title: Combined Phase-Shift and Frequency Modulation of a Dual-Active-Bridge AC–DC Converter With PFC

text: This paper presents a combined phase-shift and frequency modulation scheme of a dual-active-bridge (DAB) ac- dc converter with power factor correction (PFC) to achieve zero voltage switching (ZVS) over the full range of the ac mains voltage. The DAB consists of a half bridge with bidirectional switches on the ac side and a full bridge on the dc side of the isolation transformer to accomplish single-stage power conversion. The modulation scheme is described by means of analytical formulas, which are used in an optimization procedure to determine the optimal control variables for minimum switch commutation currents. Furthermore, an ac current controller suitable for the proposed modulation scheme is described. A loss model and measurements on a 3.3-kW electric vehicle battery charger to connect to the 230 Vrms / 50-Hz mains considering a battery voltage range of 280-420 V validate the theoretical analysis.


title: Fundamentals of Artificial Neural Networks

text: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have Historically the data pairs and logical, fashion transition while associative memories commonly? This organization of such as a, clear and algorithms search simulated annealing learning algorithm. After the utility of how to, discover form. More than three different levels of the cost function will. Dean pomerleau in reinforcement and they, discovered two chapters on. This view the application for handwriting recognition this approach by arrows originating.


title: 68 Implementing Dubins Airplane Pathson Fixed-Wing UAVs !

text: A well-known path-planning technique for mobile robots or planar aerial vehicles is to use Dubins paths, which are minimum-distance paths between two configurations subject to the constraints of the Dubins car model. An extension of this method to a three-dimensional Dubins airplane model has recently been proposed. This chapter builds on that work showing a complete architecture for implementing Dubins airplane paths on small fixed-wing UAVs. The existing Dubins airplane model is modified to be more consistent with the kinematics of a fixed-wing aircraft. The chapter then shows how a recently proposed vector-field method can be used to design a guidance law that causes the Dubins airplane model to follow straight-line and helical paths. Dubins airplane paths are more complicated than Dubins car paths because of the altitude component. Based on the difference between the altitude of the start and end configurations, Dubins airplane paths can be classified as low, medium, or high altitude gain. While for medium and high altitude gain there are many different Dubins airplane paths, this chapter proposes selecting the path that maximizes the average altitude throughout the maneuver. The proposed architecture is implemented on a six degree-of-freedom Matlab/Simulink simulation of an Aerosonde UAV, and results from this simulation demonstrate the effectiveness of the technique. 68.


title: The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers

text: When used in classrooms, personalized learning software allows students to work at their own pace, while freeing up the teacher to spend more time working one-on-one with students. Yet such personalized classrooms also pose unique challenges for teachers, who are tasked with monitoring classes working on divergent activities, and prioritizing help-giving in the face of limited time. This paper reports on the co-design, implementation, and evaluation of a wearable classroom orchestration tool for K-12 teachers: mixed-reality smart glasses that augment teachers' realtime perceptions of their students' learning, metacognition, and behavior, while students work with personalized learning software. The main contributions are: (1) the first exploration of the use of smart glasses to support orchestration of personalized classrooms, yielding design findings that may inform future work on real-time orchestration tools; (2) Replay Enactments: a new prototyping method for real-time orchestration tools; and (3) an in-lab evaluation and classroom pilot using a prototype of teacher smart glasses (Lumilo), with early findings suggesting that Lumilo can direct teachers' time to students who may need it most.


title: Shortest-path kernels on graphs

text: Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.


title: On effect size.

text: The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.


title: Models of the self: self-construals and gender.

text: The authors first describe individual differences in the structure of the self. In the independent self-construal, representations of others are separate from the self. In the interdependent self-construal, others are considered part of the self (H. Markus & S. Kitayama, 1991). In general, men in the United States are thought to construct and maintain an independent self-construal, whereas women are thought to construct and maintain an interdependent self-construal. The authors review the psychological literature to demonstrate that many gender differences in cognition, motivation, emotion, and social behavior may be explained in terms of men's and women's different self-construals. Recognition of the interdependent self-construal as a possible alternative conception of the self may stimulate new investigations into the ways the self influences a person's thinking, feeling, and behaving.


title: Color reduction based on ant colony

text: In this article a method for color reduction based on ant colony algorithm is presented. Generally color reduction involves two steps: choosing a proper palette and mapping colors to this palette. This article is about the first step. Using ant colony algorithm, pixel clusters are formed based on their colors and neighborhood information to make the final palette. A comparison between the results of the proposed method and some other methods is presented. There are some parameters in the proposed method which can be set based on user needs and priorities. This increases the flexibility of the method. 2007 Elsevier B.V. All rights reserved.


title: Subdermal neo-umbilicoplasty in abdominoplasty

text: Umbilicoplasty is an important surgical procedure in abdominoplasty, regardless of the technique used. An unaesthetic umbilicus often irreversibly affects surgical outcomes. This study describes the experience of our team with the subdermal neo-umbilicoplasty technique and assesses patient satisfaction with the appearance of the new umbilicus. Fifty-eight patients with abdominal deformity underwent abdominoplasty with subdermal neo-umbilicoplasty. Patients were followed up for at least 1 year with photographic documentation, assessment of patient satisfaction, and evaluation of eventual postoperative complications. Postoperative complications included one case of shallow umbilicus, four cases of superficial necrosis, and one case of midline deviation. No patient required surgical revision. There was a high level of patient satisfaction with the natural-looking umbilicus. Subdermal neo-umbilicoplasty resulted in low postoperative complications and provided a new, natural-looking umbilicus without external scars. Level of evidence: Level IV, therapeutic study


title: Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation

text: Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.


