential transmit-receive (T-R) switch, were designed, fabricated, and tested. The differential antenna implemented in a package in low-temperature cofired-ceramic technology achieved impedance bandwidth of 2%, radiation efficiency of 84%, and gain of 3.2 dBi at 5.425 GHz in a size of 15 x 15 x 1.6 mm3. The differential T-R switch in a standard complementary metal-oxide-semiconductor technology achieved 1.8-dB insertion loss, 15-dB isolation, and 15-dBm 1-dB power compression point (P 1dB) without using additional techniques to enhance the linearity at 5.425 GHz in a die area of 60 x 40 mum2.


title: Classroom salon: a tool for social collaboration

abstract: Classroom Salon is an on-line social collaboration tool that allows instructors to create, manage, and analyze social net- works (called Salons) to enhance student learning. Students in a Salon can cooperatively create, comment on, and modify documents. Classroom Salon provides tools that allow the instructor to monitor the social networks and gauge both student participation and individual effectiveness. This pa- per describes Classroom Salon, provides some use cases that we have developed for introductory computer science classes and presents some preliminary observations of using this tool in several computer science courses at Carnegie Mellon University.


title: EBV-negative lymphoproliferative disease with hyper-IgA, in a child with combined liver and small bowel transplantation

abstract: Abstract: A 4-year-old boy presented 14 months after liver and small bowel transplantation with fever, diarrhea, elevated liver enzymes, thrombocytopenia and autoantibodies. Total gammaglobulins level was normal but the level of plasma IgA1 was very high. The blood PCR for Epstein–Barr virus (EBV) was negative. The ileal biopsy disclosed a lymphoplasmacytic infiltration. The EBER probe was negative on the small bowel biopsies. The child was considered as suffering from a non-EBV-induced posttransplant lymphoproliferative disorder (PTLD). The high IgA level was presumed to be secreted by proliferating plasma cells in the transplanted bowel. Immunosuppression was reduced; but the efficacy was incomplete and an anti-CD20 antibody was added. There was complete resolution of symptoms and normalization of the IgA level. As IgA1 is mostly of intestinal origin, this unusual presentation of PTLD should lead to a high suspicion of a small bowel proliferating process.


title: Generating the Future with Adversarial Transformers

abstract: We learn models to generate the immediate future in video. This problem has two main challenges. Firstly, since the future is uncertain, models should be multi-modal, which can be difficult to learn. Secondly, since the future is similar to the past, models store low-level details, which complicates learning of high-level semantics. We propose a framework to tackle both of these challenges. We present a model that generates the future by transforming pixels in the past. Our approach explicitly disentangles the models memory from the prediction, which helps the model learn desirable invariances. Experiments suggest that this model can generate short videos of plausible futures. We believe predictive models have many applications in robotics, health-care, and video understanding.


title: A comparison of the grip force distribution in natural hands and in prosthetic hands

abstract: Purpose: The aim of this study is to analyse the grip force distribution for different prosthetic hand designs and the human hand fulfilling a functional task.Method: A cylindrical object is held with a power grasp and the contact forces are measured at 20 defined positions. The distributions of contact forces in standard electric prostheses, in a experimental prosthesis with an adaptive grasp, and in human hands as a reference are analysed and compared. Additionally, the joint torques are calculated and compared.Results: Contact forces of up to 24.7 N are applied by the middle and distal phalanges of the index finger, middle finger, and thumb of standard prosthetic hands, whereas forces of up to 3.8 N are measured for human hands. The maximum contact forces measured in a prosthetic hand with an adaptive grasp are 4.7 N. The joint torques of human hands and the adaptive prosthesis are comparable.Conclusions: The analysis of grip force distribution is proposed as an additional parameter to rate the perform...


title: Autonomous driving: investigating the feasibility of car-driver handover assistance

abstract: Self-driving vehicles are able to drive on their own as long as the requirements of their autonomous systems are met. If the system reaches the boundary of its capabilities, the system has to de-escalate (e.g. emergency braking) or hand over control to the human driver. Accordingly, the design of a functional handover assistant requires that it enable drivers to both take over control and feel comfortable while doing so -- even when they were "out of the loop" with other tasks. We introduce a process to hand over control from a full self-driving system to manual driving, and propose a number of handover implementation strategies. Moreover, we designed and implemented a handover assistant based on users' preferences and conducted a user study with 30 participants, whose distraction was ensured by a realistic distractor task. Our evaluation shows that car-driver handovers prompted by multimodal (auditory and visual) warnings are a promising strategy to compensate for system boundaries of autonomous vehicles. The insights we gained from the take-over behavior of drivers led us to formulate recommendations for more realistic evaluation settings and the design of future handover assistants.


title: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

abstract: While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations when given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.


title: Wideband low-profile circular polarization slot antenna based on metasurface

abstract: A wideband low-profile circular polarization (CP) slot antenna is proposed based on the corner-cut square metasurface structure. Both simulations and measurements are performed to demonstrate the CP antenna and good agreements are obtained. The proposed antenna exhibits a measured impedance matching bandwidth of 32% and an AR bandwidth of 20% centered at 5.6 GHz.


title: A compact soft actuator unit for small scale human friendly robots

abstract: This paper presents the development of a new compact soft actuation unit intended to be used in multi degree of freedom and small scale robotic systems such as the child humanoid robot “iCub” [1]. Compared to the other existing series elastic linear or rotary implementations the proposed design shows high integration density and wider passive deflection. The miniaturization of the newly developed high performance unit was achieved with a use of a new rotary spring module based on a novel arrangement of linear springs.


