title: Modified DC-Bus Voltage Balancing Algorithm for a Three-Level Neutral-Point-Clamped PMSM Inverter Drive With Reduced Common-Mode Voltage

abstract: This paper presents an improved dc-link voltage balancing algorithm for a three-level neutral-point-clamped inverter by considering phase current direction. Detailed studies on the effects of change in load current direction on the dc-link capacitor voltages are presented. A maximum value of power factor is numerically derived, above which it affects the capacitor voltage balancing capability. Compared with the previously presented research work, the inputs to the space-vector pulsewidth-modulation block are the three phase currents and the difference between the two capacitor voltages. Depending on the states of the two dc-link capacitor voltages and phase current direction, redundant voltage vector sequences are selected. The selected vectors keep the capacitor voltage deviations within 5% of the total dc-link voltage. Two zero switching vectors (i.e., PPP and NNN) are also removed from all subsectors of the earlier proposed strategy, which one used to produce higher common-mode voltages. Detailed simulation and experimental results are presented in this paper for a 6.0-kW surface permanent-magnet synchronous machine. Both the simulation and experimental results show the required performance of the proposed system.


title: Neural-based quality measurement of fingerprint images in contactless biometric systems

abstract: Traditional fingerprint biometric systems capture the user fingerprint images by a contact-based sensor. Differently, contactless systems aim to capture the fingerprint images by an approach based on a vision system without the need of any contact of the user with the sensor. The user finger is placed in front of a special CCD-based system that captures the pattern of ridges and valleys of the fingertips. This approach is less constrained by the point of view of the user, but it requires much more capability of the system to deal with the focus of the moving target, the illumination problems and the complexity of the background in the captured image. During the acquisition procedure, the quality of each frame must be carefully evaluated in order to extract only the correct frames with valuable biometric information from the sequence. In this paper, we present a neural-based approach for the quality estimation of the contactless fingertips images. The application of the neural classification models allowed for a relevant reduction of the computational complexity permitting the application in real-time. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters.


Race, Ethnicity, and the Media


title: Universal dimensions of social cognition: warmth and competence

abstract: Like all perception, social perception reflects evolutionary pressures. In encounters with conspecifics, social animals must determine, immediately, whether the ‘other' is friend or foe (i.e. intends good or ill) and, then, whether the ‘other' has the ability to enact those intentions. New data confirm these two universal dimensions of social cognition: warmth and competence. Promoting survival, these dimensions provide fundamental social structural answers about competition and status. People perceived as warm and competent elicit uniformly positive emotions and behavior, whereas those perceived as lacking warmth and competence elicit uniform negativity. People classified as high on one dimension and low on the other elicit predictable, ambivalent affective and behavioral reactions. These universal dimensions explain both interpersonal and intergroup social cognition.


title: LEAF DISEASE SEVERITY MEASUREMENT USING IMAGE PROCESSING

abstract: Fungi-caused diseases in sugarcane are the most predominant diseases which appear as spots on the leaves. If not treated on time, causes the severe loss. Excessive use of pesticide for plant diseases treatment increases the cost and environmental pollution so their use must be minimized. This can be achieved by targeting the diseases places, with the appropriate quantity and concentration of pesticide by estimating disease severity using image processing technique. Simple threshold and Triangle thresholding methods are used to segment the leaf area and lesion region area respectively. Finally diseases are catcogrise by calculating the quotient of lesion area and leaf area. The accuracy of the experiment is found to be 98.60 %. Research indicates that this method to calculate leaf disease severity is fast and accurate. Sugarcane being a long duration crop of 10 to 18 months, attacked by a number of diseases. Fungi- caused diseases in sugarcane are the most predominant diseases which appear as spots on the leaves. These spots prevent the vital process of photosynthesis to take place, hence to a large extent affects the growth and consequently the yield. In case of severe infection, the leaf becomes totally covered with spots. The various types of diseases on sugarcane determine the quality, quantity, and stability of yield. The diseases in sugarcane not only reduce the yield but also deteriorate of the variety and its withdrawal from cultivation.Excessive uses of pesticide for plant diseases treatment increases the danger of toxic residue level on agricultural products and has been identified as a major contributor to ground water contamination also pesticides are among the highest components in the production cost their use must be minimized. This can be achieved by estimating severity of disease and target the diseases places, with the appropriate quantity and concentration of pesticide.


title: Wake-up receiver operating at 433 MHz

abstract: Sensor nodes often have to work for a long time with a single battery. A change of the power source is sometimes not possible or involves effort and costs. If the node needs to be accessible for communication at any point of time it must have a radio in permanent receive state. This depletes the battery in a few days. The contradiction between long lifetime and permanent accessibility can be solved by using a separate wake-up receiver on the node. In this work we present a sensor node with included wake-up receiver working in the 433MHz ISM band. Our solution consumes 2.8 μA of current in sleep state while still maintaining a realtime behaviour. The low current consumption is achieved by modulating a 125 kHz wake-up signal on the 433MHz carrier in the sender. In the receiving node a passive demodulation circuit extracts the wake-up signal and feeds it to an 125 kHz low frequency receiver IC. An additional 16 bit address coding is used for an selective wake-up of nodes.


Biological Evidence Management for DNA Analysis in Cases of Sexual Assault


title: SMD segmentation for automated PCB recycling

abstract: Due to the complex structure and the great variability of printed circuit boards (PCBs), an accurate analysis of their material composition and consequently an efficient recycling is currently hardly possible. Facing these problems, we proposed an automated PCB recycling system with a core component for the information retrieval. As a first step towards automated recycling based on a comprehensive analysis of PCBs, the segmentation of surface-mounted devices (SMDs) was presented in this paper. Inspired by the technical background of SMDs, they were divided into two main groups: 1. small devices, such as resistors, capacitors, and 2. integrated circuits (ICs). Then assembly print-based segmentation and color distribution-based segmentation were carried out for the two groups respectively. In comparison to device template-based and solder joint-based approaches, our approach is more flexible and more reliable with respect to the variability of devices and the erosion or soiling of solder joints. As confirmed by the evaluation, satisfying results were obtained. The approach was also evaluated for different imaging conditions so that improved performance can be achieved using appropriate imaging settings.


title: A 14-b 12-MS/s CMOS pipeline ADC with over 100-dB SFDR

abstract: A 1.8-V 14-b 12-MS/s pseudo-differential pipeline analog-to-digital converter (ADC) using a passive capacitor error-averaging technique and a nested CMOS gain-boosting technique is described. The converter is optimized for low-voltage low-power applications by applying an optimum stage-scaling algorithm at the architectural level and an opamp and comparator sharing technique at the circuit level. Prototyped in a 0.18-μm 6M-1P CMOS process, this converter achieves a peak signal-to-noise plus distortion ratio (SNDR) of 75.5 dB and a 103-dB spurious-free dynamic range (SFDR) without trimming, calibration, or dithering. With a 1-MHz analog input, the maximum differential nonlinearity is 0.47 LSB and the maximum integral nonlinearity is 0.54 LSB. The large analog bandwidth of the front-end sample-and-hold circuit is achieved using bootstrapped thin-oxide transistors as switches, resulting in an SFDR of 97 dB when a 40-MHz full-scale input is digitized. The ADC occupies an active area of 10 mm 2 and dissipates 98 mW.


