title: Blind wavelet estimation using a zero-lag slice of the fourth-order statistics

abstract: This paper presents an iterative method for blind wavelet estimation using a zero-lag slice (ZLS) of the fourth-order cumulant of the seismic signal. The proposed method can estimate non-minimum phase wavelets since the higher order statistics carry the phase information. By just using the ZLS that can be estimated from the seismic data with high reliability, our method achieves good accuracy for wavelet estimation, even if only short seismic signals are available. Furthermore, the proposed method is not computationally intensive because it is performed either by applying simple 1D operations (frequency domain) or by solving a linear system (time domain) in each iteration with fast convergence, and only needs calculation of the ZLS once. Application results on synthetic and real field data sets show that our method behaves well in relatively low signal-to-noise ratios, and when the wavelet length is overspecified.


title: In-Memory Graph Databases for Web-Scale Data

abstract: A software stack relies primarily on graph-based methods to implement scalable resource description framework databases on top of commodity clusters, providing an inexpensive way to extract meaning from volumes of heterogeneous data.


title: Example-Based Cosmetic Transfer

abstract: Cosmetic makeup is used worldwide as a means to enhance beauty and express moods. An art form in its own right, cosmetic styles continuously change and evolve to reflect cultural and societal trends. While countless magazines and books are dedicated to demonstrating cosmetic art, the actual application of makeup still remains a physical endeavor. In this paper, we describe a procedure to apply cosmetic makeup to the image of a person's face with the click of a mouse. Our approach works from before- and-after example images created by professional makeup artists. Using our "cosmetic-transfer" procedure, we can realistically transfer the cosmetic style captured in the example-pair to another person's face. This greatly reduces the time and effort needed to demonstrate a cosmetic style on a new person's face. In addition, our approach can be used to mix-and- match, and even fine-tune, example styles, all virtually, without the need for any physical makeup.


title: Open Source Solution for Cloud Computing Platform Using OpenStack

abstract: OpenStack is a massively scalable open source cloud operating system that is a global collaboration of developers and cloud computing technologists producing the ubiquitous open source cloud computing platform for public and private clouds. OpenStack provides series of interrelated projects delivering various components for a cloud infrastructure solution as well as controls large pools of storage, compute and networking resources throughout a datacenter that all managed through a dashboard(Horizon) that gives administrators control while empowering their users to provision resources through a web interface. In this paper, we present a comparative study of Cloud Computing Platform such as Eucalyptus, Openstack, CloudStack and Opennebula which is open source software, cloud computing layered model, components of OpenStack, architecture of OpenStack. Further discussing about how to install Openstack as well as how to build virtual machine (VM) in Openstack cloud using CLI on RHEL 6.4 and at last covering latest OpenStack releases Icehouse, which is used for building public, private, and hybrid clouds and introduce what new features added in Icehouse. The aim of this paper is to show mainly importance of OpenStack as a Cloud provider and give the best solution for service providers as well as enterprises.


title: QDASH: a QoE-aware DASH system

abstract: Dynamic Adaptation Streaming over HTTP (DASH) enhances the Quality of Experience (QoE) for users by automatically switching quality levels according to network conditions. Various adaptation schemes have been proposed to select the most suitable quality level during video playback. Adaptation schemes are currently based on the measured TCP throughput received by the video player. Although video buffer can mitigate throughput fluctuations, it does not take into account the effect of the transition of quality levels on the QoE. In this paper, we propose a QoE-aware DASH system (or QDASH) to improve the user-perceived quality of video watching. We integrate available bandwidth measurement into the video data probes with a measurement proxy architecture. We have found that our available bandwidth measurement method facilitates the selection of video quality levels. Moreover, we assess the QoE of the quality transitions by carrying out subjective experiments. Our results show that users prefer a gradual quality change between the best and worst quality levels, instead of an abrupt switching. Hence, we propose a QoE-aware quality adaptation algorithm for DASH based on our findings. Finally, we integrate both network measurement and the QoE-aware quality adaptation into a comprehensive DASH system.


title: Causally motivated attribution for online advertising

abstract: In many online advertising campaigns, multiple vendors, publishers or search engines (herein called channels) are contracted to serve advertisements to internet users on behalf of a client seeking specific types of conversion. In such campaigns, individual users are often served advertisements by more than one channel. The process of assigning conversion credit to the various channels is called "attribution," and is a subject of intense interest in the industry. This paper presents a causally motivated methodology for conversion attribution in online advertising campaigns. We discuss the need for the standardization of attribution measurement and offer three guiding principles to contribute to this standardization. Stemming from these principles, we position attribution as a causal estimation problem and then propose two approximation methods as alternatives for when the full causal estimation can not be done. These approximate methods derive from our causal approach and incorporate prior attribution work in cooperative game theory. We argue that in cases where causal assumptions are violated, these approximate methods can be interpreted as variable importance measures. Finally, we show examples of attribution measurement on several online advertising campaign data sets.


title: Modeling and Evaluating User Interface Aesthetics Employing ISO 25010 Quality Standard

abstract: User interface aesthetics, among other design considerations, are considered to be one of the determinants of user satisfaction. Considered more of a subjective aspect, aesthetics have mostly been evaluated with intrusive approaches. However, according to the recent product quality model in ISO 25010, "aesthetics" is listed under the "usability" characteristic. It is therefore imperative to understand and evaluate aesthetics not only from users' perspective but also from the design or product perspective. In this research, we employ ISO 25010 quality standard to specify user interface aesthetics requirements from two perspectives of quality, i.e., product quality and quality in use, providing a platform for consistently evaluating and therefore improving user interface aesthetics. We also demonstrate the usefulness of our approach with a case study.


title: Revisiting iris recognition with color cosmetic contact lenses

abstract: Over the years, iris recognition has gained importance in the biometrics applications and is being used in several large scale nationwide projects. Though iris patterns are unique, they may be affected by external factors such as illumination, camera-eye angle, and sensor interoperability. The presence of contact lens, particularly color cosmetic lens, may also pose a challenge to iris biometrics as it obfuscates the iris patterns and changes the inter and intra-class distributions. This paper presents an in-depth analysis of the effect of contact lens on iris recognition performance. We also present the IIIT-D Contact Lens Iris database with over 6500 images pertaining to 101 subjects. For each subject, images are captured without lens, transparent (prescription) lens, and color cosmetic lens (textured) using two different iris sensors. The results computed using VeriEye suggest that color cosmetic lens significantly increases the false rejection at a fixed false acceptance rate. Also, the experiments on four existing lens detection algorithms suggest that incorporating lens detection helps in maintaining the iris recognition performance. However further research is required to build sophisticated lens detection algorithm that can improve iris recognition.


title: Multiple Object Recognition with Visual Attention

abstract: We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.


title: Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review

abstract: We enhance the autonomy of the continuous active learning method shown by Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted review, in which documents from a collection are retrieved and reviewed, using relevance feedback, until substantially all of the relevant documents have been reviewed. Autonomy is enhanced through the elimination of topic-specific and dataset-specific tuning parameters, so that the sole input required by the user is, at the outset, a short query, topic description, or single relevant document; and, throughout the review, ongoing relevance assessments of the retrieved documents. We show that our enhancements consistently yield superior results to Cormack and Grossman's version of continuous active learning, and other methods, not only on average, but on the vast majority of topics from four separate sets of tasks: the legal datasets examined by Cormack and Grossman, the Reuters RCV1-v2 subject categories, the TREC 6 AdHoc task, and the construction of the TREC 2002 filtering test collection.


