title: Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue Algorithms

text: Drawing ideas from previous authors, we present a new non-blocking concurrent queue algorithm and a new twolock queue algorithm in which one enqueue and one dequeue can proceed concurrently. Both algorithms are simple, fast, and practical; we were surprised not to find them in the literature. Experiments on a 12-node SGI Challenge multiprocessor indicate that the new non-blocking queue consistently outperforms the best known alternatives; it is the clear algorithm of choice for machines that provide a universal atomic primitive (e.g. compare and swap or load linked/store conditional). The two-lock concurrent queue outperforms a single lock when several processes are competing simultaneously for access; it appears to be the algorithm of choice for busy queues on machines with non-universal atomic primitives (e.g. test and set). Since much of the motivation for non-blocking algorithms is rooted in their immunity to large, unpredictable delays in process execution,we report experimental results both for systems with dedicated processors and for systems with several processes multiprogrammed on each processor.


title: Percutaneous kyphoplasty and pedicle screw fixation for the management of thoraco-lumbar burst fractures

text: The study design includes prospective evaluation of percutaneous osteosynthesis associated with cement kyphoplasty on 18 patients. The objective of the study is to assess the efficacy of a percutaneous method of treating burst vertebral fractures in patients without neurological deficits. Even if burst fractures are frequent, no therapeutic agreement is available at the moment. We report in this study the results at 2 years with a percutaneous approach for the treatment of burst fractures. 18 patients were included in this study. All the patients had burst vertebral fractures classified type A3 on the Magerl scale, between levels T9 and L2. The patients’ mean age was 53 years (range 22–78 years) and the neurological examination was normal. A percutaneous approach was systematically used and a kyphoplasty was performed via the transpedicular pathway associated with percutaneous short-segment pedicle screw osteosynthesis. The patients’ follow-up included CT scan analysis, measurement of vertebral height recovery and local kyphosis, and clinical pain assessments. With this surgical approach, the mean vertebral height was improved by 25% and a mean improvement of 11.28° in the local kyphotic angle was obtained. 3 months after the operation, none of the patients were taking class II analgesics. The mean duration of their hospital stay was 4.5 days (range 3–7 days) and the mean follow-up period was 26 months (range 17–30 months). No significant changes in the results obtained were observed at the end of the follow-up period. Minimally invasive methods of treating burst vertebral fractures can be performed via the percutaneous pathway. This approach gives similar vertebral height recovery and kyphosis correction rates to those obtained with open surgery. It provides a short hospital stay, however, and might therefore constitute a useful alternative to open surgical methods.


title: An unusual vesical calculus.

text: We report a 52 year old patient presenting with a bladder stone formed over a migrated intrauterine device (Copper-T). Her history was pertinent for intrauterine contraceptive (IUCD) device placement 10 years back. Investigations included plain ultrasound of abdomen, X-ray of abdomen, urinalysis, and urine culture. Ultrasound and plain X-ray of the pelvis confirmed a bladder stone formed over a migrated copper-T intrauterine device. The device was removed through suprapubic cystolithotomy. Of all the reported cases of vesical stone formation over a migrated IUCD, this case is unique as the patient was an elderly - 52 year old - female. All previously reported cases are of younger age.


title: The development and malleability of executive control abilities

text: Executive control (EC) generally refers to the regulation of mental activity. It plays a crucial role in complex cognition, and EC skills predict high-level abilities including language processing, memory, and problem solving, as well as practically relevant outcomes such as scholastic achievement. EC develops relatively late in ontogeny, and many sub-groups of developmental populations demonstrate an exaggeratedly poor ability to control cognition even alongside the normal protracted growth of EC skills. Given the value of EC to human performance, researchers have sought means to improve it through targeted training; indeed, accumulating evidence suggests that regulatory processes are malleable through experience and practice. Nonetheless, there is a need to understand both whether specific populations might particularly benefit from training, and what cortical mechanisms engage during performance of the tasks used in the training protocols. This contribution has two parts: in Part I, we review EC development and intervention work in select populations. Although promising, the mixed results in this early field make it difficult to draw strong conclusions. To guide future studies, in Part II, we discuss training studies that have included a neuroimaging component - a relatively new enterprise that also has not yet yielded a consistent pattern of results post-training, preventing broad conclusions. We therefore suggest that recent developments in neuroimaging (e.g., multivariate and connectivity approaches) may be useful to advance our understanding of the neural mechanisms underlying the malleability of EC and brain plasticity. In conjunction with behavioral data, these methods may further inform our understanding of the brain-behavior relationship and the extent to which EC is dynamic and malleable, guiding the development of future, targeted interventions to promote executive functioning in both healthy and atypical populations.


title: Learning Parameterized Skills

text: LEARNING PARAMETERIZED SKILLS


title: Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

text: Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.


title: Return of Frustratingly Easy Domain Adaptation

text: Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being “frustratingly easy” to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple–it can be implemented in four lines of Matlab code–CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. “Everything should be made as simple as possible, but not simpler.”


title: Unsupervised Domain Adaptation by Backpropagation

text: Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary). As the training progresses, the approach promotes the emergence of “deep” features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.


Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and test domain distributions and thus improve generalization performance. However, while generative adversarial networks (GANs) show compelling visualizations, they are not optimal on discriminative tasks and can be limited to smaller shifts. On the other hand, discriminative approaches can handle larger domain shifts, but impose tied weights on the model and do not exploit a GAN-based loss. In this work, we first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and use this generalized view to better relate prior approaches. We then propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.


title: Regularization of Neural Networks using DropConnect

text: We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.


title: Implications of integrating test-driven development into CS1/CS2 curricula

text: Many academic and industry professionals have called for more testing in computer science curricula. Test-driven development (TDD) has been proposed as a solution to improve testing in academia. This paper demonstrates how TDD can be integrated into existing course materials without reducing topic coverage. Two controlled experiments were conducted in a CS1/CS2 course in Winter 2008. Following a test-driven learning approach, unit testing was introduced at the beginning of the course and reinforced through example. Results indicate that while student work loads may increase with the incorporation of TDD, students are able to successfully develop unit tests while learning to program.


title: Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

text: Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.


title: Mechanical recycling of waste electric and electronic equipment: a review.