title: Diagnosing performance overheads in the xen virtual machine environment

abstract: Virtual Machine (VM) environments (e.g., VMware and Xen) are experiencing a resurgence of interest for diverse uses including server consolidation and shared hosting. An application's performance in a virtual machine environment can differ markedly from its performance in a non-virtualized environment because of interactions with the underlying virtual machine monitor and other virtual machines. However, few tools are currently available to help debug performance problems in virtual machine environments.In this paper, we present Xenoprof, a system-wide statistical profiling toolkit implemented for the Xen virtual machine environment. The toolkit enables coordinated profiling of multiple VMs in a system to obtain the distribution of hardware events such as clock cycles and cache and TLB misses. The toolkit will facilitate a better understanding of performance characteristics of Xen's mechanisms allowing the community to optimize the Xen implementation.We use our toolkit to analyze performance overheads incurred by networking applications running in Xen VMs. We focus on networking applications since virtualizing network I/O devices is relatively expensive. Our experimental results quantify Xen's performance overheads for network I/O device virtualization in uni- and multi-processor systems. With certain Xen configurations, networking workloads in the Xen environment can suffer significant performance degradation. Our results identify the main sources of this overhead which should be the focus of Xen optimization efforts. We also show how our profiling toolkit was used to uncover and resolve performance bugs that we encountered in our experiments which caused unexpected application behavior.


title: A new hybrid MCDM model combining DANP with VIKOR to improve e-store business

abstract: Many consumers today buy products and services from e-stores. Because e-store managers are responsible for allocating different resources, it is essential that they understand consumers' shopping behaviour to provide the best possible value for visitors to their websites. Therefore, the purpose of this article is to focus on assessing and improving strategies to reduce the gaps in customer satisfaction caused by interdependence and feedback problems among dimensions and criteria to achieve the aspiration level. We propose a new hybrid Multiple Attribute Decision Making (MADM) model, combining the Decision Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based Analytic Network Process (DANP), and VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) methods to solve these problems. Then, three real cases are used to illustrate how the proposed new hybrid Multiple Criteria Decision-Making (MCDM) model improves e-store business. These results can provide e-store managers with a knowledge-based understanding of how to create marketing strategies that reduce the performance gaps of dimensions and criteria to satisfy consumers' needs and encourage customers to purchase more.


A New Shortest Paths Ranking Algorithm


title: Ensemble-based Multi-Filter Feature Selection Method for DDoS Detection in Cloud Computing

abstract: Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.


title: A Unified Correlation-based Approach to Sampling Over Joins

abstract: Supporting sampling in the presence of joins is an important problem in data analysis, but is inherently challenging due to the need to avoid correlation between output tuples. Current solutions provide either correlated or non-correlated samples. Sampling might not always be feasible in the non-correlated sampling-based approaches -- the sample size or intermediate data size might be exceedingly large. On the other hand, a correlated sample may not be representative of the join. This paper presents a unified strategy towards join sampling, while considering sample correlation every step of the way. We provide two key contributions. First, in the case where a correlated sample is acceptable, we provide techniques, for all join types, to sample base relations so that their join is as random as possible. Second, in the case where a correlated sample is not acceptable, we provide enhancements to the state-of-the-art algorithms to reduce their execution time and intermediate data size.


title: Social media metrics for new research evaluation

abstract: This chapter approaches, from both a theoretical and practical perspective, the most important principles and conceptual frameworks that can be considered in the application of social media metrics for scientific evaluation. We propose conceptually valid uses for social media metrics in research evaluation. The chapter discusses frameworks and uses of these metrics as well as principles and recommendations for the consideration and application of current (and potentially new) metrics in research evaluation.


title: Suppression of acoustic noise in speech using spectral subtraction

abstract: A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.


title: Dynamic time warping under pointwise shape context

abstract: Similarity measures are of fundamental importance in time series data mining. Dynamic Time Warping (DTW) is a quite popular measure because it handles time distortions well. However, DTW has an inherent shortcoming in that DTW can lead to pathological alignments between time series where a single point maps onto a large subsection of another time series. To overcome this problem, we propose a novel variant of DTW named SC-DTW. SC-DTW employs shape context, a rich local shape descriptor, to replace the raw observed values considered by conventional DTW. The main novelties of SC-DTW are (1) it deeply explores both the numerical nature and shape nature of time series; and (2) neighborhood information for each point is taken into account. SC-DTW can generate a more feature-to-feature alignment between time series and thus serves as a robust similarity measure. We test the performance of SC-DTW on UCR time series datasets using the one nearest neighbor (1NN) classifier. Compared with other well-established methods, SC-DTW provides better accuracy on 24 of 34 datasets.


Complete Electrical Failure during Cardiopulmonary Bypass


title: Force field based joint optimization of strictly monotonic KPIs in wireless networks

abstract: Self-organizing networks (SONs) are expected to improve the quality of service (QoS) while reducing the operator's capital and operational expenditures. In order to achieve this ambitious goal, SON concepts are required to manage multiple key performance indicators (KPIs) by modifying several different network control parameters. Developing concepts and algorithms that cope with such complexity is not easy. In order to avoid the complexity of joint optimization, the research so far focused on concepts, which coordinate multiple algorithms [1], [2]. Usually, each of these algorithms is dedicated to managing a single use case or KPI. In contrast, we introduce and analyse a force field based joint optimization concept which considers all KPIs and network control parameters concurrently. Despite the holistic approach, our concept offers an acceptable complexity. Using an algorithm based on this concept, we can resolve a high load scenario by jointly adjusting the tilts and cell individual offsets (CIOs).


title: Blending liquids

abstract: We present a method for smoothly blending between existing liquid animations. We introduce a semi-automatic method for matching two existing liquid animations, which we use to create new fluid motion that plausibly interpolates the input. Our contributions include a new space-time non-rigid iterative closest point algorithm that incorporates user guidance, a subsampling technique for efficient registration of meshes with millions of vertices, and a fast surface extraction algorithm that produces 3D triangle meshes from a 4D space-time surface. Our technique can be used to instantly create hundreds of new simulations, or to interactively explore complex parameter spaces. Our method is guaranteed to produce output that does not deviate from the input animations, and it generalizes to multiple dimensions. Because our method runs at interactive rates after the initial precomputation step, it has potential applications in games and training simulations.


title: Topics in multiple-loop regulators and current-mode programming

abstract: Some general considerations about multiple-loop feedback are discussed, and it is concluded that incorporation of a current-programmed power stage into a “new” power stage model is both justified and useful. A new circuit-oriented model of the current feedback path is derived which augments the well-known power stage canonical circuit model.


title: Effects of Dance Movement Therapy on Adult Patients with Autism Spectrum Disorder: A Randomized Controlled Trial

abstract: This study examines the effects of dance movement therapy (DMT) on empathy for adults with autism spectrum disorder (ASD). DMT based on the embodiment approach offers body-centered interventions, such as mirroring techniques, to address the needs of ASD patients. Accordingly, findings of a feasibility study suggest that DMT may be an effective approach for clients on the ASD spectrum. The present study is a randomized controlled trial that was conducted as a multicenter study within the framework of the EU-funded research project TESIS (Toward an Embodied Science of Intersubjectivity), and employed a two-factorial between-subject design. The treatment group (n = 35) participated in a 10-week manualized DMT intervention, whereas the control group (n = 22) received treatment only after a waiting period. Empathy, measured with the Cognitive and Emotional Empathy Questionnaire (CEEQ), was the main variable of interest, analyzed by a repeated measures analysis of variance. In order to also include incomplete data cases, we used the expectation-maximization algorithm for missing data estimation. Results suggest no significant changes in overall empathy between groups. We discuss the results and limitations, as well as future research options.


title: BiNE: Bipartite Network Embedding

abstract: This work develops a representation learning method for bipartite networks. While existing works have developed various embedding methods for network data, they have primarily focused on homogeneous networks in general and overlooked the special properties of bipartite networks. As such, these methods can be suboptimal for embedding bipartite networks. In this paper, we propose a new method named BiNE, short for Bipartite Network Embedding, to learn the vertex representations for bipartite networks. By performing biased random walks purposefully, we generate vertex sequences that can well preserve the long-tail distribution of vertices in the original bipartite network. We then propose a novel optimization framework by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links) in learning the vertex representations. We conduct extensive experiments on several real datasets covering the tasks of link prediction (classification), recommendation (personalized ranking), and visualization. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our BiNE method.