title: Okapi at TREC-3

text: During the course of TREC{1 the low-level search functions were split o into a separate Basic Search System (BSS) [2], but retrieval and ranking of documents was still done using the \classical" probabilistic model of Robertson and Sparck Jones[7] with no account taken of document length or term frequency within document or query. Four runs were submitted to NIST for evaluation: automatic ad hoc, automatic routing, manual ad hoc and manual ad hoc with feedback. The results were undistinguished, although not among the worst. Of the ad hoc runs, the manual was better than the automatic (in which only the CONCEPTS elds of the topics were used), and feedback appeared bene cial.


title: Mathematical Models for Natural Gas Forecasting

text: It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers’ natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDay , which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay’s forecasting performance also is presented.


title: Real Time Vehicle Diagnostics Using Head Mounted Displays

text: This thesis evaluates how a head mounted display (HMD) can be used to increase usability compared to existing computer programs that are used during maintenance work on vehicles. Problems identified during a case study in a vehicle workshop are first described. As an attempt to solve some of the identified problems a prototype application using a HMD was developed. The prototype application aids the user during troubleshooting of systems on the vehicle by leading the mechanic with textual information and augmented reality (AR). Assessment of the prototype application was done by comparing it to the existing computer program and measuring error rate and time to completion for a predefined task. Usability was also measured using the System Usability Scale. The assessment showed that HMDs can provide higher usability in terms of efficiency and satisfaction. Furthermore, the thesis describes and discusses other possibilities and limitations that usage of HMDs and AR can lead to that were identified both from theory and during implementation.


title: Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis

text: Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics [1] and sensor array processing [2]), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states [7]). In many applications, the possibly huge tensors can be approximated well by compact multilinear models or decompositions. Tensor decompositions are more versatile tools than the linear models resulting from traditional matrix approaches. Compared to matrices, tensors have at least one extra dimension. The number of elements in a tensor increases exponentially with the number of dimensions, and so do the computational and memory requirements. The exponential dependency (and the problems that are caused by it) is called the curse of dimensionality. The curse limits the order of the tensors that can be handled. Even for a modest order, tensor problems are often large scale. Large tensors can be handled, and the curse can be alleviated or even removed by using a decomposition that represents the tensor instead of using the tensor itself. However, most decomposition algorithms require full tensors, which renders these algorithms infeasible for many data sets. If a tensor can be represented by a decomposition, this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.


title: Robust Keyframe-based Dense SLAM with an RGB-D Camera

text: In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g. fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.


title: Predictive analytics with surveillance big data

text:
In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of archived aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed.
 The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool for correlating the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization. This paper describes a scalable service architecture, implementation and the value it adds to the aviation domain.


title: Class-DE Ultrasound Transducer Driver for HIFU Therapy

text: This paper presents a practical implementation of an integrated MRI-compatible CMOS amplifier capable of directly driving a piezoelectric ultrasound transducer suitable for high-intensity focused ultrasound (HIFU) therapy. The amplifier operates in Class DE mode without the need for an output matching network. The integrated amplifier has been implemented with the AMS AG H35 CMOS process. A class DE amplifier design methodology for driving unmatched piezoelectric loads is presented along with simulation and experimental results. The proposed design achieves approximately 90% efficiency with over 800 mW of output power at 1010 kHz. The total die area including pads is 2 mm2. Compatibility with MRI was validated with B1 imaging of a phantom and the amplifier circuit.


title: WiMo: location-based emotion tagging

text: In this paper we introduce WiMo, a location-based social networking tool that enables users to share and store their emotional feelings about places. WiMo creates a mobile social network based on common interests and enables users to share not just information but also opinions, experiences and passions. The application uses a geo-emotional tagging system running on a GPS enabled mobile phone to ascribe emotions to places. In this paper we describe the development of an emotion tagging interface and present a case study scenario to situate the application in a real world setting. We then describe work-in-progress on the development of the WiMo prototype interface, and the interaction process for users. We further outline the features of the system and finally discuss next steps in the development of the application.


title: Addressing DODAG inconsistency attacks in RPL networks

text: RPL is a routing protocol for low-power and lossy constrained node networks. A malicious node can manipulate header options used by RPL to track DODAG inconsistencies, thereby causing denial of service attacks, increased control message overhead, and black-holes at the targeted node. RPL counteracts DODAG inconsistencies by using a fixed threshold, upon reaching which all subsequent packets with erroneous header options are ignored. However, the fixed threshold is arbitrary and does not resolve the black-hole issue either. To address this we present a mitigation strategy that allows nodes to dynamically adapt against a DODAG inconsistency attack. We also present the forced black-hole attack problem and a solution that can be used to mitigate it. Results from our experiments show that our proposed approach mitigates these attacks without any significant overhead.


title: Episodic and semantic memory in mild cognitive impairment

text: Little is known about episodic and semantic memory in the early predementia stage of Alzheimer's disease (AD), which is referred to as mild cognitive impairment (MCI). To explore person knowledge, item recognition and spatial associative memory, we designed the Face Place Test (FPT). A total of 75 subjects participated: 22 patients with early AD, 24 with MCI and 29 matched controls. As predicted, AD patients showed significant deficits in person naming, item recognition and recall of spatial location (placing). Surprisingly, subjects with MCI were also impaired on all components. There was no significant difference between AD and MCI except on the placing component. Analysis of the relationship between semantic (naming) and episodic (recognition and placing) components of the FPT revealed a significant association between the two episodic tasks, but not between episodic and semantic performance. Patients with MCI show deficits of episodic and semantic memory. The extent of impairment suggests dysfunction beyond the medial temporal lobe. The FPT might form the basis of a sensitive early indicator of AD.


title: Cryptanalysis of DES Implemented on Computers with Cache

text: This paper presents the results of applying an attack against the Data Encryption Standard (DES) implemented in some applications, using side-channel information based on CPU delay as proposed in [11]. This cryptanalysis technique uses side-channel information on encryption processing to select and collect effective plaintexts for cryptanalysis, and infers the information on the expanded key from the collected plaintexts. On applying this attack, we found that the cipher can be broken with 2 known plaintexts and 2 calculations at a success rate > 90%, using a personal computer with 600-MHz Pentium III. We discuss the feasibility of cache attack on ciphers that need many S-box look-ups, through reviewing the results of our experimental attacks on the block ciphers excluding DES, such as AES.


title: Differential Power Analysis

text: are the property of their respective owners. The information contained in this presentation is provided for illustrative purposes only, and is provided without any guarantee or warranty whatsoever, and does not necessarily represent official opinions of CRI or its partners. Confidential-unauthorized copying, use or redistribution is prohibited.


