title: Physician-Assisted Suicide: Why Neutrality by Organized Medicine Is Neither Neutral Nor Appropriate

abstract: It has been proposed that medical organizations adopt neutrality with respect to physician-assisted suicide (PAS), given that the practice is legal in some jurisdictions and that membership is divided. We review developments in end-of-life care and the role of medical organizations with respect to the legalization of PAS since the 1990s. We argue that moving from opposition to neutrality is not ethically neutral, but a substantive shift from prohibited to optional. We argue that medical organizations already oppose many practices that are legal in many jurisdictions, and that unanimity among membership has not been required for any other clinical or ethical policy positions. Moreover, on an issue so central to the meaning of medical professionalism, it seems important for organized medicine to take a stand. We subsequently review the arguments in favor of PAS (arguments from autonomy and mercy, and against the distinction between killing and allowing to die (K/ATD)) and the arguments against legalization (the limits of autonomy, effects on the patient-physician relationship, the meaning of healing, the validity of the K/ATD distinction, the social nature of suicide, the availability of alternatives, the propensity for incremental extension, and the meaning of control). We conclude that organized medicine should continue its opposition to PAS.


title: Estimating 3D Scene Flow from Multiple 2D Optical Flows

abstract: Scene flow is the motion of the surface points in the 3D world. For a camera, it is seen as a 2D optical flow in the image plane. Knowing the scene flow can be very useful as it gives an idea of the surface geometry of the objects in the scene and how those objects are moving. Four methods for calculating the scene flow given multiple optical flows have been explored and detailed in this paper along with the basic mathematics surrounding multi-view geometry. It was found that given multiple optical flows it is possible to estimate the scene flow to different levels of detail depending on the level of prior information present.


title: Optimized NURBS Ship Hull Fitting using Simulated Annealing

abstract: The shape of a NURBS curve or surface is defined by the location of its control points, the control points' weights, and the knot vectors. Most of the curve and surface design tried to modify the control points. However, it is still impossible to obtained accurate control points for a reconstructed NURBS curve or surface. The fitness of these surfaces is generally considered a subjective notion depending on the judgment of the designers (e.g. visually pleasing, devoid of unnecessary bumps or wiggles, satisfying certain continuity requirements). One of the restriction in ship hull design is that the existing details of a ship design on paper or a ship that already been constructed physically couldn 't be reconstructed accurately and concisely in a ship hull design software. This research strives to solve this problem by creating fair ship hull surface using NURBS. An optimized NURBS ship hull fitting approach using simulated annealing is developed. Evaluation and analysis (accuracy, fairness and speed of processing) on the proposed approach is done


title: An Analysis of the XSL Algorithm

abstract: The XSL algorithm is a method for solving systems of multivariate polynomial equations based on the linearization method. It was proposed in 2002 as a dedicated method for exploiting the structure of some types of block ciphers, for example the AES and Serpent. Since its proposal, the potential for algebraic attacks against the AES has been the source of much speculation. Although it has attracted a lot of attention from the cryptographic community, currently very little is known about the effectiveness of the XSL algorithm. In this paper we present an analysis of the XSL algorithm, by giving a more concise description of the method and studying it from a more systematic point of view. We present strong evidence that, in its current form, the XSL algorithm does not provide an efficient method for solving the AES system of equations.


title: Experimental performance comparison between TCP vs UDP tunnel using OpenVPN

abstract: The comparison between TCP and UDP tunnels have not been sufficiently reported in the scientific literature. In this work, we use OpenVPN as a platform to demonstrate the performance between TCP/UDP. The de facto belief has been that TCP tunnel provides a permanent tunnel and therefore ensures a reliable transfer of data between two end points. However the effects of transmitting TCP within a UDP tunnel has been explored and could provide a valuable attempt. The results provided in this paper demonstrates that indeed TCP in UDP tunnel provides better latency. Throughout this paper, a series of tests have been performed, UDP traffic was sent inside UDP tunnel and TCP tunnel successively. The same tests was performed using TCP traffic.


title: Spin-Transfer Torque Devices for Logic and Memory: Prospects and Perspectives

abstract: As CMOS technology begins to face significant scaling challenges, considerable research efforts are being directed to investigate alternative device technologies that can serve as a replacement for CMOS. Spintronic devices, which utilize the spin of electrons as the state variable for computation, have recently emerged as one of the leading candidates for post-CMOS technology. Recent experiments have shown that a nano-magnet can be switched by a spin-polarized current and this has led to a number of novel device proposals over the past few years. In this paper, we provide a review of different mechanisms that manipulate the state of a nano-magnet using current-induced spin-transfer torque and demonstrate how such mechanisms have been engineered to develop device structures for energy-efficient on-chip memory and logic.


title: Vectorization vs. compilation in query execution

abstract: Compiling database queries into executable (sub-) programs provides substantial benefits comparing to traditional interpreted execution. Many of these benefits, such as reduced interpretation overhead, better instruction code locality, and providing opportunities to use SIMD instructions, have previously been provided by redesigning query processors to use a vectorized execution model. In this paper, we try to shed light on the question of how state-of-the-art compilation strategies relate to vectorized execution for analytical database workloads on modern CPUs. For this purpose, we carefully investigate the behavior of vectorized and compiled strategies inside the Ingres VectorWise database system in three use cases: Project, Select and Hash Join. One of the findings is that compilation should always be combined with block-wise query execution. Another contribution is identifying three cases where "loop-compilation" strategies are inferior to vectorized execution. As such, a careful merging of these two strategies is proposed for optimal performance: either by incorporating vectorized execution principles into compiled query plans or using query compilation to create building blocks for vectorized processing.


title: Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer

abstract: This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques.


title: Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI Applications

abstract: Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument's hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects.


title: Neural Expectation Maximization

abstract: Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is accurately able to recover the constituent objects. We demonstrate that the learned representations are useful for predictive coding.


title: Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users

abstract: We examined anxiety when wireless mobile devices were unexpectedly not available.Participants felt significantly more anxious over time.Heavy wireless mobile device users were more anxious than low or moderate users.Wireless mobile device dependency may cause anxiety when device is absent.Heavy wireless mobile device users may feel separation anxiety when device is absent. Overuse of wireless mobile devices (WMDs) may be associated with a form of psychological dependency, of which a prominent feature may be anxiety arising from separation from these devices. College students, who are among the most avid consumers of WMDs, might be susceptible to the negative effects of WMD overuse. The present study examined anxiety in American college students when their WMDs were unexpectedly not available. Upon arrival, approximately one half of the 163 participants were randomly assigned to have their WMDs removed from their possession; the other half was allowed to keep their WMDs but were required to turn them off and place them out of sight. Participants were forced to sit quietly with no distractions during the study. The state portion of the State/Trait Anxiety Inventory (STAI) was administered three times, 20min apart, beginning 10min after the participants entered the room. The results showed that participants felt significantly more anxious over time. However, this pattern was evident only with heavy WMD users and with moderate WMD users whose devices were taken away. Dependency upon WMDs, mediated by an unhealthy connection to their constant use, may lead to increased anxiety when the device is absent.