title: A Photovoltaic Array Simulation Model for Matlab-Simulink GUI Environment

abstract: A photovoltaic array (PVA) simulation model to be used in Matlab-Simulink GUI environment is developed and presented in this paper. The model is developed using basic circuit equations of the photovoltaic (PV) solar cells including the effects of solar irradiation and temperature changes. The new model was tested using a directly coupled dc load as well as ac load via an inverter. Test and validation studies with proper load matching circuits are simulated and results are presented here.


title: Patterns of Connections and Movements in Dual-Map Overlays: A New Method of Publication Portfolio Analysis

abstract: Portfolio analysis of the publication profile of a unit of interest, ranging from individuals and organizations to a scientific field or interdisciplinary programs, aims to inform analysts and decision makers about the position of the unit, where it has been, and where it may go in a complex adaptive environment. A portfolio analysis may aim to identify the gap between the current position of an organization and a goal that it intends to achieve or identify competencies of multiple institutions. We introduce a new visual analytic method for analyzing, comparing, and contrasting characteristics of publication portfolios. The new method introduces a novel design of dual-map thematic overlays on global maps of science. Each publication portfolio can be added as one layer of dual-map overlays over 2 related, but distinct, global maps of science: one for citing journals and the other for cited journals. We demonstrate how the new design facilitates a portfolio analysis in terms of patterns emerging from the distributions of citation threads and the dynamics of trajectories as a function of space and time. We first demonstrate the analysis of portfolios defined on a single source article. Then we contrast publication portfolios of multiple comparable units of interest; namely, colleges in universities and corporate research organizations. We also include examples of overlays of scientific fields. We expect that our method will provide new insights to portfolio analysis.


title: Deep Learning for Image Retrieval: What Works and What Doesn't

abstract: To build an industrial content-based image retrieval system (CBIRs), it is highly recommended that feature extraction, feature processing and feature indexing need to be fully considered. Although research that bloomed in the past years suggest that the convolutional neural network (CNN) be in a leading position on feature extraction & representation for CBIRs, there are less instructions on the deep analysis of feature related topics, for example the kind of feature representation that has the best performance among the candidates provided by CNN, the extracted features generalization ability, the relationship between the dimensional reduction and the accuracy loss in CBIRs, the best distance measure technique in CBIRs and the benefit of the coding techniques in improving the efficiency of CBIRs, etc. Therefore, several practicing studies were conducted and a thorough analysis was made in this research attempting to answer the above questions. The results in the study on both ImageNet-2012 and an industrial dataset provided by Sogou demonstrate that fc4096a and fc4096b perform the best on the datasets from unseen categories. Several interesting and practicing conclusions are drawn, for instance, fc4096a and fc4096b are found to have a better generalization ability than other features of CNN and could be considered as the first choice for industrial CBIRs. Furthermore, a novel feature binarization approach is presented in this paper for better efficiency of CBIRs. More specifically, the binarization is capable of reducing 31/32 space usage of original data. To sum up, the conclusions seem to provide practical instructions on real industrial CBIRs.


title: Estimation of missing markers in human motion capture

abstract: Motion capture is a prevalent technique for capturing and analyzing human articulations. A common problem encountered in motion capture is that some marker positions are often missing due to occlusions or ambiguities. Most methods for completing missing markers may quickly become ineffective and produce unsatisfactory results when a significant portion of the markers are missing for extended periods of time. We propose a data-driven, piecewise linear modeling approach to missing marker estimation that is especially beneficial in this scenario. We model motion sequences of a training set with a hierarchy of low-dimensional local linear models characterized by the principal components. For a new sequence with missing markers, we use a pre-trained classifier to identify the most appropriate local linear model for each frame and then recover the missing markers by finding the least squares solutions based on the available marker positions and the principal components of the associated model. Our experimental results demonstrate that our method is efficient in recovering the full-body motion and is robust to heterogeneous motion data.


title: Deep Spatial Pyramid: The Devil is Once Again in the Details

abstract: In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system. We first list 5 important factors, based on both existing researches and ideas proposed in this paper. These important detailed factors include: 1) ‘2 matrix normalization is more effective than unnormalized or‘2 vector normalization, 2) the proposed natural deep spatial pyramid is very effective, and 3) a very small K in Fisher Vectors surprisingly achieves higher accuracy than normally used large K values. Along with other choices (convolutional activations and multiple scales), the proposed DSP framework is not only intuitive and efficient, but also achieves excellent classification accuracy on many benchmark datasets. For example, DSP’s accuracy on SUN397 is 59.78%, significantly higher than previous state-of-the-art (53.86%).


Developing a conceptual model for knowledge sharing


title: A brief survey on sequence classification

abstract: Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selection techniques, the dimensionality of potential features may still be very high and the sequential nature of features is difficult to capture. This makes sequence classification a more challenging task than classification on feature vectors. In this paper, we present a brief review of the existing work on sequence classification. We summarize the sequence classification in terms of methodologies and application domains. We also provide a review on several extensions of the sequence classification problem, such as early classification on sequences and semi-supervised learning on sequences.


title: NB-WiFi: IEEE 802.11 and Bluetooth Low Energy Combined for Efficient Support of IoT

abstract: Key features of the two dominating standards for the unlicensed bands, IEEE 802.11 and Bluetooth Wireless Technology, are combined to obtain a physical layer (PHY) with several desirable features for internet of things (IoT). The proposed PHY, which is referred to as Narrow-band WiFi (NB-WiFi) can be supported by an OFDM transceiver available in an IEEE 802.11 access point (AP). In addition, NB-WiFi supports concurrent use of low data rate IoT application and high data rate broadband using IEEE 802.11ax technology, based on a single IFFT#x002F;FFT in the AP. In the sensor node, Bluetooth Low Energy (BLE) hardware can be reused, making it suitable for dual mode implementation of BLE and NB-WiFi. The performance of the proposed PHY is simulated for an AWGN channel, and it achieves about 10dB improved sensitivity compared to a typical BLE receiver, due to the lower data rate.


title: Adaptive middleware for context-aware applications in smart-homes

abstract: We propose an adaptive middleware design for context-aware applications that abstracts the applications from the sensors that provide context. Further, we use application-specific utility functions to choose, given multiple alternatives for providing a specific context, one alternative at any time that provides the context for all applications, whilst maximising the applications' total "satisfaction" with the quality of context from the chosen provider. Our middleware also implements autonomic properties, such as self-configuration and resilience to failures, in the provision of context information to context-aware applications.


title: Gaze estimation using local features and non-linear regression

abstract: In this paper, we present an appearance-based gaze estimation method for a head-mounted eye tracker. The idea is to extract discriminative image descriptors with respect to gaze before applying a regression scheme. We employ multilevel Histograms of Oriented Gradients (HOG) features as our appearance descriptor. To learn the mapping between eye appearance and gaze coordinates, two learning-based approaches are evaluated : Support Vector Regression (SVR) and Relevance Vector Regression (RVR). Experimental results demonstrate that, despite the high dimensionality, our method works well and RVR provides a more efficient and generalized solution than SVR by retaining a low number of basis functions.


title: Memory Transformation Enhances Reinforcement Learning in Dynamic Environments.

abstract: Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as “memory transformation.” Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales. SIGNIFICANCE STATEMENT As time passes, memories transform from a highly detailed state to a more gist-like state, in a process called “memory transformation.” Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales.


