title: Spirituality and religion: intertwined protective factors for substance use among urban American Indian youth.

abstract: Background and objective: This article explores the aspects of spirituality and religious involvement that may be the protective factors against substance use among urban American Indian (AI) youth. Methods: Data come from AI youth (N = 123) in five urban middle schools in a southwestern metropolis. Results: Ordinary least squares regression analyses indicated that following Christian beliefs and belonging to the Native American Church were associated with lower levels of substance use. Conclusions and Scientific Significance: Following AI traditional spiritual beliefs was associated with antidrug attitudes, norms, and expectancies. Having a sense of belonging to traditions from both AI cultures and Christianity may foster integration of the two worlds in which urban AI youth live.


title: Optimization of wire connections design for power electronics

abstract: Abstract In automotive power electronics, low cost and reliability requirements both demand to optimize each part of the package in order to increase its life time during use. The wire bonding breakage accounts for one of the most common failures observed on power modules. In particular, the heel crack mechanism, mainly due to Joule self-heating, can occur in the module, leading to its death. Thus, the wire geometry design has to be set up to maximize its endurance. This paper presents an analytical method able to predict the optimal wire length regarding a wire displacement criterion. Finally, comparisons between experimental results and predictions of the analytical model are given.


title: Characterisation of teachers’ use of computers and Internet inside and outside the classroom: The need to focus on the quality

abstract: Teachers' use of ICT has mainly focused on their use in classroom context and few studies have focused on their use elsewhere. Attempting to fill this gap, this paper presents a case study of 12 secondary teachers characterising their use of ICT in and out-of-the-classroom. Results show that teachers use these tools inside the classroom for presentations and support some instructional strategies and outside the classroom for lesson preparation, administration and communication, and design of students' assignments with ICT. However, the analysis of the specific characteristics of these activities shows that beyond these apparently good purposes of ICT use, the quality of the characteristics of the activities is heterogeneous, which can have implications in the effectiveness of the use of ICT in teaching and learning. Based on this, we suggest that in order to take full advantage of the teaching and learning opportunities associated to use of digital tools in education, there is a need to develop teachers' and students' digital skills, specifically those associated to searching and selecting information available in the Internet, and developing and presenting information products.


title: Performance evaluation of an ozone generator using a phase shift PWM full bridge inverter

abstract: This paper proposes an ozone generator using a phase shift PWM full bridge inverter. The advantage of the proposed system is capable of varying ozone gas level by varying frequency of the converter whilst the output voltage is kept to be constant. In order to obtain such condition, the pulse width of the output voltage is varied. The gate signals are controlled by a microcontroller. The performance evaluation of the proposed system is given.


title: POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

abstract: From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs.-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.


title: Visual comparison for information visualization

abstract: Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.


title: Play-persona: a multifaceted concept

abstract: Gamification is but the most recent concept in the history of designing game-like applications for purposes other than pure entertainment. Prominent examples often aim to motivate a specific target behavior or follow learning goals. Nonetheless, the current practice of gamification often seems onesided regarding its motivational design as reward structures dominate most examples. One reason might be the lack of an established, holistic perspective on the user that informs the designer on personal, motivational and game-related attributes and needs within the context. This paper introduces the concept of a play-persona, that builds on multifaceted theories of personality psychology, motivational psychology, game-design and user-centered design and a series of expert interviews across this disciplines. Furthermore, an explorative study regarding correlations between personality and play preferences was conducted. The resulting design-tool is suggested to encourage strong and elaborated discussions on user's needs among the design team, thus supporting the design process.


title: Parallel Approaches for Singular Value Decomposition as Applied to Robotic Manipulator Jacobians

abstract: The system of equations that govern kinematically redundant robotic manipulators is commonly solved by finding the singular value decomposition (SVD) of the corresponding Jacobian matrix. This can require a considerable amount of time to compute, thus a parallel SVD algorithm reducing execution time is sought. The approach employed here lends itself to parallelization by using Givens rotations and information from previous decompositions. The key contribution of this research is the presentation and implementation of parallel SVD algorithms to compute the SVD for a set of Jacobians that represent various different joint failure scenarios. Results from implementation of the algorithm on a MasPar MP-1, an IBM SP2, and the PASM prototype parallel computers are compared. Specific issues considered for each implementation include: how data is mapped to the processing elements, the effect that increasing the number of processing elements has on execution time, the type of parallel architecture used, and trade-offs between modes of parallelism.


title: VALE, a switched ethernet for virtual machines

abstract: The growing popularity of virtual machines is pushing the demand for high performance communication between them. Past solutions have seen the use of hardware assistance, in the form of "PCI passthrough" (dedicating parts of physical NICs to each virtual machine) and even bouncing traffic through physical switches to handle data forwarding and replication. In this paper we show that, with a proper design, very high speed communication between virtual machines can be achieved completely in software. Our architecture, called VALE, implements a Virtual Local Ethernet that can be used by virtual machines, such as QEMU, KVM and others, as well as by regular processes. VALE achieves a throughput of over 17 million packets per second (Mpps) between host processes, and over 2 Mpps between QEMU instances, without any hardware assistance. VALE is available for both FreeBSD and Linux hosts, and is implemented as a kernel module that extends our recently proposed netmap framework, and uses similar techniques to achieve high packet rates.


title: Laser speckle contrast reveals cerebral blood flow dynamics evoked by optogenetically controlled neuronal activity

abstract: As a critical basis of functional brain imaging, neurovascular coupling describes the link between neuronal and ::: hemodynamic changes. The majority of in vivo neurovascular coupling studies was performed by inducing sensory ::: stimulation via afferent inputs. Unfortunately such an approach results in recruiting of multiple types of cells, which ::: confounds the explanation of neuronal roles in stimulus evoked hemodynamic changes. Recently optogenetics has ::: emerged to provide immediate control of neurons by exciting or inhibiting genetically engineered neurons expressing ::: light sensitive proteins. However, there is a need for optical methods capable of imaging the concurrent hemodynamic ::: changes. We utilize laser speckle contrast imaging (LSCI) to obtain high resolution display of cerebral blood flow (CBF) ::: in the vicinity of the targeted neural population. LSCI is a minimally invasive method for imaging CBF in microvessels ::: through thinned skull, and produces images with high spatiotemporal resolution, wide field of view. In the integrated ::: system light sources with different wavelengths and band-passing/blocking filters were used to allow simultaneous ::: optical manipulation of neuronal activities and optical imaging of corresponding CBF. Experimental studies were carried ::: out in a rodent model expressing channalrhodopsin (ChR2) in excitatory neurons in the somatosensory cortex (S1). The ::: results demonstrated significant increases of CBF in response to ChR2 stimulation (exciting neuronal firing) comparable ::: to the CBF response to contralateral forepaw stimulation. The approach promises to be an exciting minimally invasive ::: method to study neurovascular coupling. The complete system provides a novel approach for broad neuroscience ::: applications.


title: Contributions of the basal ganglia and functionally related brain structures to motor learning

abstract: This review discusses the cerebral plasticity, and the role of the cortico-striatal system in particular, observed as one is learning or planning to execute a newly learned motor behavior up to when the skill is consolidated or has become highly automatized. A special emphasis is given to imaging work describing the neural substrate mediating motor sequence learning and motor adaptation paradigms. These results are then put into a plausible neurobiological model of motor skill learning, which proposes an integrated view of the brain plasticity mediating this form of memory at different stages of the acquisition process.