title: A performance and energy comparison of FPGAs, GPUs, and multicores for sliding-window applications

abstract: With the emergence of accelerator devices such as multicores, graphics-processing units (GPUs), and field-programmable gate arrays (FPGAs), application designers are confronted with the problem of searching a huge design space that has been shown to have widely varying performance and energy metrics for different accelerators, different application domains, and different use cases. To address this problem, numerous studies have evaluated specific applications across different accelerators. In this paper, we analyze an important domain of applications, referred to as sliding-window applications, when executing on FPGAs, GPUs, and multicores. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that FPGAs can achieve speedup of up to 11x and 57x compared to GPUs and multicores, respectively, while also using orders of magnitude less energy.


title: Chemical composition and toxicity of the essential oils from Cunila species (Lamiaceae) on the cattle tick Rhipicephalus (Boophilus) microplus

abstract: The essential oils obtained from the aerial parts of five species of Cunila (Lamiaceae) native to Southern Brazil were analyzed by GC and GC/MS. The oil of Cunila angustifolia was characterized by sabinene; Cunila incana is rich in alpha-pinene and beta-pinene, Cunila spicata and Cunila microcephala presented menthofuran as the main component, and in the essential oil of Cunila incisa, the major component was 1,8-cineole. Laboratory tests were carried out to determine the effect of the essential oils of the above cited plants on larvae of the cattle tick Riphicephalus (Boophilus) microplus. C. angustifolia, C. incana, and C. spicata were the most active samples killing almost the totality of the larvae. C. incisa and C. microcephala showed low acaricidal effect.


title: Predicting future reviews: sentiment analysis models for collaborative filtering

abstract: This paper presents hierarchical topic models for integrating sentiment analysis with collaborative filtering. Our goal is to automatically predict future reviews to a given author from previous reviews. For this goal, we focus on differentiating author's preference, while previous sentiment analysis models process these review articles without this difference. Therefore, we propose a Latent Evaluation Topic model (LET) that infer each author's preference by introducing novel latent variables into author and his/her document layer. Because these variables distinguish the variety of words in each article by merging similar word distributions, LET incorporates the difference of writers' preferences into sentiment analysis. Consequently LET can determine the attitude of writers, and predict their reviews based on like-minded writers' reviews in the collaborative filtering approach. Experiments on review articles show that the proposed model can reduce the dimensionality of reviews to the low-dimensional set of these latent variables, and is a significant improvement over standard sentiment analysis models and collaborative filtering algorithms.


title: A Coherence Model Based on Syntactic Patterns

abstract: We introduce a model of coherence which captures the intentional discourse structure in text. Our work is based on the hypothesis that syntax provides a proxy for the communicative goal of a sentence and therefore the sequence of sentences in a coherent discourse should exhibit detectable structural patterns. Results show that our method has high discriminating power for separating out coherent and incoherent news articles reaching accuracies of up to 90%. We also show that our syntactic patterns are correlated with manual annotations of intentional structure for academic conference articles and can successfully predict the coherence of abstract, introduction and related work sections of these articles.


title: Understanding the Primary School Students’ van Hiele Levels of Geometry Thinking in Learning Shapes and Spaces: A Q-Methodology

abstract: This study was conducted using a new hybrid method of research which combined qualitative and quantitative designs to investigate the viewpoints of primary school students' conceptual understanding in learning geometry from the aspect of shapes and spaces according to van Hiele theory. Q-methodology is used in this research to find out what factors (level) of geometry understanding in van Hiele theory contributed to the geometry learning of primary school children. Q-methodology involved creating a concourse-Q set which was developed through a literature review and the statements were later refined by experts and then conducted to 30 participants aged 12 at a primary school. The respondents expressed their understanding through Q sorting, by ranking strategies according to their geometry understanding. The results indicated that students' van Hiele Levels of Geometry Thinking are at the factor (a) Level 1-Analysis (or Descriptive); and factor (b) Level 0-Recognition (or Visualization). This common outcome revealed that the students' geometry understanding according to van Hiele theory is at the lower level. Significantly, the findings indicate that the deficiency of van Hiele Levels of Geometry Thinking appears to be global in nature, crossing the boundaries of educational practices and curriculum.


title: Learning Semantic Scene Models by Trajectory Analysis

abstract: In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, sources and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.


title: Eye movements during fixation: implications to long-term viewing of small optical sources

abstract: With the easy attainability of hand-held laser devices and burgeoning Light Emitting Diode (LED) technology, safety standards for long-term viewing of continuous light sources are being scrutinized. One concern is with formalizing the effect of eye movements on smearing energy from a small optical source over the retina. This experiment describes target motion over the retina as a result of eye movements during a deliberate fixation task. Volunteers fixated, with head and chin rest support, on a lmW HeNe laser source attenuated to 0.6pW/cm2, 5μW/cm2 or 10μW/cm2 comeal irradiance. The source was masked by a 25μm aperture yielding a small source subtending 0.1 minutes of arc visual angle. A dual Purkinje Eye-Tracker measured eye position every 243μs during each 100-second fixation trial. The data showed a non-uniform retinal energy distribution with an elliptical footprint. The major axis was, depending on condition, 3 to 4 times greater than the minor axis, and oriented along the temporal/nasal retinal axi...


title: Texture Synthesis with Spatial Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN). To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs. ::: Our method has the following features which make it a state of the art algorithm for texture synthesis: high image quality of the generated textures, very high scalability w.r.t. the output texture size, fast real-time forward generation, the ability to fuse multiple diverse source images in complex textures. To illustrate these capabilities we present multiple experiments with different classes of texture images and use cases. We also discuss some limitations of our method with respect to the types of texture images it can synthesize, and compare it to other neural techniques for texture generation.


title: Hardware assisted control flow obfuscation for embedded processors

abstract: With more applications being deployed on embedded platforms, software protection becomes increasingly important. This problem is crucial on embedded systems like financial transaction terminals, pay-TV access-control decoders, where adversaries may easily gain full physical accesses to the systems and critical algorithms must be protected from being cracked. However, as this paper points out that protecting software with either encryption or obfuscation cannot completely preclude the control flow information from being leaked. Encryption has been widely studied and employed as a traditional approach for software protection, however, the control flow information is not 100% hidden with solely encrypting the code. On the other hand, pure software-based obfuscation has been proved inefficient to protect software due to its lack of theoretical foundation and considerable performance overhead introduced by complicated transformations. Moreover, even though obfuscation can prevent static reverse engineering, attacker can still successfully bypass the obfuscation by monitoring the dynamic program execution.To address all of these shortcomings, this paper presents a hardware assisted obfuscation technique that is capable of obfuscating the control flow information dynamically. Dynamic obfuscation changes memory access sequence on-the-fly and conceals recurrent instruction access sequences from being identified. Our scheme makes it provably difficult for the attacker to extract any useful information. Our results show that a high-level security protection is possible with only minor performance penalty. Finally, we show that our scheme can be implemented on embedded systems with very little hardware overhead.


title: A Review of Risk Management in Different Software Development Methodologies

abstract: ABSTRACT Different software development methodologies exist. Choosing the methodology that best fits a software project depends on several factors. One important factor is how risky the project is. Another factor is the degree to which each methodology supports risk management. Indeed, the literature is rich in such studies that aim at comparing the currently available software development process models from different perspectives. In contrast, little effort has been spent in purpose of comparing the available process models in terms of its support to risk management. In this paper, we investigate the state of risk and risk management in the most popular software development process models (i.e. waterfall, v-model, incremental development, spiral, and agile development). This trend in such studies is expected to serve in several aspects. Technically, it helps project managers adopt the methodology that best suits their projects. From another side, it will make a way for further studies that aim at improving the software development process.


