title: Accelerating pointer chasing in 3D-stacked memory: Challenges, mechanisms, evaluation

abstract: Pointer chasing is a fundamental operation, used by many important data-intensive applications (e.g., databases, key-value stores, graph processing workloads) to traverse linked data structures. This operation is both memory bound and latency sensitive, as it (1) exhibits irregular access patterns that cause frequent cache and TLB misses, and (2) requires the data from every memory access to be sent back to the CPU to determine the next pointer to access. Our goal is to accelerate pointer chasing by performing it inside main memory, thereby avoiding inefficient and high-latency data transfers between main memory and the CPU. To this end, we propose the In-Memory PoInter Chasing Accelerator (IMPICA), which leverages the logic layer within 3D-stacked memory for linked data structure traversal.


The Business Model as a Tool of Improving Value Creation in Complex Private Service System-Case : Value Network of Electric Mobility


title: Planning Bike Lanes based on Sharing-Bikes' Trajectories

abstract: Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.


title: Combining Convolutional Neural Networks and LSTMs for Segmentation-Free OCR

abstract: We present a novel end-to-end trainable OCR system combining a CNN for feature extraction with 1-D LSTMs for sequence modeling. We present results on English and Arabic handwriting data, and on English machine print data, showing state-of-the-art performance. We believe that our method is simpler than existing 2D LSTM models, and will make it easier to use techniques borrowed from CNN research in computer vision to improve OCR performance.


title: Fast Convolutional Sparse Coding

abstract: Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal sub problems and super-linear convergence.


title: Construction of a Minimal Deterministic Finite Automaton from a Regular Expression

abstract: This paper describes a method for constructing a minimal deterministic finite automaton (DFA) from a regular expression. It is based on a set of graph grammar rules for combining many graphs (DFA) to obtain another desired graph (DFA). The graph grammar rules are presented in the form of a parsing algorithm that converts a regular expression R into a minimal deterministic finite automaton M such that the language accepted by DFA M is same as the language described by regular expression R. The proposed algorithm removes the dependency over the necessity of lengthy chain of conversion, that is, regular expression → NFA with e-transitions → NFA without etransitions → DFA → minimal DFA. Therefore the main advantage of our minimal DFA construction algorithm is its minimal intermediate memory requirements and hence, the reduced time complexity. The proposed algorithm converts a regular expression of size n in to its minimal equivalent DFA in O(n.log2n) time. In addition to the above, the time complexity is further shortened to O(n.logen) for n ≥ 75. General Terms Algorithms, Complexity of Algorithm, Regular Expression, Deterministic Finite Automata (DFA), Minimal DFA.


title: Posttraumatic growth, meaning in life, and life satisfaction in response to trauma.

abstract: A model of the processes leading to posttraumatic growth and to life satisfaction following exposure to trauma was tested. Two types of repeated thought, deliberate and intrusive, posttraumatic symptoms, posttraumatic growth, and meaning in life, were assessed as predictors of general life satisfaction. Challenges to core beliefs were shown to be related to both intrusive and deliberate rumination. The two forms of rumination were in turn differentially related to posttraumatic growth and posttraumatic distress. Distress and posttraumatic growth were independently and oppositely related to meaning in life and to life satisfaction. Overall, the best fitting model was supportive of proposed posttraumatic growth models. Additional exploratory analyses examined participant groupings, based of self-reported category of resolution of the traumatic experience, and differences supportive of proposed underlying processes were found.


title: Axial Plane Optical Microscopy

abstract: We present axial plane optical microscopy (APOM) that can, in contrast to conventional microscopy, directly image a sample's cross-section parallel to the optical axis of an objective lens without scanning. APOM combined with conventional microscopy simultaneously provides two orthogonal images of a 3D sample. More importantly, APOM uses only a single lens near the sample to achieve selective-plane illumination microscopy, as we demonstrated by three-dimensional (3D) imaging of fluorescent pollens and brain slices. This technique allows fast, high-contrast, and convenient 3D imaging of structures that are hundreds of microns beneath the surfaces of large biological tissues.


title: Electrical Interactions via the Extracellular Potential Near Cell Bodies

abstract: Ephaptic interactions between a neuron and axons or dendrites passing by its cell body can be, in principle, more significant than ephaptic interactions among axons in a fiber tract. Extracellular action potentials outside axons are small in amplitude and spatially spread out, while they are larger in amplitude and much more spatially confined near cell bodies. We estimated the extracellular potentials associated with an action potential in a cortical pyramidal cell using standard one-dimensional cable theory and volume conductor theory. Their spatial and temporal pattern reveal much about the location and timing of currents in the cell, especially in combination with a known morphology, and simple experiments could resolve questions about spike initiation. From the extracellular potential we compute the ephaptically induced polarization in a nearby passive cable. The magnitude of this induced voltage can be several mV, does not spread electrotonically, and depends only weakly on the passive properties of the cable. We discuss their possible functional relevance.


title: Fatty acid contents and profiles of 16 macroalgae collected from the Irish Coast at two seasons

abstract: Due to the established health benefits of omega-3 long-chain polyunsaturated fatty acids (LC-PUFA), there is a globally increasing demand for alternative natural resources with appropriate fatty acid profiles. To assess the suitability of macroalgae as a source, 16 species (nine Phaeophyceae, five Rhodophyta and two Chlorophyta) were collected at two seasons (June and November) from the Irish west Coast, and total fatty acid contents and specific profiles were determined. Total fatty acid contents, expressed per percentage of dry weight, ranged from 6.4 % ± 0.3 (Pelvetia canaliculata, Phaeophyceae) to 0.8 % ± 0.2 (Porphyra dioica, Rhodophyta). Most common fatty acids were palmitic (16:0), oleic (OLE, 18:1 n-9), α-linolenic (ALA, 18:3 n-3), arachidonic (ARA, 20:4 n-6) and eicosapentaenoic (EPA, 20:5 n-3) acids. Fatty acid profiles were highly variable between and within algal groups; red and brown seaweeds were generally richer in LC-PUFA (e.g. 20:4 n-6 and 20:5 n-3), while high levels of saturated fatty acids such as palmitic acid (16:0) were observed in green species. Most omega-3 PUFA-rich species investigated had a omega-6/omega-3 fatty acid ratio close to 1, which is favourable for human health. The two seasonal sampling times revealed significant differences in total fatty acid and 20:5 n-3 (EPA) contents, with changes depending on species, thus implying varying suitability as potential target species for EPA production. At both times of the year, Palmaria palmata was identified as most promising species as a source of 20:5 n-3 (EPA) amongst all species investigated, with levels ranging from 0.44 to 0.58 % of dry weight in June and November, respectively.


title: The Super-High SMAS Facelift Technique with Tailor Tack Plication

abstract: BACKGROUND ::: Super-high superficial musculoaponeurotic system (SMAS) suspension and tailor tack plication are powerful facelift tools used in cases of primary facial rhytidectomy. ::: ::: ::: TECHNIQUE ::: Thorough pre-operative patient screening and counseling are completed in an outpatient cosmetic surgery center. A super-high SMAS flap is developed by undermining and incising along a line extending from the tragus to lateral canthus and dissecting the SMAS sufficiently to induce movement of the lateral nose and the oral commissure with traction on the SMAS. The SMAS is suspended from the deep temporal fascia using 4-0 Mersilene sutures. Tailor tack sutures are placed strategically in the SMAS caudal to the malar bone to eliminate any residual laxity in the SMAS close to the oral commissures and the cheek area. This is repeated until the laxity of the SMAS is completely eliminated. The orbicularis muscle is conservatively suspended laterally from the deep temporal fascia, facial volume is restored with fat grafting, the neck is contoured, if necessary, and the vest-over-pants platysma overlap technique is commonly utilized. Concomitant forehead rejuvenation, blepharoplasty, and laser resurfacing are extremely common. ::: ::: ::: RESULTS ::: Of the 72 cases of primary rhytidectomy performed by the senior author on consecutive patients included in the study, there were 64 (89%) female and 8 (11%) male patients. The average age of the patient at the time of primary facelift was 58 years old. Fifteen out of 72 (21%) patients received one dose of desmopressin (DDAVP) injection during the surgery, and one patient received DDAVP the day after surgery to maintain hemostasis. There was no incidence of facial nerve injury. Of those 72 cases, 3 (4%) developed minor hematomas that were resolved by aspiration. There were no expanding hematomas that required surgery. Of the 26 patients whose satisfaction was documented, 24 were very satisfied. Two patients expressed their dissatisfaction with the results, and one of these patients underwent a revision operation by the senior author. The other patient was mainly not pleased with the recovery duration. ::: ::: ::: CONCLUSION ::: An alteration in the SMAS elevation and suspension is described with strategic submalar plication, providing a powerful technique for primary facial rhytidectomy that offers reliable and significant malar lift, orbicularis suspension, improvement in jowls, and repositioning of the oral commissure. The technical details are demonstrated through complete video segments along with many nuances that make delivery of optimal and natural outcomes possible, while maintaining the normal anatomy. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: Design. Fabrication, and Evaluation of Tendon-Driven Multi-Fingered Foam Hands