Are Metamorphic Viruses Really Invincible


title: Multiprogramming a 64kB Computer Safely and Efficiently

abstract: Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.


title: Self-oscillating series-resonant led driver applied to reduce low-frequency current ripple transmission

abstract: This paper presents the analysis and design of an LED driver based on the series-resonant converter (SRC) and on the self-oscillating command circuit (SOCC). The use of SRC provides a reduction of the low frequency (LF) ripple in the LEDs' current, originated by the voltage ripple in the output of the power factor correction (PFC) stage (bus voltage ripple). The attenuation of the ripple transmission from the bus voltage to the LEDs' current achieves some desired goals: (1) reduction of the bulk capacitance used at the output of the PFC stage, enabling the employment of long-life film capacitors, and (2) increasing of the efficiency of the LED driver because of the low switching losses of the series-resonant converter. The SOCC stamps on the LED driver the low-cost, simplicity, and robustness features. In order to avoid any mismatch between the SRC LF current ripple reduction design guidelines and the SOCC design, a design methodology is presented. Experimental results from a self-oscillating series resonant converter to supply a 10 Watts LED lamp are presented, validating the feasibility of the developed methodology and the proposed analysis.


title: Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

abstract: Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.


title: Fuzzy identification of systems and its applications to modeling and control

abstract: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented. The premise of an implication is the description of fuzzy subspace of inputs and its consequence is a linear input-output relation. The method of identification of a system using its input-output data is then shown. Two applications of the method to industrial processes are also discussed: a water cleaning process and a converter in a steel-making process.


title: Ketamine blocks bursting in the lateral habenula to rapidly relieve depression

abstract: The N-methyl-d-aspartate receptor (NMDAR) antagonist ketamine has attracted enormous interest in mental health research owing to its rapid antidepressant actions, but its mechanism of action has remained elusive. Here we show that blockade of NMDAR-dependent bursting activity in the 'anti-reward center', the lateral habenula (LHb), mediates the rapid antidepressant actions of ketamine in rat and mouse models of depression. LHb neurons show a significant increase in burst activity and theta-band synchronization in depressive-like animals, which is reversed by ketamine. Burst-evoking photostimulation of LHb drives behavioural despair and anhedonia. Pharmacology and modelling experiments reveal that LHb bursting requires both NMDARs and low-voltage-sensitive T-type calcium channels (T-VSCCs). Furthermore, local blockade of NMDAR or T-VSCCs in the LHb is sufficient to induce rapid antidepressant effects. Our results suggest a simple model whereby ketamine quickly elevates mood by blocking NMDAR-dependent bursting activity of LHb neurons to disinhibit downstream monoaminergic reward centres, and provide a framework for developing new rapid-acting antidepressants.


title: Common Visual Pattern Discovery via Nonlinear Mean Shift Clustering

abstract: Discovering common visual patterns (CVPs) from two images is a challenging task due to the geometric and photometric deformations as well as noises and clutters. The problem is generally boiled down to recovering correspondences of local invariant features, and the conventionally addressed by graph-based quadratic optimization approaches, which often suffer from high computational cost. In this paper, we propose an efficient approach by viewing the problem from a novel perspective. In particular, we consider each CVP as a common object in two images with a group of coherently deformed local regions. A geometric space with matrix Lie group structure is constructed by stacking up transformations estimated from initially appearance-matched local interest region pairs. This is followed by a mean shift clustering stage to group together those close transformations in the space. Joining regions associated with transformations of the same group together within each input image forms two large regions sharing similar geometric configuration, which naturally leads to a CVP. To account for the non-Euclidean nature of the matrix Lie group, mean shift vectors are derived in the corresponding Lie algebra vector space with a newly provided effective distance measure. Extensive experiments on single and multiple common object discovery tasks as well as near-duplicate image retrieval verify the robustness and efficiency of the proposed approach.


title: Cross-disciplinary perspectives on meta-learning for algorithm selection

abstract: The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm is likely to perform best for my problemq Recognizing the problem as a learning task in the early 1990's, the machine learning community has developed the field of meta-learning, focused on learning about learning algorithm performance on classification problems. But there has been only limited generalization of these ideas beyond classification, and many related attempts have been made in other disciplines (such as AI and operations research) to tackle the algorithm selection problem in different ways, introducing different terminology, and overlooking the similarities of approaches. In this sense, there is much to be gained from a greater awareness of developments in meta-learning, and how these ideas can be generalized to learn about the behaviors of other (nonlearning) algorithms. In this article we present a unified framework for considering the algorithm selection problem as a learning problem, and use this framework to tie together the crossdisciplinary developments in tackling the algorithm selection problem. We discuss the generalization of meta-learning concepts to algorithms focused on tasks including sorting, forecasting, constraint satisfaction, and optimization, and the extension of these ideas to bioinformatics, cryptography, and other fields.


Presenting Historical Photos using Augmented Reality


title: Pitfalls in pediatric radiology

abstract: This essay depicts some of the diagnostic errors identified in a large academic pediatric imaging department during a 13-year period. Our aim is to illustrate potential situations in which errors are more likely to occur and more likely to cause harm, and to share our difficult cases so other radiologists might learn without having to experience those situations themselves.


title: Adjusting for Multiple Comparisons in Decision Tree Pruning

abstract: Pruning is a common technique to avoid overfitting in decision trees. Most pruning techniques do not account for one important factor — multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. Making multiple comparisons produces incorrect inferences about model accuracy. We examine a method that adjusts for multiple comparisons when pruning decision trees - Bonferroni pruning. In experiments with artificial and realistic datasets, Bonferroni pruning produces smaller trees that are at least as accurate as trees pruned using other common approaches.


abstract: Pedestrian detection in low resolution videos can be challenging. In outdoor surveillance scenarios, the size of pedestrians in the images is often very small (around 20 pixels tall). The most common and successful approaches for single frame pedestrian detection use gradient-based features and a support vector machine classifier. We propose an extension of these ideas, and develop a new algorithm that extracts gradient features from a spatiotemporal volume, consisting of a short sequence of images (about one second in duration). The additional information provided by the motion of the person compensates for the loss of resolution. On standard datasets (PETS2001, VIRAT) we show a significant improvement in performance over single-frame detection.


title: optiFLP: software for automated optimization of amplified fragment length polymorphism scoring parameters.

abstract: Amplified fragment length polymorphism (AFLP), a widely used method for DNA fingerprinting, has shifted from polyacrylamide gel to capillary electrophoresis over the last years. Currently, most AFLP data are generated in a computer-readable format, and several programs are available that automatically score raw data into binary profiles. Good scoring parameters are the key to good AFLP profiles. optiFLP is the first open source program for automatic optimization of AFLP scoring parameters. It searches parameter space to maximize the contrast among groups of AFLP profiles, with the allocation of profiles to groups in either a supervised or an unsupervised mode. The software produces output files ready for use in a range of downstream applications.


title: Polarimetric Analysis of Backscatter From the Deepwater Horizon Oil Spill Using L-Band Synthetic Aperture Radar

abstract: We analyze the fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired on June 23, 2010, from two adjacent, overlapping flight tracks that imaged the main oil slick near the Deepwater Horizon (DWH) rig site in the Gulf of Mexico. Our results show that radar backscatter from both clean water and oil in the slick is predominantly from a single surface scatterer, consistent with the tilted Bragg scattering mechanism, across the range of incidence angles from 26° to 60°. We show that the change of backscatter over the main slick is due both to a damping of the ocean wave spectral components by the oil and an effective reduction of the dielectric constant resulting from a mixture of 65-90% oil with water in the surface layer. This shows that synthetic aperture radar can be used to measure the oil volumetric concentration in a thick slick. Using the H/A/α parameters, we show that surface scattering is dominant for oil and water whenever the data are above the noise floor and that the entropy (H) and α parameters for the DWH slick are comparable to those from the clean water. The anisotropy, A, parameter shows substantial variation across the oil slick and a significant range-dependent signal whenever the backscatter in all channels is above the instrument noise floor. For slick detection, we find the most reliable indicator to be the major eigenvalue of the coherency matrix, which is approximately equal to the total backscatter power for both oil in the slick and clean sea water.