Microstrip Patch Antenna : Analysis of Surface Area for Bandwidth Improvement


title: Planning According to the ‘Scientific Conception of the World’: The Work of Otto Neurath:

abstract: Neurath, a prominent member of the Vienna Circle, was involved in much practical reform work. He was also a member of CIAM, and participated in the famous Athens meeting where its basic principles were formulated. But his plans for intensive cooperation with CIAM did not come to fruition, because of fundamental differences regarding the role of scientific evidence in decisionmaking and planning. CIAM members were looking for a solid bedrock on which to base design norms and principles. Neurath was a sceptic and emphasised the pluralistic nature of knowledge. He also held that decisions were to be taken on pragmatic grounds, reflecting one's chosen “path of life”. Experts had no superior skill in this. Neurath developed the Vienna Method of pictorial statistics to allow people to make their own inferences from such evidence as there was. Neurath's views of decisionmaking in planning are very modern, and suggest that the critique of positivism in planning needs to be reconsidered.


Trust Aware Recommender Systems : A Survey on Implicit Trust Generation Techniques


Forms of Knowledge


title: An In-Place FFT Architecture for Real-Valued Signals

abstract: This brief presents a novel scalable architecture for in-place fast Fourier transform (IFFT) computation for real-valued signals. The proposed computation is based on a modified radix-2 algorithm, which removes the redundant operations from the flow graph. A new processing element (PE) is proposed using two radix-2 butterflies that can process four inputs in parallel. A novel conflict-free memory-addressing scheme is proposed to ensure the continuous operation of the FFT processor. Furthermore, the addressing scheme is extended to support multiple parallel PEs. The proposed real-FFT processor simultaneously requires fewer computation cycles and lower hardware cost compared to prior work. For example, the proposed design with two PEs reduces the computation cycles by a factor of 2 for a 256-point real fast Fourier transform (RFFT) compared to a prior work while maintaining a lower hardware complexity. The number of computation cycles is reduced proportionately with the increase in the number of PEs.


title: The Design and Experiment of a Dual-band Omni-directional SIW Slot Array Antenna

abstract: In this paper, a new structure is proposed to implement dual-band SIW slot array omni-directional antennas. The desired radiation patterns of the two bands can be almost achieved independently due to the weak coupling between the slots for different frequency bands. A dual-band omni-directional antenna for 2.4GHz and 3.5GHz is designed and fabricated on a low loss dielectric substrate with normal printed circuit board (PCB) process, the measured results show that moderate impedance bandwidths and good omni-directional radiation patterns at both bands are achieved.


title: Dual band, dual polarized, rail mount MIMO stadium antenna

abstract: This paper presents a dual band, dual polarized rail mount MIMO antenna designed to provide mobile wireless coverage in the stadium environment. The high band (1695–2690 MHz) antenna is a dual polarized vertical printed dipole array with dual slant linear polarization. The low band (698–960 MHz) elements are vertically and horizontally polarized omnidirectional sleeve monopole antennas.


title: Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females

abstract: This article reports a comparison on childhood risk factors of males and females exhibiting ::: childhood-onset and adolescent-onset antisocial behavior, using data from the Dunedin ::: longitudinal study. Childhood-onset delinquents had childhoods of inadequate parenting, ::: neurocognitive problems, and temperament and behavior problems, whereas adolescent-onset ::: delinquents did not have these pathological backgrounds. Sex comparisons showed a ::: male-to-female ratio of 10:1 for childhood-onset delinquency but a sex ratio of only 1.5:1 for ::: adolescence-onset delinquency. Showing the same pattern as males, childhood-onset females had ::: high-risk backgrounds but adolescent-onset females did not. These findings are consistent with ::: core predictions from the taxonomic theory of life-course persistent and adolescence-limited ::: antisocial behavior.


title: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes

abstract: A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L−1 per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.


title: A POMDP Approach to Optimizing P300 Speller BCI Paradigm

abstract: To achieve high performance in brain-computer interfaces (BCIs) using P300, most of the work has been focused on feature extraction and classification algorithms. Although significant progress has been made in such signal processing methods in the lower layer, the issues in the higher layer, specifically determining the stimulus schedule in order to identify the target reliably and efficiently, remain relatively unexplored. In this paper, we propose a systematic approach to compute an optimal stimulus schedule in P300 BCIs. Our approach adopts the partially observable Markov decision process, which is a model for planning in partially observable stochastic environments. We show that the thus obtained stimulus schedule achieves a significant performance improvement in terms of the success rate, bit rate, and practical bit rate through human subject experiments.


title: Recurrent Latent Variable Networks for Session-Based Recommendation

abstract: In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.


title: Reliable Circuit Techniques for Low-Voltage Analog Design in Deep Submicron Standard CMOS: A Tutorial

abstract: We present in this paper an overview of circuit techniques dedicated to design reliable low-voltage (1-V and below) analog functions in deep submicron standard CMOS processes. The challenges of designing such low-voltage and reliable analog building blocks are addressed both at circuit and physical layout levels. State-of-the-art circuit topologies and techniques (input level shifting, bulk and current driven, DTMOS), used to build main analog modules (operational amplifier, analog CMOS switches) are covered with the implementation of MOS capacitors.


title: Designing robots for long-term social interaction

abstract: Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.


title: Validating and Restoring Defense in Depth Using Attack Graphs

abstract: Defense in depth is a common strategy that uses layers of firewalls to protect Supervisory Control and Data Acquisition (SCADA) subnets and other critical resources on enterprise networks. A tool named NetSPA is presented that analyzes firewall rules and vulnerabilities to construct attack graphs. These show how inside and outside attackers can progress by successively compromising exposed vulnerable hosts with the goal of reaching critical internal targets. NetSPA generates attack graphs and automatically analyzes them to produce a small set of prioritized recommendations to restore defense in depth. Field trials on networks with up to 3,400 hosts demonstrate that firewalls often do not provide defense in depth due to misconfigurations and critical unpatched vulnerabilities on hosts. In all cases, a small number of recommendations was provided to restore defense in depth. Simulations on networks with up to 50,000 hosts demonstrate that this approach scales well to enterprise-size networks.


title: Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation

abstract: Region-growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objective function is proposed for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.