text: The production of electric and electronic equipment (EEE) is one of the fastest growing areas. This development has resulted in an increase of waste electric and electronic equipment (WEEE). In view of the environmental problems involved in the management of WEEE, many counties and organizations have drafted national legislation to improve the reuse, recycling and other forms of recovery of such wastes so as to reduce disposal. Recycling of WEEE is an important subject not only from the point of waste treatment but also from the recovery of valuable materials.WEEE is diverse and complex, in terms of materials and components makeup as well as the original equipment's manufacturing processes. Characterization of this waste stream is of paramount importance for developing a cost-effective and environmentally friendly recycling system. In this paper, the physical and particle properties of WEEE are presented. Selective disassembly, targeting on singling out hazardous and/or valuable components, is an indispensable process in the practice of recycling of WEEE. Disassembly process planning and innovation of disassembly facilities are most active research areas. Mechanical/physical processing, based on the characterization of WEEE, provides an alternative means of recovering valuable materials. Mechanical processes, such as screening, shape separation, magnetic separation, Eddy current separation, electrostatic separation, and jigging have been widely utilized in recycling industry. However, recycling of WEEE is only beginning. For maximum separation of materials, WEEE should be shredded to small, even fine particles, generally below 5 or 10mm. Therefore, a discussion of mechanical separation processes for fine particles is highlighted in this paper. Consumer electronic equipment (brown goods), such as television sets, video recorders, are most common. It is very costly to perform manual dismantling of those products, due to the fact that brown goods contain very low-grade precious metals and copper. It is expected that a mechanical recycling process will be developed for the upgrading of low metal content scraps.


title: Query Term Suggestion in Academic Search

text: In this paper, we evaluate query term suggestion in the context of academic professional search. Our overall goal is to support scientists in their information seeking tasks. We set up an interactive search system in which terms are extracted from clicked documents and suggested to the user before every query specification step. We evaluated our method with the iSearch collection of academic information seeking behaviour and crowdsourced term relevance judgements. We found that query term suggestion can significantly improve recall in academic search.


title: Red Opal: product-feature scoring from reviews

text: Online shoppers are generally highly task-driven: they have a certain goal in mind, and they are looking for a product with features that are consistent with that goal. Unfortunately, finding a product with specific features is extremely time-consuming using the search functionality provided by existing web sites.In this paper, we present a new search system called Red Opal that enables users to locate products rapidly based on features. Our fully automatic system examines prior customer reviews, identifies product features, and scores each product on each feature. Red Opal uses these scores to determine which products to show when a user specifies a desired product feature. We evaluate our system on four dimensions: precision of feature extraction, efficiency of feature extraction, precision of product scores, and estimated time savings to customers. On each dimension, Red Opal performs better than a comparison system.


title: Online, interactive learning of gestures for human/robot interfaces

text: We have developed a gesture recognition system, based on Hidden Markov Models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 di erent gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example.


title: Enterprise Architecture: Enabling Integration, Agility And Change

text: Three core imperatives are essential for modern businesses and organizations: seamless integration of customer and operational processes, agility, and the ability to change. These imperatives are thus relevant in view of successfully executing strategic choices, but all too often not satisfied. Businesses and organizations can be viewed as complex adaptive socio-technical systems. Two fundamentally different perspectives on systems play a role: the functional (black-box) perspective and the constructional (white-box) perspective. Management and governance of businesses and organizations regard the functional, black-box perspective, which is inherently ill-suited for addressing the imperatives mentioned. Rather, establishing system integration, agility and change requires a focus on the system’s design, hence entails the constructional perspective. Both perspectives are relevant. The concept of architecture plays a fundamental role for operationalizing the constructional perspective. Next to the more familiar notion of technology architecture, the concepts of business and organizational architecture are formally introduced and elucidated. In view of the important role of information in customer and operational processes, the notion of information architecture will be additionally discussed, specifically the relationships with other architectures. Various domains within these architectures will be highlighted, whereby the importance of coherence and consistency is stressed, especially in view of the ability to change. Collectively, the four architectures are labeled Enterprise Architecture. Finally, enterprise architecture will be positioned as a crucial means for linking strategy development and execution. The functional and constructional perspective Function and construction A system may be defined as an identifiable bounded set of functionally and/or methodically related elements or principles with a certain operational purpose. Within this definition, a plethora of systems can be mentioned: a payment system, an accounting system, a transport


title: Compact substrate integrated waveguide based active and passive circularly polarized composite right/left handed leaky wave antennas

text: Two compact substrate integrated waveguide (SIW) based circularly polarized (CP) composite right/left handed (CRLH) leaky wave antennas (LWAs) are designed and presented. Besides wide-band matching enhancement, the proposed antennas are shortened by 39.8 % along the scanning plane compared to the previous work [1]. Additionally, the current amplitude distribution of the proposed antenna can be controlled by using negative resistance based reflection type amplifiers (RTAs). The compensated uniform distribution yields an enhanced directivity without compromising full space frequency scanning capability.


title: Crowd Geofencing

text: Geofencing mechanisms allow for timely message delivery to the visitors of predefined target areas. However, conventional geofencing approaches poorly support mobile data collection scenarios in which experts need in situ assistance. In this paper, we propose crowd geofencing environments, in which a large number of crowdworkers generate geofences to support mobile experts. As a first step to open up the possibilities of crowd geofencing, we have tested its feasibility by collecting more than one thousand geofences in an unfamiliar city prior to the visit to look into urban water and air quality issues. Our experience has revealed the strengths and weaknesses of crowd geofencing in terms of geofence quality and crowd-powered situated actions.