Construction and Optimization of a Tapered Amplifier System for Applications in Ultra-Cold Plasma Research


title: A WIDEBAND AND HIGH GAIN DUAL-POLARZIED ANTENNA DESIGN BY A FREQUENCY-SELECTIVE SURFACE FOR WLAN APPLICATION

abstract: A new dual-polarized antenna loading frequency-selective surface (FSS) is proposed for 5G wireless local networks (WLANs) application. The antenna consists of two orthogonal bow-tie dipoles and a ground plane. A new wideband FSS is designed comprising ring-slot connecting rectangular slots. The reflection coefficient of wideband FSS is less than 0.9 from 4-6.5 GHz. The phase of reflection coefficient is −163 ◦ at 5.5 GHz. The novel cell analyzed by the equivalent circuit is given and simulated. The wideband FSS is employed as a superstrate layer for bandwidth enhancement and radiation gain improvement of the antenna. After loading wideband FSS, the measured bandwidth is 5.3-6.3 GHz (17.2%) with S11 and S22 both less than −10 dB, which cover various 5G WLAN bandwidths. The gain of the antenna is 12.1 dBi at 5.5 GHz. The bandwidth of antenna with FSS increases 40%, and gain improves 5.6 dBi. The simulated and measured results agree well.


title: MITTS: memory inter-arrival time traffic shaping

abstract: Memory bandwidth severely limits the scalability and performance of multicore and manycore systems. Application performance can be very sensitive to both the delivered memory bandwidth and latency. In multicore systems, a memory channel is usually shared by multiple cores. Having the ability to precisely provision, schedule, and isolate memory bandwidth and latency on a per-core basis is particularly important when different memory guarantees are needed on a per-customer, per-application, or per-core basis. Infrastructure as a Service (IaaS) Cloud systems, and even general purpose multicores optimized for application throughput or fairness all benefit from the ability to control and schedule memory access on a fine-grain basis. In this paper, we propose MITTS (Memory Inter-arrival Time Traffic Shaping), a simple, distributed hardware mechanism which limits memory traffic at the source (Core or LLC). MITTS shapes memory traffic based on memory request inter-arrival time, enabling fine-grain bandwidth allocation. In an IaaS system, MITTS enables Cloud customers to express their memory distribution needs and pay commensurately. For instance, MITTS enables charging customers that have bursty memory traffic more than customers with uniform memory traffic for the same aggregate bandwidth. Beyond IaaS systems, MITTS can also be used to optimize for throughput or fairness in a general purpose multi-program workload. MITTS uses an online genetic algorithm to configure hardware bins, which can adapt for program phases and variable input sets. We have implemented MITTS in Verilog and have taped-out the design in a 25-core 32nm processor and find that MITTS requires less than 0.9% of core area. We evaluate across SPECint, PARSEC, Apache, and bhm Mail Server workloads, and find that MITTS achieves an average 1.18× performance gain compared to the best static bandwidth allocation, a 2.69× average performance/cost advantage in an IaaS setting, and up to 1.17× better throughput and 1.52× better fairness when compared to conventional memory bandwidth provisioning techniques.


title: E-learning system use and its outcomes: Moderating role of perceived compatibility

abstract: We investigated the moderating role of perceived compatibility.Compatibility moderates the relationship between e-learning use and academic performance.Compatibility does not moderate the relationship between e-learning use and learning assistance.Compatibility does not moderate the relationship between e-learning use and community building. This paper explores the moderating role of perceived compatibility on the relationship between e-learning system use and its outcomes. We conceptualize e-learning outcomes using academic performance, perceived learning assistance, and perceived community building assistance. We further hypothesize that perceived compatibility moderates the relationships between e-learning system use and these outcome variables. The model was tested by collecting data from university students (n=179) participating in hybrid courses using a popular learning management system, Moodle.The findings suggest that perceived compatibility moderates the relationship between e-learning system use and academic performance. However, it did not moderate the other two relationships, i.e. (1) the relationship between e-learning system use and perceived learning assistance, and (2) the relationship between e-learning system use and perceived community building assistance.


title: Children'S experience of three types of cartoon at two age levels.

abstract: Three cartoons were shown to 87 children at two age levels: 5-6 years, and 9 years. The children's experience was assessed in interviews. The younger children experienced the cartoons in a fragmentary manner and not as a continuous story, understood less of the cartoons, and tended to base their moral judgements of a character's behaviour on whether or not they identified with that character. Six months later, the younger children remembered best those scenes that had made them the most anxious earlier. A subgroup of children with abundant aggressive fantasies had a lower level of moral reasoning than the other children, preferred violent scenes, became less anxious while watching them and tended to give illogical explanations for the behaviour of the cartoon characters. The degree of anxiety provoked by a cartoon depended not on the amount of explicit violence shown but on the way the violence was presented. One cartoon, which contained no explicit violence, was considered the most frightening one due to its sound effects. Language: en


title: Modelling internal logistics systems through ontologies

abstract: Proposal of a standard taxonomy and classification of internal logistics resources (warehousing in particular) that is expressed into object-oriented modelling.The object-oriented modelling is ready to be included into OWL ontology models, in particular it is proposed as extension to support the logistics field representation of the Manufacturing Systems Ontology (MSO).The paper suggests the role of ontologies and semantic representation as supporting elements of the modularity and flexibility of internal logistics systems.The concepts of Storage and Transporters are analysed, classified and hierarchically represented also in relation to other relevant concepts.The paper reports also an industrial example to show how the proposed modelling can be declined in reality. Industry is facing an era characterised by unpredictable market changes and by a turbulent competitive environment. The key to compete in such a context is to achieve high degrees of responsiveness by means of high flexibility and rapid reconfiguration capabilities. The deployment of modular solutions seems to be part of the answer to face these challenges. Semantic modelling and ontologies may represent the needed knowledge representation to support flexibility and modularity of production systems, when designing a new system or when reconfiguring an existing one. Although numerous ontologies for production systems have been developed in the past years, they mainly focus on discrete manufacturing, while logistics aspects, such as those related to internal logistics and warehousing, have not received the same attention. The paper aims at offering a representation of logistics aspects, reflecting what has become a de-facto standard terminology in industry and among researchers in the field. Such representation is to be used as an extension to the already-existing production systems ontologies that are more focused on manufacturing processes. The paper presents the structure of the hierarchical relations within the examined internal logistics elements, namely Storage and Transporters, structuring them in a series of classes and sub-classes, suggesting also the relationships and the attributes to be considered to complete the modelling. Finally, the paper proposes an industrial example with a miniload system to show how such a modelling of internal logistics elements could be instanced in the real world.


title: Empirical Analysis of Influence Concerning Fiscal Expenditure on Consumption of Residents

abstract: This paper considers the influence of people’s livelihood fiscal expenditure on consumption by modifying the intertemporal substitution model. The average per capita consumption of Chinese residents is regarded as explained variable, while per capita expenditure on education; per capita health care expenditure and per capita expenditure on social security are regarded as the explanatory variable. The per capita incomes, per capita savings balance, the proportion of government spending and tax burden are as control variables. We analyze the detailed influence of three expenditures on household consumption. Through the empirical analysis the influence of the people’s livelihood fiscal expenditure on consumption, we put forward the corresponding economic strategy that promotes urban and rural residents’ consumption.


title: SLAM-based automatic extrinsic calibration of a multi-camera rig

abstract: Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.


title: A biologically inspired soft exosuit for walking assistance

abstract: We present the design and evaluation of a multi-articular soft exosuit that is portable, fully autonomous, and provides assistive torques to the wearer at the ankle and hip during walking. Traditional rigid exoskeletons can be challenging to perfectly align with a wearer's biological joints and can have large inertias, which can lead to the wearer altering their natural motion patterns. Exosuits, in comparison, use textiles to create tensile forces over the body in parallel with the muscles, enabling them to be light and not restrict the wearer's kinematics. We describe the biologically inspired design and function of our exosuit, including a simplified model of the suit's architecture and its interaction with the body. A key feature of the exosuit is that it can generate forces passively due to the body's motion, similar to the body's ligaments and tendons. These passively generated forces can be supplemented by actively contracting Bowden cables using geared electric motors, to create peak forces in the suit of up to 200 N. We define the suit-human series stiffness as an important parameter in the design of the exosuit and measure it on several subjects, and we perform human subjects testing to determine the biomechanical and physiological effects of the suit. Results from a five-subject study showed a minimal effect on gait kinematics and an average best-case metabolic reduction of 6.4%, comparing suit worn unpowered versus powered, during loaded walking with 34.6 kg of carried mass including the exosuit and actuators 2.0 kg on both legs, 10.1 kg total.