File Encryption and Decryption Using Secure RSA


title: Modeling, Control, and Implementation of DC–DC Converters for Variable Frequency Operation

abstract: In this paper, novel small-signal averaged models for dc-dc converters operating at variable switching frequency are derived. This is achieved by separately considering the on-time and the off-time of the switching period. The derivation is shown in detail for a synchronous buck converter and the model for a boost converter is also presented. The model for the buck converter is then used for the design of two digital feedback controllers, which exploit the additional insight in the converter dynamics. First, a digital multiloop PID controller is implemented, where the design is based on loop-shaping of the proposed frequency-domain transfer functions. And second, the design and the implementation of a digital LQG state-feedback controller, based on the proposed time-domain state-space model, is presented for the same converter topology. Experimental results are given for the digital multiloop PID controller integrated on an application-specified integrated circuit in a 0.13 μm CMOS technology, as well as for the state-feedback controller implemented on an FPGA. Tight output voltage regulation and an excellent dynamic performance is achieved, as the dynamics of the converter under variable frequency operation are considered during the design of both implementations.


title: Supporting Iterative Cohort Construction with Visual Temporal Queries

abstract: Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO , a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers.


title: Algorithmic tamper-proof (ATP) security: Theoretical foundations for security against hardware tampering

abstract: Traditionally, secure cryptographic algorithms provide security against an adversary who has only black-box access to the secret information of honest parties. However, such models are not always adequate. In particular, the security of these algorithms may completely break under (feasible) attacks that tamper with the secret key.


title: A neuro-stimulus chip with telemetry unit for retinal prosthetic device

abstract: In this retinal prosthesis project, a rehabilitative device is designed to replace the functionality of defective photoreceptors in patients suffering from retinitis pigmentosa (RP) and age-related macular degeneration (AMD). The device consists of an extraocular and an intraocular unit. The implantable component receives power and a data signal via a telemetric inductive link between the two units. The extraocular unit includes a video camera and video processing board, a telemetry protocol encoder chip, and an RF amplifier and primary coil. The intraocular unit consists of a secondary coil, a rectifier and regulator, a retinal chip with a telemetry protocol decoder, a stimulus signal generator, and an electrode array. This paper focuses on the design, fabrication, and testing of a microchip which serves as the telemetry protocol decoder and stimulus signal generator. It is fabricated by MOSIS with 1.2-mm CMOS technology and was demonstrated to provide the desired biphasic current stimulus pulses for an array of 100 retinal electrodes at video frame rates.


title: Tampering Detection and Localization Through Clustering of Camera-Based CNN Features

abstract: Due to the rapid proliferation of image capturing devices and user-friendly editing software suites, image manipulation is at everyone's hand. For this reason, the forensic community has developed a series of techniques to determine image authenticity. In this paper, we propose an algorithm for image tampering detection and localization, leveraging characteristic footprints left on images by different camera models. The rationale behind our algorithm is that all pixels of pristine images should be detected as being shot with a single device. Conversely, if a picture is obtained through image composition, traces of multiple devices can be detected. The proposed algorithm exploits a convolutional neural network (CNN) to extract characteristic camera model features from image patches. These features are then analyzed by means of iterative clustering techniques in order to detect whether an image has been forged, and localize the alien region.


Polyphonic Sound Event Detection with Weak Labeling


title: Learning recommender systems with adaptive regularization

abstract: Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models depends largely on the choice of good values for the regularization parameters. Without a careful selection they result in poor prediction quality as they either underfit or overfit the data. Regularization values are typically determined by an expensive search that requires learning the model parameters several times: once for each tuple of candidate values for the regularization parameters. In this paper, we present a new method that adapts the regularization automatically while training the model parameters. To achieve this, we optimize simultaneously for two criteria: (1) as usual the model parameters for the regularized objective and (2) the regularization of future parameter updates for the best predictive quality on a validation set. We develop this for the generic model class of Factorization Machines which subsumes a wide variety of factorization models. We show empirically, that the advantages of our adaptive regularization method compared to expensive hyperparameter search do not come to the price of worse predictive quality. In total with our method, learning regularization parameters is as easy as learning model parameters and thus there is no need for any time-consuming search of regularization values because they are found on-the-fly. This makes our method highly attractive for practical use.


title: Machine learning approach for predicting bumps on road

abstract: In today's days, due to increase in number of vehicles the probability of accidents are also increasing. The user should be aware of the road circumstances for safety purpose. Several methods requires installing dedicated hardware in vehicle which are expensive. so we have designed a Smart-phone based method which uses a Accelerometer and GPS sensors to analyze the road conditions. The designed system is called as Bumps Detection System(BDS) which uses Accelerometer for pothole detection and GPS for plotting the location of potholes on Google Map. Drivers will be informed in advance about count of potholes on road. we have assumed some threshold values on z-axis(Experimentally Derived)while designing the system. To justify these threshold values we have used a machine learning approach. The k means clustering algorithm is applied on the training data to build a model. Random forest classifier is used to evaluate this model on the test data for better prediction.


title: Automated Test Input Generation for Android: Are We There Yet? (E)

abstract: Like all software, mobile applications ("apps") must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: ease of use, ability to work on multiple platforms, code coverage, and ability to detect faults. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.


title: On Using Micro-Clouds to Deliver the Fog

abstract: Cloud computing has demonstrated itself to be a scalable and cost-efficient solution for many real-world applications. However, its modus operandi is not ideally suited to resource-constrained environments that are characterized by limited network bandwidth and high latencies. With the increasing proliferation and sophistication of edge devices, the idea of fog computing proposes to offload some of the computation to the edge. To this end, micro-clouds---which are modular and portable assemblies of small single-board computers---have started to gain attention as infrastructures to support fog computing by offering isolated resource provisioning at the edge in a cost-effective way. We investigate the feasibility and readiness of micro-clouds for delivering the vision of fog computing. Through a number of experiments, we showcase the potential of micro-clouds formed by collections of Raspberry Pi computers to host a range of fog-related applications, particularly for locations where there is limited network bandwidths and long latencies.


title: Generalizing Image Captions for Image-Text Parallel Corpus

abstract: The ever growing amount of web images and their associated texts offers new opportunities for integrative models bridging natural language processing and computer vision. However, the potential benefits of such data are yet to be fully realized due to the complexity and noise in the alignment between image content and text. We address this challenge with contributions in two folds: first, we introduce the new task of image caption generalization, formulated as visually-guided sentence compression, and present an efficient algorithm based on dynamic beam search with dependency-based constraints. Second, we release a new large-scale corpus with 1 million image-caption pairs achieving tighter content alignment between images and text. Evaluation results show the intrinsic quality of the generalized captions and the extrinsic utility of the new imagetext parallel corpus with respect to a concrete application of image caption transfer.


title: Chinese Poetry Generation with Recurrent Neural Networks

abstract: We propose a model for Chinese poem generation based on recurrent neural networks which we argue is ideally suited to capturing poetic content and form. Our generator jointly performs content selection (“what to say”) and surface realization (“how to say”) by learning representations of individual characters, and their combinations into one or more lines as well as how these mutually reinforce and constrain each other. Poem lines are generated incrementally by taking into account the entire history of what has been generated so far rather than the limited horizon imposed by the previous line or lexical n-grams. Experimental results show that our model outperforms competitive Chinese poetry generation systems using both automatic and manual evaluation methods.


title: Extension of phase correlation to subpixel registration

abstract: In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration. The analytic results provide a closed-form solution to subpixel translation estimation, and are used for detailed error analysis. Excellent results have been obtained for subpixel translation estimation of images of different nature and across different spectral bands.


title: The impact of agile methods on software project management

abstract: As more and more software projects engage agile methods, there are emerging patterns of success and failure. With growing adoption of agile methods, project managers increasingly need to understand the applicability to their projects and factors that drive key project performance characteristics. While some organizations affirm that agile methods solve all their problems, few have shown consistent success over a range of typical software projects. Agile methods have advantages, especially in accommodating change due to volatile requirements. However, they also present concomitant risks with managing the many dependent pieces of work distributed across a large project. Use of agile methods therefore presents a set of tradeoffs. This paper examines the impact of agile methods on the people involved in a project, the process under which a project is developed, and on the project itself in an attempt to allow project managers to evaluate the applicability using an agile method.