title: Analysis and design of block cipher constructions

text: This thesis is dedicated to symmetric cryptographic algorithms. The major focus of the work is on block ciphers themselves as well as on hash functions and message authentication codes based on block ciphers. Three main approaches to the cryptanalysis of symmetric cryptographic algorithms are pursued. First, several block cipher constructions are analyzed mathematically using statistical cryptanalysis. Second, practical attacks on real-world symmetric cryptosystems are considered. Finally, novel cryptanalytic techniques using side-channel leakage are studied with applications to block ciphers and message authentication codes. Differential and linear cryptanalyses are well-known statistical attacks on block ciphers. This thesis studies the security of unbalanced Feistel networks with contracting MDS diffusion with respect to differential and linear cryptanalysis. Upper bounds on the differential trail probabilities and linear probabilities of linear trails in such constructions are proven. It is shown that such unbalanced Feistel networks can be highly efficient and are comparable to many known balanced Feistel network constructions with respect to differential and linear cryptanalysis. Ultra-lightweight substitution-permutation networks with diffusion layers based on the co-design of S-boxes and bit permutations are proposed. This results in lightweight block ciphers and block cipher based compression functions for hash functions designed and analyzed. These constructions have very small footprint and can be efficiently implemented on the majority of RFID tags This work also studies practical attacks on real-world symmetric cryptographic systems. Attacks are proposed on the KeeLoq block cipher and authentication systems widely used for automotive access control and component identification. Cryptanalysis of the A5/2 stream cipher used for protecting GSM connections worldwide is performed. Linear slide attacks on KeeLoq are proposed resulting in the fastest known attack on the KeeLoq block cipher working for all keys. Severe weaknesses of the KeeLoq key management are identified. The KeeLoq real-world authentication protocols for access control and component identification are also analyzed. A special-purpose hardware architecture for attacking A5/2 is developed that allows for real-time key recovery within one second for different GSM channels. This engine is based on an optimized hardware algorithm for fast Gaussian elimination over binary finite fields.


title: Does Music Predictability Improve Spatial-Temporal Reasoning ?

text: In 1993, it was found that Mozart’s music temporarily enhanced performance on spatialtemporal reasoning tasks. This effect later became termed the Mozart Effect. Since then, the mechanisms of the Mozart Effect have been hotly debated among the scientific community. Recent research has called for studying the Mozart Effect by analyzing music as a series of components. The present study took the components of music into account by testing if predictable music could enhance spatial-temporal reasoning compared to non-predictable music. Participants were administered a task designed to test spatial-temporal reasoning after listening to either predictable or non-predictable music. Additionally, as a control condition, they performed the same task after listening to a short story. Listening to music did not affect reasoning performance, regardless of the predictability of the music. The results indicate that predictability in music alone may not be a major element of the Mozart Effect.


title: Wireless Hand Gesture Recognition Based on Continuous-Wave Doppler Radar Sensors

text: Short-range continuous-wave Doppler radar sensors have been mainly used for noncontact detection of various motions. In this paper, we investigate the feasibility to implement the function of a remote mouse, an input device of a computer, by recognizing human gestures based on a dual-channel Doppler radar sensor. Direct conversion architecture, symmetric subcarrier modulation, and bandpass sampling techniques are used to obtain a cost-effective solution. An arcsine algorithm and a motion imaging algorithm are proposed to linearly reconstruct the hand and finger motions in a 2-D plane from the demodulated Doppler phase shifts. Experimental results verified the effectiveness of the proposed architecture and algorithms. Different from the frequency-domain “micro-Doppler” approach, the proposed remote gesture recognition based on linear motion reconstruction is able to recognize definitive signatures for the corresponding motions, exhibiting promising potential in practical applications of human-computer interaction.


title: Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms

text: Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.


title: Glioblastoma multiforme tissue histopathology images based disease stage classification with deep CNN

text: Recently, many feature extraction methods for histopathology images have been reported for automatic quantitative analysis. One of the severe brain tumors is the Glioblastoma multiforme (GBM) and histopathology tissue images can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of tedious manual evaluation traditionally required for efficient evaluation. In this study, we consider feature extraction and disease stage classification for brain tumor histopathology images using automatic image analysis methods. In particular, we utilized an automatic feature extraction and labeling for histopathology imagery data given by The Cancer Genome Atlas (TCGA) and checked the classification accuracy of disease stages in GBM tissue images using deep Convolutional Neural Network (CNN). Experimental results indicate promise in automatic disease stage classification and high level of accuracy were obtained for tested image data.


title: Template Deformation for Point Cloud Fitting

text: The reconstruction of high-quality surface meshes from measured data is a vital stage in digital shape processing. We present a new approach to this problem that deforms a template surface to fit a given point cloud. Our method takes a template mesh and a point cloud as input, the latter typically shows missing parts and measurement noise. The deformation process is initially guided by user specified correspondences between template and data, then during iterative fitting new correspondences are established. This approach is based on a Laplacian setting for the template without need of any additional meshing of the data or cross-parameterization. The reconstructed surface fits to the point cloud while it inherits shape properties and topology of the template. We demonstrate the effectiveness of the approach for several point data sets from different sources.


title: Bystander Responses to a Violent Incident in an Immersive Virtual Environment

text: Under what conditions will a bystander intervene to try to stop a violent attack by one person on another? It is generally believed that the greater the size of the crowd of bystanders, the less the chance that any of them will intervene. A complementary model is that social identity is critical as an explanatory variable. For example, when the bystander shares common social identity with the victim the probability of intervention is enhanced, other things being equal. However, it is generally not possible to study such hypotheses experimentally for practical and ethical reasons. Here we show that an experiment that depicts a violent incident at life-size in immersive virtual reality lends support to the social identity explanation. 40 male supporters of Arsenal Football Club in England were recruited for a two-factor between-groups experiment: the victim was either an Arsenal supporter or not (in-group/out-group), and looked towards the participant for help or not during the confrontation. The response variables were the numbers of verbal and physical interventions by the participant during the violent argument. The number of physical interventions had a significantly greater mean in the in-group condition compared to the out-group. The more that participants perceived that the Victim was looking to them for help the greater the number of interventions in the in-group but not in the out-group. These results are supported by standard statistical analysis of variance, with more detailed findings obtained by a symbolic regression procedure based on genetic programming. Verbal interventions made during their experience, and analysis of post-experiment interview data suggest that in-group members were more prone to confrontational intervention compared to the out-group who were more prone to make statements to try to diffuse the situation.


title: Point cloud compression based on hierarchical point clustering

text: In this work we propose an algorithm for compressing the geometry of a 3D point cloud (3D point-based model). The proposed algorithm is based on the hierarchical clustering of the points. Starting from the input model, it performs clustering to the points to generate a coarser approximation, or a coarser level of detail (LOD). Iterating this clustering process, a sequence of LODs are generated, forming an LOD hierarchy. Then, the LOD hierarchy is traversed top down in a width-first order. For each node encountered during the traversal, the corresponding geometric updates associated with its children are encoded, leading to a progressive encoding of the original model. Special efforts are made in the clustering to maintain high quality of the intermediate LODs. As a result, the proposed algorithm achieves both generic topology applicability and good ratedistortion performance at low bitrates, facilitating its applications for low-end bandwidth and/or platform configurations.