title: One Time Password Generator System

abstract: Earlier static ID and Password are used which is vulnerable against eavesdropping and replay attack. To overcome this problem One Time Password technique is used which give different password each time. Earlier OTP is HOTP which is based on one way hash function SHA-1 and Ping Pong-128 stream cipher. In this paper we propose a method of generating OTP by using Genetic Algorithm with Elliptic Curve Cryptography. Keywords—Ant Colony Algorithm, Genetic Algorithm, Elliptic Curve, Blowfish Algorithm and EABC


title: Methodology for Characterization of Common-Mode Conducted Electromagnetic Emissions in Wide-Bandgap Converters for Ungrounded Shipboard Applications

abstract: This paper describes the development of a dedicated electromagnetic interference (EMI) characterization platform for the evaluation of wide bandgap-based converters in ungrounded architectures of the type likely to be employed on the future shipboard systems. This platform is designed to support the characterization of wide-bandgap-based converters operating at switching frequencies of hundreds of kilohertz; with power levels up to 100 kVA and with expected emissions up to 100 MHz. To illustrate the capabilities of this platform, the conducted emissions of a SiC-based half-bridge converter are evaluated (with a focus on common-mode (CM) behavior), and a parametric study is conducted which considers variations in the impedance between the half-bridge module base plate and a representative grounding structure. The focus of this work is not only the half-bridge converter which serves as the preliminary test subject, but also rather the custom metrological setup used for characterization, which is designed to discover sensitivities to resonant paths so that the design guidelines for peripheral structures and EMI mitigating components can be developed in the future studies. One major contribution of this work is the discovery that the symmetry of the parasitic capacitances within the converter’s multichip power module is an important factor which significantly influences the CM conducted emissions of the half-bridge structure, which serves as a building block for more complex converter topologies.


Proteomic profiling of HBV infected liver biopsies with different fibrotic stages


title: Closed timelike curves make quantum and classical computing equivalent

abstract: While closed timelike curves (CTCs) are not known to exist, studying their consequences has led to nontrivial insights in general relativity, quantum information, and other areas. In this paper we show that if CTCs existed, then quantum computers would be no more powerful than classical computers: both would have the (extremely large) power of the complexity class PSPACE, consisting of all problems solvable by a conventional computer using a polynomial amount of memory. This solves an open problem proposed by one of us in 2005, and gives an essentially complete understanding of computational complexity in the presence of CTCs. Following the work of Deutsch, we treat a CTC as simply a region of spacetime where a"causal consistency"condition is imposed, meaning that Nature has to produce a (probabilistic or quantum) fixed-point of some evolution operator. Our conclusion is then a consequence of the following theorem: given any quantum circuit (not necessarily unitary), a fixed-point of the circuit can be (implicitly) computed in polynomial space. This theorem might have independent applications in quantum information.


title: In touch with the remote world: remote collaboration with augmented reality drawings and virtual navigation

abstract: Augmented reality annotations and virtual scene navigation add new dimensions to remote collaboration. In this paper, we present a touchscreen interface for creating freehand drawings as world-stabilized annotations and for virtually navigating a scene reconstructed live in 3D, all in the context of live remote collaboration. Two main focuses of this work are (1) automatically inferring depth for 2D drawings in 3D space, for which we evaluate four possible alternatives, and (2) gesture-based virtual navigation designed specifically to incorporate constraints arising from partially modeled remote scenes. We evaluate these elements via qualitative user studies, which in addition provide insights regarding the design of individual visual feedback elements and the need to visualize the direction of drawings.


title: Recommendations without user preferences: a natural language processing approach

abstract: We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naive word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial systems.


title: Ensemble LDA for Face Recognition

abstract: Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1,2,3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing the overfitting problem for the two-step LDA approach, a framework of Ensemble Linear Discriminant Analysis (E n LDA) is proposed for face recognition with small number of training samples. In E n LDA, a Boosting-LDA (B-LDA) and a Random Sub-feature LDA (RS-LDA) schemes are incorporated together to construct the total weak-LDA classifier ensemble. By combining these weak-LDA classifiers using majority voting method, recognition accuracy can be significantly improved. Extensive experiments on two public face databases verify the superiority of the proposed E n LDA over the state-of-the-art algorithms in recognition accuracy.


title: Honeypot in network security: a survey

abstract: In this paper we review the recent advances in honeypot. Some notable proposals and there analysis have been discussed. The aspects of using honeypot in education and in hybrid environment with IDS have been explained. In this paper we also defines the use of signature technique in honeypot for traffic analysis. In the end we summarizes all these aspects.


title: Learning supervised scoring ensemble for emotion recognition in the wild

abstract: State-of-the-art approaches for the previous emotion recognition in the wild challenges are usually built on prevailing Convolutional Neural Networks (CNNs). Although there is clear evidence that CNNs with increased depth or width can usually bring improved predication accuracy, existing top approaches provide supervision only at the output feature layer, resulting in the insufficient training of deep CNN models. In this paper, we present a new learning method named Supervised Scoring Ensemble (SSE) for advancing this challenge with deep CNNs. We first extend the idea of recent deep supervision to deal with emotion recognition problem. Benefiting from adding supervision not only to deep layers but also to intermediate layers and shallow layers, the training of deep CNNs can be well eased. Second, we present a new fusion structure in which class-wise scoring activations at diverse complementary feature layers are concatenated and further used as the inputs for second-level supervision, acting as a deep feature ensemble within a single CNN architecture. We show our proposed learning method brings large accuracy gains over diverse backbone networks consistently. On this year's audio-video based emotion recognition task, the average recognition rate of our best submission is 60.34%, forming a new envelop over all existing records.


title: Postnatal depression and social supports in Vietnamese, Arabic and Anglo-Celtic mothers

abstract: The significance of a western woman's social supports to postnatal depression is well documented. We examine which deficits in components of their social support network are associated with postnatal depression in women from a non-English-speaking background. The social support network and postnatal mood of 105 Anglo-Celtic, 113 Vietnamese and 98 Arabic women were assessed at 6 weeks postpartum. The role of social supports in determining scores on the Edinburgh Postnatal Depression Scale (EPDS) was analysed using multiple regressions. For Anglo-Celtic women, low postnatal mood was associated with perceived need for more emotional support from partners and mothers. For Vietnamese women, low postnatal mood was associated with poor quality of relationship with the partner and a perceived need for more practical help from him. For Arabic women, low postnatal mood was associated with perceived need for more emotional support from partners. We conclude that cultural factors mediate the relation between social supports and postnatal depression.


title: Generative Adversarial Networks for Hyperspectral Image Classification

abstract: A generative adversarial network (GAN) usually contains a generative network and a discriminative network in competition with each other. The GAN has shown its capability in a variety of applications. In this paper, the usefulness and effectiveness of GAN for classification of hyperspectral images (HSIs) are explored for the first time. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs and another CNN is used to generate so-called fake inputs. The aforementioned CNNs are trained together: the generative CNN tries to generate fake inputs that are as real as possible, and the discriminative CNN tries to classify the real and fake inputs. This kind of adversarial training improves the generalization capability of the discriminative CNN, which is really important when the training samples are limited. Specifically, we propose two schemes: 1) a well-designed 1D-GAN as a spectral classifier and 2) a robust 3D-GAN as a spectral–spatial classifier. Furthermore, the generated adversarial samples are used with real training samples to fine-tune the discriminative CNN, which improves the final classification performance. The proposed classifiers are carried out on three widely used hyperspectral data sets: Salinas, Indiana Pines, and Kennedy Space Center. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods. In addition, the proposed GANs open new opportunities in the remote sensing community for the challenging task of HSI classification and also reveal the huge potential of GAN-based methods for the analysis of such complex and inherently nonlinear data.


