title: I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

abstract: As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-scale real-world data from Snapchat as an example, we develop \textit{ClusChurn}, a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that proper user clustering can help understand and predict user churn. Therefore, \textit{ClusChurn} firstly groups new users into interpretable typical clusters, based on their activities on the platform and ego-network structures. Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types. \textit{ClusChurn} is also able to predict user types, which enables rapid reactions to different types of user churn. Extensive data analysis and experiments show that \textit{ClusChurn} provides valuable insight into user behaviors, and achieves state-of-the-art churn prediction performance. The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. It is also general enough to be readily adopted by any online systems with user behavior data.


title: Impacts of sleep deprivation on vigilance, fatigue, and performance during simulated train driving

abstract: Drowsiness is one of the main factors that contribute to the occurrence of accidents, particularly in the transportation sector. While the effects of sleep deprivation on cognitive functions have been reported, the exact relationships remain a critical issue. This study aimed at quantifying the effects of extreme sleep deprivation on vigilance, fatigue, and performance during simulated train driving. A total of 12 participants were asked to drive a train simulator continuously for 4 hours, either in a sleep deprived condition (2-hr of sleep) or normal (8-hr of sleep) condition. Dependent variables obtained during the task included Psychomotor Vigilance Task (PVT) parameters, degree of fatigue (assessed via Visual Analogue Scale/VAS) and sleepiness (reported using Karolinska Sleepiness Scale/KSS), and driving performance (the number of speed limit violations). Findings from this study demonstrated substantial decrements in vigilance in the sleep-deprived condition. This condition also resulted in 75% increase in speed violation and a two-fold increase in the degree of fatigue and sleepiness. Extreme sleep deprivation was clearly associated with substantially poorer response. The exact effects, however, were dependent upon the types of responses.


title: Reinforcement Learning Neural Turing Machines - Revised

abstract: The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them. ::: The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete. ::: We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.


LOGICS FOR RESOURCE-BOUNDED AGENTS


title: Mini-Gunrock: A Lightweight Graph Analytics Framework on the GPU

abstract: Existing GPU graph analytics frameworks are typically built from specialized, bottom-up implementations of graph operators that are customized to graph computation. In this work we describe Mini-Gunrock, a lightweight graph analytics framework on the GPU. Unlike existing frameworks, Mini-Gunrock is built from graph operators implemented with generic transform-based data-parallel primitives. Using this method to bridge the gap between programmability and high performance for GPU graph analytics, we demonstrate operator performance on scale-free graphs with an average 1.5x speedup compared to Gunrock's corresponding operator performance. Mini-Gunrock's graph operators, optimizations, and applications code have 10x smaller code size and comparable overall performance vs. Gunrock.


title: Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors

abstract: In an effort toward standardization, this paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior. The results indicate that performance of these five commonly used algorithms vary dramatically, even in the case of a simple stimulus-evoked task using a single, common threshold value. The important contributions of this paper are: evaluation and comparison of performance of five algorithms to classify specific oculomotor behavior; introduction and comparison of new standardized scores to provide more reliable classification performance; logic for a reasonable threshold-value selection for any eye-movement classification algorithm based on the standardized scores; and logic for establishing a criterion-based baseline for performance comparison between any eye-movement classification algorithms. Proposed techniques enable efficient and objective clinical applications providing means to assure meaningful automated eye-movement classification.


title: Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach

abstract: Cloud Computing is a model of service delivery and access where dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a new horizon of opportunity for enterprises. It introduces new operating and business models that allow customers to pay for the resources they effectively use, instead of making heavy upfront investments. The biggest challenge in Cloud Computing is the lack of a de facto standard or single architectural method, which can meet the requirements of an enterprise cloud approach. In this paper, we explore the architectural features of Cloud Computing and classify them according to the requirements of end-users, enterprises that use the cloud as a platform, and cloud providers themselves. We show that several architectural features will play a major role in the adoption of the Cloud Computing paradigm as a mainstream commodity in the enterprise world. This paper also provides key guidelines to software architects and Cloud Computing application developers for creating future architectures.


title: Effects of augmented reality-based multidimensional concept maps on students’ learning achievement, motivation and acceptance

abstract: In this study, an augmented reality-based multidimensional concept map (ARMCM) learning system is proposed for conducting mobile learning activities. The subjects consisted of 65 students with an average age of 11 years in an elementary school in Taiwan. They were divided into an ARMCM group and a multidimensional concept map (MCM) group. An experiment was conducted to evaluate the effectiveness of the proposed approach. The experimental results suggested that students in the ARMCM group performed significantly better than those in the MCM group. Moreover, this study also found that the students using the ARMCM learning approach showed significantly higher motivation than those using the MCM learning approach, because ARMCM learning is able to simulate the complex knowledge that they needed to learn. ARMCM learning was easier to understand and easy to use, as it could simplify the content of the learning knowledge. Pedagogical implications, conclusion, and some suggestions based on this study are provided.


L-dopa slows the progression of familial parkinsonism


title: On the use of MOS varactors in RF VCOs

abstract: This paper presents two 1.8 GHz CMOS voltage-controlled oscillators (VCOs), tuned by an inversion-mode MOS varactor and an accumulation-mode MOS varactor, respectively. Both VCOs show a lower power consumption and a lower phase noise than a reference VCO tuned by a more commonly used diode varactor. The best overall performance is displayed by the accumulation-mode MOS varactor VCO. The VCOs were implemented in a standard 0.6 /spl mu/m CMOS process.


title: Software transformations to improve malware detection

abstract: Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.


title: Control and Command Systems Concepts from Early Work on a Mars Rover

abstract: We recover and develop some robotic systems concepts (on the light of present systems tools) that were originated for an intended Mars Rover in the sixties of the last century at the Instrumentation Laboratory of MIT, where one of the authors was involved. ::: ::: The basic concepts came from the specifications for a type of generalized robot inspired in the structure of the vertebrate nervous systems, where the decision system was based in the structure and function of the Reticular Formation (RF). ::: ::: The vertebrate RF is supposed to commit the whole organism to one among various modes of behavior, so taking the decisions about the present overall task. That is, it is a kind of control and command system. ::: ::: In this concepts updating, the basic idea is that the RF comprises a set of computing units such that each computing module receives information only from a reduced part of the overall, little processed sensory inputs. Each computing unit is capable of both general diagnostics about overall input situations and of specialized diagnostics according to the values of a concrete subset of the input lines. ::: ::: Slave systems to this command and control computer, there are the sensors, the representations of external environment, structures for modeling and planning and finally, the effectors acting in the external world.


title: Scaling up Dynamic Time Warping to Massive Datasets

abstract: There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a piecewise linear representation. We demonstrate that our approach allows us to outperform DTW by one to three orders of magnitude. We experimentally evaluate our approach on medical, astronomical and sign language data.


title: Team Delft's Robot Winner of the Amazon Picking Challenge 2016

abstract: This paper describes Team Delft’s robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft’s robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.