Blind Signatures for Untraceable Payments


title: On the importance of accurate weak classifier learning for boosted weak classifiers

text: Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.


title: Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network

text: In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.


title: A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

text: In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.


title: LINE: Large-scale Information Network Embedding

text: This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\footnote{\url{https://github.com/tangjianpku/LINE}}.


title: Reverse wire bonding and phosphor printing for LED wafer level packaging

text: Solid state lighting is a good alternative light source with reduced energy consumption. Light-emitting diode (LED) is very efficient in turning electrical energy into light. LED has a number of advantages over the traditional light sources. The optical performance of the LED component is very critical. In general, white light can be obtained by applying phosphor on a blue LED chip. The blue light from the LED excites the phosphor to emit yellow light. The blue and yellow light mixes together to give white light. In order to obtain a good optical performance, it is necessary to apply phosphor properly. It is challenging to distribute a right amount of phosphor on the LED die. Besides, phosphor dispensing is usually the slowest process when compared with die bonding and wire bonding. This controls the overall throughput of the LED packaging process. There are different methods to apply the phosphor. The phosphor is mixed with epoxy or silicone to form slurry and is then dispensed onto the chip. However, the spatial color distribution is poor if phosphor slurry is used. Conformal phosphor coating can be used to improve the spatial color distribution. In this paper, an innovative phosphor stencil printing method is proposed. This paper demonstrates the feasibility of the phosphor stencil printing process for wafer-level LED packaging. LEDs are first mounted on a wafer submount. Wire bonds are used as interconnect. The phosphor is stencil printed on the chip surface after wire bond. The minimum phosphor layer thickness is controlled by the wire bond loop height. In order to achieve a low loop height, reverse wire bonding is used. The first bond is on the wafer submount and the second bond is on the LED chip. The reverse wire bond has a very low profile which allows a thin layer of phosphor to be printed on the chip surface. Prototypes are successfully fabricated. A uniform layer of phosphor is stencil printed on the LED chip on the wafer submount. Experimental result shows that the proposed phosphor printing method is very effective in distributing the right amount of phosphor on the chip surface.


title: 5G cellular network integration with SDN: Challenges, issues and beyond

text: The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.


title: Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

text: Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements (p < 0.01) over the state-of-the-art in both a human observer study and the semantic interpretability score.


title: A Pose-Invariant Descriptor for Human Detection and Segmentation

text: We present a learning-based, sliding window-style approach for the problem of detecting humans in still images. Instead of traditional concatenation-style image location-based feature encoding, a global descriptor more invariant to pose variation is introduced. Specifically, we propose a principled approach to learning and classifying human/non-human image patterns by simultaneously segmenting human shapes and poses, and extracting articulation-insensitive features. The shapes and poses are segmented by an efficient, probabilistic hierarchical part-template matching algorithm, and the features are collected in the context of poses by tracing around the estimated shape boundaries. Histograms of oriented gradients are used as a source of low-level features from which our pose-invariant descriptors are computed, and kernel SVMs are adopted as the test classifiers. We evaluate our detection and segmentation approach on two public pedestrian datasets.


title: Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style

text: How much does “free shipping!” help an advertisement’s ability to persuade? This paper presents two methods for performance attribution: finding the degree to which an outcome can be attributed to parts of a text while controlling for potential confounders1. Both algorithms are based on interpreting the behaviors and parameters of trained neural networks. One method uses a CNN to encode the text, an adversarial objective function to control for confounders, and projects its weights onto its activations to interpret the importance of each phrase towards each output class. The other method leverages residualization to control for confounds and performs interpretation by aggregating over learned word vectors. We demonstrate these algorithms’ efficacy on 118,000 internet search advertisements and outcomes, finding language indicative of high and low click through rate (CTR) regardless of who the ad is by or what it is for. Our results suggest the proposed algorithms are high performance and data efficient, able to glean actionable insights from fewer than 10,000 data points. We find that quick, easy, and authoritative language is associated with success, while lackluster embellishment is related to failure. These findings agree with the advertising industry’s emperical wisdom, automatically revealing insights which previously required manual A/B testing to discover.


title: A survey on comparison of electric motor types and drives used for electric vehicles

text: In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.


title: Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

text: We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.


title: Think-aloud protocols: Analyzing three different think-aloud protocols with counts of verbalized frustrations in a usability study of an information-rich Web site

text: We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of an information-rich Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. With the goal of keeping unintended bias to a minimum, data analysis did not count the number of identified usability problems by condition, which was considered too subjective. Rather, the study collected the number of verbalized and non-verbalized counts of frustration by condition that users experienced. The study also did a count of the number of verbalized and non-verbalized instances of positive comments by condition that users expressed. Results show that there were no statistical differences in the number of counts by condition with respect to the traditional, speech communication, or coaching condition. The study concludes that simply counting the verbalizations of users by condition does not give enough information to determine whether any of the conditions would lead to a better understanding of the usability problems associated with the Web site.


title: An empirical study on the use of i* by non-technical stakeholders: the case of strategic dependency diagrams

text: Early phases of information systems engineering include the understanding of the enterprise’s context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study’s context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.