abstract: We present a novel class of tendon-actuated soft robots, which promise to be low-cost and accessible to non-experts. The primary structure of the robot consists of flexible foam, and so we term the robots created using our approach “foam robots.” A foam robot moves by driving servo mounted winches that contract (or slacken)tendons routed through the robots textile skin. We provide a methodology for fabricating these types of robots and go on to fabricate several ‘foam robots’ in the form of multi-fingered hands and perform various experiments and demonstrations to illustrate the robust applications of these robots to tasks such as dexterous manipulation.


title: From driving simulation to virtual reality

abstract: Driving simulation from the very beginning of the advent of VR technology uses the very same technology for visualization and similar technology for head movement tracking and high end 3D vision. They also share the same or similar difficulties in rendering movements of the observer in the virtual environments. The visual-vestibular conflict, due to the discrepancies perceived by the human visual and vestibular systems, induce the so-called simulation sickness, when driving or displacing using a control device (ex. Joystick). Another cause for simulation sickness is the transport delay, the delay between the action and the corresponding rendering cues. Another similarity between driving simulation and VR is need for correct scale 1:1 perception. Correct perception of speed and acceleration in driving simulation is crucial for automotive experiments for Advances Driver Aid System (ADAS) as vehicle behavior has to be simulated correctly and anywhere where the correct mental workload is an issue as real immersion and driver attention is depending on it. Correct perception of distances and object size is crucial using HMDs or CAVEs, especially as their use is frequently involving digital mockup validation for design, architecture or interior and exterior lighting. Today, the advents of high resolution 4K digital display technology allows near eye resolution stereoscopic 3D walls and integrate them in high performance CAVEs. High performance CAVEs now can be used for vehicle ergonomics, styling, interior lighting and perceived quality. The first CAVE in France, built in 2001 at Arts et Metiers ParisTech, is a 4 sided CAVE with a modifiable geometry with now traditional display technology. The latest one is Renault's 70M 3D pixel 5 sides CAVE with 4K x 4K walls and floor and with a cluster of 20 PCs. Another equipment recently designed at Renault is the motion based CARDS driving simulator with CAVE like 4 sides display system providing full 3D immersion for the driver. The separation between driving simulation and digital mockup design review is now fading though different uses will require different simulation configurations. New application domains, such as automotive AR design, will bring combined features of VR and driving simulation technics, including CAVE like display system equipped driving simulators.


title: Leveraging stereopsis for saliency analysis

abstract: Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer's attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.


title: An Anytime Algorithm for Optimal Coalition Structure Generation

abstract: Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques ranging from dynamic programming, to integer programming, to stochastic search all of which suffer from major limitations relating to execution time, solution quality, and memory requirements. With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.


title: Active Learning from Data Streams

abstract: In this paper, we address a new research problem on active learning from data streams where data volumes grow continuously and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict newly arrived instances as accurate as possible. In order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. A minimal variance principle is introduced to guide instance labeling from data streams. In addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches.


title: Design of Wideband Cavity-Backed Slot Antenna With Multilayer Dielectric Cover

abstract: A novel wideband cavity-backed slot antenna operating at X-band is proposed. Compared to conventional cavity-backed slot antennas, both the bandwidth and gain of the proposed antenna are significantly increased by adding a multilayer dielectric cover. This antenna offers a 40% (|S ::: 11 ::: | <; -10 dB) impedance bandwidth. Meanwhile, the gain of a single antenna within this band is 6-9.5 dBi, 2-3 dB higher than those without that multilayer dielectric cover at most of the bandwidth. To further validate the capability of the proposed antenna, a 4 ×4 antenna array is designed, fabricated, and tested. The measurement results corroborate with simulations well. Taking advantage of its wider bandwidth and higher gain compared to the counterparts, this antenna could find great potential application in the future.


title: Flux-weakening strategy for high speed PMSM for vehicle application

abstract: High speed Permanent Magnet Synchronous Motors (PMSM) are used in electrical vehicles because of their strong power density. The high speed implies a big electromotive force and requires flux-weakening. Usual control algorithms do flux-weakening by adding negative Id current when the required voltage by the current regulation exceeds the maximum voltage depending on the battery. If the magnet can be totally defluxed then it is better to use a Maximum Torque Per Volt (MTPV) strategy. Furthermore there is no speed regulation in the control and the driver gives a torque reference that has to be limited to the reachable operating points; the battery power limit has to be taken into account in addition to the voltage and current limits. The d-q current reference are calculated to minimize the total current magnitude required to reach the reference torque. This paper presents a salient pole PMSM control that calculates the minimum magnitude current references to obtain the reference torque while respecting the voltage, current and power limits with a flux-weakening strategy and including MTPV operation in a unified algorithm. The effectiveness of the proposed method is observed through simulations.


title: Planar waveguide feed for V-band Bull's eye antenna

abstract: A technique is presented for feeding leaky-wave Bull's eye antennas from a planar waveguide, based on a planar to rectangular waveguide transition and a milled waveguide extension. Bull's eye antennas are low-profile planar antennas that exploit leaky wave principles to achieve high gain. Currently Bull's eye antennas use hollow waveguide feeds (rectangular or circular), but lower-cost feed options open up new applications for the antenna, particularly if it can permit compact overall size due to tight integration with the underlying millimeter-wave circuit (e.g. in small satellites or communications backhaul on earth). While substrate-integrate-waveguides often offer better performance than microstrip waveguides at millimeter wave frequencies, the feed integration challenge is substantially similar and we were able to access a suitable fabrication technology for microstrip waveguides. We produced a prototype microstrip-fed Bull's eye antenna operating at 60 GHz and measured its far field radiation pattern to confirm that the antenna's radiation pattern was not substantially affected by the change in the feed.


title: Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

abstract: The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.


title: Comparative effects of Facebook and conventional media on body image dissatisfaction

abstract: BackgroundAppearance comparison has consistently been shown to engender body image dissatisfaction. To date, most studies have demonstrated this relationship between appearance comparison and body image dissatisfaction in the context of conventional media images depicting the thin-ideal. Social comparison theory posits that people are more likely to compare themselves to similar others. Since social media forums such as Facebook involve one’s peers, the current study aimed to determine whether the relationship between appearance comparison and body image dissatisfaction would be stronger for those exposed to social media images, compared to conventional media images.MethodsA sample of 193 female first year university students were randomly allocated to view a series of either Facebook or conventional media thin-ideal images. Participants completed questionnaires assessing pre- and post- image exposure measures of thin-ideal internalisation, appearance comparison, self-esteem, Facebook use and eating disorder risk.ResultsType of exposure was not found to moderate the relationship between appearance comparison and changes in body image dissatisfaction. When analysed according to exposure type, appearance comparison only significantly predicted body image dissatisfaction change for those exposed to Facebook, but not conventional media. Facebook use was found to predict higher baseline body image dissatisfaction and was associated with higher eating disorder risk.ConclusionsThe findings suggest the importance of extending the body image dissatisfaction literature by taking into account emerging social media formats. It is recommended that interventions for body image dissatisfaction and eating disorders consider appearance comparison processes elicited by thin-ideal content on social media forums, such as Facebook, in addition to conventional media.