title: Uncovering the Temporal Dynamics of Diffusion Networks

abstract: Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected – but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data – observed infection times of nodes – we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.


title: Nonparametric semantic segmentation for 3D street scenes

abstract: In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.


title: Dynamic tire friction models for vehicle traction control

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


title: Short term stock price prediction using deep learning

abstract: Short — term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short — period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. This paper plans to forecast these short — term prices of stocks. 10 unique stocks recorded on New York Stock Exchange are considered for this review. The review essentially focuses on the prediction of these short — term prices leveraging the power of technical analysis. Technical Analysis guides the framework to understand the patterns from the historical prices fed into it, and attempts to probabilistically forecast the fleeting future prices of the stock under review. The paper discusses about two distinct sorts of Artificial Neural Networks, Feed Forward Neural Networks and Recurrent Neural Networks. The review uncovers that Feed Forwards Multilayer Perceptron perform superior to Long Short-Term Memory, at predicting the short — term prices of a stock.


title: Requirements for a future EWS - Cyber Defence in the internet of the future

abstract: The emergence of new technologies and services as well as trillions of devices and petabytes of data to be processed and transferred in the Internet of the Future mean that we have to deal with new threats and vulnerabilities, in addition to handle the remaining old ones. Together with the rise of Cyber Warfare and the resulting impact on the environment means that we have to bring intelligence back to the network. Consequently, effective Cyber Defence will be more and more important. In this paper we will show that the proposed requirements for an Early Warning System are a main part of future Cyber Defence. Special attention is given on the challenges associated to the generation of early warning systems for future attacks on the Internet of the Future. The term Cyber War is used frequently but unfortunately with different intends. Therefore, we start with a definition of the term Cyber War focusing on security aspects related to the Internet of the Future, followed by an exemplification of a Cyber War, of its implications and the challenges associated to it. Then we proceed with an analysis of state of the art recent work that has been proposed on the topic. Additionally the weaknesses of these analyzed systems and approaches are presented. Finally we propose guidelines and requirements for future work which will be needed to implement a next generation early warning system for securing the Internet of the Future.


title: Management of vulvar melanoma and review of the literature.

abstract: Background: Vulvar melanoma represents a rare group of malignancies and is the second most common vulvar malignancy. Treatment options range from local excision of the tumor and sentinel lymph node dissection to radical resection involving en bloc vulvectomy and inguinofemoral lymphanedectomy. Vulvar melanomas have an overall poor prognosis, and there is lack of consensus in the published literature regarding treatment options. Objective: To discuss the management of vulvar melanomas through review of the actual literature. Methods: Identification of studies through computerized searches (January 2006) was conducted using MEDLINE (1966 to present), the Cochrane Central Register of Controlled Trials, the National Research Register and the Medical Research Council's Clinical Trials Register. The medical subject headings and text words used were: vulvar melanoma, malignant, management, case report, and therapy. The literature review was done over the past 36 years. Result: Results of these primary retrospective series have shown no improvement in the overall recovery or disease survival rates. Conclusion: Patients with malignant melanoma are often diagnosed at 70 years of age with multiple comorbidities. Less radical surgery presents a more realistic option for many patients without decreasing their survival rates. Surgery is still the gold standard of treatment and offers the best available treatment for controlling and potential curing of malignant melanomas. However, the whole concept of therapy should be tailored to meet the specific needs of individual patients.


title: Modeling and Control of Three-Port DC/DC Converter Interface for Satellite Applications

abstract: This paper presents the control strategy and power management for an integrated three-port converter, which interfaces one solar input port, one bidirectional battery port, and an isolated output port. Multimode operations and multiloop designs are vital for such multiport converters. However, control design is difficult for a multiport converter to achieve multifunctional power management because of various cross-coupled control loops. Since there are various modes of operation, it is challenging to define different modes and to further implement autonomous mode transition based on the energy state of the three power ports. A competitive method is used to realize smooth and seamless mode transition. Multiport converter has plenty of interacting control loops due to integrated power trains. It is difficult to design close-loop controls without proper decoupling method. A detailed approach is provided utilizing state-space averaging method to obtain the converter model under different modes of operation, and then a decoupling network is introduced to allow separate controller designs. Simulation and experimental results verify the converter control design and power management during various operational modes.


title: Performance assessment of Multilateration Systems - a solution to nextgen surveillance

abstract: Multilateration (MLAT) surveillance is now being used in all types of airspace for air traffic management. MLAT can be used for airport surface movement surveillance as well as for terminal and en route surveillance, using Wide Area Multilateration (WAM). MLAT is a low-cost technology that not only has major advantages in operations and maintenance, but also provides excellent performance under all conditions, especially for countries with large geographic areas or mountainous terrain to cover. This paper focuses on performance metrics from operational system data to demonstrate that the performance of Era's MLAT systems meet surveillance requirements for all surveillance applications. This includes the key performance requirements for the various surveillance applications that are evaluated, including accuracy, coverage, update rate, integrity and availability.


title: Real-time segmenting time series data

abstract: There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling: the step identifies an autoregressive moving average (ARMA) model of dynamic processes from a time series data; (2) prediction: this step makes k steps ahead prediction based on the ARMA model of the process at a crisp time point. (3) Change-points detection: the step is what fits a piecewise segmented polynomial regressive model to the time series data to determine whether it contains a new change-point. Finally, high performance of the proposed algorithm is demonstrated by comparing with Guralnik-Srivastava algorithm.


title: A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster

abstract: Erasure codes, such as Reed-Solomon (RS) codes, are being increasingly employed in data centers to combat the cost of reliably storing large amounts of data. Although these codes provide optimal storage efficiency, they require significantly high network and disk usage during recovery of missing data. In this paper, we first present a study on the impact of recovery operations of erasure-coded data on the data-center network, based on measurements from Facebook's warehouse cluster in production. To the best of our knowledge, this is the first study of its kind available in the literature. Our study reveals that recovery of RS-coded data results in a significant increase in network traffic, more than a hundred terabytes per day, in a cluster storing multiple petabytes of RS-coded data. ::: To address this issue, we present a new storage code using our recently proposed "Piggybacking" framework, that reduces the network and disk usage during recovery by 30% in theory, while also being storage optimal and supporting arbitrary design parameters. The implementation of the proposed code in the Hadoop Distributed File System (HDFS) is underway. We use the measurements from the warehouse cluster to show that the proposed code would lead to a reduction of close to fifty terabytes of cross-rack traffic per day.


title: The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time

abstract: Parents and teachers often express concern about the extensive use of social media by youngsters. Some of them see emoticons, undecipherable initialisms and loose grammar typical for social media as evidence of language degradation. In this paper, we use a simple measure of text complexity to investigate how the complexity of public posts on a popular social networking site changes over time. We analyze a unique dataset that contains texts posted by 942, 336 users from a large European city across nine years. We show that the chosen complexity measure is correlated with the academic performance of users: users from high-performing schools produce more complex texts than users from low-performing schools. We also find that complexity of posts increases with age. Finally, we demonstrate that overall language complexity of posts on the social networking site is constantly increasing. We call this phenomenon the digital Flynn effect. Our results may suggest that the worries about language degradation are not warranted.