Speech perception by humans and machines


title: Deadlock-Free Message Routing in Multiprocessor Interconnection Networks

abstract: A deadlock-free routing algorithm can be generated for arbitrary interconnection networks using the concept of virtual channels. A necessary and sufficient condition for deadlock-free routing is the absence of cycles in a channel dependency graph. Given an arbitrary network and a routing function, the cycles of the channel dependency graph can be removed by splitting physical channels into groups of virtual channels. This method is used to develop deadlock-free routing algorithms for k-ary n-cubes, for cube-connected cycles, and for shuffle-exchange networks.


title: Head motion anticipation for virtual-environment applications using kinematics and EMG energy

abstract: Real-time human-computer interaction plays an important role in virtual-environment (VE) applications. Such interaction can be improved by detecting and reacting to the user's head motion. Today's VE systems use head-mounted inertial sensors to update and spatially stabilize the image displayed to a user through a head-mounted display. Since motion can only be detected after it has already occurred, latencies in the stabilization scheme can only be reduced but never eliminated. Such latencies slow down manual control, cause inaccuracies in matching real and virtual objects through a half-transparent display, and reduce the sense of presence. This paper presents novel methods for reducing VE latencies by anticipating future head motion based on electromyographic (EMG) signals originating from the major neck muscles and head kinematics; it also reports results for anticipation of 17.5 and 35 ms. Features extracted from the EMG signals are used to train a neural network in mapping EMG data, given present head kinematics, into future head motion. The trained network is then used in real time for head-motion anticipation, which gives the VE system the time advantage necessary to compensate for the inherent latencies. The main contribution of this work is the use of EMG energy and bounded head acceleration as the key input/output information, which results in improved performance compared to the previous work


title: OPC-free and minimally irregular IC design style

abstract: Advancements in IC manufacturing technologies allow for building very large devices with billions of transistors and with complex interactions between them encapsulated in a huge number of design rules. To ease designers' efforts in dealing with electrical and manufacturing problems, regular layout style seems to be a viable option. In this paper we analyze regular layouts in an IC manufacturability context and define their desired properties. We introduce the OPC-free IC design methodology and study properties of cells designed for this layout style that have various degrees of regularity.


DNA Sequence Compression using ERGC and NRGC for Better Compression


title: Swing-up control of a triple pendulum on a cart with experimental validation

abstract: The swing-up control of a triple pendulum on a cart is presented, where the controller is based on a two-degrees-of-freedom scheme consisting of a nonlinear feedforward controller and an optimal feedback controller. The point-to-point transition task is treated as a nonlinear two-point boundary value problem with free parameters resulting from the suitably projected input-output dynamics. The main focus of the paper is on the experimental realization of the triple pendulum swing-up maneuver.


Design of Blockchain-Based Apps Using Familiar Software Patterns to Address Interoperability Challenges in Healthcare


title: Safe Driving Using Mobile Phones

abstract: As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver's overall awareness to maximize safety.


title: Faraday rotation in silicon waveguides

abstract: We investigate the possibility of using silicon waveguides as Faraday rotators for optical isolators. We employ multimode waveguides with a square cross-section. This approach offers the opportunity to introduce integrated nonreciprocal components into the silicon-on-insulator platform without complication of an additional magneto-optical material.


title: Saturated feedback control for an automated parallel parking assist system

abstract: This paper considers the parallel parking problem of automatic front-wheel steering vehicles. The problem of stabilizing the vehicle at desired position and orientation is seen as an extension of the tracking problem. A saturated control is proposed which achieves quick steering of the system near the desired position of the parking spot with desired orientation and can be successfully used in solving parking problems. In addition, in order to obtain larger area of the starting positions of the vehicle with respect to the parking spot for the first reverse maneuver of the parallel parking, an approach of using saturated control with two different levels of saturation is proposed. The vehicle can be automatically parked by using one or multiple maneuvers, depending on the size of the parking spot. Simulation results are presented to confirm the effectiveness of the proposed control schemes.


title: A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization

abstract: Habitat selection and compensation for Doppler effect are incorporated into algorithm.Algorithm possesses the quantum search operator and mechanical search operator.Self-adaptive local search is proposed.Algorithm shows significant performance in comparison with more than 20 methods. A novel bat algorithm (NBA) is proposed for optimization in this paper, which focuses on further mimicking the bats' behaviors and improving bat algorithm (BA) in view of biology. The proposed algorithm incorporates the bats' habitat selection and their self-adaptive compensation for Doppler effect in echoes into the basic BA. The bats' habitat selection is modeled as the selection between their quantum behaviors and mechanical behaviors. Having considered the bats' self-adaptive compensation for Doppler effect in echoes and the individual's difference in the compensation rate, the echolocation characteristics of bats can be further simulated in NBA. A self-adaptive local search strategy is also embedded into NBA. Simulations and comparisons based on twenty benchmark problems and four real-world engineering designs demonstrate the effectiveness, efficiency and stability of NBA compared with the basic BA and some well-known algorithms, and suggest that to improve algorithm based on biological basis should be very efficient. Further research topics are also discussed.


title: Periodic Leaky-Wave Antenna for Millimeter Wave Applications Based on Substrate Integrated Waveguide

abstract: Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.


title: Neural Machine Translation of Indian Languages

abstract: Neural Machine Translation (NMT) is a new technique for machine translation that has led to remarkable improvements compared to rule-based and statistical machine translation (SMT) techniques, by overcoming many of the weaknesses in the conventional techniques. We study and apply NMT techniques to create a system with multiple models which we then apply for six Indian language pairs. We compare the performances of our NMT models with our system using automatic evaluation metrics such as UNK Count, METEOR, F-Measure, and BLEU. We find that NMT techniques are very effective for machine translations of Indian language pairs. We then demonstrate that we can achieve good accuracy even using a shallow network; on comparing the performance of Google Translate on our test dataset, our best model outperformed Google Translate by a margin of 17 BLEU points on Urdu-Hindi, 29 BLEU points on Punjabi-Hindi, and 30 BLEU points on Gujarati-Hindi translations.


title: Simple Algorithms for Frequent Item Set Mining

abstract: In this paper I introduce SaM, a split and merge algorithm for frequent item set mining. Its core advantages are its extremely simple data structure and processing scheme, which not only make it quite easy to implement, but also very convenient to execute on external storage, thus rendering it a highly useful method if the transaction database to mine cannot be loaded into main memory. Furthermore, I review RElim (an algorithm I proposed in an earlier paper and improved in the meantime) and discuss different optimization options for both SaM and RElim. Finally, I present experiments comparing SaM and RElim with classical frequent item set mining algorithms (like Apriori, Eclat and FP-growth).


title: NEIL: Extracting Visual Knowledge from Web Data

abstract: We propose NEIL (Never Ending Image Learner), a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. NEIL uses a semi-supervised learning algorithm that jointly discovers common sense relationships (e.g., "Corolla is a kind of/looks similar to Car", "Wheel is a part of Car") and labels instances of the given visual categories. It is an attempt to develop the world's largest visual structured knowledge base with minimum human labeling effort. As of 10th October 2013, NEIL has been continuously running for 2.5 months on 200 core cluster (more than 350K CPU hours) and has an ontology of 1152 object categories, 1034 scene categories and 87 attributes. During this period, NEIL has discovered more than 1700 relationships and has labeled more than 400K visual instances.


title: Modulation of gut microbiota during probiotic-mediated attenuation of metabolic syndrome in high fat diet-fed mice

abstract: Structural disruption of gut microbiota and associated inflammation are considered important etiological factors in high fat diet (HFD)-induced metabolic syndrome (MS). Three candidate probiotic strains, Lactobacillus paracasei CNCM I-4270 (LC), L. rhamnosus I-3690 (LR) and Bifidobacterium animalis subsp. lactis I-2494 (BA), were individually administered to HFD-fed mice (108 cells day−1) for 12 weeks. Each strain attenuated weight gain and macrophage infiltration into epididymal adipose tissue and markedly improved glucose–insulin homeostasis and hepatic steatosis. Weighted UniFrac principal coordinate analysis based on 454 pyrosequencing of fecal bacterial 16S rRNA genes showed that the probiotic strains shifted the overall structure of the HFD-disrupted gut microbiota toward that of lean mice fed a normal (chow) diet. Redundancy analysis revealed that abundances of 83 operational taxonomic units (OTUs) were altered by probiotics. Forty-nine altered OTUs were significantly correlated with one or more host MS parameters and were designated ‘functionally relevant phylotypes’. Thirteen of the 15 functionally relevant OTUs that were negatively correlated with MS phenotypes were promoted, and 26 of the 34 functionally relevant OTUs that were positively correlated with MS were reduced by at least one of the probiotics, but each strain changed a distinct set of functionally relevant OTUs. LC and LR increased cecal acetate but did not affect circulating lipopolysaccharide-binding protein; in contrast, BA did not increase acetate but significantly decreased adipose and hepatic tumor necrosis factor-α gene expression. These results suggest that Lactobacillus and Bifidobacterium differentially attenuate obesity comorbidities in part through strain-specific impacts on MS-associated phylotypes of gut microbiota in mice.