title: Predicting Students' Behavioral Patterns in University Networks for Efficient Bandwidth Allocation: A Hybrid Data Mining Method (Application Paper)

abstract: The effective bandwidth management in multi-service computer networks such as university networks has become a challenge in recent years. The growth of internet traffic and limitation of bandwidth resources persuade the information technology (IT) managers to focus on effective bandwidth allocation policies. One of the important issues discussed in this domain is how to assign the bandwidth fairly based on the priority levels. In this paper, focusing on the "priority-based bandwidth allocation", a hybrid data mining method is developed to manage the limited bandwidth in a university network more effectively. This method is composed of two main steps and uses the clustering and classification techniques. The main purpose is to detect, analyze and predict students' behavioral patterns in a university network and identify the main factors that affect their tendency in using internet. The proposed method is applied on a real data of a network university. The results indicate that "degree level" and "age" are the most important factors that influence students' tendency to use internet. The results would be also useful for prediction purposes. It helps the IT managers to predict a new student's tendency to use internet given his/her characteristics. By analyzing the results, the IT managers can make better decisions to optimize the allocation of bandwidth resources.


title: Analysis and classification of speech mode: Whispered through shouted

abstract: Variation in vocal effort represents one of the most challenging problems in maintaining speech system performance for coding, speech and speaker recognition. Changes in vocal effort (or mode) result in a fundamental change in speech production which is not simply a change in volume. This is the first study to collectively consider the five speech modes: whispered, soft, neutral, loud and shouted. After corpus development, analysis is performed for i) sound intensity level, ii) duration and silence percentage, iii) frame energy distribution and iv) spectral tilt. The analysis shows vocal effort dependent traits which are used to investigate speaker recognition. Matched vocal mode conditions result in a closed-set speaker ID rate of 97.62%, with mismatch vocal conditions producing 54.02%. Finally, a speech mode classification system is developed, which has a range of classification rate from 44.5% to 98.5% confusing with adjacent vocal modes. These advancements can provide improved speech/speaker modeling information, as well as classified vocal mode knowledge to improve speech and language technology in real scenarios.


title: A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

abstract: Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.


title: Team Performance in Agile Development Teams: Findings from 18 Focus Groups

abstract: How to make teams perform well is increasingly important in software development, as agile development methods prescribe development in small teams. Team performance has been studied in a number of research fields, and there are many models of what enables team performance. A central question then is how relevant these models are for agile development teams. This article investigates the following research question: What factors do agile software practitioners perceive to influence effective teamwork, through a focus group study with 92 participants in 18 groups. The main findings are that what agile practitioners perceive foster and hinder team performance seems to comply well with what is stated in an existing research-based model. However, agile practitioners seem to place insufficient focus on backup behaviour. Agile practitioners place much emphasis on physical and technical infrastructure of the development team as enablers of team performance.


title: Domain adaptation network based on hypergraph regularized denoising autoencoder

abstract: Domain adaptation learning aims to solve the classification problems of unlabeled target domain by using rich labeled samples in source domain, but there are three main problems: negative transfer, under adaptation and under fitting. Aiming at these problems, a domain adaptation network based on hypergraph regularized denoising autoencoder (DAHDA) is proposed in this paper. To better fit the data distribution, the network is built with denoising autoencoder which can extract more robust feature representation. In the last feature and classification layers, the marginal and conditional distribution matching terms between domains are obtained via maximum mean discrepancy measurement to solve the under adaptation problem. To avoid negative transfer, the hypergraph regularization term is introduced to explore the high-order relationships among data. The classification performance of the model can be improved by preserving the statistical property and geometric structure simultaneously. Experimental results of 16 cross-domain transfer tasks verify that DAHDA outperforms other state-of-the-art methods.


Efficient Routing For Intermittently Connected Mobile Ad hoc Network


title: Comparative study between dorneneburg and rogers methods for transformer fault diagnosis based on dissolved gas analysis using Matlab Simulink Tools

abstract: Dissolved Gas Analysis (DGA) is considered a popular method to diagnose different types of faults occurring in power transformers due to different electrical and thermal stresses. These stresses can lead to arcing discharge, partial discharge or thermal faults in the power transformer. This paper focuses on the employment of the Matlab Simulink Tools with dissolved gases, in order to determine the transformer faults based on the interpretation methods in IEEE standard such as Dornenburg and Rogers methods. The constructed model is built according to Dornenburg and Rogers codes in IEEE Standard C57.104 rules. The results from the proposed Simulink model are compared with the results from lab tests and result from the literatures to test the accuracy of each method in transformer faults diagnosis. The results of this study are useful in the development of a reliable transformer diagnostic system using an easy Simulink model based on interpretation method in IEEE Standard.


title: Multiuser Scheduling with Multi Radio Access Selection

abstract: In this paper we address the problem of multiuser scheduling with multi-radio access selection, i.e., the data flow of a given user can be scheduled over multiple radio access technologies of different operators. We show that performance gains are possible and come from multiuser diversity as well as multi-radio diversity, as both the best user and the best radio access can be selected, at any given time, to get to optimal performance. We compare a terminal-based strategy where users compete to choose the best radio access, and a network-based case where the network choose the users to serve at a given time. This work assesses the performance gain due to both multi radio access and multiuser diversity with respect to the no-multi-radio case, where users are constrained to connect to the same single radio access


title: Time-domain Continuous FIR Filter Based Electronic Instrument Transducers Sampling Data Inter-substation Synchronization Arithmetic

abstract: Under electronic instrument transducer applica-tion circumstance,sampling data synchronization between substaions should be achieved as sampling clocks of substations keep independent.This paper presents the concept of time-domain continuous finite impulse response(TCFIR)filter to solve this problem.A TCFIR based continuous digital low pass filter was constructed to acquire continuous signal from EPT/ECT discrete output data and calculate sampling value at any moment.Simulation and time-frequency domain analysis using 500kV transmission line fault current with strong harmonic was carried out.With 1000Hz,2400Hz and 4000Hz sampling frequency,the RMS and DFT fundamental amplitude error is less than 0.4%.Meanwhile,the arithmetic data window length is 6 points and the time delay is only 3 sampling intervals.The result shows that the arithmatic meets the accuracy and realtime requirement of protection and distributed stability control application.


title: Self-adaptation through incremental generative model transformations at runtime

abstract: A self-adaptive system uses runtime models to adapt its architecture to the changing requirements and contexts. However, there is no one-to-one mapping between the requirements in the problem space and the architectural elements in the solution space. Instead, one refined requirement may crosscut multiple architectural elements, and its realization involves complex behavioral or structural interactions manifested as architectural design decisions. In this paper we propose to combine two kinds of self-adaptations: requirements-driven self-adaptation, which captures requirements as goal models to reason about the best plan within the problem space, and architecture-based self-adaptation, which captures architectural design decisions as decision trees to search for the best design for the desired requirements within the contextualized solution space. Following these adaptations, component-based architecture models are reconfigured using incremental and generative model transformations. Compared with requirements-driven or architecture-based approaches, the case study using an online shopping benchmark shows promise that our approach can further improve the effectiveness of adaptation (e.g. system throughput in this case study) and offer more adaptation flexibility.


title: Iterative ICI Cancellation in MIMO OFDM WiMAX System with LDPC Channel Coding

abstract: The employment of MIMO OFDM technique constitutes a cost effective approach to high throughput wireless communications. The system performance is sensitive to frequency offset which increases with the doppler spread and causes Intercarrier interference (ICI). ICI is a major concern in the design as it can potentially cause a severe deterioration of quality of service (QoS) which necessitates the need for a high speed data detection and decoding with ICI cancellation along with the intersymbol interference (ISI) cancellation in MIMO OFDM communication systems. Iterative parallel interference canceller (PIC) with joint detection and decoding is a promising approach which is used in this work. The receiver consists of a two stage interference canceller. The co channel interference cancellation is performed based on Zero Forcing (ZF) Detection method used to suppress the effect of ISI in the first stage. The latter stage consists of a simplified PIC scheme. High bit error rates of wireless communication system require employing forward error correction (FEC) methods on the data transferred in order to avoid burst errors that occur in physical channel. To achieve high capacity with minimum error rate Low Density Parity Check (LDPC) codes which have recently drawn much attention because of their error correction performance is used in this system. The system performance is analyzed for two different values of normalized doppler shift for varying speeds. The bit error rate (BER) is shown to improve in every iteration due to the ICI cancellation. The interference analysis with the use of ICI cancellation is examined for a range of normalized doppler shift which corresponds to mobile speeds varying from 5Km/hr to 250Km/hr.