title: INTERNAL COMPACT PRINTED LOOP ANTENNA WITH MATCHING ELEMENT FOR LAPTOP APPLICATIONS

text: An internal compact printed loop antenna with matching element is presented for laptop applications. Bending to the printed loop structure is employed to reduce the size of antenna. A matching element is used at backside of the substrate to enhance the bandwidth of the antenna. The matching element is of rectangular shape with inverted U shape slot in it. The proposed antenna with wideband characteristics covers GSM900, GSM1900, UMTS, LTE2300, WLAN, WiMAX 3.3/3.5/3.7 bands and is suitable for laptop applications. The antenna structure is simulated using IE3D software. Keywordsbended; multiband antenna; printed loop antenna; tablet; wideband antenna.


title: Anti-phishing detection of phishing attacks using genetic algorithm

text: An approach to detection of phishing hyperlinks using the rule based system formed by genetic algorithm is proposed, which can be utilized as a part of an enterprise solution to anti-phishing. A legitimate webpage owner can use this approach to search the web for suspicious hyperlinks. In this approach, genetic algorithm is used to evolve rules that are used to differentiate phishing link from legitimate link. Evaluating the parameters like evaluation function, crossover and mutation, GA generates a ruleset that matches only the phishing links. This ruleset is stored in a database and a link is reported as a phishing link if it matches any of the rules in the rule based system and thus it keeps safe from fake hackers. Preliminary experiments show that this approach is effective to detect phishing hyperlink with minimal false negatives at a speed adequate for online application.


title: SDN and OpenFlow for Dynamic Flex-Grid Optical Access and Aggregation Networks

text: We propose and discuss the extension of software-defined networking (SDN) and OpenFlow principles to optical access/aggregation networks for dynamic flex-grid wavelength circuit creation. The first experimental demonstration of an OpenFlow1.0-based flex-grid λ-flow architecture for dynamic 150 Mb/s per-cell 4 G Orthogonal Frequency Division Multiple Access (OFDMA) mobile backhaul (MBH) overlays onto 10 Gb/s passive optical networks (PON) without optical network unit (ONU)-side optical filtering, amplification, or coherent detection, over 20 km standard single mode fiber (SSMF) with a 1:64 passive split is also detailed. The proposed approach can be attractive for monetizing optical access/aggregation networks via on-demand support for high-speed, low latency, high quality of service (QoS) applications over legacy fiber infrastructure.


title: Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

text: We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct classes of algorithms, namely continuous time and discrete time models. The resulting neural networks form a new class of data-efficient universal function approximators that naturally encode any underlying physical laws as prior information. In this first part, we demonstrate how these networks can be used to infer solutions to partial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.


title: Children's moral motivation, sympathy, and prosocial behavior.

text: Two studies investigated the role of children's moral motivation and sympathy in prosocial behavior. Study 1 measured other-reported prosocial behavior and self- and other-reported sympathy. Moral motivation was assessed by emotion attributions and moral reasoning following hypothetical transgressions in a representative longitudinal sample of Swiss 6-year-old children (N = 1,273). Prosocial behavior increased with increasing sympathy, especially if children displayed low moral motivation. Moral motivation and sympathy were also independently related to prosocial behavior. Study 2 extended the findings of Study 1 with a second longitudinal sample of Swiss 6-year-old children (N = 175) using supplementary measures of prosocial behavior, sympathy, and moral motivation. The results are discussed in regard to the precursors of the moral self in childhood.


title: Earliest Deadline Scheduling for Real-Time Database Systems

text: Earlier studies have observed that in moderately-loaded real-time database systems, using an Earliest Deadline policy to schedule tasks results in the fewest missed deadlines. When the real-time system is overloaded, however, an Earliest Deadline schedule performs worse than most other policies. This is due to Earliest Deadline giving the highest priority to transactions that are close to missing their deadlines. In this paper, we present a new priority assignment algorithm called Adaptive Earliest Deadline (AED), which features a feedback control mechanism that detects overload conditions and modifies transaction priority assignments accordingly. Using a detailed simulation model, we compare the performance of AED with respect to Earliest Deadline and other fixed priority schemes. We also present and evaluate an extension of the AED algorithm called Hierarchical Earliest Deadline (HED), which is designed to handle applications that assign different values to transactions and where the goal is to maximize the total value of the in-time transactions.


title: Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory

text: Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings.


title: Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

text: This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.


title: Fast and extensible building modeling from airborne LiDAR data

text: This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits the typical building reconstruction pipeline, several major distinct features are developed to enhance efficiency and robustness: 1) we design a novel vegetation detection algorithm based on differential geometry properties and unbalanced SVM; 2) after roof patch segmentation, a fast boundary extraction method is introduced to produce topology-correct water tight boundaries; 3) instead of making assumptions on the angles between roof boundary lines, we propose a data-driven algorithm which automatically learns the principal directions of roof boundaries and uses them in footprint production. Furthermore, we show the extendability of our algorithm by supporting non-flat object patterns with the help of only a few user interactions. We demonstrate the efficiency and accuracy of our algorithm by showing experiment results on urban area data of several different data sets.


title: Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation

text: A new concept for the detection of small objects from modular optoelectronic multispectral scanner (MOMS-02) high spatial resolution panchromatic satellite imagery is presented. We combine supervised shape classification with unsupervised image segmentation in an iterative procedure which allows a target-oriented search for specific object shapes.


title: Implicit user modeling for personalized search

text: Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.


title: Animal-inspired design and aerodynamic stabilization of a hexapedal millirobot

text: The VelociRoACH is a 10 cm long, 30 gram hexapedal millirobot capable of running at 2.7 m/s, making it the fastest legged robot built to date, relative to scale. We present the design by dynamic similarity technique and the locomotion adaptations which have allowed for this highly dynamic performance. In addition, we demonstrate that rotational dynamics become critical for stability as the scale of a robotic system is reduced. We present a new method of experimental dynamic tuning for legged millirobots, aimed at finding stable limit cycles with minimal rotational energy. By implementing an aerodynamic rotational damper, we further reduced the rotational energy in the system, and demonstrated that stable limit cycles with lower rotational energy are more robust to disturbances. This method increased the stability of the system without detracting from forward speed.


title: Dynamic tire friction models for vehicle traction control

text: In this paper we derive a dynamic friction force model for road/tire interaction for ground vehicles. The model is based on a similar dynamic friction model for contact developed previously for contact-point friction problems, called the LuGre model [4]. We show that the dynamic LuGre friction model is able to accurately capture velocity and road/surface dependence of the tire friction force.


title: Effects of Platform-Switching on Peri-implant Soft and Hard Tissue Outcomes: A Systematic Review and Meta-analysis.

text:
PURPOSE
This systematic review and meta-analysis was aimed at evaluating the longitudinal effect of platform switching on implant survival rates as well as on soft and hard tissue outcomes.