title: Neural-net-induced Gaussian process regression for function approximation and PDE solution

abstract: Abstract Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity of deep neural networks (deep NNs) as well as the uncertainty quantification property of Gaussian processes (GPs). We generalize the current NNGP to first include a larger number of hyperparameters and subsequently train the model by maximum likelihood estimation. Unlike previous works on NNGP that targeted classification, here we apply the generalized NNGP to function approximation and to solving partial differential equations (PDEs). Specifically, we develop an analytical iteration formula to compute the covariance function of GP induced by deep NN with an error-function nonlinearity. We compare the performance of the generalized NNGP for function approximations and PDE solutions with those of GPs and fully-connected NNs. We observe that for smooth functions the generalized NNGP can yield the same order of accuracy with GP, while both NNGP and GP outperform deep NN. For non-smooth functions, the generalized NNGP is superior to GP and comparable or superior to deep NN.


title: Adversarial Perturbations Against Real-Time Video Classification Systems

abstract: Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification that does not deal with temporally varying inputs. In this paper we ask"Are adversarial perturbations possible in real-time video classification systems and if so, what properties must they satisfy?"Such systems find application in surveillance applications, smart vehicles, and smart elderly care and thus, misclassification could be particularly harmful (e.g., a mishap at an elderly care facility may be missed). We show that accounting for temporal structure is key to generating adversarial examples in such systems. We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities. More importantly, the samples also leave other activities largely unaffected making them extremely stealthy. Finally, we also surprisingly find that in many scenarios, the same perturbation can be applied to every frame in a video clip that makes the adversary's ability to achieve misclassification relatively easy.


title: Using Hard Macros to Reduce FPGA Compilation Time

abstract: The FPGA compilation process (synthesis, map, placement, routing) is a time-consuming process that limits designer productivity. Compilation time can be reduced by using pre-compiled circuit blocks (hard macros). Hard macros consist of previously synthesized, mapped, placed and routed circuitry that can be relatively placed with short tool runtimes and that make it possible to reuse previous computational effort. Two experiments were performed to demonstrate feasibility that hard macros can reduce compilation time. These experiments demonstrated that an augmented Xilinx flow designed specifically to support hard macros can reduce overall compilation time by 3x. Though the process of incorporating hard macros in designs is currently manual and error-prone, it can be automated to create compilation flows with much lower compilation time.


title: Cryptocurrency price drivers: Wavelet coherence analysis revisited

abstract: Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.


title: VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View

abstract: The automatic parking is being massively developed by car manufacturers and providers. Until now, there are two problems with the automatic parking. First, there is no openly-available segmentation labels of parking slot on panoramic surround view (PSV) dataset. Second, how to detect parking slot and road structure robustly. Therefore, in this paper, we build up a public PSV dataset. At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset. A surround-view image is made of four calibrated images captured from four fisheye cameras. We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base, with an extra efficient VH-stage for better segmenting various markings. The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively. This modification enables the network to robustly and precisely extract linear features. We evaluated our model on the PSV dataset and the results showed outstanding performance in ground markings segmentation. Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.


title: BCS-BEC crossover with a finite-range interaction

abstract: We study the crossover from BEC to BCS pairing for dilute systems but with a realistic finite-range interaction. We exhibit the changes in the excitation spectrum that provide a clean qualitative distinction between the two limits. We also study how the dilute system converges to the results from a zero-range pseudo-potential derived by Leggett.


title: An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 1: Methodology

abstract: The drafting of floor plans is mostly hand made in today's architectural design process. The use of computerized floor planning techniques may enhance the practitioner's range of solutions and expedite the design process. However, despite the research work that has been carried out, the results obtained from these techniques do not convince many practitioners to accept them as part of their design methods. The existing literature shows that every research approach is different in the way in which architectural space planning is tackled. Consequently, each approach tends to be too specific or too abstract. The Space Allocation Problem in architecture may be stated as the process of determining the position and size of several rooms and openings according to the user's specified design program requirements, and topological and geometric constraints in a two-dimensional space. This is the first part of a paper that describes an enhanced hybrid evolutionary computation scheme that couples an Evolutionary Strategy (ES) with a Stochastic Hill Climbing (SHC) technique to generate a set of floor plans to be used in the early design stages of architectural practice. It presents the mathematical model with the problem statement and how the individuals' fitness is computed, the implemented methodological approach, how the adaptive operators are implemented, the summary of the overall procedure, and conclusions.


title: Ontology Evolution: State of the Art and Future Directions

abstract: Ontologies evolve continuously throughout their lifecycle to respond to different change requirements. Several problems emanate from ontology evolution: capturing change requirements, change representation, change impact analysis and resolution, change validation, change traceability, change propagation to dependant artifacts, versioning, etc. The purpose of this chapter is to gather research and current developments to manage ontology evolution. The authors highlight ontology evolution issues and present a state-of-the-art of ontology evolution approach by describing issues raised and the ontology model considered (ontology representation language), and also the ontology engineering tools supporting ontology evolution and maintenance. Furthermore, they sum up the state-of-the-art review by a comparative study based on general characteristics, evolution functionalities supported, and specificities of the existing ontology evolution approaches. At the end of the chapter, the authors discuss future and emerging trends.


title: WEKA: The Waikato Environment for Knowledge Analysis

abstract: WEKA is a workbench designed to aid in the application of machine learning technology to real world data sets, in particular, data sets from New Zealand’s agricultural sector. In order to do this a range of machine learning techniques are presented to the user in such a way as to hide the idiosyncrasies of input and output formats, as well as allow an exploratory approach in applying the technology. The system presented is a component based one that also has application in machine learning research and education.


title: Intracellular Imaging with a Graphene-Based Fluorescent Probe

abstract: Graphene is an increasingly important nanomaterial that has shown great promise in the area of nanotechnology. In this study, fluorescein-functionalized graphene oxide (GO) is synthesized via a polyethylene glycol (PEG) bridge and its application in intracellular imaging is explored. GO is an oxide form of graphene that provides an ideal platform to prepare graphene-based functional nanomaterials via chemical modification. The PEG bridge was introduced to prevent GO-induced quenching of conjugated fluorescein. The fluorescein–PEG–GO conjugate thus prepared exhibits excellent pH-tunable fluorescent properties and, more significantly, can be efficiently taken up by cells and serve as a fluorescent nanoprobe for intracellular imaging.


Advances and Processes in Precision Glass Polishing Techniques


title: Foreign Body Granulomas after All Injectable Dermal Fillers: Part 2. Treatment Options

abstract: SUMMARY ::: Foreign body granulomas occur at certain rates with all injectable dermal fillers. They have to be distinguished from early implant nodules, which usually appear 2 to 4 weeks after injection. In general, foreign body granulomas appear after a latent period of several months at all injected sites at the same time. If diagnosed early and treated correctly, they can be diminished within a few weeks. The treatment of choice of this hyperactive granulation tissue is the intralesional injection of corticosteroid crystals (triamcinolone, betamethasone, or prednisolone), which may be repeated in 4-week cycles until the right dose is found. To lower the risk of skin atrophy, corticosteroids can be combined with antimitotic drugs such as 5-fluorouracil and pulsed lasers. Because foreign body granulomas grow fingerlike into the surrounding tissue, surgical excision should be the last option. Surgery or drainage is indicated to treat normal lumps and cystic foreign body granulomas with little tissue ingrowth. In most patients, a foreign body granuloma is a single event during a lifetime, often triggered by a systemic bacterial infection.


title: Missing data techniques in analogy-based software development effort estimation

abstract: Evaluating Analogy effort estimation techniques using three missing data approaches.Classical/Fuzzy Analogy with KNN give better accuracy than with toleration/deletion.Fuzzy Analogy generates more accurate estimates than Classical Analogy in all casesMissingness mechanisms have different negative impacts on the accuracy of Analogy. Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with two analogy-based software development effort estimation techniques: Classical Analogy and Fuzzy Analogy. More specifically, we analyze the predictive performance of these two analogy-based techniques when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques. A total of 1512 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results suggest that Fuzzy Analogy generates more accurate estimates in terms of the Standardized Accuracy measure (SA) than Classical Analogy regardless of the MD technique, the data set used, the missingness mechanism or the MD percentage. Moreover, this study found that the use of KNN imputation, rather than toleration or deletion, may improve the prediction accuracy of both analogy-based techniques. However, toleration, deletion and KNN imputation are affected by the missingness mechanism and the MD percentage, both of which have a strong negative impact upon effort prediction accuracy.


