title: 200x Low-dose PET Reconstruction using Deep Learning

abstract: Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation exposure. To minimize this potential risk in PET imaging, efforts have been made to reduce the amount of radio-tracer usage. However, lowing dose results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of which will heavily affect clinical diagnosis. Besides, the ill-conditioning of low-dose PET image reconstruction makes it a difficult problem for iterative reconstruction algorithms. Previous methods proposed are typically complicated and slow, yet still cannot yield satisfactory results at significantly low dose. Here, we propose a deep learning method to resolve this issue with an encoder-decoder residual deep network with concatenate skip connections. Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose. Different cost functions for training model are explored. Multi-slice input strategy is introduced to provide the network with more structural information and make it more robust to noise. Evaluation on ultra-low-dose clinical data shows that the proposed method can achieve better result than the state-of-the-art methods and reconstruct images with comparable quality using only 0.5% of the original regular dose.


title: A Compact X-Band Bi-Directional Phased-Array T/R Chipset in 0.13 $\mu{\hbox {m}}$ CMOS Technology

abstract: This paper presents an X-band bi-directional T/R chipset in 0.13 m CMOS. The T/R chipset consists of a bi-directional gain amplifier (BDGA), a 5-bit digital step attenuator with two BDGAs for compensating the switch losses, and a 6-bit phase shifter using DPDT switches. The phase and attenuation coverage is 360 with the LSB of 5.625°, and 31 dB with the LSB of 1 dB, respectively. The circuit has a reference state gain of >;3.5 db, and the return losses of >;11 db at 8.5-10.5 GHz. The T/R chipset has a phase shift accuracy with the RMS phase error of ;6.5 dBm and the noise figure is <;7.5 db at 8.5-10 GHz. The chip size is 2.06 × 0.58 mm2 including pads, and the DC power consumption is 154 mW only in the BDGAs. To authors' knowledge, this is the X-band CMOS T/R chipset with the competitive RF performance compared to other device technologies, which has the smallest size and the lowest power consumption to-date.


title: Integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS) – a literature review and future needs

abstract: For years, researchers have been trying to integrate Geographical Information Systems (GIS) with Building Information Modeling (BIM) systems for various purposes. An important application of BIM in construction is asset management, and the integration of BIM and GIS can provide a highly detailed and holistic picture of a project as information mined from building information models as well as associated geographical data can increase the success of asset management. The purpose of this paper is to present a systematic literature review on the approaches towards the integration of BIM with GIS systems. This paper attempts to analyze the significant contributions and advances in the field of BIM and GIS integration, while highlighting the implication for asset management. Gaps in research will be identified, which can be addressed for potential future research.


Some computer science issues in ubiquitous computing


title: A high speed soft robot based on dielectric elastomer actuators

abstract: A multilayer fabrication method has been used to create crawling soft robots based on dielectric elastomer actuators. These actuators are created without the need for pre-stretch, eliminating the need for rigid components. A four-legged, multi-gait capable crawler can be fabricated in a matter of hours and shows promise for future untethered systems. Studies on inchworm robots show them to be the fastest dielectric elastomer actuator-based systems reported to date, capable of traveling faster than 1 body length / second for the best elastomer available. Most importantly, the devices are primarily soft and deformable, with few rigid attachments.


title: Inhibited Softmax for Uncertainty Estimation in Neural Networks

abstract: We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.


title: LSHADE Algorithm with Rank-Based Selective Pressure Strategy for Solving CEC 2017 Benchmark Problems

abstract: Solving single-objective real-parameter optimization problems can still cause difficulties, for example if the optimized function is multimodal or has rotated trap problems. Such optimization problems can be found in various areas in real-world applications. Usually, these problems are very complex and computationally expensive. A new algorithm, which is a modification of the LSHADE algorithm with a rank-based selective pressure strategy, called LSHADE-RSP, is presented in this paper. The proposed algorithm is a new variant of the LSHADE algorithm, the basic idea of which consists in the adaptation of its mutation strategy using selective pressure. The experiments were performed on CEC 2018 benchmark functions. A comparison of the proposed LSHADE-RSP algorithm and the algorithm-participants of the CEC 2017 competition is presented. From the obtained results it can be concluded that LSHADE-RSP performs better in comparison with most alternative algorithms: using the CEC 2018 evaluation method, LSHADE-RSP obtained one of the best final scores among the algorithms that were winners of the previous competition.