title: Optimization for Cooperative Sensing in Cognitive Radio Networks

abstract: In cognitive radio networks, the secondary users can use the frequency bands when the primary users are not present. Hence secondary users need to constantly sense the presence of the primary users. When the primary users are detected, the secondary users have to vacate that channel. This makes the probability of detection important to the primary users as it indicates their protection level from secondary users. When the secondary users detect the presence of a primary user which is in fact not there, it is referred to as false alarm. The probability of false alarm is important to the secondary users as it determines their usage of an unoccupied channel. Depending on whose interest is of priority, either a targeted probability of detection or false alarm shall be set. After setting one of the probabilities, the other can be optimized through cooperative sensing. In this paper, we show that cooperating all secondary users in the network does not necessary achieve the optimum performance, but instead, it is achieved by cooperating a certain number of users with the highest primary user's signal to noise ratio. Computer simulations have shown that the Pd can increase from 92.03% to 99.88% and Pf can decrease from 6.02% to 0.06% in a network with 200 users.


title: Active Digital Cancellation of Transmitter Induced Modulated Spur Interference in 4G LTE Carrier Aggregation Transceivers

abstract: Mobile user equipments supporting Long Term Evolution (LTE) - carrier aggregation (CA) face several new unpredicted issues when operating in frequency division duplex mode. This paper addresses the transmit (Tx) modulated spur problem, a new issue, that appears in LTE-CA transceivers. Due to the presence of several mixers and dividers to support different bands and CA scenarios, many harmonics are generated on the chip. Unavoidable crosstalk between these harmonics can generate an unwanted continuous wave spur around the Tx frequency. Once this spur appears at the chip area where the receive (Rx) local oscillator resides, it downconverts the undesired portion of the Tx signal which leaked through the duplexer to the Rx baseband. This interference, referred to as Tx modulated spur, causes a severe degradation of the signal-to-noise ratio (SNR) of the wanted signal. To mitigate such, we propose an active digital cancellation architecture which utilizes the known information of the Tx signal. An equivalent baseband model of the Tx modulated spur is presented from which the digital cancellation architecture is derived. Simulations show that the proposed architecture cancels the Tx interference and improves the SNR of the wanted signal significantly.


title: A Survey on Software Defined Networking: Architecture for Next Generation Network

abstract: The evolution of software defined networking (SDN) has played a significant role in the development of next-generation networks (NGN). SDN as a programmable network having "service provisioning on the fly" has induced a keen interest both in academic world and industry. In this article, a comprehensive survey is presented on SDN advancement over conventional network. The paper covers historical evolution in relation to SDN, functional architecture of the SDN and its related technologies, and OpenFlow standards/protocols, including the basic concept of interfacing of OpenFlow with network elements (NEs) such as optical switches. In addition a selective architecture survey has been conducted. Our proposed architecture on software defined heterogeneous network, points towards new technology enabling the opening of new vistas in the domain of network technology, which will facilitate in handling of huge internet traffic and helps infrastructure and service providers to customize their resources dynamically. Besides, current research projects and various activities as being carried out to standardize SDN as NGN by different standard development organizations (SODs) have been duly elaborated to judge how this technology moves towards standardization.


title: Wireless LAN security and IEEE 802.11i

abstract: This article reviews wireless LAN security with a focus on the evolving new IEEE 802.11i standard. The major security enhancements in encryption and authentication defined by 802.11i are illustrated. In addition, the newly introduced key management in 802.11i is discussed. Because 802.11i incorporates IEEE 802.1X as its authentication enhancement, 802.1X with consideration of roaming users is depicted. Both intrasubnet and intersubnet roaming are illustrated.


title: Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings

abstract: Large scale deployment of sensors is essential to practical applications in cyber physical systems. For instance, instrumenting a commercial building for 'smart energy' management requires deployment and operation of thousands of measurement and metering sensors and actuators that direct operation of the HVAC system. Each of these sensors need to be named consistently and constantly calibrated. Doing this process manually is not only time consuming but also error prone given the scale, heterogeneity and complexity of buildings as well as lack of uniform naming schemas. To address this challenge, we propose Zodiac - a framework for automatically classifying, naming and managing sensors based on active learning from sensor metadata. In contrast to prior work, Zodiac requires minimal user input in terms of labelling examples while being more accurate. To evaluate Zodiac, we deploy it across four real buildings on our campus and label the ground truth metadata for all the sensors in these buildings manually. Using a combination of hierarchical clustering and random forest classifiers we show that Zodiac can successfully classify sensors with an average accuracy of 98% with 28% fewer training examples when compared to a regular expression based method.


title: A Survey of Shape Feature Extraction Techniques

abstract: "A picture is worth one thousand words". This proverb comes from Confucius a Chinese philosopher before about 2500 years ago. Now, the essence of these words is universally understood. A picture can be magical in its ability to quickly communicate a complex story or a set of ideas that can be recalled by the viewer later in time. Visual information plays an important role in our society, it will play an increasingly pervasive role in our lives, and there will be a growing need to have these sources processed further. The pictures or images are used in many application areas like architectural and engineering design, fashion, journalism, advertising, entertainment, etc. Thus it provides the necessary opportunity for us to use the abundance of images. However, the knowledge will be useless if one can't _nd it. In the face of the substantive and increasing apace images, how to search and to retrieve the images that we interested with facility is a fatal problem: it brings a necessity for image retrieval systems. As we know, visual features of the images provide a description of their content. Content-based image retrieval (CBIR), emerged as a promising mean for retrieving images and browsing large images databases. CBIR has been a topic of intensive research in recent years. It is the process of retrieving images from a collection based on automatically extracted features.


title: A low-voltage 5.1-5.8-GHz image-reject receiver with wide dynamic range

abstract: A monolithic 5-6-GHz band receiver, consisting of a differential preamplifier, dual doubly balanced mixers, cascaded injection-locked frequency doublers, and a quadrature local oscillator generator and prescaler, realizes over 45 dB of image-rejection in a mature 25-GHz silicon bipolar technology. The measured single sideband (50 /spl Omega/) noise figure is 5.1 dB with an IIP3 of -4.5 dBm and 17-dB conversion gain at 5.3 GHz. The 1.9/spl times/1.2 mm/sup 2/ IC is packaged in a standard 32-pin ceramic quad flatpack and consumes less than 50 mW from a 2.2-V supply.


title: Coverage directed test generation for functional verification using Bayesian networks

abstract: Functional verification is widely acknowledged as the bottleneck in the hardware design cycle. This paper addresses one of the main challenges of simulation based verification (or dynamic verification), by providing a new approach for coverage directed test generation (CDG). This approach is based on Bayesian networks and computer learning techniques. It provides an efficient way for closing a feedback loop from the coverage domain back to a generator that produces new stimuli to the tested design. In this paper, we show how to apply Bayesian networks to the CDG problem. Applying Bayesian networks to the CDG framework has been tested in several experiments, exhibiting encouraging results and indicating that the suggested approach can be used to achieve CDG goals.


title: Sequential Mining: Patterns and Algorithms Analysis

abstract: This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential pattern mining.


title: Low-cost intelligent static gesture recognition system

abstract: This paper presents prototype implementation of low-cost, open hardware, static — gesture recognition system. The implemented system has three major components: A Glove and Sensor Unit (GSU) — consisting of a pair of gloves embedded with custom made, low-cost flex and contact sensors, a Primary Supporting Hardware (PSH) that maps change in input values from GSU, a Secondary Supporting Hardware (SSH) that processes the input values and recognizes the gesture accurately. When a gesture is signed, the GSU tracks the change in orientation of the fingers, which results in a change in voltage levels of the sensors. This change is mapped by the PSH and passed on to SSH which comprises of two ATmega328P microcontrollers, one connected to each of the glove. The two microcontrollers are connected in a master-slave configuration and communication between them is facilitated through an XBee module. The performance of this gesture recognition system is evaluated using a data set comprising of 36 unique gestures. These gestures represent a total of 120 gestures that include all gestures across five globally used sign languages. A gesture recognition engine that resides in the master microcontroller processes the input and identifies the gesture. The gesture recognition engine comprises of a two stage selection-elimination embedded intelligence algorithm that is used to enhance the system efficiency from 83.1% to 94.5% without any additional hardware. The cost of the system is USD 30, which the authors believe on commercialization, could be brought under USD 9.