title: Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation

abstract: In this study, we developed a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists’ reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists’ gaze information were used to create a visual attention map. Next, this map was combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a region of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest found in the previous step. These cues are used to initiate a seed-based delineation process. The proposed Gaze2Segment achieved a dice similarity coefficient of 86% and Hausdorff distance of 1.45 mm as a segmentation accuracy. To the best of our knowledge, Gaze2Segment is the first true integration of eye-tracking technology into a medical image segmentation task without the need for any further user-interaction.


title: Assessing and managing the benefits of enterprise systems: the business manager's perspective

abstract: . ::: This paper focuses on the benefits that organizations may achieve from their investment in enterprise systems (ES). It proposes an ES benefit framework for summarizing benefits in the years after ES implementation. Based on an analysis of the features of enterprise systems, on the literature on information technology (IT) value, on data from 233 enterprise systems vendor-reported stories published on the Web and on interviews with managers of 34 organizations using ES, the framework provides a detailed list of benefits that have reportedly been acquired through ES implementation. This list of benefits is consolidated into five benefits dimensions: operational, managerial, strategic, IT infrastructure and organizational, and illustrated using perceived net benefit flow (PNBF) graphs. In a detailed example, the paper shows how the framework has been applied to the identification of benefits in a longitudinal case study of four organizations.


title: Trainable Citation-enhanced Summarization of Scientific Articles

abstract: This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the European Project Dr. Inventor (FP7-ICT-2013.8.1 - Grant no: 611383).


title: A comparison between extended Kalman filtering and sequential Monte Carlo techniques for simultaneous localisation and map-building

abstract: Monte Carlo Localisation has been applied to solve many dierent classes of localisation problems. In this paper, we present a possible Simultaneous Localisation and Map-building implementation using the Sequential Monte Carlo technique. Multiple particle lters are created to estimate both the robot and landmark positions simultaneously. The proposed technique shows promising results when compared with those obtained with the Extended Kalman lter.


title: FaLL: A fair and low latency queuing scheme for data center networks

abstract: Low latency, high throughput, and fairness are the stringent performance requirements of data center networks. For web applications continue to thrive, data center must remain effective by addressing these challenges. While most of the challenges have been handled by data center specific transport protocols (e.g. DCTCP), these transport protocols are not default options for data center users. Free choice of transport protocols demands careful consideration for an effective solution. We argue that queuing scheme at layer-2 switches hold the key to address these issues. Therefore, in this paper, we present FaLL, a queuing scheme for data center networks that meets following performance requirements: fair share of bandwidth, low queuing latency, high throughput, and ease of deployment. FaLL uses an efficiency module, a fairness module and a target delay based dropping scheme to meet these goals. FaLL requires very small amount of state information. Through rigorous experiments on real testbed, we show that FaLL outperforms various peer solutions in variety of network conditions.


title: Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams

abstract: Ensemble learning is a commonly used tool for building prediction models from data streams, due to its intrinsic merits of handling large volumes stream data. Despite of its extraordinary successes in stream data mining, existing ensemble models, in stream data environments, mainly fall into the ensemble classifiers category, without realizing that building classifiers requires labor intensive labeling process, and it is often the case that we may have a small number of labeled samples to train a few classifiers, but a large number of unlabeled samples are available to build clusters from data streams. Accordingly, in this paper, we propose a new ensemble model which combines both classifiers and clusters together for mining data streams. We argue that the main challenges of this new ensemble model include (1) clusters formulated from data streams only carry cluster IDs, with no genuine class label information, and (2) concept drifting underlying data streams makes it even harder to combine clusters and classifiers into one ensemble framework. To handle challenge (1), we present a label propagation method to infer each cluster's class label by making full use of both class label information from classifiers, and internal structure information from clusters. To handle challenge (2), we present a new weighting schema to weight all base models according to their consistencies with the up-to-date base model. As a result, all classifiers and clusters can be combined together, through a weighted average mechanism, for prediction. Experiments on real-world data streams demonstrate that our method outperforms simple classifier ensemble and cluster ensemble for stream data mining.


title: Distributed top-k aggregation queries at large

abstract: Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.


The Strengthening of Partisan Affect


title: A fast-transient digital LDO using a double edge-triggered comparator with a completion signal

abstract: This paper proposes a fast-transient coarse-fine digital low-dropout regulator (D-LDO). The conventional D-LDO operates a coarse shift-register and a comparator at a fast clock in coarse mode when an overshoot or undershoot voltage occurs, for a fast-transient response. After coarse mode, it operates a fine shift-register at a slow clock for low power consumption in fine mode. However, the comparator operates at a fast clock for a low ripple voltage. The proposed D-LDO reduces the response time to a half of the conventional D-LDO, by using the proposed double edge-triggered comparator and by shifting the shift-register at the double edges of clocks. As a result, it reduces the overshoot and undershoot voltages. It also reduces the power consumption of the comparator in fine mode by using a slow clock instead of a fast clock. But, it still has a low ripple voltage due to the proposed comparator with a completion signal. The proposed D-LDO was implemented using a 65nm CMOS process. In the simulation, the settling time is reduced to 340ns from 880ns of the conventional D-LDO. The overshoot and undershoot voltages are reduced to 23mV and 37mV from 129mV and 127mV, respectively. The ripple voltage is 1.5mV and the current efficiency is 99.94%.


title: Local Differential Privacy for Evolving Data

abstract: There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use. As a result, these systems do not provide meaningful privacy guarantees over long time scales. Moreover, existing techniques to mitigate this effect do not apply in the “local model” of differential privacy that these systems use. ::: In this paper, we introduce a new technique for local differential privacy that makes it possible to maintain up-to-date statistics over time, with privacy guarantees that degrade only in the number of changes in the underlying distribution rather than the number of collection periods. We use our technique for tracking a changing statistic in the setting where users are partitioned into an unknown collection of groups, and at every time period each user draws a single bit from a common (but changing) group-specific distribution. We also provide an application to frequency and heavy-hitter estimation.


title: IEEE 802.11ay: Next-Generation 60 GHz Communication for 100 Gb/s Wi-Fi

abstract: The IEEE 802.11ad amendment to the 802.11 standard ratified in 2012 created the first multi- Gb/s Wi-Fi technology by using the large swath of unlicensed spectrum at the mm-Wave band. While enabling multi-Gb/s wireless local communications was a significant achievement, throughput and reliability requirements of new applications, such as augmented reality (AR)/virtual reality (VR) and wireless backhauling, exceed what 802.11ad can offer. For this reason, building upon IEEE 802.11ad, the IEEE 802.11 Task Group ay has recently defined new PHY and MAC specifications that enable 100 Gb/s communications through a number of technical advancements. In this article, we identify and describe the main design elements of IEEE 802.11ay, including MIMO, channel bonding, improved channel access, and enhanced beamforming training. For each of these elements, we discuss how their design is impacted by mm-Wave radio propagation characteristics and present enabling mechanisms defined in IEEE 802.11ay.