title: A high-PSR LDO using a feedforward supply-noise cancellation technique

text: A feed-forward noise cancellation (FFNC) technique to improve the power supply noise rejection (PSR) of a low dropout regulator (LDO) is presented. The proposed FFNC operates in conjunction with a conventional LDO and extends the noise rejection bandwidth by nearly an order of magnitude. Fabricated in 0.18µm CMOS, at 10mA load current, the prototype achieves a PSR of −50dB and −25dB at 1MHz and 10MHz supply noise frequencies, respectively. Compared to a conventional LDO, this represents an improvement of at least 30dB at 1MHz and 15dB at 10MHz. The prototype uses only 20pF load capacitance and occupies an active area of 0.04mm2.


title: Molecular assembly on two-dimensional materials.

text: Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional (2D) materials is a developing field driven by the interest in functionalization of 2D materials in order to tune their electronic properties. This has resulted in the discovery of several rich and interesting phenomena. Here, we review this progress with an emphasis on the electronic properties of the adsorbates and the substrate in well-defined systems, as unveiled by scanning tunneling microscopy. The review covers three aspects of the self-assembly. The first one focuses on non-covalent self-assembly dealing with site-selectivity due to inherent moiré pattern present on 2D materials grown on substrates. We also see that modification of intermolecular interactions and molecule-substrate interactions influences the assembly drastically and that 2D materials can also be used as a platform to carry out covalent and metal-coordinated assembly. The second part deals with the electronic properties of molecules adsorbed on 2D materials. By virtue of being inert and possessing low density of states near the Fermi level, 2D materials decouple molecules electronically from the underlying metal substrate and allow high-resolution spectroscopy and imaging of molecular orbitals. The moiré pattern on the 2D materials causes site-selective gating and charging of molecules in some cases. The last section covers the effects of self-assembled, acceptor and donor type, organic molecules on the electronic properties of graphene as revealed by spectroscopy and electrical transport measurements. Non-covalent functionalization of 2D materials has already been applied for their application as catalysts and sensors. With the current surge of activity on building van der Waals heterostructures from atomically thin crystals, molecular self-assembly has the potential to add an extra level of flexibility and functionality for applications ranging from flexible electronics and OLEDs to novel electronic devices and spintronics.


title: Modelling Valence and Arousal in Facebook posts

text: Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologicallytrained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy – correlated at r = .65 with valence and r = .85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.


title: Gesture Recognition with a 3-D Accelerometer

text: Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.


Efficient and Effective Solutions for Video Classification


title: Literature Review of Automatic Multiple Documents Text Summarization

text: For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abundant. So, the automatic text summarization is demanded for salient information retrieval. Automatic text summarization is a system of summarizing text by computer where a text is given to the computer as input and the output is a shorter and less redundant form of the original text. An informative précis is very much helpful in our daily life to save valuable time. Research was first started naively on single document abridgement but recently information is found from various sources about a single topic in different website, journal, newspaper, text book, etc., for which multi-document summarization is required. In this paper, automatic multiple documents text summarization task is addressed and different procedure of various researchers are discussed. Various techniques are compared here that have done for multi-document summarization. Some promising approaches are indicated here and particular concentration is dedicated to describe different methods from raw level to similar like human experts, so that in future one can get significant instruction for further analysis.


title: Genome-Wide Regression and Prediction with the BGLR Statistical Package

text: Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.


title: Emotional Integration and Advertising Effectiveness : Case of Perfumes Advertising

text: This paper examines emotions in advertising, its effects and functioning. Using an interview-based experiment on 256 participants, we found that emotions perceived during an advertising exposure could play an important role in eliciting responses towards the ad and the brand. However, this process is true provided that consumers associate the perceived emotion to the exposed brand or its consumption experience. Furthermore, we have identified efficiency differences between magazine ads, depending on how they visually describe emotions. In particular, we study emotional integration in advertising, i.e. salience of emotions expressed towards the ad, the presence of core brand information and clarity of the advertising message about the hedonic attributes of consumption. Interestingly, the impact of the staging process of emotions is moderated by respondents-specific variables, including their need for emotion, tolerance for ambiguity, their need for structure and need for cognition. KeywordsEmotional Reactions ;Emotional Integration; Need for Emotion; Tolerance for Ambiguity; Need for Structure; Need for Cognition; Advertising Effectiveness.


title: A High-Speed High-Resolution Latch Comparator for Pipeline Analog-to-Digital Converters

text: A high-speed and high-resolution comparator intended to be implemented in a 12 bit 100 MHz pipeline analog-to-digital converter (ADC) for frequency wireless local area network application is proposed. The designed comparator presents a rail-to-rail input range preamplifier without any capacitance required. This comparator with a novel architecture of output stage achieves very high speed at a low kickback noise. The simulation results using a 0.35 mum TSMC CMOS process technology show that this comparator exhibits a propagation delay of 2.8 ns and has a very high resolution for a rail-to-rail input signal range, while consumes only 1.0 mW of power with 5.0 V voltage supply.


title: CMOS analog circuit design via geometric programming

text: This talk presents a new method for optimizing and automating component sizing in CMOS analog circuits. It is shown that a wide variety of circuit performance measures have a special form, i.e., they are posynomial functions of the design variables. As a result, circuit design problems can be posed as geometric programs, a special type of convex optimization problem for which very efficient global optimization methods exist. The synthesis method is therefore fast, and determines the globally optimal design; in particular, the final solution is completely independent of the starting point, and infeasible specifications are unambiguously detected. Also, because the method is highly efficient, in practice it can be used to carry out robust designs and quickly explore the design space. We show that despite the restricted form of geometric programs, a large variety of circuit design problems can be posed as geometric programs.