title: Gas Leakage Detection and Smart Alerting and prediction using IoT

abstract: IoT is an expanding network of physical devices that are linked with different types of sensors and with the help of connectivity to the internet, they are able to exchange data. Through IoT, internet has now extended its roots to almost every possible thing present around us and is no more limited to our personal computers and mobile phones. Safety, the elementary concern of any project, has not been left untouched by IoT. Gas Leakages in open or closed areas can prove to be dangerous and lethal. The traditional Gas Leakage Detector Systems though have great precision, fail to acknowledge a few factors in the field of alerting the people about the leakage. Therefore we have used the IoT technology to make a Gas Leakage Detector having Smart Alerting techniques involving calling, sending text message and an e-mail to the concerned authority and an ability to predict hazardous situation so that people could be made aware in advance by performing data analytics on sensor readings.


title: Image Encryption Based on Diffusion and Multiple Chaotic Maps

abstract: In the recent world, security is a prime important issue, and encryption is one of the best alternative way to ensure security. More over, there are many image encryption schemes have been proposed, each one of them has its own strength and weakness. This paper presents a new algorithm for the image encryption/decryption scheme. This paper is devoted to provide a secured image encryption technique using multiple chaotic based circular mapping. In this paper, first, a pair of sub keys is given by using chaotic logistic maps. Second, the image is encrypted using logistic map sub key and in its transformation leads to diffusion process. Third, sub keys are generated by four different chaotic maps. Based on the initial conditions, each map may produce various random numbers from various orbits of the maps. Among those random numbers, a particular number and from a particular orbit are selected as a key for the encryption algorithm. Based on the key, a binary sequence is generated to control the encryption algorithm. The input image of 2-D is transformed into a 1- D array by using two different scanning pattern (raster and Zigzag ) and then divided into various sub blocks. Then the position permutation and value permutation is applied to each binary matrix based on multiple chaos maps. Finally the receiver uses the same sub keys to decrypt the encrypted images. The salient features of the proposed image encryption method are loss-less, good peak signal –to noise ratio (PSNR), Symmetric key encryption, less cross correlation, very large number of secret keys, and key-dependent pixel value replacement.


title: A compact Vivaldi antenna with microstrip feed line for the 2–18GHz ultra-wideband applications

abstract: Design of a microstrip fed double exponentially tapered Vivaldi antenna is presented for the 2–18 GHz ultra-wideband applications. Wideband operation is obtained by integrating a compact ultra-wideband balun with the antenna through an impedance transformer. Rectangular dimension of the feeding network including the balun and the matching section fabricated on a 0.813 mm thick RO4003C substrate is 100.2 mm × 82.75 mm. Measured results show that the overall input reflection loss is below 10 dB over 2–18 GHz. The antenna also maintains good gain characteristics over this band.


title: Machine learning versus ray-tracing to forecast irradiance for an edge-computing SkyImager

abstract: Increasing penetration of photovoltaics (PV) into the electric grid necessitates accurate intra-hour solar irra-diance forecasting. The authors have previously developed a low cost all-sky imaging system at the University of Texas at San Antonio. The SkyImager hardware is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in an all-weather enclosure. The software to process the images and to make minutes-ahead forecasts is written in Python 2.7 and utilizes the open source computer vision package OpenCV. A two-step approach for the forecasts uses optical flow to track low-level cumulus clouds and ray-tracing to predict the location of cloud shadows. This paper proposes to replace the ray-tracing approach which is traditionally known to be an ill-posed inverse problem with a machine learning strategy that utilizes a novel multi-layer perceptron (MLP) to classify cloud-cover in sub-images of the circumsolar region. The developed SkyImager was deployed at the National Renewable Energy Laboratory (NREL) in 2015, where it successfully collected months of all-sky image data. In 2016 a second SkyImager was integrated into a microgrid management system at Joint Base San Antonio. Results are presented to validate the proposed methodology.


title: Crossing Generative Adversarial Networks for Cross-View Person Re-identification

abstract: Abstract Person re-identification (re-id) refers to matching pedestrians across disjoint yet non-overlapping camera views. The most effective way to match these pedestrians undertaking significant visual variations is to seek reliably invariant features that can describe the person of interest faithfully. Most of existing methods are presented in a supervised manner to produce discriminative features by relying on labeled paired images in correspondence. However, annotating pair-wise images is prohibitively expensive in labours, and thus not practical in large-scale networked cameras. Moreover, seeking comparable representations across camera views demands a flexible model to address the complex distributions of images. In this work, we study the co-occurrence statistic patterns between pairs of images, and propose to crossing Generative Adversarial Network (Cross-GAN) for learning a joint distribution for cross-image representations in an unsupervised manner. Given a pair of person images, the proposed model consists of the variational auto-encoder to encode the pair into respective latent variables, a proposed cross-view alignment to reduce the view disparity, and an adversarial layer to seek the joint distribution of latent representations. The learned latent representations are well-aligned to reflect the co-occurrence patterns of paired images. We empirically evaluate the proposed model against challenging datasets, and our results show the importance of joint invariant features in improving matching rates of person re-id with comparison to semi/unsupervised state-of-the-arts.


Music Structure Boundaries Estimation Using Multiple Self-Similarity Matrices as Input Depth of Convolutional Neural Networks


title: A resonant switched capacitor based 4-to-1 bus converter achieving 2180 W/in3 power density and 98.9% peak efficiency

abstract: Hybrid switched-capacitor (SC) converters are known to have the potential to achieve higher efficiency and higher power density than conventional SC and magnetic-based converters, thanks to the efficient utilization of both active and passive components. By comparing the total switch stress and the minimal passive component volume, the cascaded 2:1 resonant SC topology is found to be excellent for applications with large conversion ratio (e.g. data center power delivery). A 36–60 V input, 4:1 fixed ratio intermediate bus converter is built based on this topology. The prototype achieves 98.9% peak efficiency and up to 2180 W/in3 power density, both of which are significantly higher than the state-of-the-art.


title: An information retrieval approach for automatically constructing software libraries

abstract: A technology for automatically assembling large software libraries which promote software reuse by helping the user locate the components closest to her/his needs is described. Software libraries are automatically assembled from a set of unorganized components by using information retrieval techniques. The construction of the library is done in two steps. First, attributes are automatically extracted from natural language documentation by using an indexing scheme based on the notions of lexical affinities and quantity of information. Then a hierarchy for browsing is automatically generated using a clustering technique which draws only on the information provided by the attributes. Due to the free-text indexing scheme, tools following this approach can accept free-style natural language queries. >


title: Protection devices for aircraft electrical power distribution systems: a survey

abstract: The development of all electric aircraft has provided new opportunities in the area of electronic devices and power electronics. One of the most interesting fields is related to the protection devices area and the loads management by means of the Solid State Power Controllers (SSPC). This is mainly due to the noticeable increase of these devices in the architectures of electrical power distribution system used in the new airplanes such as A380 and B787. This paper presents a survey of conventional and future trends of protections in on-board platforms. In addition, a virtual test bench is developed in order to analyze the potential problems that could appear by using SSPC technology in the new onboard platforms.