Harvest: A High-Performance Fundamental Frequency Estimator from Speech Signals


title: Feature learning and deep architectures: new directions for music informatics

abstract: As we look to advance the state of the art in content-based music informatics, there is a general sense that progress is decelerating throughout the field. On closer inspection, performance trajectories across several applications reveal that this is indeed the case, raising some difficult questions for the discipline: why are we slowing down, and what can we do about it? Here, we strive to address both of these concerns. First, we critically review the standard approach to music signal analysis and identify three specific deficiencies to current methods: hand-crafted feature design is sub-optimal and unsustainable, the power of shallow architectures is fundamentally limited, and short-time analysis cannot encode musically meaningful structure. Acknowledging breakthroughs in other perceptual AI domains, we offer that deep learning holds the potential to overcome each of these obstacles. Through conceptual arguments for feature learning and deeper processing architectures, we demonstrate how deep processing models are more powerful extensions of current methods, and why now is the time for this paradigm shift. Finally, we conclude with a discussion of current challenges and the potential impact to further motivate an exploration of this promising research area.


title: Efficient and Expressive Stream Reasoning with Object-Oriented Complex Event Processing

abstract: RDF Stream Processing (RSP) engines systems able to continuously answer queries upon semantically annotated information flows empirically proved that Stream Reasoning (SR) is feasible. However, existing RSP engines do not investigate the trade-off between the reasoning expressiveness and the performance typical of information flow processing (IFP) systems: either an high throughputs with a low expressiveness (e.g. ρDF) or an high expressiveness (e.g., EL) with a low throughputs are provided. Can the systematic exploration of this trade-off lead SR to continuously execute expressive reasoning without loosing the efficiency typical of IFP systems? In this paper, we propose a Systematic Comparative Research Approach (SCRA) to investigate the RSP solution space. Moreover, in contrast with the state-of-the-art trend of adding IFP capabilities to reasoners, we discuss how to realize an Efficient and Expressive Stream Reasoning by adding reasoning capabilities into IFP systems (in particular to Object-Oriented Complex Event Processors).


title: Salient object detection via bootstrap learning

abstract: We propose a bootstrap learning algorithm for salient object detection in which both weak and strong models are exploited. First, a weak saliency map is constructed based on image priors to generate training samples for a strong model. Second, a strong classifier based on samples directly from an input image is learned to detect salient pixels. Results from multiscale saliency maps are integrated to further improve the detection performance. Extensive experiments on six benchmark datasets demonstrate that the proposed bootstrap learning algorithm performs favorably against the state-of-the-art saliency detection methods. Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.


title: Blind source separation based on subband ICA and beamforming

abstract: ICSLP2000: the 6th International Conference on Spoken Language Processing, October 16-20, 2000, Beijing, China.


title: Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding

abstract: Ambiguity or uncertainty is a pervasive element of many real world decision making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research had proven that decision making by human is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming scene images are mutually exclusive; and therefore, focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are non-mutually exclusive, and propose the Fuzzy Qualitative Rank Classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in term of qualitative and quantitative using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the non-mutually exclusive scene images.


title: Automated design of digital microfluidic lab-on-chip under pin-count constraints

abstract: Digital microfluidic biochips, as referred to as lab-on-a-chip, are revolutionizing DNA sequencing, immunoassays, and clinical diagnostics. Bioassays steps are mapped to a sequence of microfludic operations on a two dimensional array of electrodes. The number of independent input pins used to control the electrodes is an important cost-driver, especially for disposable PCB devices that are being developed for clinical and point-of care diagnostics. We review two design-automation methods for such pin-count-constrained biochips. The first design procedure relies on a droplet-trace-based array partitioning scheme and an efficient pin assignment technique, referred to as the "Connect-5 algorithm". The second pin-constrained design method relies on cross-referencing addressing based on "rows" and "columns" to access electrodes. An efficient droplet manipulationmethod is presented for this cross-referencing technique based on a mapping of the droplet-movement problem to the clique-partitioning problem from graph theory


title: Continuously Learning Neural Dialogue Management

abstract: We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradient-based algorithms on one single model. The experiments demonstrate the supervised model's effectiveness in the corpus-based evaluation, with user simulation, and with paid human subjects. The use of reinforcement learning further improves the model's performance in both interactive settings, especially under higher-noise conditions.


title: License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study

abstract: Most vehicle license plate recognition use neural network techniques to enhance its computing capability. The image of the vehicle license plate is captured and processed to produce a textual output for further processing. This paper reviews image processing and neural network techniques applied at different stages which are preprocessing, filtering, feature extraction, segmentation and recognition in such way to remove the noise of the image, to enhance the image quality and to expedite the computing process by converting the characters in the image into respective text. An exemplar experiment has been done in MATLAB to show the basic process of the image processing especially for license plate in Malaysia case study. An algorithm is adapted into the solution for parking management system. The solution then is implemented as proof of concept to the algorithm.


title: Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks

abstract: In semantic scene segmentation, every pixel of an image is assigned a category label. This task can be made easier by incorporating depth information, which structured light sensors provide. Depth, however, has very different properties from RGB image channels. In this paper, we present a novel method to provide depth information to convolutional neural networks. For this purpose, we apply a simplified version of the histogram of oriented depth (HOD) descriptor to the depth channel. We evaluate the network on the challenging NYU Depth V2 dataset and show that with our method, we can reach competitive performance at a high frame rate.


title: Supervised learning models to predict firm performance with annual reports: An empirical study

abstract: Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict change in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: a We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and b our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.


title: A Solution for Internet of Things based on Blockchain Technology

abstract: Blockchain technology is a generic term for data organization structures, cryptography algorithms, distributed consensus mechanisms, and peer-to-peer communications that are used to implement distributed ledgers. Its core value lies in the establishment of mutual trust between non-coordinated participants. However, the current blockchain applications are inclined towards software and network, missing strong connection with the real world. The Internet of Things technology connects various smart devices and sensors to Internet to facilitate recognition and management of information. This paper presents a blockchain-based Internet of Things solution, where RFID chips with built-in asymmetric encryption algorithm and scanners uploading information directly to blockchain. Compared to traditional Internet of Things designs, the proposed solution combines the advantages of both decentralized blockchain, and the Waltonchain implementation demonstrates such advantages with both data security and application flexibility.


title: Word sense disambiguation of Bangla sentences using statistical approach

abstract: Word sense disambiguation is the process of identifying appropriate sense or meaning of a word in a sentence, when the word has multiple meanings. In this work, we propose a disambiguation system using Naive Bayes classifier and ANN to disambiguate the Bangla words. Naive Bayes classifier for training phase and using statistical information gained from the training dataset the unknown ambiguous words are predicted and gives detecting the correct sense using ANN. For the testing phase we used general regression neural network. The result of the evaluation shows that the proposed word sense disambiguation system can disambiguate the ambiguous Bangla word with 82% accuracy.


title: Investigating the Relationship Between Affective Commitment and Unethical Pro-Organizational Behaviors: The Role of Moral Identity

abstract: In this study, we investigate the relationship between affective commitment and unethical proorganizational behaviors (UPBs), which are unethical behaviors conducted by employees meant to potentially benefit the organization (Umphress, Bingham, & Mitchell, 2010). We predicted that affective commitment would be positively related to UPBs and that moral identity would moderate and weaken this relationship. The results support our hypotheses, indicating that individuals with high levels of affective organizational commitment are more likely to engage in UPBs when they hold a lower level or moral identity. Theoretical and practical implications are discussed.


title: STAR: A System for Ticket Analysis and Resolution

abstract: In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.