title: Return loss and bandwidth enhancement of microstrip antenna using Defected Ground Structure (DGS)

abstract: In this paper a modified inset-feed microstrip antenna is proposed to increase band width and return loss. The operating frequency of the antenna is 2.4 GHz ISM band. To enhance the performance of the antenna one DGS has been introduced under the patch area that is on the other side of substrate and little modification in the dimension of feed gap. The DGS has been optimized to provide a stop band at 2.4GHz. The stop band of the DGS added with stop band of the inset-feed rectangular antenna which is responsible for enhancement of return loss. The DGS on the ground plane increases fringing field which introduces parasitic capacitance. This parasitic capacitance increases the coupling between the conducting patch and the ground plane which is responsible for the enhancement of the bandwidth. The performance of the antenna is depends on the physical dimension of the antenna as well as location and shape of the DGS. Zealand IE3D is used for simulation and analysis of the proposed antenna.


title: An FPGA controlled DC/DC step-up converter for automotive applications

abstract: In automotive applications piezoelectric actuators (used as injectors) typically require more than 150V in order to be driven in a smaller time with respect to magnetic actuators, allowing for example more precise injections in the same motor cycle. This is a big concern in automotive environment, where the 12V battery voltage is still the main power source available. In this paper an efficient high-step up DC/DC converter is presented for interfacing the standard 12V battery with the high voltage DC BUS used for piezoelectric actuators system. In the proposed converter, two coupled inductor boost converters are interleaved and controlled by an FPGA. Design and analysis of the proposed converter are presented. Finally experimental results are provided for verification of the proposed converter.


title: Towards Good Practices for Image Retrieval Based on CNN Features

abstract: Recent works have demonstrated that Convolutional Neural Networks (CNNs) achieve state-of-the-art results in several computer vision tasks. CNNs have also shown their ability to provide effective descriptors for image retrieval. In this paper, we focus on CNN feature extraction for instance-level image search. We started by studying in depth several methods proposed to improve the Regional Maximal Activation (RMAC) approach. Then, we selected some of these advances and introduced a new approach that combines multi-scale and multi-layer feature extraction with feature selection. We also propose an approach for local RMAC descriptor extraction based on class activation maps. Our parameter-free approach provides short descriptors and achieves state-of-the-art performance without the need of CNN finetuning or additional data in any way. In order to demonstrate the effectiveness of our approach, we conducted extensive experiments on four well known instance-level image retrieval benchmarks (the INRIA Holidays dataset, the University of Kentucky Benchmark, Oxford5k and Paris6k).


title: STAR, a sprawl tuned autonomous robot

abstract: This paper presents a six-legged, sprawl-tuned autonomous robot (STAR). This novel robot has a variable leg sprawl angle in the transverse plane to adapt its stiffness, height, and leg-to-surface contact angle. The sprawl angle can be varied from nearly positive 60 degrees to negative 90 degrees, enabling the robot to run in a planar configuration, upright, or inverted (see movie). STAR is fitted with spoke wheel-like legs which provide high electromechanical conversion efficiency and enable the robot to achieve legged performance over rough surfaces and obstacles, using a high sprawl angle, and nearly wheel-like performance over smooth surfaces for small sprawl angles. Our model and experiments show that the contact angle and normal contact forces are substantially reduced when the sprawl angle is low, and the velocity increases over smooth surfaces, with stable running at all velocities up to 5.2m/s and a Froude number of 9.8.


title: Single-Stage Soft-Switching Converter With Boost Type of Active Clamp for Wide Input Voltage Ranges

abstract: A single-stage soft-switching converter is proposed for universal line voltage applications. A boost type of active-clamp circuit is used to achieve zero-voltage switching operation of the power switches. A simple DC-link voltage feedback scheme is applied to the proposed converter. A resonant voltage-doubler rectifier helps the output diodes to achieve zero-current switching operation. The reverse-recovery losses of the output diodes can be eliminated without any additional components. The DC-link capacitor voltage can be reduced, providing reduced voltage stresses of switching devices. Furthermore, power conversion efficiency can be improved by the soft-switching operation of switching devices. The performance of the proposed converter is evaluated on a 160-W (50 V/3.2 A) experimental prototype. The proposed converter complies with International Electrotechnical Commission (IEC) 1000-3-2 Class-D requirements for the light-emitting diode power supply of large-sized liquid crystal displays, maintaining the DC-link capacitor voltage within 400 V under the universal line voltage (90-265 Vrms).


title: Addressing supply chain risks of microelectronic devices through computer vision

abstract: Microelectronics are at the heart of nearly all modern devices, ranging from small embedded integrated circuits (ICs) inside household products to complex microprocessors that power critical infrastructure systems. Devices often consist of numerous ICs from a variety of different manufacturers and procured through different vendors, all of whom may be trusted to varying degrees. Ensuring the quality, safety, and security of these components is a critical challenge. One possible solution is to use automated imaging techniques to check devices' physical appearance against known reference models in order to detect counterfeit or malicious components. This analysis can be performed at both a macro level (i.e., ensuring that the packaging of the IC appears legitimate and undamaged) and a micro level (i.e., comparing microscopic, transistor-level imagery of the circuit itself to detect suspicious deviations from a reference model). This latter analysis in particular is very challenging, considering that modern devices can contain billions of transistors. In this paper, we review the problem of microelectronics counterfeiting, discuss the potential application of computer vision to microelectronics inspection, present initial results, and recommend directions for future work.


title: A survey of Tactile Human–Robot Interactions

abstract: Robots come into physical contact with humans in both experimental and operational settings. Many potential factors motivate the detection of human contact, ranging from safe robot operation around humans, to robot behaviors that depend on human guidance. This article presents a review of current research within the field of Tactile Human-Robot Interactions (Tactile HRI), where physical contact from a human is detected by a robot during the execution or development of robot behaviors. Approaches are presented from two viewpoints: the types of physical interactions that occur between the human and robot, and the types of sensors used to detect these interactions. We contribute a structure for the categorization of Tactile HRI research within each viewpoint. Tactile sensing techniques are grouped into three categories, according to what covers the sensors: (i) a hard shell, (ii) a flexible substrate or (iii) no covering. Three categories of physical HRI likewise are identified, consisting of contact that (i) interferes with robot behavior execution, (ii) contributes to behavior execution and (iii) contributes to behavior development. We populate each category with the current literature, and furthermore identify the state-of-the-art within categories and promising areas for future research.


title: A Simple, Fast Diverse Decoding Algorithm for Neural Generation

abstract: In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an inter-sibling ranking penalty, favoring choosing hypotheses from diverse parents. We evaluate the proposed model on the tasks of dialogue response generation, abstractive summarization and machine translation. We find that diverse decoding helps across all tasks, especially those for which reranking is needed. ::: We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL). We observe a further performance boost from this RL technique. This paper includes material from the unpublished script "Mutual Information and Diverse Decoding Improve Neural Machine Translation" (Li and Jurafsky, 2016).


title: A new medium voltage PWM inverter topology for adjustable speed drives

abstract: In this paper, a new pulsewidth modulation (PWM) inverter topology suitable for medium-voltage (2300/4160 V) adjustable-speed drive systems is proposed. The modular inverter topology is derived by combining three standard three-phase inverter modules and a 0.33-pu output transformer. The output voltage is high-quality multistep PWM with low dv/dt. Further, the approach also guarantees balanced operation and 100% utilization of each three-phase inverter module over the entire speed range. These features enable the proposed topology to be suitable for powering constant-torque, as well as variable-torque type loads. Clean power utility interface of the proposed inverter system can be achieved via an 18-pulse input transformer. Analysis, simulation and experimental results are shown to validate the concepts.