title: The sense of naturally transcribed antisense RNAs in plants.

abstract: Naturally occurring antisense transcripts are well documented in mammals and prokaryotes but little is known about their existence and effects in plants. Generally, antisense RNAs are believed to control gene expression negatively by annealing to the complementary sequences of the sense transcript. The resulting double-stranded RNAs are thought either to affect RNA stability, transcription and/or translation directly, or to generate a signal for gene silencing and defense against viruses.


title: Inflatable antenna for cubesat: fabrication, deployment and results of experimental tests

abstract: CubeSats and small satellites have potential to provide means to explore space and to perform science in a more affordable way. As the goals for these spacecraft become more ambitious in space exploration, moving from Low Earth Orbit (LEO) to Geostationary Earth Orbit (GEO) or further, the communication systems currently implemented will not be able to support those missions. One of the bottlenecks is the antennas’ size, due to the close relation between antenna gain and dimensions. Current antennas for CubeSats are mostly dipole or patch antennas with limited gain. Deployable (not inflatable) antennas for CubeSats are currently being investigated, but these solutions are affected by the challenge of packaging the whole deployable structure in a small spacecraft. The work that we propose represents the first attempt to develop an inflatable antenna for CubeSats. Inflatable structures and antennas can be packaged efficiently, occupying a small amount of space, and they can provide, once deployed, large dish dimension and correspondent gain. Inflatable antennas have been previously tested in space (Inflatable Antenna Experiment, STS-77). However they have never been developed for small spacecraft such as CubeSats, where the packaging efficiency, the deployment, and the inflation represent a challenge. Previous works developed by the authors described trade-off analysis, preliminary design and radiation model for the antenna. The research presented in this paper is focused specifically on implementation and testing. Details of the antenna’s fabrication and related issues are illustrated as well as the mechanism to fold and deploy the antenna in space. Finally, results of the experimental tests (vacuum chamber and anechoic chamber) are described. Future work in the development of the antenna will include the improvement of the fabrication process and the design of a 3U CubeSat mission to be proposed as a technical demonstration.


title: Improving the IATA delay data coding system for enhanced data analytics

abstract: Aviation delays inconvenience travelers and result in financial losses for stakeholders. Without complex data pre-processing, delay data collected by the existing IATA delay coding system are inadequate to support advanced delay analytics, e.g. large-scale delay propagation tracing in an airline network. Consequently, we developed three new coding schemes aiming at improving the current IATA system. These schemes were tested with specific analysis tasks using simulated delay data and were benchmarked against the IATA system. It was found that a coding scheme with a well-designed reporting style can facilitate automated data analytics and data mining, and an improved grouping of delay codes can minimise potential confusion at the data entry and recording stages.


title: Myomodulation with Injectable Fillers: An Innovative Approach to Addressing Facial Muscle Movement

abstract: Consideration of facial muscle dynamics is underappreciated among clinicians who provide injectable filler treatment. Injectable fillers are customarily used to fill static wrinkles, folds, and localized areas of volume loss, whereas neuromodulators are used to address excessive muscle movement. However, a more comprehensive understanding of the role of muscle function in facial appearance, taking into account biomechanical concepts such as the balance of activity among synergistic and antagonistic muscle groups, is critical to restoring facial appearance to that of a typical youthful individual with facial esthetic treatments. Failure to fully understand the effects of loss of support (due to aging or congenital structural deficiency) on muscle stability and interaction can result in inadequate or inappropriate treatment, producing an unnatural appearance. This article outlines these concepts to provide an innovative framework for an understanding of the role of muscle movement on facial appearance and presents cases that illustrate how modulation of muscle movement with injectable fillers can address structural deficiencies, rebalance abnormal muscle activity, and restore facial appearance. 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: Printed passive matrix addressed electrochromic displays

abstract: Flexible displays are attracting considerable attention as a visual interface for applications such as in electronic papers and paper electronics. Passive or active matrix-addressing of individual ...


title: Video analysis of mouth movement using motion templates for computer-based lip-reading

abstract: This thesis presents a novel lip-reading approach to classifying utterances from video data, without evaluating voice signals. This work addresses two important issues which are • the efficient representation of mouth movement for visual spe


title: Distributed Bayesian Posterior Sampling via Moment Sharing

abstract: We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.


title: Opposition-Based Adaptive Fireworks Algorithm

abstract: A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.


Fully Spectral Partial Shape Matching


title: Forensicloud: An Architecture for Digital Forensic Analysis in the Cloud

abstract: The amount of data that must be processed in current digital forensic examinations continues to rise. Both the volume and diversity of data are obstacles to the timely completion of forensic investigations. Additionally, some law enforcement agencies do not have the resources to handle cases of even moderate size. To address these issues we have developed an architecture for a cloud-based distributed processing platform we have named Forensicloud. This architecture is designed to reduce the time taken to process digital evidence by leveraging the power of a high performance computing platform and by adapting existing tools to operate within this environment. Forensicloud’s Software and Infrastructure as a Service service models allow investigators to use remote virtual environments for investigating digital evidence. These environments allow investigators the ability to use licensed and unlicensed tools that they may not have had access to before and allows some of these tools to be run on computing clusters.


title: Design of a Novel Ultrawideband Digital Receiver for Pulse Ground-Penetrating Radar

abstract: A new ultrawideband-digital-sampling technology is presented. This novel sampling technology is the first to employ both the successive-approximation-register technology and the equivalent-time-sampling technology to improve the bandwidth of the receiver. Accordingly, a new low-cost and compact ultrawideband digital receiver for pulse ground-penetrating radar is designed. The merit of this receiver is that it can convert radio-frequency signal to digital signal without any analog sampling gate or analog-to-digital converter. The equivalent sampling rate of the receiver is up to 33 GHz. The measurements show that this receiver has more than 3-GHz bandwidth and nearly 100% conversion efficiency.


title: Micro-Drone for Wind Vector Estimation and Gas Distribution Mapping

abstract: This paper presents the development and validation of an autonomous gas-sensitive micro-drone, which is capable of estimating the wind vector in real time using only the on-board control unit of the micro-drone and performing gas distribution mapping. Two different sampling approaches are suggested in order to address this problem. On one hand, a predefined trajectory is used to explore the target area with the micro- drone in a real-world gas distribution mapping experiment. As an alternative sampling approach we introduce an adaptive strategy, which suggests next sampling points based on an Artificial Potential Field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas distribution mapping and its use for gas source localization. Index Terms—anemometric sensor, autonomous Micro UAV, chemical sensing, gas distribution modeling, gas source localiza- tion, gas sensors, mobile sensing system, quadrocopter, sensor planning, artificial potential field.


title: Cognitive security: securing the burgeoning landscape of mobile networks

abstract: The rapid proliferation of personal wearable as well as embedded devices point to the emergence of networks of unprecedented size and complexity in the near future. Unfortunately, traditional network security solutions fall short of addressing the unique security requirements of the emerging environment given their general emphasis on administratively managed, preconfigured security context and strong physical security mechanisms. To cope with the security challenges of this emerging environment, novel cognitive-inspired security architectures have been proposed that emphasize dynamic, autonomous trust management. Cognitive security systems take advantage of sensing and computing capabilities of smart devices to analyze raw sensor data and apply machine learning techniques to make security decisions. In this article, we present a canonical representation of cognitive security architectures and examine the practicality of using these architectures to address the security challenges of rapidly growing networks of mobile/embedded autonomous devices including the ability to identify threats simply based on symptoms, without necessarily understanding attack methods. Using authentication as the main focus, we introduce our canonical representation and define various categories of contextual information commonly used by cognitive security architectures to handle authentication requirements, and highlight key advantages and disadvantages of each category. We then examine three grand challenges facing the cognitive security research including the tension between automation and security, the unintended consequences of using machine learning techniques as a basis for making security decisions, and the revocation problem in the context of cognitive security. We conclude by offering some insight into solution approaches to these challenges.