title: Certain Topics in Telegraph Transmission Theory

text: The most obvious method for determining the distortion of telegraph signals is to calculate the transients of the telegraph system. This method has been treated by various writers, and solutions are available for telegraph lines with simple terminal conditions. It is well known that the extension of the same methods to more complicated terminal conditions, which represent the usual terminal apparatus, leads to great difficulties. The present paper attacks the same problem from the alternative standpoint of the steady-state characteristics of the system. This method has the advantage over the method of transients that the complication of the circuit which results from the use of terminal apparatus does not complicate the calculations materially. This method of treatment necessitates expressing the criteria of distortionless transmission in terms of the steady-state characteristics. Accordingly, a considerable portion of the paper describes and illustrates a method for making this translation. A discussion is given of the minimum frequency range required for transmission at a given speed of signaling. In the case of carrier telegraphy, this discussion includes a comparison of single-sideband and double-sideband transmission. A number of incidental topics is also discussed.


title: A low-kickback-noise latched comparator for high-speed flash analog-to-digital converters

text: In traditional comparators especially for flash ADCs, one serious problem is the kick back noise, which disturbs the input signal voltages and consequently might cause errors at the outputs of the ADCs. In this paper, we propose a novel CMOS latched comparator with very low kickback noise for high-speed flash ADCs. The proposed comparator separates analog preamplifier from the positive feedback digital dynamic latch so as to reduce the influence of the kickback noise. Simulation results based on a mixed signal CMOS 0.35 /spl mu/m technology show that, this comparator can work at a maximum clock frequency of 500 MHz with very reduced kickback noise compared with conventional architectures.


title: A 0.35 /spl mu/m CMOS comparator circuit for high-speed ADC applications

text: A high-speed differential clocked comparator circuit is presented. The comparator consists of a preamplifier and a latch stage followed by a dynamic latch that operates as an output sampler. The output sampler circuit consists of a full transmission gate (TG) and two inverters. The use of this sampling stage results in a reduction in the power consumption of this high-speed comparator. Simulations show that charge injection of the TG adds constructively to the sampled signal value, therefore amplifying the sampled signal with a modest gain of 1.15. Combined with the high gain of the inverters, the sampled signals are amplified toward the rail voltages. This comparator is designed and fabricated in a 0.35 /spl mu/m standard digital CMOS technology. Measurement results show a sampling frequency of 1 GHz with 16 mV resolution for a 1 V input signal range and 2 mW power consumption from a 3.3 V supply. The architecture can be scaled down to smaller feature sizes and lower supply voltages.


title: Personalization and Customization Technologies

text: The article presents a survey of personalization and customization technologies. It starts with an analysis of user profiling and explains how user profiles can be used for generating personalized products and services. The article explains some standard personalization techniques such as rule-based filtering, collaborative filtering, and content-based filtering. Web usage mining is also explained and it is shown how data mining can be used in learning customer behavior and for providing personalized content. Several applications are discussed such as adaptive websites, recommender systems, adaptive web stores, and intelligent agents. The article concludes by summarizing current trends.


title: Control optimization for a power-split hybrid vehicle

text: Toyota hybrid system (THS) is used in the current best-selling hybrid vehicle on the market - the Toyota Prius. This hybrid system contains a power split planetary gear system which combines the benefits of series and parallel hybrid vehicles. This paper first develops a dynamic model to investigate the control strategy of the THS power train. An equivalent consumption minimization strategy (ECMS) is developed which is based on instantaneous optimization concept. The dynamic programming (DP) technique is then utilized to obtain a performance benchmark and insight toward fine-tuning of the ECMS algorithm for better performance


title: Grounding Language for Interactive Task Learning

text: This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agent's perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain, along with test versions of the world model and ontology, and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.


title: Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

text: This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model


title: Accelerated Attributed Network Embedding

text: Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. It has been observed that network topological structure and node attributes are often strongly correlated with each other. Thus modeling and incorporating node attribute proximity into network embedding could be potentially helpful, though non-trivial, in learning better vector representations. Meanwhile, real-world networks often contain a large number of nodes and features, which put demands on the scalability of embedding algorithms. To bridge the gap, in this paper, we propose an accelerated attributed network embedding algorithm AANE, which enables the joint learning process to be done in a distributed manner by decomposing the complex modeling and optimization into many sub-problems. Experimental results on several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.


title: Tweeting celebrity suicides: Users' reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides.

text: A substantial amount of evidence indicates that news coverage of suicide deaths by celebrities is followed by an increase in suicide rates, suggesting a copycat behavior. However, the underlying process by which celebrity status and media coverage leads to increases in subsequent suicides is still unclear. This study collected over 1 million individual messages ("tweets") posted on Twitter that were related to 26 prominent figures in Japan who died by suicide between 2010 and 2014 and investigated whether media reports on suicide deaths that generated a greater level of reactions by the public are likely to be followed by a larger increase in actual suicides. We also compared the number of Twitter posts and the number of media reports in newspaper and on television to understand whether the number of messages on Twitter in response to the deaths corresponds to the amount of coverage in the traditional media. Using daily data from Japan's national death registry between 2010 and 2014, our analysis found an increase in actual suicides only when suicide deaths generated a large reaction from Twitter users. In contrast, no discernible increase in suicide counts was observed when the analysis included suicide deaths to which Twitter users did not show much interest, even when these deaths were covered considerably by the traditional media. This study also found suicides by relatively young entertainers generated a large number of posts on Twitter. This sharply contrasts with the relatively smaller volume of reaction to them generated by traditional forms of media, which focuses more on the deaths of non-entertainers. The results of this study strongly suggest that it is not sufficient to examine only traditional news media when investigating the impact of media reports on actual suicides.