title: An experiment in using IMUNES and Conpot to emulate honeypot control networks

abstract: Honeypots are used as a security measure both to divert the attention of a potential attackers intentions and to reveal the attacker since the only reason someone would interact with honeypots is if they are looking for a vulnerable target. Honeypots emulate only a part of the machine they are supposed to represent and contain no valuable data. ICS (Industrial Control System) is a term that is used for a system that monitors industrial plants, distributed control systems or other systems that mostly contain PLCs (Programmable Logic Controllers). Conpot is an open source honeypot that emulates PLC devices so it can be used in ICSs. However, Conpot can not emulate complex honeypot networks. The aim of this project is to make a tool that can be used to design a honeypot network which emulates an ICS. A network designed with that tool will be simulated as a part of this project and the data collected during the simulation will be analyzed.


title: A new power divider architecture for suspended strip line

abstract: In this paper the authors present a novel power divider architecture intended to be used with the Suspended Strip Line (SSL) transmission line. The new antenna array systems, that are designed to work in the millimetre wave ranges, are suffering from the losses in their beam forming networks. In order to mitigate the losses one can use special types of transmission lines. One of them is the so-called Suspended Strip Line (SSL). In this paper the authors present a novel RF power divider intended to be used with the SSL. It uses the properties of the suspended stripline geometry in order to give the designer an increased number of degrees of freedom to achieve the desired power division ratio and a good input match. The advantages of the proposed geometry are compactness, good power handling, no need of any soldering or introducing lumped elements and the sole use of standard 50Ω lines.


title: Self-Interference Channel Estimation for Full-Duplex MIMO-OFDM Systems with Phase Noise

abstract: In full-duplex (FD) multi-input multi-output (MIMO)- orthogonal frequency division multiplexing (OFDM) systems, the performance of self-interference (SI) cancellation suffers severe degradations due to the presence of stochastic phase noise (PN), which introduces certain estimation error for the SI channel state information (CSI). Following a new design principle to minimize the residual SI power, we look for an optimal linear SI channel estimator for the FD MIMO- OFDM systems with PN. After formulating the residual SI power, a closed-form solution of the optimal linear SI channel estimator is derived by solving a series of quadratic programming problems. Such an optimal SI channel estimator requires information including the covariances between different taps of the SI channel, the relative PN bandwidth, the noise power, and the power of the transmitted signal. Simulation and numerical results show that our proposed scheme outperforms the conventional least square (LS) or minimum mean square error (MMSE) channel estimators, in terms of achieving higher digital SI cancellation ability under various scenarios.


title: Curiosity Driven Exploration of Learned Disentangled Goal Spaces

abstract: Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions. They have been applied successfully to real world robots to discover repertoires of policies producing a wide diversity of effects. Often these algorithms relied on engineered goal spaces but it was recently shown that one can use deep representation learning algorithms to learn an adequate goal space in simple environments. However, in the case of more complex environments containing multiple objects or distractors, an efficient exploration requires that the structure of the goal space reflects the one of the environment. In this paper we show that using a disentangled goal space leads to better exploration performances than an entangled goal space. We further show that when the representation is disentangled, one can leverage it by sampling goals that maximize learning progress in a modular manner. Finally, we show that the measure of learning progress, used to drive curiosity-driven exploration, can be used simultaneously to discover abstract independently controllable features of the environment.


Adapting Young ’ s Internet Addiction Test for Massively Multiplayer Online Role-Playing Game Users : A Factor Analysis


title: Ddelta: A deduplication-inspired fast delta compression approach

abstract: Abstract Delta compression is an efficient data reduction approach to removing redundancy among similar data chunks and files in storage systems. One of the main challenges facing delta compression is its low encoding speed, a worsening problem in face of the steadily increasing storage and network bandwidth and speed. In this paper, we present Ddelta, a deduplication-inspired fast delta compression scheme that effectively leverages the simplicity and efficiency of data deduplication techniques to improve delta encoding/decoding performance. The basic idea behind Ddelta is to (1) accelerate the delta encoding and decoding processes by a novel approach of combining Gear-based chunking and Spooky-based fingerprinting for fast identification of duplicate strings for delta calculation, and (2) exploit content locality of redundant data to detect more duplicates by greedily scanning the areas immediately adjacent to already detected duplicate chunks/strings. Our experimental evaluation of a Ddelta prototype based on real-world datasets shows that Ddelta achieves an encoding speedup of 2.5×–8× and a decoding speedup of 2×–20× over the classic delta-compression approaches Xdelta and Zdelta while achieving a comparable level of compression ratio.


title: Bandit Algorithms for Tree Search

abstract: Bandit based methods for tree search have recently gained popularity when applied to huge trees, e.g. in the game of go [6]. Their efficient exploration of the tree enables to return rapidly a good value, and improve precision if more time is provided. The UCT algorithm [8], a tree search method based on Upper Confidence Bounds (UCB) [2], is believed to adapt locally to the effective smoothness of the tree. However, we show that UCT is "over-optimistic" in some sense, leading to a worst-case regret that may be very poor. We propose alternative bandit algorithms for tree search. First, a modification of UCT using a confidence sequence that scales exponentially in the horizon depth is analyzed. We then consider Flat-UCB performed on the leaves and provide a finite regret bound with high probability. Then, we introduce and analyze a Bandit Algorithm for Smooth Trees (BAST) which takes into account actual smoothness of the rewards for performing efficient "cuts" of sub-optimal branches with high confidence. Finally, we present an incremental tree expansion which applies when the full tree is too big (possibly infinite) to be entirely represented and show that with high probability, only the optimal branches are indefinitely developed. We illustrate these methods on a global optimization problem of a continuous function, given noisy values.


title: Latency-aware elastic scaling for distributed data stream processing systems

abstract: Elastic scaling allows a data stream processing system to react to a dynamically changing query or event workload by automatically scaling in or out. Thereby, both unpredictable load peaks as well as underload situations can be handled. However, each scaling decision comes with a latency penalty due to the required operator movements. Therefore, in practice an elastic system might be able to improve the system utilization, however it is not able to provide latency guarantees defined by a service level agreement (SLA). In this paper we introduce an elastic scaling system, which optimizes the utilization under certain latency constraints defined by a SLA. Specifically, we present a model, which estimates the latency spike created by a set of operator movements. We use this model to built a latency-aware elastic operator placement algorithm, which minimizes the number of latency violations. We show that our solution is able to reduce the 90th percentile of the end to end latency by up to 30% and reduce the number of latency violations by 50%. The achieved system utilization for our approach is comparable to a scaling strategy, which does not use latency as optimization target.


title: How Important Is Deliberate Practice? Reply to Ericsson (2016)

abstract: Ericsson and colleagues’ view that individual differences in expertise can largely be accounted for by accumulated deliberate practice is not supported by the available empirical evidence. Extending earlier work (Macnamara, Hambrick, & Oswald, 2014), we found that deliberate practice accounted for a sizeable amount of variance in sports performance (18%), but it left a much larger amount unexplained. Ericsson’s (2016, this issue) evaluation of our research is undercut by contradictions, omissions, and errors. We agree with Ericsson that future longitudinal research will deepen understanding of expertise, but our goal was to evaluate the importance of deliberate practice based on existing evidence.