MATERIALS AND METHODS
An electronic search of the databases of the National Center for Biotechnology Information, PubMed, Ovid (MEDLINE), EMBASE, Web of Science, and Cochrane Collaboration Library was conducted in February 2015. Studies published in English with at least 10 human participants and a 12-month post-loading follow-up were included. Random effects meta-analyses of selected studies were applied to compare the primary and secondary outcomes of platform-switched (PS) and regular-platform (RP) implants, as well as the experimental designs and clinical outcomes.

RESULTS
A total of 26 studies involving 1,511 PS implants and 1,123 RP implants were evaluated. Compared to RP implants, PS implants showed a slight increase in vertical marginal bone loss (VMBL) and pocket depth reduction (weighted mean differences were -0.23 mm and -0.20 mm, respectively). The PS implants had a mean VMBL of 0.36 ± 0.15 mm within the first year of service. The meta-regression suggested a trend of decreased bone resorption at sites with thick soft tissues at baseline.

CONCLUSION
This study suggested that platform switching may have an indirect protective effect on implant hard tissue outcomes.


title: Parallel Database Recovery for Multicore Main-Memory Databases

text: Main-memory database systems for multicore servers can ach ieve excellent performance when processing massive volume of OL TP workloads. But crash-resilience mechanism, or namely logg ingand-replay, can either degrade the performance of transact ion processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it i s possible to achieve speedy failure recovery without introduci ng any costly logging overhead to the execution of online transact ions. We propose TARS, a parallel log-recovery mechanism that is specifically designed for lightweight, coarse-grained commandlogging approach. TARS leverages a combination of static and dynamic analyses to parallelize the log replay: at compile time, T ARS decomposes stored procedures by carefully analyzing depende ncies within and across programs; at recovery time, a re-executio n schedule with a high degree of parallelism is attained through lig tweight one-pass scans over transaction parameter values. As such, recovery latency is remarkably minimized. We evaluated T ARS in a main-memory database prototype running on a 40-core serve r. Compared to the state-of-the-art mechanisms, T ARS yields significantly higher recovery rate without compromising the effici en y of transaction processing.


title: A convenient nucleic acid test on the basis of the capillary convective PCR for the on-site detection of enterovirus 71.

text: The recent and continuing epidemic of enterovirus 71 in China has affected millions of children and resulted in thousands of deaths. Timely diagnosis and management is essential for disease control. Current enterovirus 71 molecular tests require resources that are unavailable for on-site testing. We have developed a simple-to-operate nucleic acid test, the convenient and integrated nucleic acid test, for local medical institutions. It uses a convective PCR for rapid amplification, a dipstick for visual detection of PCR products, and a simple commercial kit for nucleic acid extraction. By using a specially designed reagent and reaction tube containing a dipstick, the amplification and detection processes are well integrated and simplified. Moreover, cross contamination that may be caused by an open-tube detection system can be avoided. On the basis of the convenient and integrated nucleic acid test, an enterovirus 71 assay for on-site testing was developed. After evaluating known hand, foot, and mouth disease virus stocks of 17 strains of 11 different serotypes, this assay showed a favorable detection spectrum and no cross-reactivity. Its clinical performance was established by testing 141 clinical samples and comparing the results with a nested RT-PCR method. The assay showed a clinical sensitivity and specificity of 98.5% and 100%, respectively. Our results suggest that this convenient and integrated nucleic acid test enterovirus 71 assay may serve as an on-site diagnosis tool.


title: An 8.3 nW −72 dBm event driven IoE wake up receiver RF front end

text: This work presents an ultra-low power event driven wake-up receiver (WuRx) fabricated in a RF CMOS 130 nm process. The receiver consists of an off-chip lumped element matching network, an envelope detector, a decision circuit capable of detecting sub-mV baseband signal voltages and a clock source consuming 1.3 nW. This receiver has demonstrated a sensitivity of −72 dBm while consuming a total of 8.3 nW from 1 V and 0.65 V sources.


title: Standardization of marketing strategy 107 Standardization of international marketing strategy by firms from a developing country

text: A major debate in the international marketing literature deals with the globalization of markets and the extent to which a company’s international marketing strategy can be standardized (Buzzell, 1968; Cavusgil et al., 1993; Douglas and Wind, 1987; Hill and Still, 1984; Jain, 1989; Levitt, 1983; Sorenson and Wiechmann, 1975). Significant progress has been made with respect to the extent to which international marketing strategy can be standardized across national borders (Cavusgil et al., 1993; Jain, 1989). However, almost all previous research has been conducted from a US-firm perspective. Even though a few studies dealt with this debate in the context of less developed economies, the issues studied were viewed from the perspective of US firms’ operating activities in those economies (e.g. Grosse and Zinn, 1990; Hill and Still, 1984; Kacker, 1972). A major gap in the literature is whether our current knowledge can be generalized to foreign companies in other nations, especially the developing world. Firms from developing countries assume an increasingly important role in international competition, since many of the fastest growing economies in the world can be found in these nations. In addition, since developing countries are often culturally different from developed countries, they provide a suitable context to assess the generalizability of the existing knowledge in the standardization literature. Consequently, it is of interest to researchers and practitioners of international marketing to know the perspective of firms from developing countries regarding the standardization and adaptation of marketing strategy. Another void in the literature is that previous studies implicitly assume that standardization concepts are unidimensional at either the overall marketing programme level or the 4-P level (e.g. Akaah, 1991; Boddewyn et al., 1986;


title: Numerically Grounded Language Models for Semantic Error Correction

text: Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction. Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities. Our approach uses language models grounded in numbers within the text. Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases. Our evaluation on clinical reports shows that numerical grounding improves perplexity by 33% and F1 for semantic error correction by 5 points when compared to ungrounded approaches. Conditioning on a knowledge base yields further improvements.