title: DATAFLASKS: Epidemic Store for Massive Scale Systems

abstract: Very large scale distributed systems provide some of the most interesting research challenges while at the same time being increasingly required by nowadays applications. The escalation in the amount of connected devices and data being produced and exchanged, demands new data management systems. Although new data stores are continuously being proposed, they are not suitable for very large scale environments. The high levels of churn and constant dynamics found in very large scale systems demand robust, proactive and unstructured approaches to data management. In this paper we propose a novel data store solely based on epidemic (or gossip-based) protocols. It leverages the capacity of these protocols to provide data persistence guarantees even in highly dynamic, massive scale systems. We provide an open source prototype of the data store and correspondent evaluation.


title: Out-of-order transmission enabled congestion and scheduling control for multipath TCP

abstract: With development of wireless communication technologies, mobile devices are commonly equipped with multiple network interfaces and ready to adopt emerging transport layer protocols such as multipath TCP (MPTCP). The protocol is specifically useful for Internet of Things streaming applications with critical latency and bandwidth demands. To achieve full potential of MPTCP, major challenges on congestion control, fairness, and path scheduling are identified and draw considerable research attention. In this paper, we propose a joint congestion control and scheduling algorithm allowing out-of-order transmission as an overall solution. It is achieved by adaptive window coupling, congestion discrimination, and delay-aware packet ordering. The algorithm is implemented in the Linux kernel for real-world experiments. Favorable results are obtained in both shared or distinct bottleneck scenarios.


title: Dissociative symptoms in individuals with gender dysphoria: Is the elevated prevalence real?

abstract: This study evaluated dissociative symptomatology, childhood trauma and body uneasiness in 118 individuals with gender dysphoria, also evaluating dissociative symptoms in follow-up assessments after sex reassignment procedures were performed. We used both clinical interviews (Dissociative Disorders Interview Schedule) and self-reported scales (Dissociative Experiences Scale). A dissociative disorder of any kind seemed to be greatly prevalent (29.6%). Moreover, individuals with gender dysphoria had a high prevalence of lifetime major depressive episode (45.8%), suicide attempts (21.2%) and childhood trauma (45.8%), and all these conditions were more frequent in patients who fulfilled diagnostic criteria for any kind of dissociative disorder. Finally, when treated, patients reported lower dissociative symptoms. RESULTS confirmed previous research about distress in gender dysphoria and improved mental health due to sex reassignment procedures. However, it resulted to be difficult to ascertain dissociation in the context of gender dysphoria, because of the similarities between the two conditions and the possible limited application of clinical instruments which do not provide an adequate differential diagnosis. Therefore, because the body uneasiness is common to dissociative experiences and gender dysphoria, the question is whether dissociation is to be seen not as an expression of pathological dissociative experiences but as a genuine feature of gender dysphoria. Language: en


title: The Case of Jeffrey Dahmer: Sexual Serial Homicide from a Neuropsychiatric Developmental Perspective

abstract: Sexual serial homicidal behavior has received considerable attention during the last three decades. Substantial progress has been made in the development of methods aimed at identifying and apprehending individuals who exhibit these behaviors. In spite of these advances, the origins of sexual serial killing behavior remain for the most part unknown. In this article we propose a biopsychosocial psychiatric model for understanding the origins of sexual serial homicidal behavior from both neuropsychiatric and developmental perspectives, using the case of convicted serial killer Jeffrey Dahmer as the focal point. We propose that his homicidal behavior was intrinsically associated with autistic spectrum psychopathology, specifically Asperger's disorder. The relationship of Asperger's disorder to other psychopathology and to his homicidal behavior is explored. We discuss potential implications of the proposed model for the future study of the causes of sexual serial homicidal crime. Language: en


title: Signal quality measures for pulse oximetry through waveform morphology analysis.

abstract: Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.


title: Intrusion detection system by fuzzy interpolation

abstract: Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated.


title: Depth recovery using an adaptive color-guided auto-regressive model

abstract: This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We formulate the depth recovery task into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. Experimental results show that our method outperforms existing state-of-the-art schemes, and is versatile for both mainstream depth sensors: ToF camera and Kinect.


title: Autonomous Forest Fire Detection

abstract: Forest fire detection is a very important issue in the pre-suppression process. Timely detection allows the suppression units to reach the fire in its initial stages and this will reduce the suppression costs considerably. The autonomous forest fire detection principle is based on temporal contrast differences with the natural background and spatial characteristics of the smoke plume. The images are obtained from multiple staring black and white video cameras installed on a small platform in a tower. To reduce the bandwidth of the needed communication link with the operational centre, all processing is performed locally at the tower, The detection algorithm sends alarm messages with the co-ordinates of the location of the fire automatically and on request images or system status are sent.


title: SnapShot: Visualization to Propel Ice Hockey Analytics

abstract: Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.


title: Motion deblurring for optical character recognition

abstract: This paper investigates the problem of blurring caused by motion during image capture of text documents. Motion blurring prevents proper optical character recognition of the document text contents. One area of such applications is to deblur name card images obtained from handheld cameras. In this paper, a complete motion deblurring procedure for document images has been proposed. The method handles both uniform linear motion blur and uniform acceleration motion blur. Experiments on synthetic and real-life blurred images prove the feasibility and reliability of this algorithm provided that the motion is not too irregular. The restoration procedure consumes only small amount of computation time.


Streaming Graph-Parallel Algorithms for Dynamic Community Detection using Spark GraphX


title: Cyclic Prefix-Based Universal Filtered Multicarrier System and Performance Analysis

abstract: Recently proposed universal filtered multicarrier (UFMC) system is not an orthogonal system in multipath channel environments and might cause significant performance loss. In this paper, the authors propose a cyclic prefix (CP) based UFMC system and first analyze the conditions for interference-free one-tap equalization in the absence of transceiver imperfections. Then the corresponding signal model and output signal-to-noise ratio expression are derived. In the presence of carrier frequency offset, timing offset, and insufficient CP length, the authors establish an analytical system model as a summation of desired signal, intersymbol interference, intercarrier interference, and noise. New channel equalization algorithms are proposed based on the derived analytical signal model. Numerical results show that the derived model matches the simulation results precisely, and the proposed equalization algorithms improve the UFMC system performance in terms of bit error rate.


title: Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms

abstract: Network Traffic Classification is a central topic nowadays in the field of computer science. It is a very essential task for internet service providers (ISPs) to know which types of network applications flow in a network. Network Traffic Classification is the first step to analyze and identify different types of applications flowing in a network. Through this technique, internet service providers or network operators can manage the overall performance of a network. There are many methods traditional technique to classify internet traffic like Port Based, Pay Load Based and Machine Learning Based technique. The most common technique used these days is Machine Learning (ML) technique. Which is used by many researchers and got very effective accuracy results. In this paper, we discuss network traffic classification techniques step by step and real time internet data set is develop using network traffic capture tool, after that feature extraction tool is use to extract features from the capture traffic and then four machine learning classifiers Support Vector Machine, C4.5 decision tree, Naive Bays and Bayes Net classifiers are applied. Experimental analysis shows that C4.5 classifiers gives very good accuracy result as compare to other classifies.


title: ‘What’ and ‘where’ in the human brain

abstract: Multiple visual areas in the cortex of nonhuman primates are organized into two hierarchically organized and functionally specialized processing pathways, a 'ventral stream' for object vision and a 'dorsal stream' for spatial vision. Recent findings from positron emission tomography activation studies have localized these pathways within the human brain, yielding insights into cortical hierarchies, specialization of function, and attentional mechanisms.