title: What Can Economists Learn From Happiness Research?

abstract: Over the past few years, there has been a steadily increasing interest on the part of economists in happiness research. We argue that reported subjective well-being is a satisfactory empirical approximation to individual utility and that happiness research is able to contribute important insights for economics. We report how the economic variables income, unemployment and inflation affect happiness as well as how institutional factors, in particular the type of democracy and the extent of government decentralization, systematically influence how satisfied individuals are with their life. We discuss some of the consequences for economic policy and for economic theory.


title: A Collaborative Approach to In-Place Sensor Calibration

abstract: Numerous factors contribute to errors in sensor measurements. In order to be useful, any sensor device must be calibrated to adjust its accuracy against the expected measurement scale. In large-scale sensor networks, calibration will be an exceptionally difficult task since sensor nodes are often not easily accessible and manual device-by-device calibration is intractable. In this paper, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its first phase, the algorithm derives relative calibration relationships between pairs of co-located sensors, while in the second phase, it maximizes the consistency of the pair-wise calibration functions among groups of sensor nodes. The key idea in the first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) to derive the function relating their bias in amplitude. We formulate the second phase as an optimization problem and present an algorithm suitable for localized implementation. We evaluate the performance of the first phase of the algorithm using empirical and simulated data.


title: Wireless Technology in Industrial Networks

abstract: With the success of wireless technologies in consumer electronics, standard wireless technologies are envisioned for the deployment in industrial environments as well. Industrial applications involving mobile subsystems or just the desire to save cabling make wireless technologies attractive. Nevertheless, these applications often have stringent requirements on reliability and timing. In wired environments, timing and reliability are well catered for by fieldbus systems (which are a mature technology designed to enable communication between digital controllers and the sensors and actuators interfacing to a physical process). When wireless links are included, reliability and timing requirements are significantly more difficult to meet, due to the adverse properties of the radio channels. In this paper, we thus discuss some key issues coming up in wireless fieldbus and wireless industrial communication systems: 1) fundamental problems like achieving timely and reliable transmission despite channel errors; 2) the usage of existing wireless technologies for this specific field of applications; and 3) the creation of hybrid systems in which wireless stations are incorporated into existing wired systems.


title: High dynamic range display systems

abstract: The dynamic range of many real-world environments exceeds the capabilities of current display technology by several orders of magnitude. In this paper we discuss the design of two different display systems that are capable of displaying images with a dynamic range much more similar to that encountered in the real world. The first display system is based on a combination of an LCD panel and a DLP projector, and can be built from off-the-shelf components. While this design is feasible in a lab setting, the second display system, which relies on a custom-built LED panel instead of the projector, is more suitable for usual office workspaces and commercial applications. We describe the design of both systems as well as the software issues that arise. We also discuss the advantages and disadvantages of the two designs and potential applications for both systems.


title: An authentication scheme for wireless healthcare monitoring sensor network

abstract: In this paper, a user-authentication scheme that is designed to ensure privacy and security of health-information exchange in cloud computing is proposed. The proposed system will allow health care centers and doctors to securely and efficiently monitor patients' health at their homes using secure integration of medical devices and healthcare systems such as: body sensor networks, wearable devices, and the Internet of Things. The incremental patient data will be captured in real time and sent to the cloud/data center asynchronously to be analyzed and stored. Cloud based medical advice, then, can be provided to ensure accessibility of patient's data from anywhere and via almost any computing devices. The system will also be able to follow the progress of patient's recovery and determine if further care is necessary. In this system, the mobile communication is authenticated and stored in an encrypted database that is isolated from the Internet. The information security service will be provided by highly trusted cloud network that allows exchanging and sharing sensitive medical information.


title: Port-based modeling of mechatronic systems

abstract: Many engineering activities, including mechatronic design, require that a multidomain or 'multi-physics' system and its control system be designed as an integrated system. This contribution discusses the background and tools for a port-based approach to integrated modeling and simulation of physical systems and their controllers, with parameters that are directly related to the real-world system, thus improving insight and direct feedback on modeling decisions.


title: PHD extended target tracking using an incoherent X-band radar: Preliminary real-world experimental results

abstract: X-band radar systems represent a flexible and low-cost tool for ship detection and tracking. These systems suffer the interference of the sea-clutter but at the same time they can provide high measurement resolutions, both in space and time. Such features offer the opportunity to get accurate information about the target's state and shape. Accordingly, here we exploit an extended target tracking methodology based on the popular Probability Hypothesis Density to get information about the targets observed in an actual X-band radar dataset. For each target track we estimate the target's position, velocity and acceleration, as well as its size and the expected number of radar returns.


title: How does Bayesian reverse-engineering work?

abstract: Bayesian models of cognition and behavior are particularly promising when they are used in reverse-engineering explanations: explanations that descend from the computational level of analysis to the algorithmic and implementation levels. Unfortunately, it remains unclear exactly how Bayesian models constrain and influence these lower levels of analysis. In this paper, we review and reject two widespread views of Bayesian reverse-engineering, and propose an alternative view according to which Bayesian models at the computational level impose pragmatic constraints that facilitate the generation of testable hypotheses at the algorithmic and implementation levels.


title: Identification and control of dynamic systems using recurrent fuzzy neural networks

abstract: Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.


title: Human gut microbiome: the second genome of human body

abstract: The human body is actually a super-organism that is composed of 10 times more microbial cells than our body cells. Metagenomic study of the human microbiome has demonstrated that there are 3.3 million unique genes in human gut, 150 times more genes than our own genome, and the bacterial diversity analysis showed that about 1000 bacterial species are living in our gut and a majority of them belongs to the divisions of Firmicutes and Bacteriodetes. In addition, most people share a core microbiota that comprises 50-100 bacterial species when the frequency of abundance at phylotype level is not considered, and a core microbiome harboring more than 6000 functional gene groups is present in the majority of human gut surveyed till now. Gut bacteria are not only critical for regulating gut metabolism, but also important for host immune system as revealed by animal studies.


title: Interactive motion generation from examples

abstract: There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who must move. In this paper, we present a framework that generates human motions by cutting and pasting motion capture data. Selecting a collection of clips that yields an acceptable motion is a combinatorial problem that we manage as a randomized search of a hierarchy of graphs. This approach can generate motion sequences that satisfy a variety of constraints automatically. The motions are smooth and human-looking. They are generated in real time so that we can author complex motions interactively. The algorithm generates multiple motions that satisfy a given set of constraints, allowing a variety of choices for the animator. It can easily synthesize multiple motions that interact with each other using constraints. This framework allows the extensive re-use of motion capture data for new purposes.


title: The potentiality of botanicals and their products as an alternative to chemical insecticides to sandflies (Diptera: Psychodidae): a review.

abstract: Use of chemical pesticides is the current method for controlling sandflies. However, resistance is being developed in sandflies against the insecticide of choice that is DDT (dichlorodiphenyl trichloroethane). Botanicals have potential to act as an alternative to chemical insecticides as the crude extracts and active molecules of some plants show insecticidal effect to sandflies. This will lead to safe, easy and environment friendly method for control of sandflies. Therefore, information regarding botanicals acting as alternative to chemical insecticide against sandflies assumes importance in the context of development of resistance to insecticides as well as to prevent environment from contamination. This review deals with some plants and their products having repellent and insecticidal effect to sandflies in India and abroad. Different methods of extraction and their bioassay on sandflies have been emphasized in the text. Various extracts of some plants like Ricinus communis (Euphorbiaceae), Solanum jasminoides (Solanaceae), Bougainvillea glabra (Nyctaginaceae), Capparis spinosa (Capparidaceae), Acalypha fruticosa (Euphorbiaceae) and Tagetes minuta (Asteraceae) had shown repellent/insecticidal effect on sandflies. This review will be useful in conducting the research work to find out botanicals of Indian context having insecticidal effect on sandflies.