title: Evaluating Students Performance by Artificial Neural Network using WEKA

abstract: Data mining is the process of extracting hidden patterns and useful information from large set of data is now becoming part of current inventions. Data mining now can be applied to different fields like marketing, education; health etc. Data mining in field of education is named as educational data mining. Educational data mining can help institutions to predict the performance of their students so as to improve their academic results. In this paper artificial neural network is used to predict the performance of student. Multilayer Perceptron Neural Network is used for the implementation of prediction strategy. Experiment is conducted using weka and real time dataset available.


title: Beyond Diathesis Stress: Differential Susceptibility to Environmental Influences

abstract: Evolutionary-biological reasoning suggests that individuals should be differentially susceptible to environmental influences, with some people being not just more vulnerable than others to the negative effects of adversity, as the prevailing diathesis-stress view of psychopathology (and of many environmental influences) maintains, but also disproportionately susceptible to the beneficial effects of supportive and enriching experiences (or just the absence of adversity). Evidence consistent with the proposition that individuals differ in plasticity is reviewed. The authors document multiple instances in which (a) phenotypic temperamental characteristics, (b) endophenotypic attributes, and (c) specific genes function less like “vulnerability factors” and more like “plasticity factors,” thereby rendering some individuals more malleable or susceptible than others to both negative and positive environmental influences. Discussion focuses upon limits of the evidence, statistical criteria for distinguishing differential susceptibility from diathesis stress, potential mechanisms of influence, and unknowns in the differentialsusceptibility equation.


title: Comparison and evaluation of ontology visualizations

abstract: In the last couple of years a large number of software aiming at visualizing ontologies were introduced, causing difficulties in selecting an objectively suitable visualizing tool. This work lists free and commercial tools in four groups named by the visualizing method used by members of each group. Furthermore a detailed examination of each tool in a separate section is presented. Finally the document comes to an end with an overview of all listed tools and a recommendation of the most powerful ones of each group.


title: Agentic and Communal Values: Their Scope and Measurement

abstract: Agency is the meta-concept associated with self-advancement in social hierarchies; communion is the partner concept associated with maintenance of positive relationships. Despite the wealth of data documenting the conceptual utility of agency and communion (A & C) as superordinate metaconcepts, no direct measures of global A & C value dimensions are currently available. The first part of this article presents structural analyses of data from 4 diverse data sets (3 archival and 1 new): Each included a broad inventory of values or life goals. All 4 data sets revealed higher order A & C dimensions that were either apparent or implicit. The second part details the development of the ACV, a 24-item questionnaire measuring global A and C values, and documents its psychometric properties. Four studies support their joint construct validity by positioning the value measures within a nomological network of interpersonal traits, self-favorability biases, ideology dimensions, gender, socio-sexuality, and religious a...


title: Mobile Cloud Computing: A Comparison of Application Models

abstract: Cloud computing is an emerging concept combining many fields of computing. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information. However, the actual realization of these benefits is far from being achieved for mobile applications and open many new research questions. In order to better understand how to facilitate the building of mobile cloud-based applications, we have surveyed existing work in mobile computing through the prism of cloud computing principles. We give a definition of mobile cloud coputing and provide an overview of the results from this review, in particular, models of mobile cloud applications. We also highlight research challenges in the area of mobile cloud computing. We conclude with recommendations for how this better understanding of mobile cloud computing can help building more powerful mobile applications.


title: Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses

abstract: We introduce a new method for robot control that combines prediction learning with a fixed, crafted response---the robot learns to make a temporally-extended prediction during its normal operation, and the prediction is used to select actions as part of a fixed behavioral response. Our method is inspired by Pavlovian conditioning experiments in which an animal's behavior adapts as it learns to predict an event. Surprisingly the animal's behavior changes even in the absence of any benefit to the animal (i.e. the animal is not modifying its behavior to maximize reward). Our method for robot control combines a fixed response with online prediction learning, thereby producing an adaptive behavior. This method is different from standard non-adaptive control methods and also from adaptive reward-maximizing control methods. We show that this method improves upon the performance of two reactive controls, with visible benefits within 2.5 minutes of real-time learning on the robot. In the first experiment, the robot turns off its motors when it predicts a future over-current condition, which reduces the time spent in unsafe over-current conditions and improves efficiency. In the second experiment, the robot starts to move when it predicts a human-issued request, which reduces the apparent latency of the human-robot interface.


title: Challenges and Future Directions of Software Technology: Secure Software Development

abstract: Developing large scale software systems has major security challenges. This paper describes the issues involved and then addresses two topics: formal methods for emerging secure systems and secure services modeling.


title: Local Rademacher Complexity Bounds based on Covering Numbers

abstract: This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm. This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions. We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.


title: TMACS: A Robust and Verifiable Threshold Multi-Authority Access Control System in Public Cloud Storage

abstract: Attribute-based Encryption (ABE) is regarded as a promising cryptographic conducting tool to guarantee data owners’ direct control over their data in public cloud storage. The earlier ABE schemes involve only one authority to maintain the whole attribute set, which can bring a single-point bottleneck on both security and performance. Subsequently, some multi-authority schemes are proposed, in which multiple authorities separately maintain disjoint attribute subsets. However, the single-point bottleneck problem remains unsolved. In this paper, from another perspective, we conduct a threshold multi-authority CP-ABE access control scheme for public cloud storage, named TMACS, in which multiple authorities jointly manage a uniform attribute set. In TMACS, taking advantage of ( $t,n$ ) threshold secret sharing, the master key can be shared among multiple authorities, and a legal user can generate his/her secret key by interacting with any $t$ authorities. Security and performance analysis results show that TMACS is not only verifiable secure when less than $t$ authorities are compromised, but also robust when no less than $t$ authorities are alive in the system. Furthermore, by efficiently combining the traditional multi-authority scheme with TMACS, we construct a hybrid one, which satisfies the scenario of attributes coming from different authorities as well as achieving security and system-level robustness.


title: Does Transcranial Direct Current Stimulation Improve Healthy Working Memory?: A Meta-analytic Review

abstract: Transcranial direct current stimulation tDCS has been reported to improve working memory WM performance in healthy individuals, suggesting its value as a means of cognitive enhancement. However, recent meta-analyses concluded that tDCS has little or no effect on WM in healthy participants. In this article, we review reasons why these meta-analyses may have underestimated the effect of tDCS on WM and report a more comprehensive and arguably more sensitive meta-analysis. Consistent with our interest in enhancement, we focused on anodal stimulation. Thirty-one articles matched inclusion criteria and were included in four primary meta-analyses assessing the WM effects of anodal stimulation over the left and right dorsolateral pFC DLPFC and right parietal lobe as well as left DLPFC stimulation coupled with WM training. These analyses revealed a small but significant effect of left DLPFC stimulation coupled with WM training. Left DLPFC stimulation alone also enhanced WM performance, but the effect was reduced to nonsignificance after correction for publication bias. No other effects were significant, including a variety of tested moderators. Additional meta-analyses were undertaken with study selection criteria based on previous meta-analyses, to reassess the findings from these studies using the analytic methods of this study. These analyses revealed a mix of significant and nonsignificant small effects. We conclude that the primary WM enhancement potential of tDCS probably lies in its use during training.


title: Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

abstract: As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.


title: Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

abstract: Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this paper, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.


title: Clustering with Deep Learning: Taxonomy and New Methods

abstract: Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.


title: When Timing Is Everything: Age of First-Language Acquisition Effects on Second-Language Learning.

abstract: The present paper summarizes three experiments that investigate the effects of age of acquisition on first-language (L1) acquisition in relation to second-language (L2) outcome. The experiments use the unique acquisition situations of childhood deafness and sign language. The key factors controlled across the studies are age of L1 acquisition, the sensory–motor modality of the language, and level of linguistic structure. Findings consistent across the studies show age of L1 acquisition to be a determining factor in the success of both L1 and L2 acquisition. Sensory–motor modality shows no general or specific effects. It is of importance that the effects of age of L1 acquisition on both L1 and L2 outcome are apparent across levels of linguistic structure, namely, syntax, phonology, and the lexicon. The results demonstrate that L1 acquisition bestows not only facility with the linguistic structure of the L1, but also the ability to learn linguistic structure in the L2.