Rhythm and Flow in Hip-Hop Music: A Corpus Study


title: FDCNet: filtering deep convolutional network for marine organism classification

abstract: Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).


title: Road Detection through Supervised Classification

abstract: Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification and hand-crafted feature vectors.


title: Managing vulnerabilities in networked systems

abstract: Most organizations recognize the importance of cyber security and are implementing various forms of protection. However, many are failing to find and fix known security problems in the software packages they use as the building blocks of their networks and systems, a vulnerability that a hacker can exploit to bypass all other efforts to secure the enterprise. The Common Vulnerabilities and Exposures (CVE) initiative seeks to avoid such disasters and transform this area from a liability to a key asset in the fight to build and maintain secure systems. Coordinating international, community-based efforts from industry, government and academia, CVE strives to find and fix software product vulnerabilities more rapidly, predictably, and efficiently. The initiative seeks the adoption of a common naming practice for describing software vulnerabilities. Once adopted, these names will be included within security tools and services and on the fix sites of commercial and open source software package providers. As vendors respond to more users requests for CVE-compatible fix sites, securing the enterprise will gradually include the complete cycle of finding, analyzing, and fixing vulnerabilities.


title: Bringing together BPM and Social Software

abstract: Abstract Traditional BPM approaches are well understood for hierarchical organizations that cope with highly repetitive and well-defined processes. Unfortunately, they do not work well for the most prevalent types of processes and exclude most of the employees from participation. Especially collaborative ad-hoc processes in a team-based organization are not covered by BPM methods. Social software is a widely accepted and promising approach to support participation but lacks mechanisms for controlling processes. The paper presents a novel approach using roles. Based on a systematic literature review, this paper contributes a solution-architecture for collaborative BPM and presents collaboration patterns expressed through role models. This helps overcoming the model-reality-divide and lost innovation in the business process modeling area. Furthermore, role-model based collaboration patterns can be used to monitor and control collaborative ad-hoc processes. Keywords Social BPM, virtual communities, social software, ad-hoc processes, knowledge intensive processes, collaborative processes, conceptual modeling.


title: On Randomizing the Sending Times in TCP and

abstract: Current implementations of TCP suffer from serious performance problems like unfairness to flows with higher round trip times (RTTs), synchronization of windows and phase effects in flows and correlated losses leading to throughput degradations under a wide range of scenarios. In this paper we show that by randomizing the packet sending times at TCP sources (called Randomized TCP) we can address a majority of these issues. Specifically, we propose to space successive packet transmissions with a time interval Δ = RTT(1 + x)/cwnd, where x is a zero mean random number drawn from an Uniform distribution. We find that an Uniform distribution on U[-1,1] is optimal with respect to metrics like throughput, fairness, timeouts, losses and de-synchronization. We show that the scheme is better than Paced as well as TCP Reno in a large number of scenarios. The proposed scheme is also fair with TCP Reno. We show through simple simulations that Randomized TCP reduces phase effects and synchronization even when multiplexed with TCP Reno. Also, we extend randomization to other window based schemes like the binomial schemes and show that fairness to TCP Reno increases dramatically.


Understanding the Digital Economy: Data, Tools, and Research


title: Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces

abstract: In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved: (i) how to design reliable, convergent, and robust reinforcement learning algorithms (ii) how to guarantee that reinforcement learning satisfies pre-specified "safety" guarantees, and remains in a stable region of the parameter space (iii) how to design "off-policy" temporal difference learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of stochastic optimization. In this paper, we provide detailed answers to all these questions using the powerful framework of proximal operators. ::: The key idea that emerges is the use of primal dual spaces connected through the use of a Legendre transform. This allows temporal difference updates to occur in dual spaces, allowing a variety of important technical advantages. The Legendre transform elegantly generalizes past algorithms for solving reinforcement learning problems, such as natural gradient methods, which we show relate closely to the previously unconnected framework of mirror descent methods. Equally importantly, proximal operator theory enables the systematic development of operator splitting methods that show how to safely and reliably decompose complex products of gradients that occur in recent variants of gradient-based temporal difference learning. This key technical innovation makes it possible to finally design "true" stochastic gradient methods for reinforcement learning. Finally, Legendre transforms enable a variety of other benefits, including modeling sparsity and domain geometry. Our work builds extensively on recent work on the convergence of saddle-point algorithms, and on the theory of monotone operators.


title: Design of anthropomorphic robot hand with IMC joints

abstract: This paper presents a novel anthropomorphic robot hand with IMC joints. IMC joints are applied to robot hand to grasp various objects regardless of the size and get large overlapped workspace between thumb and other fingers, which allow all the fingertips to be located on a single spot in a wide range. Desinged IMC joints of robot hand are always rotated with 1:1 ratio by mechanical constraint using passive tendon. Actuation module consists of miniature BLDC motor and ballscrew drives two IMC joints with a single motor. To evaluate the advantages of robot hand with IMC joints, grasping experiments are performed.


title: The relationship of Brand Commitment, Brand Credibility, Perceived Quality, Customer Satisfaction and brand loyalty: an empirical study on Stylo shoes

abstract: Group advertisement can help build brands, but dependability is what makes them preceding. If people believe they share values with an organization, they will remain loyal to the particular brand.” There is only one boss ‘the customer’ and he can fire everybody in the company, simply investing money somewhere else, he wants. Keeping in mind that costumers are a challenging aspect for organizations, our study aims to contribute in the brand loyalty. There is a rapid increase seen in the needs of brands in the market, now a day. Customers feel good, safe, proud, sophisticated, satisfied and obliged having brands, explained by research. As customers want brands and the main purpose of this research paper was to study the contribution of brand equity, perceived quality, consumer satisfaction, brand commitment, brand credibility, and brand trust for building brand loyalty. Also, present the role of brand loyalty on brand equity. Our study also explains that how can be the customers remain loyal with a brand and what they need in it and what will be the future of all this. A self-administered questionnaire was developed to collect data from randomly selected 200 female consumers of stylo shoes in Pakistan. We constructed a structural model and tested it. The SPSS analysis was performed in order to check the fit of conceptual model presented. To evaluate the relationship between variables, correlation and regression analysis was also used. The findings of this study demonstrate that all the variables positively and significantly effects brand loyalty. Additionally, the relationship of brand loyalty and brand equity was also seen. This article and its findings have numerous limitations and give directions for future work.


title: Adversarial Generation of Natural Language

abstract: Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.


title: Radio resource allocation scheme for device-to-device communication in cellular networks using fractional frequency reuse

abstract: Device-to-Device (D2D) communication is the technology enabling user equipments (UEs) to directly communicate with each other without help of evolved nodeB (eNB). Due to this characteristic, D2D communication can reduce end-to-end delay and traffic load offered to eNB. However, by applying D2D communication into cellular systems, interference between D2D and eNB relaying UEs can occur if D2D UEs reuse frequency band for eNB relaying UEs. In cellular systems, fractional frequency reuse (FFR) is used to reduce inter-cell interference of cell outer UEs. In this paper, we propose a radio resource allocation scheme for D2D communication underlaying cellular networks using FFR. In the proposed scheme, D2D and cellular UEs use the different frequency bands chosen as users' locations. The proposed radio resource allocation scheme can alleviate interference between D2D and cellular UEs if D2D device is located in cell inner region. If D2D UEs is located in cell outer region, D2D and cellular UEs experience tolerable interference. By simulations, we show that the proposed scheme improves the performance of D2D and cellular UEs by reducing interference between them.


title: Bit-split string-matching engines for intrusion detection and prevention

abstract: Network Intrusion Detection and Prevention Systems have emerged as one of the most effective ways of providing security to those connected to the network and at the heart of almost every modern intrusion detection system is a string-matching algorithm. String matching is one of the most critical elements because it allows for the system to make decisions based not just on the headers, but the actual content flowing through the network. Unfortunately, checking every byte of every packet to see if it matches one of a set of thousands of strings becomes a computationally intensive task as network speeds grow into the tens, and eventually hundreds, of gigabits/second. To keep up with these speeds, a specialized device is required, one that can maintain tight bounds on worst-case performance, that can be updated with new rules without interrupting operation, and one that is efficient enough that it could be included on-chip with existing network chips or even into wireless devices. We have developed an approach that relies on a special purpose architecture that executes novel string matching algorithms specially optimized for implementation in our design. We show how the problem can be solved by converting the large database of strings into many tiny state machines, each of which searches for a portion of the rules and a portion of the bits of each rule. Through the careful codesign and optimization of our architecture with a new string-matching algorithm, we show that it is possible to build a system that is 10 times more efficient than the currently best known approaches.