title: Human-swarm interaction using spatial gestures

abstract: This paper presents a machine vision based ap- proach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i.e., colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs. I. INTRODUCTION Without the use of teleoperated and hand-held interaction devices, human operators generally face difficulties in select- ing and commanding individual and groups of robots from a relatively large group of spatially distributed robots (i.e., a swarm). However, due to the widespread availability of cost effective digital cameras onboard UGVs and UAVs, it is increasing the attention towards developing uninstrumented methods (i.e., methods that do not use sophisticated hardware devices from the human side) for human-swarm interaction (HSI). In previous work, we focused on learning efficient features incrementally (online) from multi-viewpoint images of multiple gestures that were acquired by a swarm of ground robots (1). In this paper, we present a cascaded supervised machine learning approach to deal with the machine vision problem of selecting 3D spatially-situated robots from a networked swarm based on the recognition of spatial hand gestures. These are a natural, easy recognizable, and device- less way to enable human operators to easily interact with external artifacts such as robots. Inspired by natural human behavior, we propose an ap- proach that combines face engagement and pointing gestures to interact with a swarm of robots: standing in front of a population of robots, by looking at them and pointing at them with spatial gestures, a human operator can designate individual or groups of robots of determined size. Robots cooperate to combine their independent observations of the human's face and gestures to cooperatively determine which robots were addressed (i.e., selected). While state of the art computer vision techniques pro- vide excellent face detection, human skeleton, and gesture recognition in ideal conditions, there are often occlusions,


title: Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

abstract: Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ segmentation. Existing CNN-based segmentation methods mainly focus on looking for increasingly powerful network architectures, but pay less attention to data sampling strategies for training networks more effectively. In this paper, we present a simple but effective sample selection method for training multi-organ segmentation networks. Sample selection exhibits an exploitation-exploration strategy, i.e., exploiting hard samples and exploring less frequently visited samples. Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB). Compared with other sample selection policies, e.g., Upper Confident Bound (UCB), it exploits a range of hard samples rather than being stuck with a small set of very hard ones, which mitigates the influence of annotation errors during training. We apply this new sample selection policy to training a multi-organ segmentation network on a dataset containing 120 abdominal CT scans and show that it boosts segmentation performance significantly.


title: Learning Personalized Preference of Strong and Weak Ties for Social Recommendation

abstract: Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in social relations originated from social sciences, and then propose anovel social recommendation method based on a new Probabilistic Matrix Factorization model that incorporates the distinction of strong and weak ties for improving recommendation performance. The proposed method is capable of simultaneously classifying different types of social ties in a social network w.r.t. optimal recommendation accuracy, and learning a personalized tie type preference for each user in addition to other parameters. We conduct extensive experiments on four real-world datasets by comparing our method with state-of-the-art approaches, and find encouraging results that validate the efficacy of the proposed method in exploiting the personalized preferences of strong and weak ties for social recommendation.


title: Analysis of the Bridged T-Coil Circuit Using the Extra-Element Theorem

abstract: The bridged T-coil (BTC) circuit is often employed to extend the bandwidth of a wideband amplifier beyond the transition frequency fT of the driver device. Although the optimal design solution is well known, extreme algebraic complexity has caused its derivation to remain arcane. This brief presents an analysis of the BTC circuit using the extra element theorem which breaks the overall problem down into a series of simple network analyses and produces the final result as the product of the transfer function without the "extra element" and a multiplicative correction factor. The design is completed by using pole-zero cancellation to reduce the order of the transfer functions


title: DDoS Attacks in Cloud Computing: Issues, Taxonomy, and Future Directions

abstract: Security issues related to the cloud computing are relevant to various stakeholders for an informed cloud adoption decision. Apart from data breaches, the cyber security research community is revisiting the attack space for cloud-specific solutions as these issues affect budget, resource management, and service quality. Distributed Denial of Service (DDoS) attack is one such serious attack in the cloud space. In this paper, we present developments related to DDoS attack mitigation solutions in the cloud. In particular, we present a comprehensive survey with a detailed insight into the characterization, prevention, detection, and mitigation mechanisms of these attacks. Additionally, we present a comprehensive solution taxonomy to classify DDoS attack solutions. We also provide a comprehensive discussion on important metrics to evaluate various solutions. This survey concludes that there is a strong requirement of solutions, which are designed keeping utility computing models in mind. Accurate auto-scaling decisions, multi-layer mitigation, and defense using profound resources in the cloud, are some of the key requirements of the desired solutions. In the end, we provide a definite guideline on effective solution building and detailed solution requirements to help the cyber security research community in designing defense mechanisms. To the best of our knowledge, this work is a novel attempt to identify the need of DDoS mitigation solutions involving multi-level information flow and effective resource management during the attack.


title: Machine learning and computer vision approaches for phenotypic profiling

abstract: With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.


title: A Novel UHF Near-Field RFID Reader Antenna Deploying CSRR Elements

abstract: A novel ultrahigh frequency (UHF) near-field radio-frequency identification (RFID) reader antenna based on the complementary split ring resonator (CSRR) elements is presented in this communication. The antenna consists of a power divider and two arms. The two arms are terminated with two 50- $\Omega $ terminations. First arm is forward arm, which is a microstrip transmission line. The second arm is backward arm, which is loaded with CSRR elements, instigating backward wave propagation. The oppositely directed currents are generated by this configuration to produce strong and uniform magnetic field over the antenna plane for UHF near-field RFID operations. The proposed antenna operates from 0.76 to 0.88 GHz, and a total impedance bandwidth of 120 MHz is obtained. A near-field read range of 100 mm and an interrogation area of 220 mm $\times180$ mm over the antenna plane at a height of 50 mm is reported for this antenna.


title: Can GAN Learn Topological Features of a Graph?

abstract: This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generative adversarial networks (GANs) can successfully capture topological features of any arbitrary graph, and rank edge sets by different stages according to their contribution to topology reconstruction. Moreover, in addition to acting as an indicator of graph reconstruction, we find that these stages can also preserve important topological features in a graph.


Augmenting Voting Interfaces to Improve Accessibility and Performance


title: Human reidentification with transferred metric learning

abstract: Human reidentification is to match persons observed in non-overlapping camera views with visual features for inter-camera tracking. The ambiguity increases with the number of candidates to be distinguished. Simple temporal reasoning can simplify the problem by pruning the candidate set to be matched. Existing approaches adopt a fixed metric for matching all the subjects. Our approach is motivated by the insight that different visual metrics should be optimally learned for different candidate sets. We tackle this problem under a transfer learning framework. Given a large training set, the training samples are selected and reweighted according to their visual similarities with the query sample and its candidate set. A weighted maximum margin metric is online learned and transferred from a generic metric to a candidate-set-specific metric. The whole online reweighting and learning process takes less than two seconds per candidate set. Experiments on the VIPeR dataset and our dataset show that the proposed transferred metric learning significantly outperforms directly matching visual features or using a single generic metric learned from the whole training set.


title: The clinical use of structural MRI in Alzheimer disease

abstract: Structural imaging based on magnetic resonance is an integral part of the clinical assessment of patients with suspected Alzheimer dementia. Prospective data on the natural history of change in structural markers from preclinical to overt stages of Alzheimer disease are radically changing how the disease is conceptualized, and will influence its future diagnosis and treatment. Atrophy of medial temporal structures is now considered to be a valid diagnostic marker at the mild cognitive impairment stage. Structural imaging is also included in diagnostic criteria for the most prevalent non-Alzheimer dementias, reflecting its value in differential diagnosis. In addition, rates of whole-brain and hippocampal atrophy are sensitive markers of neurodegeneration, and are increasingly used as outcome measures in trials of potentially disease-modifying therapies. Large multicenter studies are currently investigating the value of other imaging and nonimaging markers as adjuncts to clinical assessment in diagnosis and monitoring of progression. The utility of structural imaging and other markers will be increased by standardization of acquisition and analysis methods, and by development of robust algorithms for automated assessment.


title: Short-segment fixation without fusion for thoracolumbar burst fractures with neurological deficit can preserve thoracolumbar motion without resulting in post-traumatic disc degeneration: a 10-year follow-up study.