title: Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles

text: A 1D electrochemical, lumped thermal model is used to explore pulse power limitations and thermal behavior of a 6 Ah, 72 cell, 276 V nominal i-ion hybrid-electric vehicle (HEV) battery pack. Depleted/saturated active material Li surface concentrations in the negative/positive electrodes onsistently cause end of high-rate (∼25 C) pulse discharge at the 2.7 V cell−1 minimum limit, indicating solid-state diffusion is the limiting echanism. The 3.9 V cell−1 maximum limit, meant to protect the negative electrode from lithium deposition side reaction during charge, is overly onservative for high-rate (∼15 C) pulse charges initiated from states-of-charge (SOCs) less than 100%. Two-second maximum pulse charge rate rom the 50% SOC initial condition can be increased by as much as 50% without risk of lithium deposition. Controlled to minimum/maximum oltage limits, the pack meets partnership for next generation vehicles (PNGV) power assist mode pulse power goals (at operating temperatures 16 ◦C), but falls short of the available energy goal. In a vehicle simulation, the pack generates heat at a 320 W rate on a US06 driving cycle at 25 ◦C, with more heat generated at lower temperatures. ess aggressive FUDS and HWFET cycles generate 6–12 times less heat. Contact resistance ohmic heating dominates all other mechanisms, ollowed by electrolyte phase ohmic heating. Reaction and electronic phase ohmic heats are negligible. A convective heat transfer coefficient of = 10.1 W m−2 K−1 maintains cell temperature at or below the 52 ◦C PNGV operating limit under aggressive US06 driving. 2006 Elsevier B.V. All rights reserved.


title: Safe and automatic live update for operating systems

text:
Increasingly many systems have to run all the time with no downtime allowed. Consider, for example, systems controlling electric power plants and e-banking servers. Nevertheless, security patches and a constant stream of new operating system versions need to be deployed without stopping running programs. These factors naturally lead to a pressing demand for live update---upgrading all or parts of the operating system without rebooting. Unfortunately, existing solutions require significant manual intervention and thus work reliably only for small operating system patches.
 In this paper, we describe an automated system for live update that can safely and automatically handle major upgrades without rebooting. We have implemented our ideas in Proteos, a new research OS designed with live update in mind. Proteos relies on system support and nonintrusive instrumentation to handle even very complex updates with minimal manual effort. The key novelty is the idea of state quiescence, which allows updates to happen only in safe and predictable system states. A second novelty is the ability to automatically perform transactional live updates at the process level, ensuring a safe and stable update process. Unlike prior solutions, Proteos supports automated state transfer, state checking, and hot rollback. We have evaluated Proteos on 50 real updates and on novel live update scenarios. The results show that our techniques can effectively support both simple and complex updates, while outperforming prior solutions in terms of flexibility, security, reliability, and stability of the update process.


title: RPA Antagonizes Microhomology-Mediated Repair of DNA Double-Strand Breaks

text: Microhomology-mediated end joining (MMEJ) is a Ku- and ligase IV–independent mechanism for the repair of DNA double-strand breaks that contributes to chromosome rearrangements. Here we used a chromosomal end-joining assay to determine the genetic requirements for MMEJ in Saccharomyces cerevisiae. We found that end resection influences the ability to expose microhomologies; however, it is not rate limiting for MMEJ in wild-type cells. The frequency of MMEJ increased by up to 350-fold in rfa1 hypomorphic mutants, suggesting that replication protein A (RPA) bound to the single-stranded DNA (ssDNA) overhangs formed by resection prevents spontaneous annealing between microhomologies. In vitro, the mutant RPA complexes were unable to fully extend ssDNA and were compromised in their ability to prevent spontaneous annealing. We propose that the helix-destabilizing activity of RPA channels ssDNA intermediates from mutagenic MMEJ to error-free homologous recombination, thus preserving genome integrity.


title: A New Distance-weighted k-nearest Neighbor Classifier

text: In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.


title: Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design

text: Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time.


title: 0 BLIS : A Framework for Rapid Instantiation of BLAS Functionality

text: The BLAS Libray Instantiation Software (BLIS) is a new framework for the rapid instantiation of Basic Linear Algebra Subprograms (BLAS) functionality. The fundamental innovation is the insight that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed in terms of very simple kernels. While others had made similar insights, BLIS brings this set down to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in standard C so that it can be reused and/or re-parameterized for different operations (as well as different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any and all BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the BLIS native interface (which is a C interface that corrects a few known idiosyncracies of the BLAS interface), the traditional Fortran BLAS interface, or through any other higher level interface that chooses to build upon the BLIS interface. Preliminary experimental performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).


title: Smart homes automation using Z-wave protocol

text: The rapid development of information technology and computer networks make them part of almost everything in our daily life and it became impossible to abandon their use. One of the main and important applications of technology in homes is home automation including controlling and automation of electronic and electrical machines remotely. Wireless Home Automation Networks (WHANs) are used in homes to connect the different devices together and to the Internet. In order to control home devices remotely, there are many popular protocols such as INSTEON, ZigBee, and Home Plug. In this paper, we focus on the relatively new protocol called Z-Wave protocol and we discuss it development and applications in smart homes. This wireless protocol has many advantages over the popular and widely used ZigBee protocol as it provides better reliability, low radio rebirth, easy usage, and easy Interoperability.


title: SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports

text: We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.


title: ViewDroid: towards obfuscation-resilient mobile application repackaging detection

text: In recent years, as mobile smart device sales grow quickly, the development of mobile applications (apps) keeps accelerating, so does mobile app repackaging. Attackers can easily repackage an app under their own names or embed advertisements to earn pecuniary profits. They can also modify a popular app by inserting malicious payloads into the original app and leverage its popularity to accelerate malware propagation. In this paper, we propose ViewDroid, a user interface based approach to mobile app repackaging detection. Android apps are user interaction intensive and event dominated, and the interactions between users and apps are performed through user interface, or views. This observation inspires the design of our new birthmark for Android apps, namely, feature view graph, which captures users' navigation behavior across app views. Our experimental results demonstrate that this birthmark can characterize Android apps from a higher level abstraction, making it resilient to code obfuscation. ViewDroid can detect repackaged apps at a large scale, both effectively and efficiently. Our experiments also show that the false positive and false negative rates of ViewDroid are both very low.