title: Robust Hash for Detecting and Localizing Image Tampering

abstract: An image hash should be (1) robust to allowable operations and (2) sensitive to illegal manipulations and distinct queries. Some applications also require the hash to be able to localize image tampering. This requires the hash to contain both robust content and alignment information to meet the above criterion. Fulfilling this is difficult because of two contradictory requirements. First, the hash should be small and second, to verify authenticity and then localize tampering, the amount of information in the hash about the original required would be large. Hence a tradeoff between these requirements needs to be found. This paper presents an image hashing method that addresses this concern, to not only detect but also localize tampering using a small signature (< 1kB). Illustrative experiments bring out the efficacy of the proposed method compared to existing methods.


title: Deep Refinement Convolutional Networks for Human Pose Estimation

abstract: This work introduces a novel Convolutional Networkarchitecture (ConvNet) for the task of human poseestimation, that is the localization of body joints in a singlestatic image. The proposed coarse to fine architecture addressesshortcomings of the baseline architecture that stem from thefact that large inaccuracies of its coarse ConvNet cannot becorrected by the refinement ConvNet that refines the estimationwithin small windows of the coarse prediction. This is achievedby a) changes in architectural parameters that both increase theaccuracy of the coarse model and make the refinement modelmore capable of correcting the errors of the coarse model,b) the introduction of a Markov Random Field (MRF)-basedspatial model network between the coarse and the refinementmodel that introduces geometric constraints and c) a trainingscheme that adapts the data augmentation and the learning rateaccording to the difficulty of the data examples. The proposedarchitecture is trained in an end-to-end fashion. Experimentalresults show that the proposed method improves the baselinemodel and provides state of the art results on the FashionPose[8] and MPII benchmarks [1].


title: Using social networks to distort users' profiles generated by web search engines

abstract: The Internet is one of the most important sources of knowledge in the present time. It offers a huge volume of information which grows dramatically every day. Web search engines (e.g. Google, Yahoo...) are widely used to find specific data among that information. However, these useful tools also represent a privacy threat for the users: the web search engines profile them by storing and analyzing all the searches that they have previously submitted. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. In this paper, we propose a new scheme designed to protect the privacy of the users from a web search engine that tries to profile them. Our system uses social networks to provide a distorted user profile to the web search engine. The proposed protocol submits standard queries to the web search engine; thus it does not require any change in the server side. In addition to that, this scheme does not require the server to collaborate with the users. Our protocol improves the existing solutions in terms of query delay. Besides, the distorted profiles still allow the users to get a proper service from the web search engines.


title: Predicting bursts and popularity of hashtags in real-time

abstract: Hashtags have been widely used to annotate topics in tweets (short posts on Twitter.com). In this paper, we study the problems of real-time prediction of bursting hashtags. Will a hashtag burst in the near future? If it will, how early can we predict it, and how popular will it become? Based on empirical analysis of data collected from Twitter, we propose solutions to these challenging problems. The performance of different features and possible solutions are evaluated.


title: A 79-GHz 2 $\times $ 2 MIMO PMCW Radar SoC in 28-nm CMOS

abstract: In this paper, the concept of phase modulated MIMO radars is explained and demonstrated with a 28-nm CMOS fully integrated 79-GHz radar SoC. It includes two transmitters, two receivers, and the mm-wave frequency generation. The receivers’ outputs are digitized by on-chip ADCs and processed by a custom designed digital core, which performs correlation and accumulation with a pseudorandom sequence used in transmission. The SoC consumes 1 W to achieve 7.5 cm range resolution. A module with antennas allows for 5° resolution over ±60° elevation and azimuth scan in 2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>2 code domain MIMO operation. A 4<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>4 MIMO system is also demonstrated by means of two SoCs mounted on the same module.


title: Recent improvements of efficiency and power density of DC-DC converters for automotive applications

abstract: Automotive industry is considered to be one of the main contributors to environmental pollution and global warming. Therefore, many car manufacturers are in near future planning to introduce hybrid electric vehicles (HEV), fuel cell electric vehicles (FCEV) and pure electric vehicles (EV) to make our cars more environmentally friendly. These new vehicles require highly efficient and small power converters. In recent years, considerable improvements were made in designing such converters. In this paper, an approach based on so called Snubber Assisted Zero Voltage and Zero Current Switching topology otherwise also know as SAZZ is presented. This topology has evolved to be one of the leaders in the field of highly efficient converters with high power densities. Evolution and main features of this topology are briefly discussed. Capabilities of the topology are demonstrated on two case study prototypes based on different design approaches. The prototypes are designed to be fully bi-directional for peak power output of 30 kW. Both designs reached efficiencies close to 99 % in wide load range. Power densities over 40 kW/litre are attainable in the same time. Combination of MOSFET technology and SAZZ topology is shown to be very beneficial to converters designed for EV applications.


title: ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

abstract: This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.


title: SoS-centric Middleware Services for Interoperability in Smart Cities Systems

abstract: Modern cities are supported by many IT systems managed by distinct public and private agents. Such legacy systems are often incompatible since, in general, they use old, dependent and non-standardised technologies. This results in an environment in which there is no interoperability among smart city systems, preventing richer and more interesting applications to be used by citizens, companies, and city administration. An alternative to solve the lack of interoperability is the adoption of a System-of-Systems (SoS) approach. A SoS is a set of independent and heterogeneous constituent systems that interoperate to accomplish a global mission. The collaboration among such constituent systems enables a SoS to offer new functionalities that cannot be provided by any of these systems working as individual entities. The goal of this paper is to propose SoS-centric middleware services to support the management and execution of SoS in Smart Cities environments in a dynamic, transparent and scalable way. The proposed services, once integrated into a smart city platform, support interoperability among different systems operating in a city. Moreover, this paper also presents a motivational case study to make it clear the issues that must be addressed when multiple independent systems are brought together to provide a new Smart City service or application.


title: Empirical assessment of machine learning models for agile software development effort estimation using story points

abstract: In the present day developing houses, the procedures adopted during the development of software using agile methodologies are acknowledged as a better option than the procedures followed during conventional software development due to its innate characteristics such as iterative development, rapid delivery and reduced risk. Hence, it is desirable that the software development industries should have proper planning for estimating the effort required in agile software development. The existing techniques such as expert opinion, analogy and disaggregation are mostly observed to be ad hoc and in this manner inclined to be mistaken in a number of cases. One of the various approaches for calculating effort of agile projects in an empirical way is the story point approach (SPA). This paper presents a study on analysis of prediction accuracy of estimation process executed in order to improve it using SPA. Different machine learning techniques such as decision tree, stochastic gradient boosting and random forest are considered in order to assess prediction more qualitatively. A comparative analysis of these techniques with existing techniques is also presented and analyzed in order to critically examine their performance.


title: Fully implantable, battery-free wireless optoelectronic devices for spinal optogenetics

abstract: AbstractThe advent of optogenetic tools has allowed unprecedented insights into the organization of neuronal networks. Although recently developed technologies have enabled implementation of optogenetics for studies of brain function in freely moving, untethered animals, wireless powering and device


title: YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

abstract: Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy. The required computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs. Recent efforts in designing CNN Application-Specific Integrated Circuits (ASICs) and accelerators for System-On-Chip (SoC) integration have achieved very promising results. Unfortunately, even these highly optimized engines are still above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage. On the algorithmic side, highly competitive classification accuracy canbe achieved by properly training CNNs with binary weights. This novel algorithm approach brings major optimization opportunities in the arithmetic core by removing the need for the expensive multiplications as well as in the weight storage and I/O costs. In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a corearea of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE, respectively.