title: Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets

abstract: The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in viewpoint, scale, illumination or planar transformations. While drastically altering the visual appearance, these changes are orthogonal to recognition and should not be reflected in the representation or feature encoding used for learning. We introduce a framework for weakly supervised learning of image embeddings that are robust to transformations and selective to the class distribution, using sets of transforming examples (orbit sets), deep parametrizations and a novel orbit-based loss. The proposed loss combines a discriminative, contrastive part for orbits with a reconstruction error that learns to rectify orbit transformations. The learned embeddings are evaluated in distance metric-based tasks, such as one-shot classification under geometric transformations, as well as face verification and retrieval under more realistic visual variability. Our results suggest that orbit sets, suitably computed or observed, can be used for efficient, weakly-supervised learning of semantically relevant image embeddings.


title: Optimizing Stencil Computations for NVIDIA Kepler GPUs

abstract: We present a series of optimization techniques for stencil computations on NVIDIA Kepler GPUs. Stencil computations with regular grids had been ported to the older generations of NVIDIA GPUs with signicant performance improvements thanks to the higher memory bandwidth than conventional CPU-only systems. However, because of the architectural changes introduced with the latest generation of the GPU architecture, Kepler, we show that existing implementation strategies used for such older GPUs are not as eective on Kepler as before. To fully exploit the potential performance of the latest generation of the GPU architecture, our implementation method uses shared memory for better data locality combined with warp specialization for higher instruction throughput. Our method achieves approximately 80% of the estimated peak performance by the rooine model, and even higher performance with temporal blocking.


title: Active SLAM and Loop Prediction with the Segmented Map Using Simplified Models

abstract: We previously introduced the SegSLAM algorithm, an approach to the simultaneous localization and mapping (SLAM) problem that divides the environment up into segments, or submaps, using heuristic methods.We investigate a realtime method for Active SLAM with SegSLAM, in which actions are selected in order to reduce uncertainty in both the local metric submap and the global topological map. Recent work in the area of Active SLAM has been built on the theoretical basis of information entropy. Due to the complexity of the SegSLAM belief state, as encoded in the SegMap representation, it is not feasible to estimate the expected entropy of the full belief state. Instead, we use a simplified model to heuristically select entropy-reducing actions without explicitly evaluating the full belief state.We discuss the relation of this heuristic method to the full entropy estimation method, and present results from applying our planning method in real-time onboard a mobile robot.


title: Low-cost antennas for mm-Wave sensing applications using inkjet printing of silver nano-particles on liquid crystal polymers

abstract: For the first time, we demonstrate the possibility of realizing low-cost mm-Wave antennas using inkjet printing of silver nano-particles. It is widely spread that fabrication of mm-Wave antennas and microwave circuits using the typical (deposit/pattern/etch) scheme is a challenging and costly process, due to the strict limitations on permissible tolerances. Such fabrication technique becomes even more challenging when dealing with flexible substrate materials, such as liquid crystal polymers. On the other hand, inkjet printing of conductive inks managed to form an emerging fabrication technology that has gained lots of attention over the last few years. Such process allows the deposition of conductive particles directly at the desired location on a substrate of interest, without need for mask productions, alignments, or etching. This means the inkjet printing of conductive materials could present the future of environment-friendly low-cost rapid manufacturing of RF circuits and antennas.


title: Image segmentation with a bounding box prior

abstract: User-provided object bounding box is a simple and popular interaction paradigm considered by many existing interactive image segmentation frameworks. However, these frameworks tend to exploit the provided bounding box merely to exclude its exterior from consideration and sometimes to initialize the energy minimization. In this paper, we discuss how the bounding box can be further used to impose a powerful topological prior, which prevents the solution from excessive shrinking and ensures that the user-provided box bounds the segmentation in a sufficiently tight way. The prior is expressed using hard constraints incorporated into the global energy minimization framework leading to an NP-hard integer program. We then investigate the possible optimization strategies including linear relaxation as well as a new graph cut algorithm called pinpointing. The latter can be used either as a rounding method for the fractional LP solution, which is provably better than thresholding-based rounding, or as a fast standalone heuristic. We evaluate the proposed algorithms on a publicly available dataset, and demonstrate the practical benefits of the new prior both qualitatively and quantitatively.


The biosynthesis of shikimate metabolites


Relaxation Response and Resiliency Training and Its Effect on Healthcare Resource Utilization


title: Face-to-face transfer of wafer-scale graphene films

abstract: High-quality graphene is grown on copper and then transferred to the underlying substrate, typically silicon oxide or quartz, by simply etching away the copper; the graphene is held in place during etching by capillary bridges.


title: Native support of multi-tenancy in RDBMS for software as a service

abstract: Software as a Service (SaaS) facilitates acquiring a huge number of small tenants by providing low service fees. To achieve low service fees, it is essential to reduce costs per tenant. For this, consolidating multiple tenants onto a single relational schema instance turned out beneficial because of low overheads per tenant and scalable manageability. This approach implements data isolation between tenants, per-tenant schema extension and further tenant-centric data management features in application logic. This is complex, disables some optimization opportunities in the RDBMS and represents a conceptual misstep with Separation of Concerns in mind. Therefore, we contribute first features of a RDBMS to support tenant-aware data management natively. We introduce tenants as first-class database objects and propose the concept of a tenant context to isolate a tenant from other tenants. We present a schema inheritance concept that allows sharing a core application schema among tenants while enabling schema extensions per tenant. Finally, we evaluate a preliminary implementation of our approach.


title: Detecting Uyghur text in complex background images with convolutional neural network

abstract: Uyghur text detection is crucial to a variety of real-world applications, while little researches put their attention on it. In this paper, we develop an effective and efficient region-based convolutional neural network for Uyghur text detection in complex background images. The characteristics of the network include: (1) Three region proposal networks are used to improve the recall, which simultaneously utilize feature maps from different convolutional layers. (2) The overall architecture of our network is in the form of fully convolutional network, and global average pooling is applied to replace the fully connected layers in the classification and bounding box regression layers. (3) To fully utilize the baseline information, Uyghur text lines are detected directly by the network in an end-to-end fashion. Experiment results on benchmark dataset show that our method achieves an F-measure of 0.83 and detection time of 0.6 s for each image in a single K20c GPU, which is much faster than the state-of-the-art methods while keeps competitive accuracy.


title: Field-aware Factorization Machines in a Real-world Online Advertising System

abstract: Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only effective to win challenges but is also valuable in a real-world prediction system. We also discuss some specific challenges and solutions to reduce the training time, namely the use of an innovative seeding algorithm and a distributed learning mechanism.


title: Introduction to Planning in Multiagent Systems

abstract: In most multiagent systems planning on forehand can help to seriously improve the efficiency of executing actions. The main difference between centrally creating a plan and constructing a plan for a system of agents lies in the fact that in the latter coordination plays the main part. This introduces a number of additional difficulties. This special issue discusses some of these difficulties in detail. To place these in a context, this introduction gives a brief overview of multiagent planning problems, and most multiagent planning techniques.


title: Spatial Working Memory and Arithmetic Deficits in Children With Nonverbal Learning Difficulties

abstract: Visuospatial working memory and its involvement in arithmetic were examined in two groups of 7- to 11-year-olds: one comprising children described by teachers as displaying symptoms of nonverbal learning difficulties (N = 21), the other a control group without learning disabilities (N = 21). The two groups were matched for verbal abilities, age, gender, and sociocultural level. The children were presented with a visuospatial working memory battery of recognition tests involving visual, spatial-sequential and spatial-simultaneous processes, and two arithmetic tasks (number ordering and written calculations). The two groups were found to differ on some spatial tasks but not in the visual working memory tasks. On the arithmetic tasks, the children with nonverbal learning difficulties made more errors than controls in calculation and were slower in number ordering. A discriminant function analysis confirmed the crucial role of spatial-sequential working memory in distinguishing between the two groups. Results...