title: Regression Based Model for Autosteering of a Car with Delayed Steering Response

abstract: In our work we consider the area of intelligent road vehicles, especially, the topic of automated vehicles. Our objective is to automatically develop - by applying a supervised learning approach - the steering of a realistically simulated car featuring a steering delay and rate limit of turning of front wheels. Due to the adopted physical constrains, the typically used steering mechanisms (based on servo-control model) result in a non-stable, oscillating behavior of the controlled car. The proposed approach of automated development of steering for such a realistically simulated car employs the perception-action relationship obtained from the sample runs of an ideal car (featuring instant steering response) steered by the servo-control model. Then, implying that offsetting the perceptions in the obtained relationship back in time to the value equal to the steering delay would mimic the behavior of the car featuring steering delays, we used the so modified perception-action relationship to train the proposed regression based model. The experimental results verify that the derived automated steering controls the car featuring steering delays of 100 ms and 200 ms in a much similar way as the servo-control steers an ideal car. Moreover, for delay of 400 ms the steering, developed via proposed approach provides a better quality of control than that obtained from one of the most versatile unsupervised machine learning approaches - genetic programming. approach - the steering of a realistically simulated car featuringa steering delay and rate limit of turning of front wheels. Dueto the adopted physical constrains, the typically used steeringmechanisms (based on servo-control model) result in a non-stable, oscillating behavior of the controlled car. The proposed approachof automated development of steering for such a realistically simulatedcar employs the perception-action relationship obtainedfrom the sample runs of an ideal car (featuring instant steeringresponse) steered by the servo-control model. Then, implyingthat offsetting the perceptions in the obtained relationship backin time to the value equal to the steering delay would mimicthe behavior of the car featuring steering delays, we used theso modified perception-action relationship to train the proposedregression based model. The experimental results verify that thederived automated steering controls the car featuring steeringdelays of 100 ms and 200 ms in a much similar way as theservo-control steers an ideal car. Moreover, for delay of 400ms the steering, developed via proposed approach provides abetter quality of control than that obtained from one of the mostversatile unsupervised machine learning approaches - geneticprogramming.


title: Cellular texture generation

abstract: We propose an approach for modeling surface details such as scales, feathers, or thorns. These types of cellular textures require a representation with more detail than texture-mapping but are inconvenient to model with hand-crafted geometry. ::: ::: We generate patterns of geometric elements using a biologically-motivated cellular development simulation together with a constraint to keep the cells on a surface. The surface may be defined by an implicit function, a volume dataset, or a polygonal mesh. Our simulation combines and extends previous work in developmental ::: models and constrained particle systems.


Intrinsic Classification by MML—the Snob Program


title: Automatic on-the-fly extrinsic camera calibration of onboard vehicular cameras

abstract: This paper presents an on-the-fly procedure for obtaining extrinsic camera parameters of onboard vehicular cameras, as well as augmented reality applications for driver assistance systems and road inspection. The proposed approach employs a lane detection algorithm to extract the lane boundaries of the road, and estimates the distance between adjacent lane markings based on the speed of the vehicle. Then, a rectangular portion of the road is used to obtain the desired extrinsic camera parameters. With the complete set of camera parameters, virtual objects can be coherently inserted into the video sequence captured by the camera, so that synthetic traffic signs may be added to increase safety. Experimental results illustrate the performance of the proposed camera calibration systems, as well as possible applications involving augmented reality.


title: Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks

abstract: Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, such as wifi, bluetooth, and physical connections, they can access a car's controller area network (CAN) bus. On the CAN bus, commands can be sent to control the car, for example cutting the brakes or stopping the engine. While securing the car's interfaces to the outside world is an important part of mitigating this threat, the last line of defence is detecting malicious behaviour on the CAN bus. We propose an anomaly detector based on a Long Short-Term Memory neural network to detect CAN bus attacks. The detector works by learning to predict the next data word originating from each sender on the bus. Highly surprising bits in the actual next word are flagged as anomalies. We evaluate the detector by synthesizing anomalies with modified CAN bus data. The synthesized anomalies are designed to mimic attacks reported in the literature. We show that the detector can detect anomalies we synthesized with low false alarm rates. Additionally, the granularity of the bit predictions can provide forensic investigators clues as to the nature of flagged anomalies.


title: Deep learning for skin lesion segmentation

abstract: Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains dermoscopic images paired with their corresponding lesion binary masks, provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection, and compare against the benchmark results submitted by other participants. The experimental results show that our proposed method can outperform the submissions in terms of segmentation accuracy.


title: Enhancing the reliability of services in NFV with the cost-efficient redundancy scheme

abstract: Network Function Virtualization (NFV) transforms the traditional service provision architecture into a software-based structure. In NFV, Virtual Network Functions Forwarding Graph (VNF FG) [1] is used to describe the logic connections among the VNFs placed on the Physical Network (PN). And end-to-end services are represented as flows traverse the VNF FG. To improve the reliability of the services, redundancy is proposed as an effective method. Existing redundancy method protects the unreliable VNF for end-to-end service independently and embeds the backup without considering the reliability of the PN hardware. Such a scheme ignores the global information of the VNF FG and leads to an inaccurate reliability estimation for the services. These deficiencies result in over backup and the utilization reduction of the underlying resource. To overcome these shortcomings, we propose a novel redundancy scheme. By using the Cost-aware Importance Measure (CIM), the structure of the VNF FG is involved in selecting the qualified backups. Meanwhile, with CIM, the placement decision is made by mapping these backups to the PN nodes with high reliability. In our simulation, our method performs well for cutting down the backup cost for up to 46% with respect to the present algorithms and keeping high cost-efficiency.


title: Reachability in MDPs: Refining Convergence of Value Iteration

abstract: Markov Decision Processes (MDP) are a widely used model including both non-deterministic and probabilistic choices. Minimal and maximal probabilities to reach a target set of states, with respect to a policy resolving non-determinism, may be computed by several methods including value iteration. This algorithm, easy to implement and efficient in terms of space complexity, consists in iteratively finding the probabilities of paths of increasing length. However, it raises three issues: (1) defining a stopping criterion ensuring a bound on the approximation, (2) analyzing the rate of convergence, and (3) specifying an additional procedure to obtain the exact values once a sufficient number of iterations has been performed. The first two issues are still open and for the third one a “crude” upper bound on the number of iterations has been proposed. Based on a graph analysis and transformation of MDPs, we address these problems. First we introduce an interval iteration algorithm, for which the stopping criterion is straightforward. Then we exhibit convergence rate. Finally we significantly improve the bound on the number of iterations required to get the exact values.