title: Measuring Metrics - A forty year longitudinal cross-validation of citations, downloads, and peer review in Astrophysics

abstract: Citation measures, and newer altmetric measures such as downloads are now commonly used to inform personnel decisions. How well do or can these measures measure or predict the past, current, or future scholarly performance of an individual? Using data from the Smithsonian/NASA Astrophysics Data System we analyze the publication, citation, download, and distinction histories of a cohort of 922 individuals who received a U.S. PhD in astronomy in the period 1972-1976. By examining the same and different measures at the same and different times for the same individuals we are able to show the capabilities and limitations of each measure. Because the distributions are lognormal, measurement uncertainties are multiplicative; we show that in order to state with 95% confidence that one person's citations and downloads are significantly higher than another person's, the log difference in the ratio of counts must be at least 0.3dex, which corresponds to a multiplicative factor of 2.


title: Technique for preprocessing of digital mammogram

abstract: Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. It provides us opportunities to develop algorithms for computer aided detection (CAD). In this paper we have proposed three distinct steps. The initial step involves contrast enhancement by using the contrast limited adaptive histogram equalization (CLAHE) technique. Then define the rectangle to isolate the pectoral muscle from the region of interest (ROI) and finally suppress the pectoral muscle using our proposed modified seeded region growing (SRG) algorithm. The proposed algorithms were extensively applied on all the 322 mammogram images in MIAS database resulting in complete pectoral muscle suppression in most of the images. Our proposed algorithm is compared with other segmentation methods showing superior results in comparison.


title: Zone Divisional Hierarchical Routing Protocol for Wireless Sensor Network

abstract: Clustering prolongs energy resources, improves scalability and preserves communication bandwidth. Clustering is either classified as static and dynamic or as equal and unequal. In cluster based routing protocols that employ multi-hop communication, imbalanced energy consumption among the nodes results in hot-spots. Unequal clustering overcomes hot-spots but requires a high overhead and is prone to connectivity issues. To offer guaranteed connectivity and alleviate the hot-spot problem, a zone divisional hierarchical routing protocol has been proposed in this paper. The network is divided into equal sized static rectangular clusters which are assigned to two zones namely near zone and far zone. The zone facing the base station is known as the near zone and rest of the network space makes up the far zone which is further divided into sub-zones. Dual cluster heads approach for sharing the reception, aggregation and forwarding tasks is proposed. The performance evaluation of the proposed protocol against the existing protocols reveals that the method offers energy-efficient multi-hop communication support, uses negligible overhead, prevents creation of hot-spots, avoids early death of nodes, uses balanced energy consumption across the network and maximizes the network lifetime.


title: Discretization methods of fractional parallel PID controllers

abstract: This work addresses employing direct and indirect discretization methods to obtain a rational discrete approximation of continuous time parallel fractional PID controllers. The different approaches are illustrated by implementing them on an example.


title: A Book Dewarping System by Boundary-Based 3D Surface Reconstruction

abstract: Non-contact imaging devices such as digital cameras and overhead scanners can convert hardcopy books to digital images without cutting them to individual pages. However, the captured images have distinct distortions. A book dewarping system is proposed to remove the perspective and geometric distortions automatically from single images. A book boundary model is extracted, and a 3D book surface is reconstructed. And then the horizontal and vertical metrics of each column are restored from it. Experimental results show the good dewarping and speed performance. Since no additional equipments and no restrictions to specific book layouts or contents are needed, the proposed system is very practical in real applications.


title: Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering

abstract: Monolingual alignment models have been shown to boost the performance of question answering systems by ”bridging the lexical chasm” between questions and answers. The main limitation of these approaches is that they require semistructured training data in the form of question-answer pairs, which is difficult to obtain in specialized domains or lowresource languages. We propose two inexpensive methods for training alignment models solely using free text, by generating artificial question-answer pairs from discourse structures. Our approach is driven by two representations of discourse: a shallow sequential representation, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora from different genres and domains: one from Yahoo! Answers and one from the biology domain, and two types of non-factoid questions: manner and reason. We show that these alignment models trained directly from discourse structures imposed on free text improve performance considerably over an information retrieval baseline and a neural network language model trained on the same data.


title: Knockdown of human AMPK using the CRISPR-Cas9 genome-editing system

abstract: AMP activated protein kinase (AMPK) is a critical energy sensor, regulating signaling networks involved in pathology including metabolic diseases and cancer. This increasingly recognized role of AMPK has prompted tremendous research efforts to develop new pharmacological AMPK activators. To precisely study the role of AMPK, and the specificity and activity of AMPK activators in cellular models, genetic AMPK inactivating tools are required. We report here methods for genetic inactivation of AMPK $\alpha1/ \alpha2$ catalytic subunits in human cell lines by the CRISPR/Cas9 technology, a recent breakthrough technique for genome editing.


title: A Security Benchmark for OpenStack

abstract: The cloud computing paradigm entails a radical change in IT provisioning, which must be understood and correctly applied especially when security requirements are considered. Security requirements do not cover anymore just the application itself, but involve the whole cloud supply chain from the hosting infrastructure to the final applications. This scenario requires, on one side, new security mechanisms protecting the cloud against misbehaviors/malicious attacks and, on the other side, a continuous and adaptive assurance process evaluating the observed cloud security behavior against the expected one. In this paper, we focus on the evaluation of the security assurance of OpenStack, a major open source cloud infrastructure. We first define a security benchmark for OpenStack, inspired by Center for Internet Security (CIS) benchmark for cloud infrastructures. We then present a platform, called Moon Cloud, for cloud security assurance evaluation, showing an application of our benchmark and platform to the in-production OpenStack deployment of the University of Milan.


title: High Speed Whole Body Dynamic Motion Experiment with Real Time Master-Slave Humanoid Robot System

abstract: In this paper, we propose novel methods suitable for online real time whole body master-slave control with real life-sized humanoid robot. We conducted some dynamic whole body master-slave experiment with life-sized humanoid robot, and we achieved speedier and flexible master-slave operation compared to conventional study. Conventionally, master-slave operations with humanoid robots were available with only the upper body of the humanoid robot, and the COM movement was limited to be static. In our previous study, we introduced LIP model based restrictions to ensure the balance stability. In this study, we extend the safety restrictions by introducing foot landing delay prediction and trajectory smoothing method suitable for real robot. We conducted master-slave tennis swing experiment and high kick motion experiment with life-sized humanoid robot “JAXON”, and we evaluated the effectiveness of our proposed methods and system.


title: Adversarial Imitation via Variational Inverse Reinforcement Learning

abstract: We consider a problem of learning a reward and policy from expert examples under unknown dynamics in high-dimensional scenarios. Our proposed method builds on the framework of generative adversarial networks and exploits reward shaping to learn near-optimal rewards and policies. Potential-based reward shaping functions are known to guide the learning agent whereas in this paper we bring forward their benefits in learning near-optimal rewards. Our method simultaneously learns a potential-based reward shaping function through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our method on various high-dimensional complex control tasks. We also evaluate our learned rewards in transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. Our experimentation shows that our proposed method not only learns near-optimal rewards and policies matching expert behavior, but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.