title: Autoenucleation in a 84-year-old dementia patient

text: Background Autoenucleation is a severe, rare form of self-mutilation. The majority of cases have been reported in the 15- to 60-year age group, usually in psychiatric patients with a history of depression or schizophrenia, sometimes caused by drug abuse. Case report We report a case of left-sided autoenucleation in an 84-year-old dementia patient suffering from reactive depression. Medical reports mentioned a suicide attempt 2 weeks prior to the incident, whereupon the patient was admitted to the locked ward of a psychiatric hospital. During one night of inpatient stay, the patient manually autoenucleated his left eye. Inspection of the enucleated organ revealed a completely intact globe with an attached optic nerve 5.5 cm in length. The orbit was filled with a massive haematoma. A contrast-enhanced computed tomography (CT) scan showed an orbital haematoma, absence of the left globe and optic nerve and a chiasmatic lesion, accompanied by an intracranial bleeding into the subarachnoid space. Primary wound closure was performed without complications. Visual acuity of the right eye could not be tested due to the patient's lack of cooperation. Conclusion To the best of our knowledge, this is the only reported case of an elderly patient with primary dementia who performed autoenucleation. Other aspects, such as patient history, suicide attempt, manual eye extraction and chiasma lesion are similar to cases reported earlier. The identification and evaluation of intracranial bleedings and chiasmatic lesions that can be associated with autoenucleation requires a contrast-enhanced CT, especially if a long optic nerve fragment is attached to the enucleated globe.


title: Selective Pooling Vector for Fine-Grained Recognition

text: We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.


title: An Open Source Tracking Testbed and Evaluation Web Site

text: We have implemented a GUI-based tracking testbed system in C and Intel OpenCV, running within the Microsoft Windows environment. The motivation for the testbed is to run and log tracking experiments in near real-time. The testbed allows a tracking experiment to be repeated from the same starting state using different tracking algorithms and parameter settings, thereby facilitating comparison of algorithms. We have also developed a tracking evaluation web site to encourage third-party self-evaluation of state-of-the-art tracking algorithms. The site hosts source code for the tracking testbed, a set of ground-truth datasets, and a method for on-line evaluation of uploaded tracking results.


title: A Survey of Securing Networks Using Software Defined Networking

text: Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of networks ranging from the data center to the core, enterprise, and home. The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.


title: Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

text: In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user's search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.


title: Small-footprint keyword spotting using deep neural networks

text: Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.


title: Convolutional neural networks for small-footprint keyword spotting

text: We explore using Convolutional Neural Networks (CNNs) for a small-footprint keyword spotting (KWS) task. CNNs are attractive for KWS since they have been shown to outperform DNNs with far fewer parameters. We consider two different applications in our work, one where we limit the number of multiplications of the KWS system, and another where we limit the number of parameters. We present new CNN architectures to address the constraints of each applications. We find that the CNN architectures offer between a 27-44% relative improvement in false reject rate compared to a DNN, while fitting into the constraints of each application.


Convolutional networks for images, speech, and time series


title: UWB antenna for brain stroke and brain tumour detection

text: UWB Pentagon antenna is designed to detect brain stroke and brain tumour. It is operating at a band from 3.3568– 12.604 GHz in free space and from 3.818 to 9.16 GHz on the normal head model. The antenna has dimensions of 44×30mm2. It is fabricated on FR4-substrate with relative permittivity 4.4 and thickness 1.5mm. The antenna is simulated on the CST Microwave Studio and measured using the network analyser. There is a good agreement between the measured and simulated results of the return loss of the antenna on human's head and head phantom.


title: An ultracapacitor integrated power conditioner for intermittency smoothing and improving power quality of distribution grid

text: Summary form only given. Penetration of various types of distributed energy resources (DERs) like solar, wind and PHEVs onto the distribution grid is on the rise. There is a corresponding increase in power quality problems and intermittencies on the distribution grid. In order to reduce the intermittencies and improve the power quality of the distribution grid an Ultracapacitor (UCAP) integrated power conditioner is proposed in this paper. UCAP integration gives the power conditioner active power capability which is useful in tackling the grid intermittencies and in improving the voltage sag and swell compensation. UCAPs have low energy density, high power density and fast charge/discharge rates which are all ideal characteristics for meeting high power low energy events like grid intermittencies, sags/swells. In this paper UCAP is integrated into dc-link of the power conditioner through a bi-directional dc-dc converter which helps in providing a stiff dc-link voltage. The integration helps in providing active/reactive power support, intermittency smoothing and sag/swell compensation. Design and control of both the dc-ac inverters and the dc-dc converter are discussed. The simulation model of the overall system is developed and compared to the experimental hardware setup.


title: Improving bug localization using structured information retrieval

text: Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing bugs to a small number of files. Our key insight is that structured information retrieval based on code constructs, such as class and method names, enables more accurate bug localization. We present BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available. We build BLUiR on a proven, open source IR toolkit that anyone can use. Our work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice. We evaluate BLUiR on four open source projects with approximately 3,400 bugs. Results show that BLUiR matches or outperforms a current state-of-the-art tool across applications considered, even when BLUiR does not use bug similarity data used by the other tool.


title: Context-assisted face clustering framework with human-in-the-loop

text: Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of “common scene”, people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Finally, we discuss a novel methodology for integrating human-in-the-loop feedback mechanisms that leverage human interaction to achieve the high-quality clustering results. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.


title: Face Detection and Gesture Recognition for Human-Computer Interaction

text: Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the face detection and gesture recognition for human computer interaction book. You can open the device and get the book by on-line.