Candidate re-ranking for SMT-based grammatical error correction


HUMAN ACTION RECOGNITION IN VIDEOS : A SURVEY


title: The mediating role of interpersonal competence between adolescents' empathy and friendship quality: A dyadic approach

abstract: Abstract The current study examined the effect of empathy on friendship quality in the context of dyadic same-sex friendships, and how such an effect might be mediated by interpersonal competence. A special version of the actor–partner interdependence model (APIM) was used to examine this hypothesis in 146 same-sex friend dyads in 10th grade. Results showed that empathy was positively related to intimacy and conflict management competences. Also, adolescents higher in intimacy and conflict management competences had more friendship closeness and less discord, respectively, as perceived by both members. Consistent with our hypothesis, the relationship between empathy and self- and friend-reports of friendship closeness and discord were mediated by adolescents' intimacy and conflict management competence, respectively. These findings emphasize the importance of empathy and interpersonal competence in adolescent friendships, and of considering the interdependence of these constructs in friend dyads.


title: Exposure to a Northern Contaminant Mixture (NCM) Alters Hepatic Energy and Lipid Metabolism Exacerbating Hepatic Steatosis in Obese JCR Rats

abstract: Non-alcoholic fatty liver disease (NAFLD), defined by the American Liver Society as the buildup of extra fat in liver cells that is not caused by alcohol, is the most common liver disease in North America. Obesity and type 2 diabetes are viewed as the major causes of NAFLD. Environmental contaminants have also been implicated in the development of NAFLD. Northern populations are exposed to a myriad of persistent organic pollutants including polychlorinated biphenyls, organochlorine pesticides, flame retardants, and toxic metals, while also affected by higher rates of obesity and alcohol abuse compared to the rest of Canada. In this study, we examined the impact of a mixture of 22 contaminants detected in Inuit blood on the development and progression of NAFLD in obese JCR rats with or without co-exposure to 10% ethanol. Hepatosteatosis was found in obese rat liver, which was worsened by exposure to 10% ethanol. NCM treatment increased the number of macrovesicular lipid droplets, total lipid contents, portion of mono- and polyunsaturated fatty acids in the liver. This was complemented by an increase in hepatic total cholesterol and cholesterol ester levels which was associated with changes in the expression of genes and proteins involved in lipid metabolism and transport. In addition, NCM treatment increased cytochrome P450 2E1 protein expression and decreased ubiquinone pool, and mitochondrial ATP synthase subunit ATP5A and Complex IV activity. Despite the changes in mitochondrial physiology, hepatic ATP levels were maintained high in NCM-treated versus control rats. This was due to a decrease in ATP utilization and an increase in creatine kinase activity. Collectively, our results suggest that NCM treatment decreases hepatic cholesterol export, possibly also increases cholesterol uptake from circulation, and promotes lipid accumulation and alters ATP homeostasis which exacerbates the existing hepatic steatosis in genetically obese JCR rats with or without co-exposure to ethanol.


Exemplar Guided Unsupervised Image-to-Image Translation


title: Event-based Dynamic Face Detection and Tracking Based on Activity.

abstract: We present the first purely event-based approach for face detection using an ATIS, a neuromorphic camera. We look for pairs of blinking eyes by comparing local activity across the input frame to a predefined range, which is defined by the number of events per second in a specific location. If within range, the signal is checked for additional constraints such as duration, synchronicity and distance between the eyes. After a valid blink is registered, we await a second blink in the same spot to initiate Gaussian trackers above the eyes. Based on their position, a bounding box around the estimated outlines of the face is drawn. The face can then be tracked until it is occluded.


title: Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment

abstract: This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach.


title: Structure in 5's: A Synthesis of the Research on Organization Design

abstract: The elements of organizational structuring-which show a curious tendency to appear in five's-suggest a typology of five basic configurations: Simple Structure, Machine Bureaucracy, Professional Bureaucracy, Divisionalized Form, and Adhocracy. ::: ::: The elements include 1 five basic parts of the organization-the operating core, strategic apex, middle line, technostructure, and support staff; 2 five basic mechanisms of coordination-mutual adjustment, direct supervision, and the standardization of work processes, outputs, and skills; 3 the design parameters-job specialization, behavior formalization, training and indoctrination, unit grouping, unit size, action planning and performance control systems, liaison devices such as integrating managers, teams, task forces, and matrix structure, vertical decentralization delegation to line managers, and horizontal decentralization power sharing by nonmanagers; and 4 the contingency factors-age and size, technical system, environment, and power. ::: ::: Each of the five configurations relies on one of the five coordinating mechanism and tends to favor one of the five parts. In Simple Structure, the key part is the strategic apex, which coordinates by direct supervision; the structure is minimally elaborated and highly centralized; it is associated with simple, dynamic environments and strong leaders, and tends to be found in smaller, younger organizations or those facing severe crises. The Machine Bureaucracy coordinates primarily by the imposition of work standards from the technostructure; jobs are highly specialized and formalized, units functional and very large at the operating level, power centralized vertically at the strategic apex with limited horizontal decentralization to the technostructure; this structure tends to be found in simple, stable environments, and is often associated with older, larger organizations, sometimes externally controlled, and mass production technical systems. The Professional Bureaucracy relies on the standardization of skills in its operating core for coordination; jobs are highly specialized but minimally formalized, training is extensive and grouping is on a concurrent functional and market basis, with large sized operating units, and decentralization is extensive in both the vertical and horizontal dimensions; this structure is typically found in complex but stable environments, with technical systems that are simple and non-regulating. In the Divisionalized Form, a good deal of power is delegated to market-based units in the middle line limited vertical decentralization, whose efforts are coordinated by the standardization of outputs, through the extensive use of performance control systems; such structures are typically found in very large, mature organizations, above all operating in diversified markets. Adhocracy coordinates primarily by mutual adjustment among all of its parts, calling especially for the collaboration of its support staff; jobs are specialized, involving extensive training but little formalization, units are small and combine functional and market bases in matrix structures, liaison devices are used extensively, and the structure is decentralized selectively in both the vertical and horizontal dimensions; these structures are found in complex, dynamic environments, and are often associated with highly sophisticated and automated technical systems. ::: ::: In conclusion, it is claimed that the effective Organization will favor some sort of configuration-some type of a logically consistent clustering of its elements-as it searches for harmony in its internal processes and consonance with its environment. But some organizations will inevitably be driven to hybrid structures as they react to contradictory pressures or while they effect a transition from one configuration to another, and here too it is believed that the typology of five can serve as a diagnostic tool in organizational design.


title: User-Driven Access Control: Rethinking Permission Granting in Modern Operating Systems

abstract: Modern client platforms, such as iOS, Android, Windows Phone, Windows 8, and web browsers, run each application in an isolated environment with limited privileges. A pressing open problem in such systems is how to allow users to grant applications access to user-owned resources, e.g., to privacy- and cost-sensitive devices like the camera or to user data residing in other applications. A key challenge is to enable such access in a way that is non-disruptive to users while still maintaining least-privilege restrictions on applications. In this paper, we take the approach of user-driven access control, whereby permission granting is built into existing user actions in the context of an application, rather than added as an afterthought via manifests or system prompts. To allow the system to precisely capture permission-granting intent in an application's context, we introduce access control gadgets (ACGs). Each user-owned resource exposes ACGs for applications to embed. The user's authentic UI interactions with an ACG grant the application permission to access the corresponding resource. Our prototyping and evaluation experience indicates that user-driven access control is a promising direction for enabling in-context, non-disruptive, and least-privilege permission granting on modern client platforms.


Some Simultaneous Confidence Bounds in Normal Models with Restricted Alternatives


title: Autonomous vision-based exploration and mapping using hybrid maps and Rao-Blackwellised particle filters

abstract: This paper addresses the problem of exploring and mapping an unknown environment using a robot equipped with a stereo vision sensor. The main contribution of our work is a fully automatic mapping system that operates without the use of active ranger sensors (such as laser or sonic transducers), can operate in real-time and can consistently produce accurate maps of large-scale environments. Our approach implements a Rao-Blackwellised particle filter (RBPF) to solve the simultaneous localization and mapping problem and uses efficient data structures for real-time data association, mapping, and spatial reasoning. We employ a hybrid map representation that infers 3D point landmarks from image features to achieve precise localization, coupled with occupancy grids for safe navigation. This paper describes our framework and implementation, and presents our exploration method, and experimental results illustrating the functionality of the system.


title: Error modeling and improved position estimation for optical incremental encoders by means of time stamping

abstract: Optical incremental encoders are extensively used for position measurements in motion systems. The number of slits on the encoder disk defines the resolution of the encoder and bounds the accuracy of the position measurement. The encoder position measurements suffer from quantization errors. Moreover, encoder imperfections occur due to manufacturing tolerances. In this paper a method, which is based on time stamping, is proposed to obtain more accurate position and velocity estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, which are captured at a high resolution clock. Low order polynomial fitting through these encoder events is combined with active compensation of the encoder errors. The proposed method is applied in real-time experiments to a motion system. The results show an improvement in the position accuracy of up to 87% in terms of the maximum error. The estimated velocity is also much more accurate than the differentiated quantized encoder output signal.