title: A Hybrid Method of Superpixel Segmentation Algorithm and Deep Learning Method in Histopathological Image Segmentation

abstract: Manual analysis of cell morphology in high resolutional histopathological images is a tedious and time consuming task for pathologists. In recent years, computer assisted diagnostic systems have gained considerable importance in order to assist the pathologists for analyzing cellular structures. In this study, the simple linear iterative clustering (SLIC) superpixel segmentation method and convolutional neural network are combined to segment the cellular structures in histopathological images. The proposed study is mainly composed of two stages. First, SLIC superpixel method was used as a pre-segmentation algorithm to perform segmentation of cellular superpixels and non-cellular superpixels. Then convolutional neural networks (CNN) based deep learning algorithm is used to classify those superpixels in order to obtain the final segmentation of the whole image. The overall accuracy of the system at classifying the superpixels was observed to be 0.9876. The analysis and confusion matrix of the study was also presented in experimental studies section.


title: Fast camera self-calibration for synthesizing Free Viewpoint soccer Video

abstract: Recently, non-fixed camera-based free viewpoint sports video synthesis has become very popular. Camera calibration is an indispensable step in free viewpoint video synthesis, and the calibration has to be done frame by frame for a non-fixed camera. Thus, calibration speed is of great significance in real-time application. In this paper, a fast self-calibration method for a non-fixed camera is proposed to estimate the homography matrix between a camera image and a soccer field model. As far as we know, it is the first time to propose constructing feature vectors by analyzing crossing points of field lines in both camera image and field model. Therefore, different from previous methods that evaluate all the possible homography matrices and select the best one, our proposed method only evaluates a small number of homography matrices based on the matching result of the constructed feature vectors. Experimental results show that the proposed method is much faster than other methods with only a slight loss of calibration accuracy that is negligible in final synthesized videos.


title: Quantifying the local resolution of cryo-EM density maps

abstract: We propose a definition of local resolution for three-dimensional electron cryo-microscopy (cryo-EM) density maps that uses local sinusoidal features. Our algorithm has no free parameters and is applicable to other imaging modalities, including tomography. By evaluating the local resolution of single-particle reconstructions and subtomogram averages for four example data sets, we report variable resolution across a 4- to 40-Å range.


Evaluating Improvements to the Shapelet Transform


title: Detecting Masked Faces in the Wild with LLE-CNNs

abstract: Detecting faces with occlusions is a challenging task due to two main reasons: 1) the absence of large datasets of masked faces, and 2) the absence of facial cues from the masked regions. To address these two issues, this paper first introduces a dataset, denoted as MAFA, with 30, 811 Internet images and 35, 806 masked faces. Faces in the dataset have various orientations and occlusion degrees, while at least one part of each face is occluded by mask. Based on this dataset, we further propose LLE-CNNs for masked face detection, which consist of three major modules. The Proposal module first combines two pre-trained CNNs to extract candidate facial regions from the input image and represent them with high dimensional descriptors. After that, the Embedding module is incorporated to turn such descriptors into a similarity-based descriptor by using locally linear embedding (LLE) algorithm and the dictionaries trained on a large pool of synthesized normal faces, masked faces and non-faces. In this manner, many missing facial cues can be largely recovered and the influences of noisy cues introduced by diversified masks can be greatly alleviated. Finally, the Verification module is incorporated to identify candidate facial regions and refine their positions by jointly performing the classification and regression tasks within a unified CNN. Experimental results on the MAFA dataset show that the proposed approach remarkably outperforms 6 state-of-the-arts by at least 15.6%.


title: Historical Roots of Agile Methods: Where Did “Agile Thinking” Come From?

abstract: The appearance of Agile methods has been the most noticeable change to software process thinking in the last fifteen years [16], but in fact many of the “Agile ideas” have been around since 70’s or even before. Many studies and reviews have been conducted about Agile methods which ascribe their emergence as a reaction against traditional methods. In this paper, we argue that although Agile methods are new as a whole, they have strong roots in the history of software engineering. In addition to the iterative and incremental approaches that have been in use since 1957 [21], people who criticised the traditional methods suggested alternative approaches which were actually Agile ideas such as the response to change, customer involvement, and working software over documentation. The authors of this paper believe that education about the history of Agile thinking will help to develop better understanding as well as promoting the use of Agile methods. We therefore present and discuss the reasons behind the development and introduction of Agile methods, as a reaction to traditional methods, as a result of people's experience, and in particular focusing on reusing ideas from history


title: Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

abstract: The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.


title: Adversarial Diversity and Hard Positive Generation

abstract: State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs. In this paper, we present a new psychometric perceptual adversarial similarity score (PASS) measure for quantifying adversarial images, introduce the notion of hard positive generation, and use a diverse set of adversarial perturbations - not just the closest ones - for data augmentation. We introduce a novel hot/cold approach for adversarial example generation, which provides multiple possible adversarial perturbations for every single image. The perturbations generated by our novel approach often correspond to semantically meaningful image structures, and allow greater flexibility to scale perturbation-amplitudes, which yields an increased diversity of adversarial images. We present adversarial images on several network topologies and datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that fine-tuning with a diverse set of hard positives improves the robustness of these networks compared to training with prior methods of generating adversarial images.


title: Magnetic hysteresis for multi-state addressable magnetic microrobotic control

abstract: We present a new scheme of remote addressable magnetic actuation for sub-mm microrobotics which uses the hysteresis characteristics of multiple magnetic materials to achieve advanced state control of many magnetic actuators sharing the same magnetic control inputs. Using this standard approach, remote magnetic actuation of a single magnet has been achieved for untethered motion control with a single magnetic control input. We propose the simultaneous use of multiple magnetic materials with varying hysteresis characteristics to effectively gain multiple control inputs as different applied magnetic field strengths. As a first experimental implementation of this idea, we present a set of three heterogeneous magnetic modules floating on a liquid surface which can be remotely reconfigured by application of a field of varying magnitude. As a second implementation, we present a team of up to six independently actuated walking microrobots made from a composite material whose net magnetic moment can be selectively turned on or off by application of a large magnetic field pulse. We also demonstrate a team of two addressable microrobots performing a task requiring cooperative teamwork. The presented concept providing multiple magnetic control inputs could be applicable in various areas of milli- or microrobotics to address multiple magnetic elements for motion or actuation control.


title: Improving the search performance of SHADE using linear population size reduction

abstract: SHADE is an adaptive DE which incorporates success-history based parameter adaptation and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function. We evaluated the performance of L-SHADE on CEC2014 benchmarks and compared its search performance with state-of-the-art DE algorithms, as well as the state-of-the-art restart CMA-ES variants. The experimental results show that L-SHADE is quite competitive with state-of-the-art evolutionary algorithms.


title: Induced sensitivity of Bacillus subtilis colony morphology to mechanical media compression

abstract: Bacterial from several taxa, including Kurthia zopfii, Myxococcus xanthus, and Bacillus mycoides, have been reported to align growth of their colonies to small features on the surface of solid media, including anisotropies created by compression. While the function of this phenomenon is unclear, it may help organisms navigate on solid phases, such as soil. The origin of this behavior is also unknown: it may be biological (that is, dependent on components that sense the environment and regulate growth accordingly) or merely physical. Here we show that B. subtilis, an organism that typically does not respond to media compression, can be induced to do so with two simple and synergistically perturbations: a mutation that maintains cells in the swarming (chained) state, and the addition of EDTA to the growth media, which further increases chain length. EDTA apparently increases cell length by inducing defects in cell separation, as the treatment has only marginal effects on the length of individual cells. These results lead us to three conclusions. First, the wealth of genetic tools available to B. subtilis will provide a new, tractable chassis for engineering compression sensitive organisms. Second, the sensitivity of colony morphology to media compression in Bacillus is a physical rather than biological phenomenon dependent on a simple physical property of rod-shaped cells. And third, colony morphology under compression holds promise as a rapid, simple, and low-cost way to screen for changes in the length of rod-shaped cells or chains thereof.


title: Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization

abstract: This paper presents a hybrid differential evolution (DE) with quantum-behaved particle swarm optimization (QPSO) for the unmanned aerial vehicle (UAV) route planning on the sea. The proposed method, denoted as DEQPSO, combines the DE algorithm with the QPSO algorithm in an attempt to further enhance the performance of both algorithms. The route planning for UAV on the sea is formulated as an optimization problem. A simple method of pretreatment to the terrain environment is proposed. A novel route planner for UAV is designed to generate a safe and flyable path in the presence of different threat environments based on the DEQPSO algorithm. To show the high performance of the proposed method, the DEQPSO algorithm is compared with the real-valued genetic algorithm, DE, standard particle swarm optimization (PSO), hybrid particle swarm with differential evolution operator, and QPSO in terms of the solution quality, robustness, and the convergence property. Experimental results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization algorithms.