title: Sequential Self-Folding Structures by 3D Printed Digital Shape Memory Polymers

text: Folding is ubiquitous in nature with examples ranging from the formation of cellular components to winged insects. It finds technological applications including packaging of solar cells and space structures, deployable biomedical devices, and self-assembling robots and airbags. Here we demonstrate sequential self-folding structures realized by thermal activation of spatially-variable patterns that are 3D printed with digital shape memory polymers, which are digital materials with different shape memory behaviors. The time-dependent behavior of each polymer allows the temporal sequencing of activation when the structure is subjected to a uniform temperature. This is demonstrated via a series of 3D printed structures that respond rapidly to a thermal stimulus, and self-fold to specified shapes in controlled shape changing sequences. Measurements of the spatial and temporal nature of self-folding structures are in good agreement with the companion finite element simulations. A simplified reduced-order model is also developed to rapidly and accurately describe the self-folding physics. An important aspect of self-folding is the management of self-collisions, where different portions of the folding structure contact and then block further folding. A metric is developed to predict collisions and is used together with the reduced-order model to design self-folding structures that lock themselves into stable desired configurations.


title: A Hybrid Indexing Method for Approximate String Matching

text: We present a new indexing method for the approximate string matching problem. The method is based on a suffix array combined with a partitioning of the pattern. We analyze the resulting algorithm and show that the average retrieval time is , for some that depends on the error fraction tolerated and the alphabet size . It is shown that for approximately , where . The space required is four times the text size, which is quite moderate for this problem. We experimentally show that this index can outperform by far all the existing alternatives for indexed approximate searching. These are also the first experiments that compare the different existing schemes.


title: Extracting Business Process Models Using Natural Language Processing (NLP) Techniques

text: This Doctoral Consortium paper discusses how NLP can be applied in the domain of BPM in order to automatically generate business process models from existing documentation within the organization. The main idea is that from the syntactic and grammatical structure of a sentence, the components of a business process model can be derived (i.e. activities, resources, tasks, patterns). The result would be a business process model depicted using BPMN - a dedicated business process modeling technique


title: Extraction and Adaptation of Fuzzy Rules for Friction Modeling and Control Compensation

text: Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems.


title: Offline Evaluation of Response Prediction in Online Advertising Auctions

text: Click-through rates and conversion rates are two core machine learning problems in online advertising. The evaluation of such systems is often based on traditional supervised learning metrics that ignore how the predictions are used. These predictions are in fact part of bidding systems in online advertising auctions. We present here an empirical evaluation of a metric that is specifically tailored for auctions in online advertising and show that it correlates better than standard metrics with A/B test results.


title: Automatically generating features for learning program analysis heuristics for C-like languages

text: We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been developed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the analysis designers, but they do not remove it completely; they still leave the nontrivial task of designing so called features to the hands of the designers. Our technique aims at automating this feature design process. The idea is to use programs as features after reducing and abstracting them. Our technique goes through selected program-query pairs in codebases, and it reduces and abstracts the program in each pair to a few lines of code, while ensuring that the analysis behaves similarly for the original and the new programs with respect to the query. Each reduced program serves as a boolean feature for program-query pairs. This feature evaluates to true for a given program-query pair when (as a program) it is included in the program part of the pair. We have implemented our approach for three real-world static analyses. The experimental results show that these analyses with automatically-generated features are cost-effective and consistently perform well on a wide range of programs.


title: Visualizing Linked Data with JavaScript

text: Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.


title: TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment

text: A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers’ opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker’s opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.


title: Semi-supervised feature learning for improving writer identification

text: Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we proposed a weighted label smoothing regularization (WLSR) method for data augmentation, which assigned the weighted uniform label distribution to the extra unlabeled data. The WLSR method could regularize the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semisupervised feature learning approach could significantly improve the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline write identification.


title: A Temporal Attentional Model for Rumor Stance Classification

text: Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.


title: Modeling WiFi Active Power/Energy Consumption in Smartphones

text: We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.


title: Fingerstroke time estimates for touchscreen-based mobile gaming interaction.

text: The growing popularity of gaming applications and ever-faster mobile carrier networks have called attention to an intriguing issue that is closely related to command input performance. A challenging mirroring game service, which simultaneously provides game service to both PC and mobile phone users, allows them to play games against each other with very different control interfaces. Thus, for efficient mobile game design, it is essential to apply a new predictive model for measuring how potential touch input compares to the PC interfaces. The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smartphone interface (i.e., tapping, pointing, dragging, and flicking). A modified KLM, tentatively called the fingerstroke-level model (FLM), is proposed using time estimates on regression models.


title: A fast transient LDO based on dual loop FVF with high PSRR

text: In this work, the design of a dual loop flipped voltage follower (FVF) based low-dropout regulator (LDO) to achieve fast transient response is proposed for all-digital phase locked loop (ADPLL) in the RFID application. With the help of an additional reference generation loop in FVF LDO and the cascaded structure, high power supply rejection ratio (PSRR) is achievable. This design is fabricated in GF 40nm CMOS technology. The FVF LDO core only occupies small area of 0.036 mm2. This area also includes 80pF on chip capacitor. Without large off-chip capacitor, this LDO is suitable for system-on-chip (SoC) requirement. Post layout simulation shows that fast response of 45ns and high PSRR of −42dB through up to 10GHz frequency range.


title: New compact OMT based on a septum solution

text: This paper presents a new compact orthomode transducer (OMT) able to extract or combine two orthogonal linear polarizations in a very compact manner with simple manufacturing process. The component looks similar to the well-known septum polarizer but contrary to this one, the new component is able to handle dual linear polarization.


title: PIXHAWK: A system for autonomous flight using onboard computer vision

text: We provide a novel hardware and software system for micro air vehicles (MAV) that allows high-speed, low-latency onboard image processing. It uses up to four cameras in parallel on a miniature rotary wing platform. The MAV navigates based on onboard processed computer vision in GPS-denied in- and outdoor environments. It can process in parallel images and inertial measurement information from multiple cameras for multiple purposes (localization, pattern recognition, obstacle avoidance) by distributing the images on a central, low-latency image hub. Furthermore the system can utilize low-bandwith radio links for communication and is designed and optimized to scale to swarm use. Experimental results show successful flight with a range of onboard computer vision algorithms, including localization, obstacle avoidance and pattern recognition.