title: Hybrid Antenna Module Concept for 28 GHz 5G Beamsteering Cellular Devices

abstract: This paper presents a novel hybrid antenna module concept at millimeter-waves to realize spherical beamsteering coverage that is structurally and systematically compatible with present day cellular devices. The hybrid antenna module concept coherently combines two existing concepts - the AiP (Antenna-in-Package) and AoD (Antenna-on-Display) to steer the antenna main lobe in the end-fire and broadside direction. A compact end-fire antenna array (AiP) and a fully optically transparent antenna array (AoD) achieve impedance bandwidth (2:1 VSWR) of 1.67 GHz and 0.85 GHz respectively at 28 GHz. Investigation in the far-field ascertains hat the 4-element end-fire AiP and the 16-element broadside AoD exhibit a realized gain of 9.2 dBi and 12.8 dBi in the θ = 90° and θ = 0° respectively.


title: Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

abstract: Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.


Integration of angle of arrival information for multimodal sensor network localization using semidefinite programming


title: Strategy as Action: from Porter to Anti-Porter

abstract: In the post-crisis era, new concepts are emerging, while some old and dysfunctional ones are being discarded. Strategy is about making series of decisions that drive corporate action under specific coupling with companyâ€™s environment and context. Because decisions are actions, the strategy itself is action, not just a description of action. In the world of traditional strategy, descriptions (information) have replaced action (knowledge), and talk has replaced walk. We start from the premise that strategy is what company does, and what company does is its strategy. One cannot run a company just on descriptions and framed mission statements. The role of customers is crucial: the customer shapes strategy and triggers corporate action. Without respecting the customer, there is no viable strategy. This is why we label the action-based strategic thinking as â€œAnti-Porterâ€ : consumers do not want tradeoffs and thus truly effective strategy cannot be rooted in Porterâ€™s tradeoffs.


title: E-mail Header Injection Vulnerabilities

abstract: Abstract E-mail Header Injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-mail Header Injection is possible when the mailing script fails to check for the presence of e-mail headers in user input (either form fields or URL parameters). The vulnerability exists in the reference implementation of the built-in mail functionality in popular languages such as PHP, Java, Python, and Ruby. With the proper injection string, this vulnerability can be exploited to inject additional headers, modify existing headers, and alter the content of the e-mail.


title: Deep learning based gastric cancer identification

abstract: Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.


title: A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks

abstract: Segmenting vertebrae and disks in medical images using algorithmic methods promises to improve diagnostics and treatment selection for lower back pain. While most existing spinal segmentation methods are model-based, we propose a deep learning approach that uses a series of four pixel-wise segmentation networks. Each network segments MR images at a different scale. Results from one network in the chain are fed as input to the next network in the chain. By using both the original image and results from the previous network as input, each successive network produces an increasingly refined segmentation result. This approach improves vertebrae and disk segmentations results by 1.3% and 4.9%, respectively, when compared to a state-of-the-art deep-learning segmentation method—the U-net. It also provides additional benefits over U-net architectures by simplifying simultaneous multi-class segmentation, streamlining the overall use of the network, and removing the need for post-processing to filter erroneous classifications from the output.


Identifying Opposing Views in Online Discourse


title: Observational Pain Scales in Critically Ill Adults

abstract: Pain is a common and distressing symptom in critically ill patients. Uncontrolled pain places patients at risk for numerous adverse psychological and physiological consequences, some of which may be life-threatening. A systematic assessment of pain is difficult in intensive care units because of the high percentage of patients who are noncommunicative and unable to self-report pain. Several tools have been developed to identify objec- tive measures of pain, but the best tool has yet to be identified. A comprehensive search on the reliability and validity of observational pain scales indicated that although the Critical-Care Pain Observation Tool was supe- rior to other tools in reliably detecting pain, pain assessment in individuals incapable of spontaneous neuro- muscular movements or in patients with concurrent conditions, such as chronic pain or delirium, remains an enigma. (Critical Care Nurse. 2013;33(3):68-79)


title: A cost comparison of datacenter network architectures

abstract: There is a growing body of research exploring new network architectures for the data center. These proposals all seek to improve the scalability and cost-effectiveness of current data center networks, but adopt very different approaches to doing so. For example, some proposals build networks entirely out of switches while others do so using a combination of switches and servers. How do these different network architectures compare? For that matter, by what metrics should we even begin to compare these architectures? Understanding the tradeoffs between different approaches is important both for operators making deployment decisions and to guide future research. In this paper, we take a first step toward understanding the tradeoffs between different data center network architectures. We use high-level models of different classes of data center networks and compare them on cost using both current and predicted trends in cost and power consumption.