Millimeter-Wave Receiver Concepts for 77 GHz Automotive Radar in Silicon-Germanium Technology


title: Studying the Impact of Managers on Password Strength and Reuse

abstract: Despite their well-known security problems, passwords are still the incumbent authentication method for virtually all online services. To remedy the situation, end-users are very often referred to password managers as a solution to the password reuse and password weakness problems. However, to date the actual impact of password managers on password security and reuse has not been studied systematically. ::: In this paper, we provide the first large-scale study of the password managers' influence on users' real-life passwords. From 476 participants of an online survey on users' password creation and management strategies, we recruit 170 participants that allowed us to monitor their passwords in-situ through a browser plugin. In contrast to prior work, we collect the passwords' entry methods (e.g., human or password manager) in addition to the passwords and their metrics. Based on our collected data and our survey, we gain a more complete picture of the factors that influence our participants' passwords' strength and reuse. We quantify for the first time that password managers indeed benefit the password strength and uniqueness, however, also our results also suggest that those benefits depend on the users' strategies and that managers without password generators rather aggravate the existing problems.


title: REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

abstract: Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. ::: ::: REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.


title: Where is Scrum in the current Agile world?

abstract: A variety of methodologies in software processes exist nowadays with the Agile software development having gained significant ground since the introduction of the Agile manifesto in 2001. Scrum is a representative Agile development method employed in the software industry. Since trends come and go, it is vital to see where they stand in the real world. In order to gain an insight into how Scrum is viewed nowadays, we have conducted an online study on the current state of the adoption of Scrum. The study targeted in demonstrating where the success or failure success factors of Scrum lie and in viewing Scrum properties in comparison to heavyweight approaches. This paper presents the results of the study that constitute an instructive view into the above aspects of Scrum development.


title: Long-term video interpolation with bidirectional predictive network

abstract: This paper considers the challenging task of long-term video interpolation. Unlike most existing methods that only generate few intermediate frames between existing adjacent ones, we attempt to speculate or imagine the procedure of an episode and further generate multiple frames between two non-consecutive frames in videos. In this paper, we present a novel deep architecture called bidirectional predictive network (BiPN) that predicts intermediate frames from two opposite directions. The bidirectional architecture allows the model to learn scene transformation with time as well as generate longer video sequences. Besides, our model can be extended to predict multiple possible procedures by sampling different noise vectors. A joint loss composed of clues in image and feature spaces and adversarial loss is designed to train our model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes and UCF101 and report competitive results to recent approaches.


title: The Importance of Micro-Crack Evolution under Oxidation/Creep Conditions in Component Failure Life Predictions

abstract: The oxidation, environmental corrosion and creep damage are limiting factors in the life assessment of engineering components operating at elevated temperatures. Experimental observations have shown that environmental effects (i.e. oxidation) during components operation time can harden the material [1] and lead to evolution of damage in the form of surface cracks (see Figure 1). The regions subjected to environmental damage which show higher hardness are structurally weaker and more brittle. When components operate in the creep regime, the initiated micro cracks may lead to accelerated creep crack growth in the material. The aim of this paper is to highlight the importance of oxidation effects and microcracks on creep in component life assessments.


title: A MOSFET Power Amplifier with Error Correction

abstract: The rapid evolution of power MOSFETs during the last few years has brought them to the point where they are now very attractive for use in audio amplifier power output stages. Important improvements include in creased voltage, current, and dissipation ratings, re duced "on" resistance, availability of complementary pairs, and greatly reduced cost. Although a 75-W MOSFET is still more expensive than a 150-W bipolar transistor, the premium is small when considered rel ative to total amplifier cost and improved performance. The purpose of this paper is to demonstrate the level of performance achievable with current technology and to illustrate practical circuit techniques for achieving this performance. Power MOSFETs have several fundamental advan tages over bipolar power transistors, most notably speed and freedom from secondary breakdown. The latter provides higher "usable" power dissipation, improved reliability, and freedom from safe-area limiter circuits, which can misbehave and cause audible degradation . MOSFETs also have some disadvantages in comparison with bipolar transistors. These include higher turn-on voltage drive requirements and smaller transconduct ance at low current levels . The former tends to contradict generalizations that have been made to the effect that drive circuits for power MOSFETs are less expensive, at least for the reliable source-follower configuration. The latter results in transconductance droop in the cross over region if bias currents are not fairly high. Such transconductance droop can result in crossover distortion.


title: Wearable wireless biophotonic and biopotential sensors for canine health monitoring

abstract: There is an increasing interest from dog handlers and veterinarians in the ability to continuously monitor dogs' vital signs (heart rate, heart rate variability and respiration rate) with the aim of identifying physiological correlations to stress and excitement outside laboratory environments. We present our latest efforts towards a novel non-invasive wearable sensor system including photoplethysmogram (PPG) and electrocardiogram (ECG) to remotely and continuously monitor vital signs of animals. To overcome the limitations imposed by the dense hair layer, we investigated the use of pointed style electrodes, traditionally used in training (shock) collars, as passive ECG recording electrodes. We also studied the incorporation of light guides and optical fibers for efficient optical coupling to the skin. The ECG and PPG sensors were interfaced to a system on chip (SoC) with Bluetooth capability for transferring the data to a nearby smartphone or computer for data storage and analysis.


title: Big Data : New Tricks for Econometrics

abstract: Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.


title: Machine Learning Meets Databases

abstract: Machine Learning has become highly popular due to several success stories in data-driven applications. Prominent examples include object detection in images, speech recognition, and text translation. According to Gartner’s 2016 Hype Cycle for Emerging Technologies, Machine Learning is currently at its peak of inflated expectations, with several other application domains trying to exploit the use of Machine Learning technology. Since data-driven applications are a fundamental cornerstone of the database community as well, it becomes natural to ask how these fields relate to each other. In this article, we will therefore provide a brief introduction to the field of Machine Learning, we will discuss its interplay with other fields such as Data Mining and Databases, and we provide an overview of recent data management systems integrating Machine Learning functionality.


title: A Map of Recent Positive Selection in the Human Genome

abstract: The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP) data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest approximately 250 signals of recent selection in each population.


title: Ontology-based healthcare context information model to implement ubiquitous environment

abstract: To establish real u-healthcare environments, it is necessary to receive the context information obtained from various platforms at the proper time in portable devices which operate using both wired and wireless communication. Moreover, a knowledge model is required that reflects the information and characteristics needed for such services while remaining appropriate for medical reference. This paper develops an ontology-based healthcare context information model to implement a ubiquitous environment. Contextual information will be extracted and classified to implement the healthcare services using the context information model. The healthcare context information model can be defined using the ontology, and a common healthcare model will be developed by considering medical references and service environments. Application and healthcare service developers can use the sensed information in various environments by authoring device- and space-specific ontologies based on this common ontology. In addition, this paper designs a personalized u-healthcare service system. The validity of the model used in this study is evaluated for the food and exercise recommendation in u-healthcare services.