title: Nasal reconstruction using the Washio retroauricular temporal flap

abstract: Reconstruction of an external nasal defect presents a challenge to the reconstructive surgeon. Transferring retroauricular tissue on a temporal pedicle was first described by Washio as a means to repair the central portion of the face. This paper describes our experience with the Washio retroauricular temporal flap for nasal reconstruction in twelve patients, together with our modifications to simplify the planning and raising of the flap. There was one patient with a stitch abscess, one case of hair loss from the pedicle which recovered within one month and one elderly patient who developed thromboembolic complications. There was no instance of flap necrosis. Our results confirm that the Washio retroauricular flap is an excellent technique for difficult nasal reconstruction in young patients.


title: Oil spill detection by satellite remote sensing

abstract: This paper presents the state of the art for oil spill detection in the world oceans. We discuss different satellite sensors and oil spill detectability under varying conditions. In particular, we concentrate on the use of manual and automatic approaches to discriminate between oil slicks and look-alikes based on pattern recognition. We conclude with a discussion of suggestions for further research with respect to oil spill detection systems.


title: Electrowetting droplet microfluidics on a single planar surface

abstract: Electrowetting refers to an electrostatically induced reduction in the contact angle of an electrically conductive liquid droplet on a surface. Most designs ground the droplet by either sandwiching the droplet with a grounding plate on top or by inserting a wire into the droplet. Washizu and others have developed systems capable of generating droplet motion without a top plate while allowing the droplet potential to float. In contrast to these designs, we demonstrate an electrowetting system in which the droplet can be electrically grounded from below using thin conductive lines on top of the dielectric layer. This alternative method of electrically grounding the droplet, which we refer to as grounding-from-below, enables more robust droplet translation without requiring a top plate or wire. We present a concise electrical-energy analysis that accurately describes the distinction between grounded and non-grounded designs, the improvements in droplet motion, and the simplified control strategy associated with grounding-from-below designs. Electrowetting on a single planar surface offers flexibility for interfacing to liquid-handling instruments, utilizing droplet inertial dynamics to achieve enhanced mixing of two droplets upon coalescence, and increasing droplet translation speeds. In this paper, we present experimental results and a number of design issues associated with the grounding-from-below approach.


title: Enhancing small satellite communication through effective antenna system design

abstract: A Cube-Satellite (CubeSat) is a small satellite weighing no more than one kilogram. CubeSats are used for space research, but their low-rate communication capability limits functionality. As greater payload and instrumentation functions are sought, increased data rate is needed. Since most CubeSats currently transmit at a 437 MHz frequency, several directional antenna types were studied for a 2.45 GHz, larger bandwidth transmission. This higher frequency provides the bandwidth needed for increasing the data rate. A deployable antenna mechanism maybe needed because most directional antennas are bigger than the CubeSat size constraints. From the study, a deployable hemispherical helical antenna prototype was built. Transmission between two prototype antenna equipped transceivers at varying distances tested the helical performance. When comparing the prototype antenna's maximum transmission distance to the other commercial antennas, the prototype outperformed all commercial antennas, except the patch antenna. The root cause was due to the helical antenna's narrow beam width. Future work can be done in attaining a more accurate alignment with the satellite's directional antenna to downlink with a terrestrial ground station.


title: Restoration of Degraded Historical Document Image

abstract: Restoration plays a very important role in enhancing the degraded noisy images. To enhance the degraded image, the numerous algorithms have been designed. Since image processing algorithms are subjective, not all algorithms that developed will address all type of degradedness. To address specific type of problem the suitable algorithms need to be selected. In this paper a combination of spatial domain method along with set theory operations are used to enhance the historical image. The proposed method eliminates noise, uneven background and enhances the contrast of the script image. The result of the proposed method is compared with Mean and Gaussian filter. Performance of the proposed method proved to be better than these methods. The restored images will have clear uniform background and foreground with enhanced character appearance.


The Nature of Intestinal Radiation Death1


title: A Generational Comparison of Social Networking Site Use: The Influence of Age and Social Identity:

abstract: An online survey (N = 256) compared social networking site (SNS) use among younger (millennial: 18–29) and older (baby-boomer: 41–64) subscribers focusing on the influence of collective self-esteem and group identity on motives for SNS use. Younger participants reported higher positive collective self-esteem, social networking site use for peer communication, and social compensation. Regardless of age, participants reporting high collective self-esteem and group identity were more likely to use social networking sites for peer communication and social identity gratifications, while those reporting negative collective self-esteem were more likely to use social networking sites for social compensation. The theoretical implications of the strong relationship between social identity gratifications and social compensation are discussed.


title: Reconfigurable circularly polarized antenna for short-range communication systems

abstract: This paper describes the design rules of a compact microstrip patch antenna with polarization reconfigurable features (right-handed circular polarization (CP)/left-handed CP). The basic antenna is a circular coplanar-waveguide (CPW)-fed microstrip antenna excited by a diagonal slot and the CPW open end. This device is developed for short-range communications or contactless identification systems requiring polarization reconfigurability to optimize the link reliability. First, experimental and simulated results are presented for the passive version of the antenna excited by an asymmetric slot. A reconfigurable antenna using beam-lead p-i-n diodes to switch the polarization sense is then simulated with an electrical modeling of the diodes. Finally, the efficiency reduction resulting from the diode losses is discussed.


title: A CMOS subbandgap reference circuit with 1-v power supply voltage

abstract: A CMOS subbandgap reference circuit with 1-V supply voltage is described. To obtain subbandgap reference voltages with a 1-V supply voltage, threshold voltage reduction and subthreshold operation techniques are used. Large /spl Delta/V/sub BE/ (100 mV) as well as a 90-dB operational amplifier are used to circumvent the amplifier offset. A power-on-reset (POR) circuit is used as startup. This circuit has been implemented using a standard 0.5-/spl mu/m CMOS process, and its size is 940 /spl mu/m/spl times/1160/spl mu/m. The temperature coefficient is 17 ppm from -40/spl deg/C to 125/spl deg/C after resistor trimming and the minimum power supply voltage is 0.95 V. The measured total current consumption is below 10 /spl mu/A and the measured output voltage is 0.631 V at room temperature.


title: Parametric thermal modeling of heat transfer in handheld electronic devices

abstract: The dimensions and power dissipation information of various commercial handheld electronics were collected to find benchmarking size and power ranges. The relationship between device size parameter, defined as total volume divided by external surface area, and the power dissipation was determined. Three dimensional finite element models were used to model the conduction, natural convection, and surface-to-surface radiation phenomena inside of the devices. Simulations were performed of four designs of handheld mockups with three boundary conditions on the enclosure modeling the device operating in free air, handheld, and in a clothing pocket. Various combinations of external case and circuit board conductivity were modeled. The ratio between the thermal resistance internal to the device and the resistance from the case surface to the ambient were also determined.


title: Autophagy in Pulmonary Innate Immunity

abstract: Autophagy is a major intracellular digestion system that delivers cytoplasmic components for degradation and recycling. In this capacity, autophagy plays an important role in maintaining cellular homeostasis by mediating the degradation of cellular macromolecules and dysfunctional organelles and regeneration of nutrients for cell growth. Autophagy is important in innate immunity, as it is responsible for the clearance of various pathogens. Deficiency of intracellular autophagy can result in exaggerated activation of the inflammasome. The latter is an innate immune complex that senses diverse pathogen-associated or danger-associated molecular patterns and activates the expression of inflammatory cytokines. In autophagy-deficient cells, accumulation of damaged organelles, misfolded proteins, and reactive oxygen species contribute to inflammasome activation. The lung is continuously exposed to pathogens from the environment, rendering it vulnerable to infection. The lung innate immune cells act as a crucial initial barrier against the continuous threat from pathogens. In this review, we will summarize recent findings on the regulation of autophagy and its interaction with innate immunity, focusing on the lung.