Modified M-Sequence UWB-Radar


title: Development and validation of the Bullying and Cyberbullying Scale for Adolescents: A multi‐dimensional measurement model

abstract: Intervention on adolescent bullying is reliant on valid and reliable measurement of victimization and perpetration experiences across different behavioural expressions. This study developed and validated a survey tool that integrates measurement of both traditional and cyber bullying to test a theoretically driven multi-dimensional model. Adolescents from 10 mainstream secondary schools completed a baseline and follow-up survey (N = 1,217; M = 14 years; 66.2% male). The Bullying and cyberbullying Scale for Adolescents (BCS-A) developed for this study comprised parallel victimization and perpetration subscales, each with 20 items. Additional measures of bullying (Olweus Global Bullying and the Forms of Bullying Scale [FBS]), as well as measures of internalizing and externalizing problems, school connectedness, social support, and personality, were used to further assess validity. Factor structure was determined, and then, the suitability of items was assessed according to the following criteria: (1) factor interpretability, (2) item correlations, (3) model parsimony, and (4) measurement equivalence across victimization and perpetration experiences. The final models comprised four factors: physical, verbal, relational, and cyber. The final scale was revised to two 13-item subscales. The BCS-A demonstrated acceptable concurrent and convergent validity (internalizing and externalizing problems, school connectedness, social support, and personality), as well as predictive validity over 6 months. The BCS-A has sound psychometric properties. This tool establishes measurement equivalence across types of involvement and behavioural forms common among adolescents. An improved measurement method could add greater rigour to the evaluation of intervention programmes and also enable interventions to be tailored to subscale profiles.


title: Deep Learning without Poor Local Minima

abstract: In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). Moreover, we prove that the same four statements hold for deep linear neural networks with any depth, any widths and no unrealistic assumptions. As a result, we present an instance, for which we can answer to the following question: how difficult to directly train a deep model in theory? It is more difficult than the classical machine learning models (because of the non-convexity), but not too difficult (because of the nonexistence of poor local minima and the property of the saddle points). We note that even though we have advanced the theoretical foundations of deep learning, there is still a gap between theory and practice.


title: Development of Transgenic Papaya through Agrobacterium-Mediated Transformation

abstract: Transgenic papaya plants were regenerated from hypocotyls and immature zygotic embryo after cocultivation with Agrobacterium tumefaciens LBA-4404 carrying a binary plasmid vector system containing neomycin phosphotransferase (nptII) gene as the selectable marker and β-glucuronidase (GUS) as the reporter gene. The explants were co-cultivated with Agrobacterium tumefaciens on regeneration medium containing 500 mg/L carbenicillin + 200 mg/L cefotaxime for one week. The cocultivated explants were transferred into the final selection medium containing 500 mg/L carbenicillin + 200 mg/L cefotaxime + 50 mg/L kanamycin for callus induction as well as plant regeneration. The callus derived from the hypocotyls of Carica papaya cv. Shahi showed the highest positive GUS activities compared to Carica papaya cv. Ranchi. The transformed callus grew vigorously and formed embryos followed by transgenic plantlets successfully. The result of this study showed that the hypocotyls of C. papaya cv. Shahi and C. papaya cv. Ranchi are better explants for genetic transformation compared to immature embryos. The transformed C. papaya cv. Shahi also showed the maximum number of plant regeneration compared to that of C. papaya cv. Ranchi.


title: Computationally efficient convolved multiple output gaussian processes

abstract: Recently there has been an increasing interest in regression methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance function that, whilst being positive semi-definite, captures the dependencies between all the data points and across all the outputs. One approach to account for non-trivial correlations between outputs employs convolution processes. Under a latent function interpretation of the convolution transform we establish dependencies between output variables. The main drawbacks of this approach are the associated computational and storage demands. In this paper we address these issues. We present different efficient approximations for dependent output Gaussian processes constructed through the convolution formalism. We exploit the conditional independencies present naturally in the model. This leads to a form of the covariance similar in spirit to the so called PITC and FITC approximations for a single output. We show experimental results with synthetic and real data, in particular, we show results in school exams score prediction, pollution prediction and gene expression data.


High-Resolution Angle Estimation for an Automotive FMCW Radar Sensor