title: Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn

abstract: One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typically needed for the position, what are their alternatives, which companies are likely to have such candidates, etc. However, listing examples of suitable candidates for a given position is a relatively easy job. Therefore, in order to help searchers overcome this challenge, we design a next generation of talent search paradigm at LinkedIn: Search by Ideal Candidates. This new system only needs the searcher to input one or several examples of suitable candidates for the position. The system will generate a query based on the input candidates and then retrieve and rank results based on the query as well as the input candidates. The query is also shown to the searcher to make the system transparent and to allow the searcher to interact with it. As the searcher modifies the initial query and makes it deviate from the ideal candidates, the search ranking function dynamically adjusts an refreshes the ranking results balancing between the roles of query and ideal candidates. As of writing this paper, the new system is being launched to our customers.


title: A comparative performance evaluation of neural network based approach for sentiment classification of online reviews

abstract: The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs) are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN), probabilistic neural network (PNN) & homogeneous ensemble of PNN (HEN)) using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs) outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA) as a feature reduction technique provides superior performance in terms of training time also.


title: Using partial-run-time reconfigurable hardware to accelerate video processing in driver assistance system

abstract: In this paper we show a reconfigurable hardware architecture for the acceleration of video-based driver assistance applications in future automotive systems. The concept is based on a separation of pixel-level operations and high level application code. Pixel-level operations are accelerated by coprocessors, whereas high level application code is implemented fully programmable on standard PowerPC CPU cores to allow flexibility for new algorithms. In addition, the application code is able to dynamically reconfigure the coprocessors available on the system, allowing for a much larger set of hardware accelerated functionality than would normally fit onto a device. This process makes use of the partial dynamic reconfiguration capabilities of Xilinx Virtex FPGAs.


title: GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

abstract: Abstract. In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.


title: COMPRESSED SENSING IMAGE RECONSTRUCTION VIA RECURSIVE SPATIALLY ADAPTIVE FILTERING

abstract: We introduce a new approach to image reconstruction from highly incomplete data. The available data are assumed to be a small collection of spectral coefficients of an arbitrary linear transform. This reconstruction problem is the subject of intensive study in the recent field of "compressed sensing" (also known as "compressive sampling"). Our approach is based on a quite specific recursive filtering procedure. At every iteration the algorithm is excited by injection of random noise in the unobserved portion of the spectrum and a spatially adaptive image denoising filter, working in the image domain, is exploited to attenuate the noise and reveal new features and details out of the incomplete and degraded observations. This recursive algorithm can be interpreted as a special type of the Robbins-Monro stochastic approximation procedure with regularization enabled by a spatially adaptive filter. Overall, we replace the conventional parametric modeling used in CS by a nonparametric one. We illustrate the effectiveness of the proposed approach for two important inverse problems from computerized tomography: Radon inversion from sparse projections and limited-angle tomography. In particular we show that the algorithm allows to achieve exact reconstruction of synthetic phantom data even from a very small number projections. The accuracy of our reconstruction is in line with the best results in the compressed sensing field.


title: A new method for pulse oximetry possessing inherent insensitivity to artifact

abstract: A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.


title: A Survey of Model Driven Engineering Tools for User Interface Design

abstract: The introduction of new technologies leads to a more and more complex interactive systems design. In order to describe the future interactive system, the human computer interaction (HCI) domain uses specific models and tools. In another way, the Model Driven Engineering (MDE) approach has been proposed in software engineering domain in order to provide techniques and tools for dealing with models in an automated way. MDE approach is based on models, meta-models, models transformation and models weaving and aims to produce productive models, i.e. models concentrated on their generative power. Considering these two domains and the already existing HCI works in MDE, the goal of this paper is to understand actual HCI design needs and to study how MDE tools can support HCI needs. As a first response, it proposes a survey of existing MDE tools in regards to HCI model management.


title: Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

abstract: ANFIS based tool wear/condition monitoring is demonstrated for end milling GFRP composites.Grid portioning and subtractive clustering are used to partition the experimental data.Using feed force produces better prediction capability compared to cutting force.Statistical analyses are used to evaluate the predictive capability. The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r2 and root mean square error (RMSE), performed on training as well as checking datasets.


title: A Comparison of Scheduling Latency in Linux, PREEMPT RT, and LITMUS RT

abstract: Scheduling latency under Linux and its principal real-time variant, the PREEMPT RT patch, are typically measured using cyclictest, a tracing tool that treats the kernel as a black box and directly reports scheduling latency. LITMUS, a real-time extension of Linux focused on algorithmic improvements, is typically evaluated using Feather-Trace, a finedgrained tracing mechanism that produces a comprehensive overhead profile suitable for overhead-aware schedulability analysis. This difference in tracing tools and output has to date prevented a direct comparison. This paper reports on a port of cyclictest to LITMUS and a case study comparing scheduling latency on a 16-core Intel platform. The main conclusions are: (i) LITMUS introduces only minor overhead itself, but (ii) it also inherits mainline Linux’s severe limitations in the presence of I/O-bound background tasks.


title: Diaphragmatic Breathing Reduces Exercise-Induced Oxidative Stress

abstract: Diaphragmatic breathing is relaxing and therapeutic, reduces stress, and is a fundamental procedure of Pranayama Yoga, Zen, transcendental meditation and other meditation practices. Analysis of oxidative stress levels in people who meditate indicated that meditation correlates with lower oxidative stress levels, lower cortisol levels and higher melatonin levels. It is known that cortisol inhibits enzymes responsible for the antioxidant activity of cells and that melatonin is a strong antioxidant; therefore, in this study, we investigated the effects of diaphragmatic breathing on exercise-induced oxidative stress and the putative role of cortisol and melatonin hormones in this stress pathway. We monitored 16 athletes during an exhaustive training session. After the exercise, athletes were divided in two equivalent groups of eight subjects. Subjects of the studied group spent 1 h relaxing performing diaphragmatic breathing and concentrating on their breath in a quiet place. The other eight subjects, representing the control group, spent the same time sitting in an equivalent quite place. Results demonstrate that relaxation induced by diaphragmatic breathing increases the antioxidant defense status in athletes after exhaustive exercise. These effects correlate with the concomitant decrease in cortisol and the increase in melatonin. The consequence is a lower level of oxidative stress, which suggests that an appropriate diaphragmatic breathing could protect athletes from long-term adverse effects of free radicals.


title: TORA by varying Number of Nodes with FTP and HTTP Applications over MANETs

abstract: years have witnessed an extreme growth in research and development in the field of Wireless Networks. The special focus has been on Ad-hoc Networks especially Mobile Ad-hoc Network (MANET). Mobile ad-hoc network is a dynamic instant mutihop radio infrastructure-less temporary network of wireless mobile nodes in which each participating nodes act as host and router at the same time. Routing is a crucial activity and plays an important role in the success of the communication in these structures. Many routing protocols have been presented for Mobile Ad-hoc network since last decade. The major difference between these protocols lies in the mechanism of searching, maintenance and recovering the route path. In the recent researches, there are numerous MANET routing protocols aiming to find the most suitable path from source to destination. In this paper, a simulation based comparative analysis is performed on various types of routing protocols over MANET. Ad Hoc On- Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Temporally-Ordered Routing Algorithm (TORA), Optimized Link State Routing (OLSR) and Geographic Routing Protocol (GRP) has been considered for investigation in this paper based on throughput, delay, load and data dropped performance metrics using OPNET Modeler .