title: Privacy preference modeling and prediction in a simulated campuswide IoT environment

abstract: With the advent of the Internet of Things (IoT), users are more likely to have privacy concerns since their personal information could be collected, analyzed, and utilized without notice by the networked IoT devices and services. Users may want to control all such activities by explicitly expressing their privacy preferences. However, it is becoming increasingly difficult for users to do so, not only because of the cognitive burden of continuously making privacy decisions for IoT services, but also because IoT devices have no, or only very restricted, user interfaces. Intelligent software helping users make better privacy decisions will be an important component of privacy-preserving IoT environments. In order to construct such a component, we aim to verify whether it will be possible to computationally model and predict users' privacy preferences in IoT. To that end, we survey 172 participants in a simulated campuswide IoT environment about their privacy preferences regarding hypothetical personal information tracking scenarios. Then, we cluster the scenarios based on the survey responses, arriving at four clusters with distinct associated privacy preferences. Based on the clustering results, we uncover contextual factors that induce privacy violations in IoT. Finally, we build machine learning models to predict users' privacy decisions, using both contextual information and the corresponding cluster membership as training data. The final trained model shows 77% accuracy in predicting users' decisions whether or not to allow the respective IoT scenario.


title: Face Alignment Across Large Poses: A 3D Solution

abstract: Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45 degree), lacking the ability to align faces in large poses up to 90 degree. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.


title: Proactive Load Balancing in Heterogeneous Cellular Networks

abstract: Recent exponential growth of data over cellular networks has cause the progression from conventional mobile communication networks to heterogeneous cellular networks (HetNets). Quality of experience (QoE)-aware traffic load balancing in such dense HetNets is considered a major problem. Current HetNets exploit reactive load balancing schemes that hinder in achieving desired QoE gain. In this paper, we propose a novel proactive load balancing framework which leverages content caching and mobility prediction to improve user QoE. We use Semi-Markov model to predict users' future cells and a novel proactive caching algorithm is proposed to pre-fetch user future demands at these cells. This allows us to reduce cell congestion and offers better average downlink rates. To validate the effectiveness of the proposed framework, system level simulations are performed and compared with state of the art approaches. Encouragingly, our system achieves up-to $98.7\%$ cell load balancing in terms of Jain's fairness index, reduces $20\%$ backhaul load, and provides $31\%$ improved downlink rates at congested cells in contrast to reactive approaches.


A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement


title: Celeb-500K: A Large Training Dataset for Face Recognition

abstract: In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observations on face recognition study. First, metric learning methods have limited performance gain when the training dataset contains a large number of identities. Second, in order to develop an efficient training dataset, the number of identities is more important than the average image number of each identity from the perspective of face recognition performance. Extensive experimental results show the superiority of Celeb-500K and provide a strong support to the two observations.


title: Deep 3D Human Pose Estimation Under Partial Body Presence

abstract: This paper addresses the problem of 3D human pose estimation when not all body parts are present in the input image, i.e., when some body joints are present while other joints are fully absent (we exclude self-occlusion). State-of-the-art is not designed and thus not effective for such cases. We propose a deep CNN to regress the human pose directly from an input image; we design and train this network to work under partial body presence. Parallel to this, we train a detection network to classify the presence or absence of each of the main body joints in the input image. The outputs of our detection and regression networks are a) joints that are present and b) joints that are absent. With these outputs, our method reconstructs the full body skeleton. Evaluations on the Hu-man3.6M dataset yield promising results compared to related work.


title: Commercial latex test for Clostridium difficile toxin A does not detect toxin A.

abstract: The rapid latex test recently marketed by Marion Scientific (Div. Marion Laboratories, Inc., Kansas City, Mo.) for the detection of Clostridium difficile toxin A does not react with the toxin, based on the following findings: culture filtrates from nontoxigenic strains of C. difficile gave positive reactions in the test, culture filtrate in which toxin A had been removed gave positive reactions, purified toxin A did not react in the test, and the latex reagent bound an antigen which is distinct from toxin A and which is produced in various amounts by both toxigenic and nontoxigenic strains of C. difficile.


title: Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition

abstract: 3-D shape analysis has attracted extensive research efforts in recent years, where the major challenge lies in designing an effective high-level 3-D shape feature. In this paper, we propose a multi-level 3-D shape feature extraction framework by using deep learning. The low-level 3-D shape descriptors are first encoded into geometric bag-of-words, from which middle-level patterns are discovered to explore geometric relationships among words. After that, high-level shape features are learned via deep belief networks, which are more discriminative for the tasks of shape classification and retrieval. Experiments on 3-D shape recognition and retrieval demonstrate the superior performance of the proposed method in comparison to the state-of-the-art methods.


title: Prevalence of Internet Addiction in Students of Sabzevar University of Medical Sciences in 2015

abstract: Background: One of the factors jeopardizing individual and social status of persons is internet over-use. Considering the impor- tance of the internet to learn new information, faster relationships between people, and widespread use among students, more precise knowledge regarding the potential harms of internet over-use is necessary. Methods: This descriptive cross-sectional study included 1000 medical students in 2015 from Sabzevar University. The collected data include demographic information and a standard evaluation of internet addiction, called the Young questionnaire. Data were ana- lyzed by SPSS software (version16) with the help of Pearson's correlation test and Independent t- test. P value < 0.05 was considered statistically significant. Results: The age average of 638 participants was 21.1 3.5 years (72.8% female). 22.9% were married and 47.9% were from Sabzevar originally. The average internet addition score was mild, about 27.9 18.2. Thirty-six percent (n = 229) had used the internet nor- mally, 51.1% (n = 325) had mild addiction, 12.3% (n = 78) had moderate addiction, and 0.6% (n = 4) had severe addiction. There was a significant relationship between internet addiction and sex (P = 0.003), marital status (P = 0.006) and indigenous status (P = 0.03). There was no significant relation between age and internet addiction in this population (P = 0.329). Conclusions: A significant number of students are at risk of internet addiction and its related physical and psychological harms. It is necessary to take the appropriate steps to prevent internet addiction. Educating students in terms of qualified use of the internet may be very eective for this purpose.


title: Gene expression profiles of primary breast tumors maintained in distant metastases

abstract: It has been debated for decades how cancer cells acquire metastatic capability. It is unclear whether metastases are derived from distinct subpopulations of tumor cells within the primary site with higher metastatic potential, or whether they originate from a random fraction of tumor cells. Here we show, by gene expression profiling, that human primary breast tumors are strikingly similar to the distant metastases of the same patient. Unsupervised hierarchical clustering, multidimensional scaling, and permutation testing, as well as the comparison of significantly expressed genes within a pair, reveal their genetic similarity. Our findings suggest that metastatic capability in breast cancer is an inherent feature and is not based on clonal selection.


title: FREE-p: Protecting non-volatile memory against both hard and soft errors

abstract: Emerging non-volatile memories such as phase-change RAM (PCRAM) offer significant advantages but suffer from write endurance problems. However, prior solutions are oblivious to soft errors (recently raised as a potential issue even for PCRAM) and are incompatible with high-level fault tolerance techniques such as chipkill. To additionally address such failures requires unnecessarily high costs for techniques that focus singularly on wear-out tolerance. In this paper, we propose fine-grained remapping with ECC and embedded pointers (FREE-p). FREE-p remaps fine-grained worn-out NVRAM blocks without requiring large dedicated storage. We discuss how FREE-p protects against both hard and soft errors and can be extended to chipkill. Further, FREE-p can be implemented purely in the memory controller, avoiding custom NVRAM devices. In addition to these benefits, FREE-p increases NVRAM lifetime by up to 26% over the state-of-the-art even with severe process variation while performance degradation is less than 2% for the initial 7 years.