abstract: STUDY DESIGN ::: Prospective consecutive series. ::: ::: ::: OBJECTIVE ::: To evaluate the post-traumatic disc degeneration and range of motion 10 years after short-segment fixation without fusion for thoracolumbar burst fractures with neurological deficit. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Early clinical results of short-segment fixation without fusion for thoracolumbar burst fractures were satisfactory. However, the long-term results have not been reported, and post-traumatic disc degeneration and preservation of thoracolumbar motion have not been elucidated. ::: ::: ::: METHODS ::: Twelve patients who had thoracolumbar burst fractures and associated incomplete neurological deficit, operatively treated within 4 days of admission and had their implants removed within 1 year, were prospectively followed for at least 10 years. Following indirect reduction and pedicle screw fixation, transpedicular intracorporeal hydroxyapatite grafting to the fractured vertebrae was performed. ::: ::: ::: RESULTS ::: Sagittal alignment was improved from a mean preoperative kyphosis of 17° to -2° (lordosis) by operation, but was found to have slightly deteriorated to 2° at the final follow-up observation. With respect to back pain, 8 patients did not report back pain. Three reported occasional minimal pain, and 1 reported moderate pain. None reported severe pain or needed daily dosages of analgesics.Regarding disc degeneration, the shape of the disc adjacent to the fractured vertebra had not changed from the preoperative to the 10-year postoperative magnetic resonance image (MRI). Although signal intensity of the disc had decreased by 1 grade from the preoperative to the 2-year postoperative MRI, the intensity had not changed from the 2-year postoperative MRI to the 10-year postoperative MRI. At the 10-year follow-up, flexion-extension radiographs revealed that a mean range of motion at the disc adjacent to the fractured vertebra was 12º (range; 5-19). ::: ::: ::: CONCLUSION ::: This unprecedented 10-year follow-up study demonstrated that posterior indirect reduction, transpedicular hydroxyapatite grafting, and pedicle screw fixation does not require fusion to a segment, thereby preserves thoracolumbar motion without resulting in post-traumatic disc degeneration. ::: ::: ::: LEVEL OF EVIDENCE ::: 4.


title: To appear in “Large-Scale Kernel Machines”,

abstract: Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.ContributorsLeon Bottou, Yoshua Bengio, Stephane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaelle Loosli, Joaquin Quinonero-Candela, Carl Edward Rasmussen, Gunnar Ratsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Soren Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-TovLeon Bottou is a Research Scientist at NEC Labs America. Olivier Chapelle is with Yahoo! Research. He is editor of Semi-Supervised Learning (MIT Press, 2006). Dennis DeCoste is with Microsoft Research. Jason Weston is a Research Scientist at NEC Labs America.


title: Short-circuit evaluation and overcurrent protection for SiC power MOSFETs

abstract: In this paper, the short-circuit (SC) performance of two different SiC MOSFETs is experimentally investigated for different input voltages, biasing voltages and case temperatures. The measurement results are compared to simulations, and a good agreement is achieved. For fault handling, two different overcurrent protection (OCP) circuits are designed and applied to the SiC MOSFETs. The desaturation method is successfully tested with a hardware solution substituting the blanking time delay. The second method is based on sensing the voltage drop across the parasitic inductance at the source pin. The experimental and simulation results show that both OCP methods have the capability to detect a short circuit condition in the SiC MOSFET within safe SC time avoiding device failure.


title: Annotating photo collections by label propagation according to multiple similarity cues

abstract: This paper considers the emerging problem of annotating personal photo collections that are taken by digital cameras and may have been subsequently organized by customers. Unlike the images from the web searching engine or commercial image banks (e.g. the Corel database), the photos in the same personal collection are related to each other in time, location, and content. Advanced technologies can record the GPS coordinates for each photo, and thus provide a richer source of context to model and enforce the correlation between the photos in the same collection. Recognizing the well-known limitations ("semantic gap") of visual recognition algorithms, we exploit the correlation between the photos to enhance the annotation performance. In our approach, high-confidence annotation labels are first obtained for certain photos and then propagated to the remaining photos in the same collection, according to time, location, and visual proximity (or similarity). A novel generative probabilistic model is employed, which outperforms the pervious linear propagation scheme. Experimental results have shown the advantages of the proposed annotation scheme.


title: Spiking Optical Flow for Event-Based Sensors Using IBM's TrueNorth Neurosynaptic System

abstract: This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on TrueNorth with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity. The spiking neural network is a variant of the Barlow Levick method for optical flow estimation. The system is evaluated on two recordings for which ground truth motion is available, and achieves an Average Endpoint Error of 11% at an estimated power budget of under 80mW for the sensor and computation.


title: Context-Aware Elevator Scheduling

abstract: Research on context-aware systems is usually user- centric and thus focussed on the context of a specific user to serve his or her needs in an optimized way. In this paper, we want to apply core concepts developed in research on context-awareness in a system-centric way, namely to elevator systems. We show with three different examples that the performance of an elevator system can be significantly improved if the elevator control has access to contextual knowledge. The first example demonstrates that an appropriate elevator group scheduling algorithm can be chosen and adjusted to the current and near-future passenger traffic situation. The second example incorporates a second predominant technology used in many ubiquitous computing environments, namely RFID tags, to predict the passengers destination. The third example shows that adaptation to a specific emergency situation can further increase the transportation capacity of an elevator system which is ultimately important to save as many lifes as possible in an emergency. Evidence for the increased efficiency is provided by using a comprehensive simulation of a diverse set of scheduling algorithms and scenarios.


title: Effect of Wideband Beam Squint on Codebook Design in Phased-Array Wireless Systems

abstract: Analog beamforming with phased arrays is a promising technique for 5G wireless communication at millimeter wave frequencies. Using a discrete codebook consisting of multiple analog beams, each beam focuses on a certain range of angles of arrival or departure and corresponds to a set of fixed phase shifts across frequency due to practical hardware considerations. However, for sufficiently large bandwidth, the gain provided by the phased array is actually frequency dependent, which is an effect called beam squint, and this effect occurs even if the radiation pattern of the antenna elements is frequency independent. This paper examines the nature of beam squint for a uniform linear array (ULA) and analyzes its impact on codebook design as a function of the number of antennas and system bandwidth normalized by the carrier frequency. The criterion for codebook design is to guarantee that each beam's minimum gain for a range of angles and for all frequencies in the wideband system exceeds a target threshold, for example 3 dB below the array's maximum gain. Analysis and numerical examples suggest that a denser codebook is required to compensate for beam squint. For example, 54% more beams are needed compared to a codebook design that ignores beam squint for a ULA with 32 antennas operating at a carrier frequency of 73 GHz and bandwidth of 2.5 GHz. Furthermore, beam squint with this design criterion limits the bandwidth or the number of antennas of the array if the other one is fixed.


title: Graphene: Its Fundamentals to Future Applications

abstract: Currently, graphene is a topic of very active research fields from science to potential applications. For various RF circuit applications, including low-noise amplifiers, the unique ambipolar nature of graphene field-effect-transistors can be utilized for high-performance frequency multipliers, mixers, and high-speed radiometers. Potential integration of graphene on silicon substrates with CMOS compatibility would also benefit future RF systems. The future success of the RF circuit applications depends on vertical and lateral scaling of graphene MOSFETs to minimize parasitics and improve gate modulation efficiency in the channel with zero or a small bandgap. In this paper, we highlight recent progress in graphene materials, devices, and circuits for RF applications. For passive RF applications, we show its transparent electromagnetic shielding in Ku-band and transparent antennas, where its success depends on the quality of materials. We also attempt to discuss future applications and challenges of graphene.


title: Pre-implementation attitudes and organizational readiness for implementing an Enterprise Resource Planning system

abstract: Abstract Implementing an Enterprise Resource Planning (ERP) system is expensive and time consuming. A substantial cost is associated with pre-implementation involvement and training designed to encourage acceptance and effective implementation of the system. The results of this study indicate that, contrary to conventional wisdom, extensive organizational investments in shaping pre-implementation attitudes do not always achieve the desired effects. Despite extensive time, money and effort, length of time with the firm and position had a greater impact on attitudes toward ERP capabilities, value, acceptance and timing than high levels of pre-implementation involvement.