title: Some Correlates of Electronic Health Information Management System Success in Nigerian Teaching Hospitals

text: Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. The population of study comprised 442 health information management personnel in five teaching hospitals that had implemented EHIMS in Nigeria. A self-developed questionnaire was used as an instrument for data collection. The findings revealed that there is a positive, close relationship between all the identified factors and EHIMS's success: technical factors (r = 0.564, P < 0.05); social factors (r = 0.616, P < 0.05); organizational factors (r = 0.621, P < 0.05); financial factors (r = 0.705, P < 0.05); and political factors (r = 0.589, P < 0.05). We conclude that consideration of all the identified factors was highly significant for the success of EHIMS in Nigerian teaching hospitals.


title: Deep Discriminative Representation Learning with Attention Map for Scene Classification

text: Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.


title: Fire and smoke detection without sensors: Image processing based approach

text: In this paper, novel models for fire and smoke detection using image processing is provided. The models use different colour models for both fire and smoke. The colour models are extracted using a statistical analysis of samples extracted from different type of video sequences and images. The extracted models can be used in complete fire/smoke detection system which combines colour information with motion analysis.


title: Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction

text: In this work, we study parameter tuning towards the M2 metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M2 as a scorer in the Moses tuning framework, we investigate interactions of dense and sparse features, different optimizers, and tuning strategies for the CoNLL-2014 shared task. We notice erratic behavior when optimizing sparse feature weights with M2 and offer partial solutions. To our surprise, we find that a bare-bones phrase-based SMT setup with task-specific parameter-tuning outperforms all previously published results for the CoNLL-2014 test set by a large margin (46.37% M2 over previously 40.56%, by a neural encoder-decoder model) while being trained on the same data. Our newly introduced dense and sparse features widen that gap, and we improve the state-ofthe-art to 49.49% M2.


title: Real-time categorization of driver's gaze zone using the deep learning techniques

text: This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.


title: New Branch Prediction Vulnerabilities in OpenSSL and Necessary Software Countermeasures

text: Software based side-channel attacks allow an unprivileged spy process to extract secret information from a victim (cryptosystem) process by exploiting some indirect leakage of “side-channel” information. It has been realized that some components of modern computer microarchitectures leak certain side-channel information and can create unforeseen security risks. An example of such MicroArchitectural Side-Channel Analysis is the Cache Attack — a group of attacks that exploit information leaks from cache latencies [4, 7, 13, 15, 17]. Public awareness of Cache Attack vulnerabilities lead software writers of OpenSSL (version 0.9.8a and subsequent versions) to incorporate countermeasures for preventing these attacks. In this paper, we present a new and yet unforeseen side channel attack that is enabled by the recently published Simple Branch Prediction Analysis (SBPA) which is another type of MicroArchitectural Analysis, cf. [2, 3]. We show that modular inversion — a critical primitive in public key cryptography — is a natural target of SBPA attacks because it typically uses the Binary Extended Euclidean algorithm whose nature is an input-centric sequence of conditional branches. Our results show that SBPA can be used to extract secret parameters during the execution of the Binary Extended Euclidean algorithm. This poses a new potential risk to crypto-applications such as OpenSSL, which already employs Cache Attack countermeasures. Thus, it is necessary to develop new software mitigation techniques for BPA and incorporate them with cache analysis countermeasures in security applications. To mitigate this new risk in full generality, we apply a security-aware algorithm design methodology and propose some changes to the CRT-RSA algorithm flow. These changes either avoid some of the steps that require modular inversion, or remove the critical information leak from this procedure. In addition, we also show by example that, independently of the required changes in the algorithms, careful software analysis is also required in order to assure that the software implementation does not inadvertently introduce branches that may expose the application to SBPA attacks. These offer several simple ways for modifying OpenSSL in order to mitigate Branch Prediction Attacks.


title: Omnidirectional planar dipole array antenna for WLAN access point

text: In this paper, a low-cost planar dipole array antenna with an omnidirectional radiation characteristic is presented. The proposed omnidirectional planar dipole array antenna comprises two 1/spl times/2 dipole arrays arranged back to back, and can be easily constructed by printing on both sides of a dielectric substrate. By selecting the central ground plane to have a small width (about 6 mm for operating at about 2.4 GHz), the proposed array antenna can have a good omnidirectional radiation pattern in the azimuthal plane (gain variations less than 2 dBi). Prototypes of the proposed antenna for WLAN operation in the 2.4 GHz band (2400-2484 MHz) were constructed and studied.


title: An ear-worn continuous ballistocardiogram (BCG) sensor for cardiovascular monitoring

text: Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.


title: SimICS/Sun4m: A Virtual Workstation

text: System level simulators allow computer architects and system software designers to recreate an accurate and complete replica of the program behavior of a target system, regardless of the availability, existence, or instrumentation support of such a system. Applications include evaluation of architectural design alternatives as well as software engineering tasks such as traditional debugging and performance tuning. We present an implementation of a simulator acting as a virtual workstation fully compatible with the sun4m architecture from Sun Microsystems. Built using the system-level SPARC V8 simulator SimICS, SimICS/sun4m models one or more SPARC V8 processors, supports user-developed modules for data cache and instruction cache simulation and execution profiling of all code, and provides a symbolic and performance debugging environment for operating systems. SimICS/sun4m can boot unmodified operating systems, including Linux 2.0.30 and Solaris 2.6, directly from snapshots of disk partitions. To support essentially arbitrary code, we implemented binary-compatible simulators for several devices, including SCSI, console, interrupt, timers, EEPROM, and Ethernet. The Ethernet simulation hooks into the host and allows the virtual workstation to appear on the local network with full services available ( NFS, NIS, rsh, etc). Ethernet and console traffic can be recorded for future playback. The performance of SimICS/sun4m is sufficient to run realistic workloads, such as the database benchmark TPC-D, scaling factor 1/100, or an interactive network application such as Mozilla. The slowdown in relation to native hardware is in the range of 25 to 75 (measured using SPECint95). We also demonstrate some applications, including modeling an 8-processor sun4m version (which does not exist), modeling future memory hierarchies, and debugging an operating system.