title: An Energy-Efficient Diagonal-Based Directed Diffusion for Wireless Sensor Networks

abstract: Advances in processor, memory, and radio technology will enable small and cheap nodes capable of sensing, communication, and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed-diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed-diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.


title: Indirect method to estimate distance measurement based on single visual cameras

abstract: In this paper, we proposed an indirect method to measure the distance of an object accurately by single visual cameras using triangulation. The object can be seen as the third point of a triangle with two known sides and one known angle. Distance to object can be determined indirectly on the base of known sides and angle, rather than being measured directly. It would be very useful in case there is no line of sight to object (inaccessible) or an obstacle interrupts it. Furthermore, the results show that the measured distance using the indirect method has a less measurement error than the one using the direct method. This method establishes a basis for the implementation of the position algorithm into the navigation subsystem of swarm robots and will be very helpful especially in robot cooperation.


title: Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks

abstract: Abstract This work shows two innovative hybrid methodologies capable of performing short and long term wind speed predictions from the mathematical junction of two classical time series models the Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) and the Holt-Winters (HW), both combined with Artificial Neural Networks (ANN). The first hybrid model (ARIMAX and ANN) is made from the physical relations between pressure, temperature and precipitation with the wind speed, that is, this model is considered as multivariate. The second hybrid model (HW and ANN) is considered as univariate, i.e. allowing only wind speed inputs. By means of statistical analysis of error it is verified that the proposed hybrid models offer perfect adjustments to the observed data at the regions of study, and thus, better comparisons with traditional ones from the literature. It is possible to find in this analysis percentage error of 5.0% and efficiency coefficient (Nash-Sutcliffe) of approximately 0.96. The confirmation of accuracy by the hybrid models reveals that they provide time series that are able to follow the observed time series profiles with similarities of maximum and minimum values between both series. Therefore, it became an important indicative in the representation of characteristics of seasonality by the models.


title: A Novel Search Engine for Virtual Screening of Very Large Databases

abstract: Virtual screening of large chemical databases using the structure of the receptor can be computationally very demanding. We present a novel strategy that combines exhaustive similarity searches directly in SMILES format with the docking of flexible ligands, whose 3D structure is generated on the fly from the SMILES representation. Our strategy makes use of the recently developed LINGO tools to extract implicit chemical information from SMILES strings and integrates LINGO similarities into a pseudo-evolutionary algorithm. The algorithm represents a combination of a fast target-independent similarity method with a slower but information richer target-focused method. A virtual search of FactorXa ligands provided 62% of the potential hits after docking only 6.5% of a database of nearly 1 million molecules. The set of solutions showed good diversity, indicating that the method shows good scaffold hopping capabilities.


title: Sutureless circumcision: a prospective randomised controlled study

abstract: Our aim was to study the advantages of glue versus sutures for circumcision in children. A randomised prospective controlled study was conducted with 152 boys; glue was used on 80 and sutures on 72). The procedures were quicker and the duration and severity of postoperative pain were significantly less (p<0.001) in the cases in which glue was used. The tissue glue is a perfectly feasible alternative to sutures for circumcision in children and has potentially significant advantages.


title: Recommendations on basic requirements for intensive care units: structural and organizational aspects

abstract: Objective: To provide guidance and recommendations for the planning or renovation of inten- sive care units (ICUs) with respect to the specific characteristics relevant to organizational and structural aspects of intensive care medicine. Methodology: The Working Group on Quality Improvement (WGQI) of the European Society of Intensive Care Medicine (ESICM) identified the basic requirements for ICUs by a comprehensive literature search and an iterative process with several rounds of consensus finding with the participation of 47 intensive care physicians from 23 countries. The starting point of this process was an ESICM recommendation published in 1997 with the need for an updated version. Results: The document consists of operational guidelines and design recommendations for ICUs. In the first part it covers the definition and objectives of an ICU, functional criteria, activity criteria, and the management of equipment. The sec- ond part deals with recommendations with respect to the planning process, floorplan and connections, accom- modation, fire safety, central services, and the necessary communication systems. Conclusion: This docu- ment provides a detailed framework for the planning or renovation of ICUs based on a multinational con- sensus within the ESICM.


title: A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing

abstract: This paper presents a programmable in-memory-computing processor, demonstrated in a 65nm CMOS technology. For data-centric workloads, such as deep neural networks, data movement often dominates when implemented with today's computing architectures. This has motivated spatial architectures, where the arrangement of data-storage and compute hardware is distributed and explicitly aligned to the computation dataflow, most notably for matrix-vector multiplication. In-memory computing is a spatial architecture where processing elements correspond to dense bit cells, providing local storage and compute, typically employing analog operation. Though this raises the potential for high energy efficiency and throughput, analog operation has significantly limited robustness, scale, and programmability. This paper describes a 590kb in-memory-computing accelerator integrated in a programmable processor architecture, by exploiting recent approaches to charge-domain in-memory computing. The architecture takes the approach of tight coupling with an embedded CPU, through accelerator interfaces enabling integration in the standard processor memory space. Additionally, a near-memory-computing datapath both enables diverse computations locally, to address operations required across applications, and enables bit-precision scalability for matrix/input-vector elements, through a bit-parallel/bit-serial (BP/BS) scheme. Chip measurements show an energy efficiency of 152/297 1b-TOPS/W and throughput of 4.7/1.9 1b-TOPS (scaling linearly with the matrix/input-vector element precisions) at VDD of 1.2/0.85V. Neural network demonstrations with 1-b/4-b weights and activations for CIFAR-10 classification consume 5.3/105.2 $\mu$J/image at 176/23 fps, with accuracy at the level of digital/software implementation (89.3/92.4 $\%$ accuracy).


title: ROLE OF INTERCONNECTIONS IN POROUS BIOCERAMICS ON BONE RECOLONIZATION IN VITRO AND IN VIVO

abstract: The interconnections in a porous biomaterial are the pathways between the pores. They conduct cells and vessels between pores. Thus they favour bone ingrowth inside ceramics. The aim of our study was to determine the effect on bone ingrowth of interconnections in two ceramics: hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) with the same porosity of about 50% and a mean pores size of 100–300 μm and a mean interconnection size of 30–100 μm. In vitro, four discs for osteoblast culture were studied after 14 and 28 days of incubation. The results show that human osteoblasts can penetrate interconnections over 20 μm in size, and colonize and proliferate inside macropores, but the most favourable size is over 40 μm. In vivo, eight cylinders were implanted in the middle shaft of both rabbit femurs for 12 or 24 weeks. The histomorphometric results show that interconnections in porous ceramics favour bone ingrowth inside the macropores. In the HA group the rate of calcification and bone ingrowth do not differ, and chondroid tissue is observed inside pores. But in β-TCP, the calcification rate and the bone ingrowth increased significantly. At week 12 significant correlation between new bone ingrowth and the size of the interconnections is observed between new bone ingrowth and the density of pores. In conclusion we notice that in vivo a 20 μm interconnection size only allows cell penetration and chondroid tissue formation; however the size of the interconnections must be over 50 μm to favour new bone ingrowth inside the pores. We propose the concept of “interconnection density” which expresses the quantity of links between pores of porous materials. It assures cell proliferation and differentiation with blood circulation and extracellular liquid exchange. In resorbable materials, pore density and interconnection density are more important than their size, contrary to unresorbable materials in which the sizes and the densities are equally important. © 1999 Kluwer Academic Publishers


title: Blind source separation methods for deconvolution of complex signals in cancer biology

abstract: Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.