Integrating Order Information and Event Relation for Script Event Prediction


Data Mining Techniques for Anti Money Laundering


Anomaly Detection over Noisy Data using Learned Probability Distributions


title: Learning from Television News: A Critique of the Research.

abstract: This paper offers a critical review of some approaches to research on the effectiveness of television newscasts. The importance of behavioral science issues to such research is stressed.


title: MOOCex: Exploring Educational Video via Recommendation

abstract: Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos across multiple courses and content platforms to provide a choice of perspectives on topics of interest. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.


title: What's It Mean to "Be Social" in VR?: Mapping the Social VR Design Ecology

abstract: The emerging ecology of commercial social VR currently includes a diverse set of applications and competing models of what it means to be social in VR. This study maps a slice of this ecology, comparing and contrasting ways different applications frame, support, shape, or constrain social interaction. We deploy a method of design-oriented autobiographical landscape research to examine five platforms: Facebook Spaces, Rec Room, High Fidelity, VRChat, and AltspaceVR. We analyze design choices underlying these environments and draw attention to issues of space and place, locomotion, and social mechanics. Drawing on this analysis, we identify key issues and concerns for future research and design in social VR.


title: DOOBNet: Deep Object Occlusion Boundary Detection from an Image

abstract: Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. This is challenging to solve as encountering the extreme boundary/non-boundary class imbalance during training an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoder-decoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the state-of-the-art on the PIOD dataset (ODS F-score of .702) and the BSDS ownership dataset (ODS F-score of .555), as well as improving the detecting speed to as 0.037s per image on the PIOD dataset.


title: Multimodal Continuous Prediction of Emotions in Movies using Long Short-Term Memory Networks

abstract: Predicting emotions that movies are designed to evoke, can be useful in entertainment applications such as content personalization, video summarization and ad placement. Multimodal input, primarily audio and video, helps in building the emotional content of a movie. Since the emotion is built over time by audio and video, the temporal context of these modalities is an important aspect in modeling it. In this paper, we use Long Short-Term Memory networks (LSTMs) to model the temporal context in audio-video features of movies. We present continuous emotion prediction results using a multimodal fusion scheme on an annotated dataset of Academy Award winning movies. We report a significant improvement over the state-of-the-art results, wherein the correlation between predicted and annotated values is improved from 0.62 vs 0.84 for arousal, and from 0.29 to 0.50 for valence.


title: Knowledge Graph Inference for spoken dialog systems

abstract: We propose Inference Knowledge Graph, a novel approach of remapping existing, large scale, semantic knowledge graphs into Markov Random Fields in order to create user goal tracking models that could form part of a spoken dialog system. Since semantic knowledge graphs include both entities and their attributes, the proposed method merges the semantic dialog-state-tracking of attributes and the database lookup of entities that fulfill users' requests into one single unified step. Using a large semantic graph that contains all businesses in Bellevue, WA, extracted from Microsoft Satori, we demonstrate that the proposed approach can return significantly more relevant entities to the user than a baseline system using database lookup.


Process Mining and Fraud Detection


title: Effectiveness of an Integrated Handwriting Program for First-Grade Students: A Pilot Study

abstract: We developed and piloted a program for first-grade students to promote development of legible handwriting and writing fluency. The Write Start program uses a coteaching model in which occupational therapists and teachers collaborate to develop and implement a handwriting-writing program. The small-group format with embedded individualized supports allows the therapist to guide and monitor student performance and provide immediate feedback. The 12-wk program was implemented with 1 class of 19 students. We administered the Evaluation of Children's Handwriting Test, Minnesota Handwriting Assessment, and Woodcock-Johnson Fluency and Writing Samples test at baseline, immediately after the Write Start program, and at the end of the school year. Students made large, significant gains in handwriting legibility and speed and in writing fluency that were maintained at 6-mo follow-up. The Write Start program appears to promote handwriting and writing skills in first-grade students and is ready for further study in controlled trials.


title: Fuzzy logic controllers are universal approximators

abstract: In this paper, we consider a fundamental theoretical question on why does fuzzy control have such a good performance for a wide variety of practical problems. We try to answer this fundamental question by proving that for each fixed fuzzy logic belonging to a wide class of fuzzy logics, and for each fixed type of membership function belonging to a wide class of membership functions, the fuzzy logic control systems using these two and any method of defuzzification are capable of approximating any real continuous function on a compact set to arbitrary accuracy. On the other hand, this result can be viewed as an existence theorem of an optimal fuzzy logic control system for a wide variety of problems. >


Leveraging Entrepreneurial Orientation to Enhance SME Export Performance. Prepared for the SBA, Office of Advocacy


title: Very Low Resolution Face Recognition Problem

abstract: This paper addresses the very low resolution (VLR) problem in face recognition in which the resolution of the face image to be recognized is lower than 16 × 16. With the increasing demand of surveillance camera-based applications, the VLR problem happens in many face application systems. Existing face recognition algorithms are not able to give satisfactory performance on the VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a VLR face image. To overcome this problem, this paper proposes a novel approach to learn the relationship between the high-resolution image space and the VLR image space for face SR. Based on this new approach, two constraints, namely, new data and discriminative constraints, are designed for good visuality and face recognition applications under the VLR problem, respectively. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.


title: Understanding Alternating Minimization for Matrix Completion

abstract: Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by and are still poorly understood. This is in part because the heuristic is iterative and non-convex in nature. We give a new algorithm based on Alternating Minimization that provably recovers an unknown low-rank matrix from a random subsample of its entries under a standard incoherence assumption. Our results reduce the sample size requirements of the Alternating Minimization approach by at least a quartic factor in the rank and the condition number of the unknown matrix. These improvements apply even if the matrix is only close to low-rank in the Frobenius norm. Our algorithm runs in nearly linear time in the dimension of the matrix and, in a broad range of parameters, gives the strongest sample bounds among all subquadratic time algorithms that we are aware of. Underlying our work is a new robust convergence analysis of the well-known Power Method for computing the dominant singular vectors of a matrix. This viewpoint leads to a conceptually simple understanding of Alternating Minimization. In addition, we contribute a new technique for controlling the coherence of intermediate solutions arising in iterative algorithms based on a smoothed analysis of the QR factorization. These techniques may be of interest beyond their application here.