title: Application of PID controller in controlling refrigerator temperature

abstract: This paper presents the comparison of performances between a PID temperature controller and a conventional on-off temperature controller for home-applied refrigerator. A new PID temperature controller was designed to replace the conventional on-off temperature controller to maintain the inner temperature of a refrigerator. Both temperature controllers of the conventional system and the PID were designed and evaluated using MATLAB Simulink software. The simulation shows that the PID controller was found to be more consistent and effective in maintaining the inner temperature of the refrigerator despite rapid changes in the outer temperature, than the conventional on-off controller. The both simulation results provide full graphical development which is analyzed and have been compared. The effectiveness of the PID control system and its ability to maintain the inner temperature of the refrigerator consistently than the conventional on-off controller is shown.


title: Safe Charging for Wireless Power Transfer

abstract: As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology, which allows the power to be transferred from a charger to ambient devices wirelessly, receives intensive interests. Existing schemes mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the safe charging problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold $R_{t}$ . We show that SCP is NP-hard and propose a solution, which provably outperforms the optimal solution to SCP with a relaxed EMR threshold $(1-\epsilon )R_{t}$ . Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when $\epsilon = 0.1$ , while a naive greedy algorithm is 34.6% below our solution.


title: FPGA PUF using programmable delay lines

abstract: This paper proposes a novel approach for efficient implementation of a real-valued arbiter-based physical unclon-able function (PUF) on FPGA. We introduce a high resolution programmable delay logic (PDL) implemented by lookup table (LUT) internal structure. Using the PDL, we perform fine tuning to cancel out delay skews caused by asymmetries in routing and systematic variations. We devise a symmetric switch structure that can be easily implemented on FPGA. To mitigate the arbiter metastability problem, we present and analyze methods for majority voting of responses. Lastly, a method to classify and group challenges into different robustness sets is introduced, to further increase the corresponding responses' stability in the face of environmental variations. Experimental evaluations show that the responses to robust challenges have an average error rate of less than 2% under temperature variations from −10°C to 75°C.


title: A Depth-Based Fall Detection System Using a Kinect® Sensor

abstract: We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.


title: Genital Trauma in the Pediatric and Adolescent Female

abstract: Genital injuries in female children and adolescents may occur accidentally or as the result of an act of violence. This article discusses the etiologies and management of genital trauma. Awareness needs to be heightened among individual providers of medical care, as well as in the international community, to protect young girls from becoming victims of violence and to provide avenues for recovery.


title: Caregiver Support and Child Sexual Abuse: Why Does It Matter?.

abstract: Coohey’s paper is a valuable investigation of the substantiation of mothers for failure to protect their children from child sexual abuse (CSA). Drawing on concerns regarding the possible inconsistency of decisions to substantiate, the author sought to determine the factors relied on by CPS investigators in the decision-making process. Multivariate analyses revealed the importance of maternal reactions to abuse,


title: Individual Differences in Moral Development: Does Intelligence Really Affect Children’s Moral Reasoning and Moral Emotions?

abstract: This study investigates the relationship between intelligence and individual differences in children’s moral development across a range of different moral transgressions. Taking up prior research that showed morality and intelligence to be related in adolescents and adults, the current study wants to test if these findings can be extended to younger children. The study was designed to address some of the shortcomings in prior research by examining young children aged between 6 years; 4 months and 8 years; 10 months, using a broad concept of moral development including emotional aspects and applying an approach that is closely connected to children’s daily lives. Participants (N = 129) completed a standardized intelligence test and were presented four moral transgression stories to assess moral development. Results demonstrated that findings from prior research with adolescents or adults cannot simply be extended to younger participants. No significant correlations of moral development and intelligence were found for any of the presented stories. This provides first evidence that – at least in middle childhood – moral developmental status seems to be independent from children’s general intelligence assessed by figural inductive reasoning tests.


Dense In Dense : Training Segmentation from Scratch


title: Fully automated assessment of the severity of Parkinson's disease from speech

abstract: For several decades now, there has been sporadic interest in automatically characterizing the speech impairment due to Parkinson’s disease (PD). Most early studies were confined to quantifying a few speech features that were easy to compute. More recent studies have adopted a machine learning approach where a large number of potential features are extracted and the models are learned automatically from the data. In the same vein, here we characterize the disease using a relatively large cohort of 168 subjects, collected from multiple (three) clinics. We elicited speech using three tasks – the sustained phonation task, the diadochokinetic task and a reading task, all within a time budget of 4 min, prompted by a portable device. From these recordings, we extracted 1582 features for each subject using openSMILE, a standard feature extraction tool. We compared the effectiveness of three strategies for learning a regularized regression and find that ridge regression performs better than lasso and support vector regression for our task. We refine the feature extraction to capture pitch-related cues, including jitter and shimmer, more accurately using a time-varying harmonic model of speech. Our results show that the severity of the disease can be inferred from speech with a mean absolute error of about 5.5, explaining 61% of the variance and consistently well-above chance across all clinics. Of the three speech elicitation tasks, we find that the reading task is significantly better at capturing cues than diadochokinetic or sustained phonation task. In all, we have demonstrated that the data collection and inference can be fully automated, and the results show that speech-based assessment has promising practical application in PD. The techniques reported here are more widely applicable to other paralinguistic tasks in clinical domain. © 2013 Elsevier Ltd. All rights reserved.


title: VFunc: a Deep Generative Model for Functions

abstract: We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and parameter noise. In this short paper we describe our model, situate it in the context of prior work, and present proof-of-concept experiments for regression and RL.


title: Challenges and problems in release management process: A case study

abstract: The IT Infrastructure Library (ITIL) is the most widely used IT service management framework and a de facto standard. ITIL provides instructions for IT service providers and organizations how to design, manage and support IT services. However, adopting best practices of ITIL can be difficult for many IT companies. The main contribution of this paper is to present challenges and problems that were identified during a case study focusing on improvement of the release management process. Key challenges included the lack of process description, too high release distribution rate, and poor traceability between incident records and release records.


abstract: THIS collection of verses began with a few songs written for ‘community singing’ at the annual dinners of the research students of the Cavendish Laboratory. The first collection was privately printed in 1904, and similar editions with new songs added appeared in 1906, 1907, and 1911. Some of these were of a purely ephemeral interest and were omitted in the first published edition of 1920. The present edition was published in connexion with the dinner held in celebration of the seventieth birthday of Sir J. J. Thomson. Several of the songs are tributes to him, including a “Biographical Sketch,” which gives a versified account of his career up to date. The author of this, as of most of the best songs in the volume, shelters himself modestly behind the initials “A. A. R.” The ‘J. J.’ songs reveal the extraordinarily happy personal relations which have always bound the research students to their professor, while the songs in honour of Sir Ernest Rutherford are sufficient evidence that the old tradition is being carried on. The remaining songs deal with important branches of modern physics from the electromagnetic theory to the quantum theory. All old Cavendish students will welcome the appearance of this new edition, which is greatly improved in form, and a much wider public will find it both interesting and entertaining.Post-Prandial Proceedings of the Cavendish Society. Sixth edition. Pp. 37. (Cambridge: Bowes and Bowes, 1926.) 2s. net.


title: Using the CPU and GPU for real-time video enhancement on a mobile computer

abstract: Real-time video enhancement is generally achieved using costly specialized hardware that have specific functions and outputs. Commercial off-the-shelf hardware, such as desktop computers with Graphics Processing Units (GPUs), are also commonly used as cost effective solutions for real-time video processing. In the past, the limitations in computer hardware meant that real-time video enhancement was mainly done on desktop GPUs with minimal use of the Central Processing Unit (CPU). These algorithms were simple and easily parallelizable in nature, which enabled them to achieve real-time performance. However, complex enhancement algorithms also require the sequential processing of data and this cannot be easily achieved in real-time on a GPU. In this paper, the current advances in mobile CPU and GPU hardware are used to implement video enhancement algorithms in a new way on a mobile computer. Both the CPU and GPU are used effectively to achieve realtime performance for complex image enhancement algorithms that require both sequential and parallel processing operations. Results are presented for histogram equalization, local adaptive histogram equalization, contrast enhancement using tone mapping and exposure fusion of multiple 8-bit grey scale videos of size up to 1600×1200 pixels.