title: Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition

text: When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transformmethods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures.


title: Least squares quantization in PCM

text: It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quantization schemes for 26 quanta, b = 1,2, t ,7, are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.


title: Robust Face Recognition via Sparse Representation

text: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.


title: High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence

text: Given i.i.d. observations of a random vector X ∈ R, we study the problem of estimating both its covariance matrix Σ, and its inverse covariance or concentration matrix Θ = (Σ). When X is multivariate Gaussian, the non-zero structure of Θ is specified by the graph of an associated Gaussian Markov random field; and a popular estimator for such sparse Θ is the l1-regularized Gaussian MLE. This estimator is sensible even for for non-Gaussian X, since it corresponds to minimizing an l1-penalized log-determinant Bregman divergence. We analyze its performance under high-dimensional scaling, in which the number of nodes in the graph p, the number of edges s, and the maximum node degree d, are allowed to grow as a function of the sample size n. In addition to the parameters (p, s, d), our analysis identifies other key quantities that control rates: (a) the l∞-operator norm of the true covariance matrix Σ; and (b) the l∞ operator norm of the sub-matrix Γ∗SS , where S indexes the graph edges, and Γ∗ = (Θ) ⊗ (Θ); and (c) a mutual incoherence or irrepresentability measure on the matrix Γ∗ and (d) the rate of decay 1/f(n, δ) on the probabilities {|Σ̂ij − Σ∗ij | > δ}, where Σ̂ is the sample covariance based on n samples. Our first result establishes consistency of our estimate Θ̂ in the elementwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o( √ s). In our second result, we show that with probability converging to one, the estimate Θ̂ correctly specifies the zero pattern of the concentration matrix Θ∗. We illustrate our theoretical results via simulations for various graphs and problem parameters, showing good correspondences between the theoretical predictions and behavior in simulations. AMS 2000 subject classifications: Primary 62F12; secondary 62F30.


title: Malware detection using adaptive data compression

text: A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of existing malware, are extracted by malware analysts from known malware samples, and stored in a database often referred to as a virus dictionary. This process often involves a significant amount of human efforts. In addition, there are two major limitations in this technique. First, not all malicious programs have bit patterns that are evidence of their malicious nature. Therefore, some malware is not recorded in the virus dictionary and can not be detected through signature matching. Second, searching for specific bit patterns will not work on malware that can take many forms--obfuscated malware. Signature matching has been shown to be incapable of identifying new malware patterns and fails to recognize obfuscated malware. This paper presents a malware detection technique that discovers malware by means of a learning engine trained on a set of malware instances and a set of benign code instances. The learning engine uses an adaptive data compression model--prediction by partial matching (PPM)--to build two compression models, one from the malware instances and the other from the benign code instances. A code instance is classified, either as "malware" or "benign", by minimizing its estimated cross entropy. Our preliminary results are very promising. We achieved about 0.94 true positive rate with as low as 0.016 false positive rate. Our experiments also demonstrate that this technique can effectively detect unknown and obfuscated malware.


title: An updated survey of GA-based multiobjective optimization techniques

text: After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.


title: Understanding PubMed® user search behavior through log analysis

text: This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.


title: Pattern classification of foot strike type using body worn accelerometers

text: The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.


title: SUPPRESSION OF GATE INDUCED DRAIN LEAKAGE CURRENT ( GIDL ) BY GATE WORKFUNCTION ENGINEERING : ANALYSIS AND MODEL

text: Leakage current reduction is of primary importance as the technology scaling trends continue towards deep sub-micrometer regime. One of the leakage mechanisms which contribute significantly to power dissipation is the Gate Induced Drain Leakage (GIDL). GIDL sets an upper limit on the VLSI MOSFET scaling and may even lead to device breakdown. Thus, in order to improve performance, static power consumption becomes a major concern in such miniaturized devices. With the CMOS technology in the nanometer regime, metal gates emerge as a powerful booster over the poly-Si gates, keeping the leakage mechanisms in control. This work presents a systematic study of the Gate Induced Drain Leakage current reduction by changing the gate workfunction. In this work, an attempt has been made to model the metal gates in the field equations in the gate-drain overlap region. The modeling has been accomplished by taking the physical property of the metals into account i.e. the workfunction. The analytical model has been used to study the impact of gate workfunction engineering on GIDL. The model includes the calculation of band bending, the resultant electric field in the gate-drain overlap region and the GIDL current. It has been observed that the electric field and the GIDL current decreases as the gate workfunction is increased continuously from 4.0~5.2 eV. Further, the results are more interesting at low applied voltages where the decrease in the current is of about 6 orders of magnitude as compared to that at higher voltages.


title: On the Interpolation of Data with Normally Distributed Uncertainty for Visualization

text: In many fields of science or engineering, we are confronted with uncertain data. For that reason, the visualization of uncertainty received a lot of attention, especially in recent years. In the majority of cases, Gaussian distributions are used to describe uncertain behavior, because they are able to model many phenomena encountered in science. Therefore, in most applications uncertain data is (or is assumed to be) Gaussian distributed. If such uncertain data is given on fixed positions, the question of interpolation arises for many visualization approaches. In this paper, we analyze the effects of the usual linear interpolation schemes for visualization of Gaussian distributed data. In addition, we demonstrate that methods known in geostatistics and machine learning have favorable properties for visualization purposes in this case.


title: Two-factor authentication for the Bitcoin protocol

text: We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter (Int J Inf Secur 2(3–4):218–239, 2004. doi: 10.1007/s10207-004-0041-0 ) in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.


title: Jointly Detecting and Separating Singing Voice: A Multi-Task Approach

text: A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns to solve related auxiliary tasks on additional datasets to exploit their correlation. While intuitive in principle, it can be challenging to identify related tasks and construct the model to optimally share information between tasks. In this paper, we explore vocal activity detection as an additional task to stabilise and improve the performance of vocal separation. Further, we identify problematic biases specific to each dataset that could limit the generalisation capability of separation and detection models, to which our proposed approach is robust. Experiments show improved performance in separation as well as vocal detection compared to single-task baselines. However, we find that the commonly used Signal-to-Distortion Ratio (SDR) metrics did not capture the improvement on non-vocal sections, indicating the need for improved evaluation methodologies.