title: Programming by demonstration: dual-arm manipulation tasks for humanoid robots

abstract: This paper deals with easy programming methods of dual-arm manipulation tasks for humanoid robots. Hereby a programming by demonstration system is used in order to observe, learn and generalize tasks performed by humans. A classification for dual-arm manipulations is introduced, enabling a segmentation of tasks into adequate subtasks. Further it is shown how the generated programs are mapped on and executed by a humanoid robot.


title: MicroTE: fine grained traffic engineering for data centers

abstract: The effects of data center traffic characteristics on data center traffic engineering is not well understood. In particular, it is unclear how existing traffic engineering techniques perform under various traffic patterns, namely how do the computed routes differ from the optimal routes. Our study reveals that existing traffic engineering techniques perform 15% to 20% worse than the optimal solution. We find that these techniques suffer mainly due to their inability to utilize global knowledge about flow characteristics and make coordinated decision for scheduling flows. To this end, we have developed MicroTE, a system that adapts to traffic variations by leveraging the short term and partial predictability of the traffic matrix. We implement MicroTE within the OpenFlow framework and with minor modification to the end hosts. In our evaluations, we show that our system performs close to the optimal solution and imposes minimal overhead on the network making it appropriate for current and future data centers.


title: A general approach to network configuration analysis

abstract: We present an approach to detect network configuration errors, which combines the benefits of two prior approaches. Like prior techniques that analyze configuration files, our approach can find errors proactively, before the configuration is applied, and answer "what if" questions. Like prior techniques that analyze data-plane snapshots, our approach can check a broad range of forwarding properties and produce actual packets that violate checked properties. We accomplish this combination by faithfully deriving and then analyzing the data plane that would emerge from the configuration. Our derivation of the data plane is fully declarative, employing a set of logical relations that represent the control plane, the data plane, and their relationship. Operators can query these relations to understand identified errors and their provenance. We use our approach to analyze two large university networks with qualitatively different routing designs and find many misconfigurations in each. Operators have confirmed the majority of these as errors and have fixed their configurations accordingly.


title: Design of intelligent PID/PIλDµ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm

abstract: This paper deals with the design, implementation and analysis of an integer order (IO) and fractional order (FO) based Proportional Integral Derivative (PID) controller, for speed regulation in a chopper fed Direct Current (DC) motor drive. The interdependent parameters of PID and FOPID controllers are designed in both time and frequency domain. In both domains, designs of controllers are formulated as a single objective optimization problem based on time indices integrals and frequency domain rules. Time domain based design of controllers focus mainly on minimization of indices like rise time, settling time etc. On the other hand frequency domain based design of controllers focuses on achieving iso-damping characteristics, which tries to meet user specified gain crossover frequency or phase margin while also maintaining constant overshoot for wide range of motor gain apart from maintaining optimum time indices. A newly evolved artificial bee colony (ABC) algorithm enhanced with opposition search has been used to perform the optimization task. This type of multiple designs enables users to choose the controller based on their requirement. A comparative study has been made to highlight the advantage of using fractional order controller over conventional integer order PID control scheme for speed regulation. To illustrate the efficacy of the opposition based ABC, we also compared the performance with the conventional ABC, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Computer simulations and extensive analysis over results obtained shows the effectiveness of the proposed approach.


title: Global-and-local attention networks for visual recognition

abstract: State-of-the-art deep convolutional networks (DCNs) such as squeeze-and- excitation (SE) residual networks implement a form of attention, also known as contextual guidance, which is derived from global image features. Here, we explore a complementary form of attention, known as visual saliency, which is derived from local image features. We extend the SE module with a novel global-and-local attention (GALA) module which combines both forms of attention -- resulting in state-of-the-art accuracy on ILSVRC. We further describe ClickMe.ai, a large-scale online experiment designed for human participants to identify diagnostic image regions to co-train a GALA network. Adding humans-in-the-loop is shown to significantly improve network accuracy, while also yielding visual features that are more interpretable and more similar to those used by human observers.


title: Semantic Explanations of Predictions

abstract: The main objective of explanations is to transmit knowledge to humans. This work proposes to construct informative explanations for predictions made from machine learning models. Motivated by the observations from social sciences, our approach selects data points from the training sample that exhibit special characteristics crucial for explanation, for instance, ones contrastive to the classification prediction and ones representative of the models. Subsequently, semantic concepts are derived from the selected data points through the use of domain ontologies. These concepts are filtered and ranked to produce informative explanations that improves human understanding. The main features of our approach are that (1) knowledge about explanations is captured in the form of ontological concepts, (2) explanations include contrastive evidences in addition to normal evidences, and (3) explanations are user relevant.


About uncertainty estimation for ADC spectral figures of merit


title: A survey of industrial model predictive control technology

abstract: This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities. r 2002 Elsevier Science Ltd. All rights reserved.


title: The unbearable automaticity of being

abstract: What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-mom ent psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and behavior. Three major forms of automatic self-regulation are identified: an automatic effect of perception on action, automatic goal pursuit, and a continual automatic evaluation of one's experience. From the accumulating evidence, the authors conclude that these various nonconscious mental systems perform the lion's share of the self-regulatory burden, beneficently keeping the individual grounded in his or her current environment. The strongest knowledge—that of the total unfreedom of the human will—is nonetheless the poorest in successes, for it always has the strongest opponent: human vanity. —Nietzsche, Human, All Too Human


title: A Randomized Singular Value Decomposition Algorithm For Image Processing Applications

abstract: The main contribution of this paper is to demonstrate that a new randomized SVD algorithm, proposed by Drineas et. al. in [4], is not only of theoretical interest but also a viable and fast alternative to traditional SVD algorithms in applications (e.g. image processing). This algorithm samples a constant number of rows (or columns) of the matrix, scales them appropriately to form a small matrix, say S, and then computes the SVD of S (which is a good approximation to the SVD of the original matrix). We experimentally evaluate the accuracy and speed of this algorithm for image matrices, using various probability distributions to perform the sampling.


title: Open challenges for data stream mining research

abstract: Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application-relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.


title: Preoperative neutrophil-lymphocyte ratio predicts the risk of microvascular invasion in hepatocellular carcinoma: A meta-analysis

abstract: Background:Preoperative estimation of microvascular invasion is of great significance for the clinical decision making in hepatocellular carcinoma. Neutrophil-lymphocyte ratio (NLR) has been report...


title: Adaptive hypertext navigation based on user goals and context

abstract: Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system.


title: Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005

abstract: This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city’s rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.


abstract: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.


title: Crawling a country: better strategies than breadth-first for web page ordering

abstract: This article compares several page ordering strategies for Web crawling under several metrics. The objective of these strategies is to download the most "important" pages "early" during the crawl. As the coverage of modern search engines is small compared to the size of the Web, and it is impossible to index all of the Web for both theoretical and practical reasons, it is relevant to index at least the most important pages.We use data from actual Web pages to build Web graphs and execute a crawler simulator on those graphs. As the Web is very dynamic, crawling simulation is the only way to ensure that all the strategies considered are compared under the same conditions. We propose several page ordering strategies that are more efficient than breadth- first search and strategies based on partial Pagerank calculations.