title: Procedural Modelling of Urban Road Networks

abstract: We present a model for growing procedural road networks in and close to cities. The main idea of our paper is that a city cannot be meaningfully simulated without taking its neighbourhood into account. A simple traffic simulation that considers this neighbourhood is then used to grow new major roads and to influence the locations of minor road growth. Waterways are introduced and used to help position the city nuclei on the map. The resulting cities are formed by allowing several smaller settlements to grow together and to form a rich road structure, much like in real world, and require only minimal per-city input, allowing for batch generation.


title: Bi-Lingual Text to Speech Synthesis System for Urdu and Sindhi

abstract: Text to Speech Synthesis along with the Speech Recognition is widely used throughout the world to enhance the accessibility of the information and enable even the disabled persons to interact with the computers in order to get the potential benefit from this high-tech revolution. In this paper we introduce a bi-lingual novel algorithm for the synthesis of Urdu and Sindhi language text. The devised bi-lingual algorithm uses knowledge based approach along with the hybrid rule based and concatenative acoustic methods to provide efficient and accurate conversion of Urdu and Sindhi text into the high quality speech. The algorithm has been implemented in the VB programming language with a GUI based interface. The proposed system works with high accuracy and has a great potential to be used for variety of applications. The system is versatile enough and can be used for speech recognition also.


title: The Relationship Between Vertical Jump Power Estimates and Weightlifting Ability: A Field-Test Approach

abstract: The purpose of this ::: study was to assess the usefulness of the vertical jump and estimated ::: vertical-jump power as a field test for weightlifting. Estimated ::: PP output from the vertical jump was correlated with ::: lifting ability among 64 USA national-level weightlifters (junior ::: and senior men and women). Vertical jump was measured using ::: the Kinematic Measurement System, consisting of a switch mat ::: interfaced with a laptop computer. Vertical jumps were measured ::: using a hands-on-hips method. A counter-movement vertical ::: jump (CMJ) and a static vertical jump (SJ, 908 knee angle) ::: were measured. Two trials were given for each condition. Testretest ::: reliability for jump height was intra-class correlation ::: (ICC) 5 0.98 (CMJ) and ICC 5 0.96 (SJ). Athletes warmed up ::: on their own for 2�3 minutes, followed by 2 practice jumps at ::: each condition. Peak power (PP) was estimated using the equations ::: developed by Sayers et al. (24). The athletes� current lifting ::: capabilities were assessed by a questionnaire, and USA national ::: coaches checked the listed values. Differences between groups ::: (i.e., men versus women, juniors versus resident lifters) were ::: determined using t-tests (p <= 0.05). Correlations were determined ::: using Pearson�s r. Results indicate that vertical jumping ::: PP is strongly associated with weightlifting ability. Thus, these ::: results indicate that PP derived from the vertical jump (CMJ or ::: SJ) can be a valuable tool in assessing weightlifting performance.


title: A weak link in metabolism: the metabolic capacity for glycine biosynthesis does not satisfy the need for collagen synthesis

abstract: In a previous paper, we pointed out that the capability to synthesize glycine from serine is constrained by the stoichiometry of the glycine hydroxymethyltransferase reaction, which limits the amount of glycine produced to be no more than equimolar with the amount of C 1 units produced. This constraint predicts a shortage of available glycine if there are no adequate compensating processes. Here, we test this prediction by comparing all reported fl uxes for the production and consumption of glycine in a human adult. Detailed assessment of all possible sources of glycine shows that synthesis from serine accounts for more than 85% of the total, and that the amount of glycine available from synthesis, about 3 g/day, together with that available from the diet, in the range 1.5-3.0 g/day, may fall significantly short of the amount needed for all metabolic uses, including collagen synthesis by about 10 g per day for a 70 kg human. This result supports earlier suggestions in the literature that glycine is a semi-essential amino acid and that it should be taken as a nutritional supplement to guarantee a healthy metabolism.


title: Three Myths about Dynamic Time Warping Data

abstract: The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.


title: Innovative moments and poor outcome in narrative therapy

abstract: Abstract Aims: To analyse a poor outcome case of narrative therapy with a woman victim of intimate violence. Method: The Innovative Moments Coding System: version 1 was applied to all sessions to track the innovative moments (i-moments) in the therapeutic process. I–moments are the narrative details that occur in psychotherapeutic conversations that are outside the influence of the problematic narrative. This research aims to describe the processes involved in the stability of meanings in psychotherapy through a dialogical approach to meaning making. Findings: Contrarily to what usually occurs in good outcome cases, re-conceptualization i-moments are absent. Moreover, two specific types of i-moments emerged with higher duration: reflection and protest. Qualitative analysis showed that the potential meanings of these i-moments were surpassed by a return to the problematic narrative. Conclusion: The therapeutic stability seems to be maintained by a systematic return to the problematic narrative after the em...


title: A Wireless Sensor Network Solution for Precision Agriculture Based on Zigbee Technology

abstract: The article has been retracted due to the investigation of complaints received against it. ::: The Editorial Board found that substantial portions of the text quite like the ::: one published in other journal. ::: The problem is under investigation. ::: This paper published in Vol.4 No.1, 2012, has been removed from this site ::: temporally.


title: Pipelined implementation of AES encryption based on FPGA

abstract: This paper presents the outer-round only pipelined architecture for a FPGA implementation of the AES-128 encryption processor. The proposed design uses the Block RAM storing the S-box values and exploits two kinds of Block RAM. By combining the operations in a single round, we can reduce the critical delay. Therefore, our design can achieve a throughput of 34.7 Gbps at 271.15 Mhz and 2389 CLB Slices with 200 BRAM. We can get the much higher efficiency than any other implementation reported in the literature.


title: Mechanical breakdown of aged insulating paper around continuously transposed conductors for power transformers under the influence of short-circuit forces - analysis by numerical simulations

abstract: The cellulose based oil impregnated insulating system of power transformers is exposed to significant aging due to heat, oxygen, moisture and acids during operation time. This degradation affects the mechanical strength of the cellulose. It is generally accepted that a degree of polymerization (DP) below 200 can be defined as the end of life of an insulation system. In literature however there exist little investigations dealing with the consequences of a reduced mechanical strength. Especially the thin paper insulation wrapped around the conductors is weakened due to aging and might fail under the influence of short-circuit forces on the windings. Thus this contribution presents a mechanical simulation model for such paper insulated power transformer conductors. Investigations concerning the beginning of paper insulation failure are conducted for different aging states, conductor geometries and number of insulating layers. The aging dependent material definition for the insulating paper is based on accelerated aging test. Simulation results show that an increase in radial conductor width leads to higher mechanical stress in the paper layers and for this reason to a higher probability of paper failure. Flat paper wrapped wires generate lower stress inside the insulating layers than large Continuously Transposed Conductors (CTCs). The maximum stress inside the paper is hardly dependent on the number of layers. The maximum paper tensile stress that can be correlated to the DP however is a main indicator for the beginning of failure inside the cellulose insulation system. Furthermore the aging dependent Young's Modulus of paper also affects its stress exposure.


title: Random forest for credit card fraud detection

abstract: Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive fraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques, and then utilize these features to check if a transaction is fraud or not. In this paper, two kinds of random forests are used to train the behavior features of normal and abnormal transactions. We make a comparison of the two random forests which are different in their base classifiers, and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China.


Scalable Multi-party Computation for zk-SNARK Parameters in the Random Beacon Model.


title: Informative path planning for an autonomous underwater vehicle

abstract: We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths while ensuring that the vehicle stays out of high-traffic areas during predesignated time intervals. In our objective function we explicitly account for the fact that underwater vehicles typically take measurements while moving, and that they do not have the ability to communicate until they resurface. We present field results from ocean trials on planning paths for a specific AUV, an underwater glider.


Patient Classification System Evaluation Part 1: Essential System Elements