title: A Differentially-Driven Dual-Polarized Magneto-Electric Dipole Antenna

abstract: A novel differentially-driven dual-polarized antenna is proposed in this communication. It is a magneto-electric dipole antenna whose gain and beamwidth keep constant along frequency within the operation bandwidth. If the antenna is ideally symmetrical, its differential port-to-port isolation is theoretically infinite. Due to the differentially-driven scheme, its cross-polarization level can be very low. Measurement shows that the proposed antenna achieves a wide impedance bandwidth of 68% (0.95 to 1.92 GHz) for differential reflection coefficients less than -10 dB and high differential port-to-port isolation of better than 36 dB within the bandwidth. The 3-dB-gain bandwidth of the proposed antenna is 62% (1.09 to 2.08 GHz), and the radiation pattern across it is stable and unidirectional. The broadside gain within the 3-dB-gain bandwidth ranges from 6.6 to 9.6 dBi. The cross-polarization level is lower than -23 dB across the 3-dB-gain bandwidth. The proposed antenna is the first differentially-driven dual-polarized magneto-electric dipole antenna. A feeding structure is specially designed to fit the differentially-driven scheme, and also to achieve wide impedance and gain bandwidths.


title: Humanoid robot HRP-4 - Humanoid robotics platform with lightweight and slim body

abstract: This paper presents the development of humanoid robotics platform - 4 (or HRP-4 for short). The high-density implementation used for HRP-4C, the cybernetic human developed by AIST, is also applied to HRP-4. HRP-4 has a total of 34 degrees of freedom, including 7 degrees of freedom for each arm to facilitate object handling and has a slim, lightweight body with a height of 151 [cm] and weight 39 [kg]. The software platform OpenRTM-aist and a Linux kernel with the RT-Preempt patch are used in the HRP-4 software system. Design concepts and mechanisms are presented with its basic specification in this paper.


title: [Internet- and computer game addiction: phenomenology, comorbidity, etiology, diagnostics and therapeutic implications for the addictives and their relatives].

abstract: OBJECTIVE ::: Excessive and addictive internet use and computer game playing is reported as an increasing problem in outpatient care. The aim of this paper is to give an overview about the current scientific discussion of the overuse and addiction of internet and computer game playing. ::: ::: ::: METHODS ::: Pubmed was used for a systematic literature research considering original papers and review articles dealing with Internet/computer game addiction. ::: ::: ::: RESULTS ::: Recent epidemiological data from Germany suggest that 1.5-3.5 % of adolescent computer and internet users show signs of an overuse or addictive use of computer and video games. Moreover there is evidence that the disorder is associated with higher rates of depression, anxiety, as well as lower achievements e. g. at school. Although the nosological assignment still remains unclear there is some evidence from neurobiological data that the disorder can be conceptualized as behavioral addiction. As treatment strategy CBT-techniques have been proposed, but there is still a lack of controlled clinical trials concerning their efficacy. ::: ::: ::: CONCLUSIONS ::: Since the addicted persons often show little motivation for a behavioural change we consider it a promising approach to treat and train their relatives with the aim of increasing the motivation for a behavioural change of the addicted person.


title: Estimating Human Drug Oral Absorption Kinetics From Caco-2 Permeability Using an Absorption-Disposition Model : Model Development and Evaluation and Derivation of Analytical Solutions for ka and Fa

abstract: Intestinal transcellular permeability (P(m)), measured across cell lines such as Caco-2 cells in vitro, is often used for assessing oral drug absorption potential in humans. However, the quantitative link between in vitro permeability and apparent in vivo absorption kinetics, based on drug appearance in plasma, is poorly understood. In the current study, a novel absorption-disposition kinetic model that links traditional pharmacokinetic and mass transfer models was developed. Analytical solutions of k(a) and F(a) were deduced, and using Caco-2 permeability, F(a) in humans was predicted for 51 structurally diverse compounds. Predicted F(a) values were similar to and correlated highly with their corresponding experimental values with an average error of 1.88 +/- 1.06% (-17 to 22%) and r2 = 0.934. Simulated concentration profiles for 17 of 18 drugs corresponded to observed plasma concentration profiles in healthy volunteers. The equilibrium solution for k(a) (k(a,eq)) was found to be a key determinant of F(a), whereas under sink conditions, k(a) is likely to be a determinant of plasma concentration kinetics. The current version of the model offers a quantitative approach for predicting human oral absorption kinetics from in vitro permeability. It also establishes, for the first time, a quantitative link between P(m) and k(a) and between k(a,eq) and F(a). This will facilitate better in vitro or in situ-in vivo correlations since it establishes a basis for incorporating permeability coefficients from the various experimental formats based on drug loss or appearance that are commonly used in the laboratory for permeability determination.


title: Multi-domain evaluation framework for named entity recognition tools

abstract: Abstract Extracting structured information from unstructured text is important for the qualitative data analysis. Leveraging NLP techniques for qualitative data analysis will effectively accelerate the annotation process, allow for large-scale analysis and provide more insights into the text to improve the performance. The first step for gaining insights from the text is Named Entity Recognition (NER). A significant challenge that directly impacts the performance of the NER process is the domain diversity in qualitative data. The represented text varies according to its domain in many aspects including taxonomies, length, formality and format. In this paper we discuss and analyse the performance of state-of-the-art tools across domains to elaborate their robustness and reliability. In order to do that, we developed a standard, expandable and flexible framework to analyse and test tools performance using corpora representing text across various domains. We performed extensive analysis and comparison of tools across various domains and from various perspectives. The resulting comparison and analysis are of significant importance for providing a holistic illustration of the state-of-the-art tools.


title: Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis

abstract: We present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DefIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations.


title: Technologies for 3D mesh compression: A survey

abstract: Three-dimensional (3D) meshes have been widely used in graphic applications for the representation of 3D objects. They often require a huge amount of data for storage and/or transmission in the raw data format. Since most applications demand compact storage, fast transmission, and efficient processing of 3D meshes, many algorithms have been proposed to compress 3D meshes efficiently since early 1990s. In this survey paper, we examine 3D mesh compression technologies developed over the last decade, with the main focus on triangular mesh compression technologies. In this effort, we classify various algorithms into classes, describe main ideas behind each class, and compare the advantages and shortcomings of the algorithms in each class. Finally, we address some trends in the 3D mesh compression technology development.


title: A direct adaptive method for faster backpropagation learning: the RPROP algorithm

abstract: A learning algorithm for multilayer feedforward networks, RPROP (resilient propagation), is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behavior of the error function. Contrary to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforeseeable influence of the size of the derivative, but only dependent on the temporal behavior of its sign. This leads to an efficient and transparent adaptation process. The capabilities of RPROP are shown in comparison to other adaptive techniques. >


title: On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders

abstract: Latent fingerprint identification is of critical importance in criminal investigation. FBI’s Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated latent fingerprint identification is limited due to imprecise automated feature (minutiae) extraction, specifically due to noisy ridge pattern and presence of background noise. In this paper, we propose a novel descriptor based minutiae detection algorithm for latent fingerprints. Minutia and non-minutia descriptors are learnt from a large number of tenprint fingerprint patches using stacked denoising sparse autoencoders. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia patch. Experiments performed on the NIST SD-27 database shows promising results on latent fingerprint matching.


title: A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation

abstract: In this paper, a fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park. The model is trained using a genetic algorithm-based learning scheme. The training set includes wind speed and direction data, measured at neighboring sites up to 30 km away from the wind turbine clusters. Extensive simulation results are shown for two application cases, providing wind speed forecasts from 30 min to 2 h ahead. It is demonstrated that the suggested model achieves an adequate understanding of the problem while it exhibits significant improvement compared to the persistent method.


title: Vulnerability Extrapolation: Assisted Discovery of Vulnerabilities Using Machine Learning

abstract: Rigorous identification of vulnerabilities in program code is a key to implementing and operating secure systems. Unfortunately, only some types of vulnerabilities can be detected automatically. While techniques from software testing can accelerate the search for security flaws, in the general case discovery of vulnerabilities is a tedious process that requires significant expertise and time. In this paper, we propose a method for assisted discovery of vulnerabilities in source code. Our method proceeds by embedding code in a vector space and automatically determining API usage patterns using machine learning. Starting from a known vulnerability, these patterns can be exploited to guide the auditing of code and to identify potentially vulnerable code with similar characteristics--a process we refer to as vulnerability extrapolation. We empirically demonstrate the capabilities of our method in different experiments. In a case study with the library FFmpeg, we are able to narrowthe search for interesting code from 6,778 to 20 functions and discover two security flaws, one being a known flaw and the other constituting a zero-day vulnerability.