title: Authenticity and Subjective Wellbeing within the Context of a Religious Organization

text: Although authenticity has a long history as a philosophical and psychological idea, this concept has received scarce attention in the business literature until very lately. Nevertheless, scholars belonging to a broad array of disciplines have pointed out the escalation in the individuals' search for authenticity within developed societies. Hence, the purpose of this paper is to assess the link between authenticity and subjective wellbeing within the rarely explored context of faith-driven organizations, where the management of emotions attains a particular significance. Specifically, this study links authenticity with subjective wellbeing among the distinct groups that shape a large international Catholic organization. This study uses Partial Least Squares (PLS) to test our research model and hypotheses. This paper covers two noteworthy research gaps. On the one hand, it provides evidence of the relationship between authenticity and subjective wellbeing within the context of religious organizations. On the other hand, our results suggest that this relationship is not homogeneous among the distinct groups that shape the organization. Implications of the research are finally discussed.


title: Data-Driven Feature Characterization Techniques for Laser Printer Attribution

text: Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.


title: A Low-delay Protocol for Multihop Wireless Body Area Networks

text: Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.


title: The Role of Smart Meters in Enabling Real-Time Energy Services for Households : The Italian Case

text: The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.


title: Ichthyosis congenita, harlequin fetus type: a case report.

text: Ichthyosis is a very heterogeneous family of skin disorders with harlequin ichthyosis being the most severe genetic form. It is a rare autosomal recessive condition, characterized by dry, severely thickened skin with large plates of hyperkeratotic scale, separated by deep fissures. Infants are very susceptible to metabolic abnormalities and infections. They usually do not survive for very long, but several long term survivals have been noted. The vast majority of affected individuals are homozygous for mutations in the ABCA12 gene, which cause a deficiency of the epidermal lipid transporter, resulting in hyperkeratosis and abnormal barrier function. We report a case of a newborn with harlequin ichthyosis, born to unrelated parents, who had a favorable evolution with topical treatment and intensive care.


title: Classification accuracy is not enough

text: We argue that an evaluation of system behavior at the level of the music is required to usefully address the fundamental problems of music genre recognition (MGR), and indeed other tasks of music information retrieval, such as autotagging. A recent review of works in MGR since 1995 shows that most (82 %) measure the capacity of a system to recognize genre by its classification accuracy. After reviewing evaluation in MGR, we show that neither classification accuracy, nor recall and precision, nor confusion tables, necessarily reflect the capacity of a system to recognize genre in musical signals. Hence, such figures of merit cannot be used to reliably rank, promote or discount the genre recognition performance of MGR systems if genre recognition (rather than identification by irrelevant confounding factors) is the objective. This motivates the development of a richer experimental toolbox for evaluating any system designed to intelligently extract information from music signals.


title: A game-theoretic method of fair resource allocation for cloud computing services

text: As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.


title: Time-frequency Analysis 3.1 Time-frequency Analysis

text: Let f (t) be a mathematical idealization of some physical signal depending on time t. Perhaps f can be considered as a superposition of oscillating components but these oscillations have to be limited to some finite extension in time. This is a fundamental problem of the Fourier inversion formula which states that, for a well-behaved signal f one has f (t) = ∞ −∞ˆf (ξ)e 2πitξ dξ expressing a complex signal f as a superposition of exponentials e 2πitξ. If f vanishes outside some finite set then the exponentials, which extend over all time, must cancel each other in some fantastic way that makes it virtually impossible to quantify in any intuitive way which frequencies play a dominant role at any particular time t. 3.1.2 Frequency Local in time Consider a physical signal to be a square integrable real-valued function of time, x(t). One can define a complex extension z(t) = x(t) + iy(t) bey letting y(t) be the inverse Fourier transform of −isgn (t)ˆ x(ξ) where sgn denotes the signum function ξ/|ξ|. In this casê z = ˆ x + iˆy has only positive frequencies. Exercise 3.1.1. Explain why z(t) has an extension to a complex argument t + is, s > 0 that is analytic in the upper half plane {t + is : s > 0}. You may assume that x(t) is a continuous, bounded, absolutely integrable function. The analytic signal z has the polar form r(t)e iθ(t) where r = x 2 + y 2 and θ = arctan y/x. The instantaneous frequency can be defined as dθ/dt. This point of view however is a little too simple because x(t) can be a superposition of multiple oscillating components and the instantaneous frequency cannot resolve multiple oscillating contributions. We will return to this point later in this chapter. First we want to consider some fundamental issues governing the impossibility of joint time-frequency localization and, in view of these limitations, mathematical tools that aim to characterize compositions of signals in terms of time localized oscillations in view of such limitations. One typically refers to such tools as time-frequency representations. As with the case of Fourier transforms we will encounter both continuous and discrete parameter time-frequency representations. 3.1.3 The Heisenberg-Wiener inequality Variance inequalities Theorem 3.1.2. (Heisenberg uncertainty principle) If f ∈ L 2 (R) with f 2 = 1 then xf (x) 2 ξ ˆ f (ξ) 2 ≥ …


title: Effect of subjective perspective taking during simulation of action: a PET investigation of agency

text: Perspective taking is an essential component in the mechanisms that account for intersubjectivity and agency. Mental simulation of action can be used as a natural protocol to explore the cognitive and neural processing involved in agency. Here we took PET measurements while subjects simulated actions with either a first-person or a third-person perspective. Both conditions were associated with common activation in the SMA, the precentral gyrus, the precuneus and the MT/V5 complex. When compared to the first-person perspective, the third-person perspective recruited right inferior parietal, precuneus, posterior cingulate and frontopolar cortex. The opposite contrast revealed activation in left inferior parietal and somatosensory cortex. We suggest that the right inferior parietal, precuneus and somatosensory cortex are specifically involved in distinguishing self-produced actions from those generated by others.


title: Agile Software Development Methods: Review and Analysis

text: Agile – denoting “the quality of being agile; readiness for motion; nimbleness, activity, dexterity in motion” – software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked a substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being ”agile” against the defined criteria. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.