title: Diverse M-Best Solutions in Markov Random Fields

abstract: Much effort has been directed at algorithms for obtaining the highest probability (MAP) configuration in probabilistic (random field) models. In many situations, one could benefit from additional high-probability solutions. Current methods for computing the M most probable configurations produce solutions that tend to be very similar to the MAP solution and each other. This is often an undesirable property. In this paper we propose an algorithm for the Diverse M-Best problem, which involves finding a diverse set of highly probable solutions under a discrete probabilistic model. Given a dissimilarity function measuring closeness of two solutions, our formulation involves maximizing a linear combination of the probability and dissimilarity to previous solutions. Our formulation generalizes the M-Best MAP problem and we show that for certain families of dissimilarity functions we can guarantee that these solutions can be found as easily as the MAP solution.


title: LCL filter startup diagnostics for an Active Front End converter

abstract: Reliability of Active Front End (AFE) converter can be improved if converter faults can be identified before startup. Startup diagnostics of the LCL filter can be an useful tool to accomplish this. An algorithm based on Fast Fourier Transform (FFT) of the filter step response is shown to be able to detect variation of filter components. This is extended to consider conditions of short circuit and open circuit of filter components. This method can identify failed components in a LCL filter before operating the converter, which otherwise may lead to undesirable operation. Analytical expressions are derived for the frequency spectrum of the LCL filter during step excitation using continuous Fourier Transform and Discrete Fourier Transform (DFT). A finite state machine Finite State Machine (FSM) based algorithm is use to sequence the startup diagnostics before commencing normal operation of the power converter. It is shown that the additional computational resource required to perform the diagnostic algorithm is small when compared with the overall inverter control program. The diagnostic functions can be readily implemented in advanced digital controllers that are available today. The spectral analysis is supported by simulations and experimental results which validate the proposed method.


title: A CHiME-3 challenge system: Long-term acoustic features for noise robust automatic speech recognition

abstract: The paper describes an automatic speech recognition (ASR) system for the 3rd CHiME challenge that addresses noisy acoustic scenes within public environments. The proposed system includes a multi-channel speech enhancement front-end including a microphone channel failure detection method that is based on cross-comparing the modulation spectra of speech to detect erroneous microphone recordings. The main focus of the submission is the investigation of the amplitude modulation filter bank (AMFB) as a method to extract long-term acoustic cues prior to a Gaussian mixture model (GMM) or deep neural network (DNN) based ASR classifier. It is shown that AMFB features outperform the commonly used frame splicing technique of filter bank features even on a performance optimized ASR challenge system. I.e., temporal analysis of speech by hand-crafted and auditory motivated AMFBs is shown to be more robust compared to a data-driven method based on extracting temporal dynamics with a DNN. Our final ASR system, which additionally includes adaptation of acoustic features to speaker characteristics, achieves an absolute word error rate reduction of approx. 21.53 % relative to the best CHiME-3 baseline system on the "real" test condition.


title: Neural Tangent Kernel: Convergence and Generalization in Neural Networks

abstract: At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function $f_\theta$ (which maps input vectors to output vectors) follows the kernel gradient of the functional cost (which is convex, in contrast to the parameter cost) w.r.t. a new kernel: the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and it stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We prove the positive-definiteness of the limiting NTK when the data is supported on the sphere and the non-linearity is non-polynomial. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function $f_\theta$ follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit.


title: ACCURATE VISUAL LOCALIZATION IN OUTDOOR AND INDOOR ENVIRONMENTS EXPLOITING 3D IMAGE SPACES AS SPATIAL REFERENCE

abstract: Abstract. In this paper, we present a method for visual localization and pose estimation based on 3D image spaces. The method works in indoor and outdoor environments and does not require the presence of control points or markers. The method is evaluated with different sensors in an outdoor and an indoor test field. The results of our research show the viability of single image localization with absolute position accuracies at the decimetre level for outdoor environments and 5 cm or better for indoor environments. However, the evaluation also revealed a number of limitations of single image visual localization in real-world environments. Some of them could be addressed by an alternative AR-based localization approach, which we also present and compare in this paper. We then discuss the strengths and weaknesses of the two approaches and show possibilities for combining them to obtain accurate and robust visual localization in an absolute coordinate frame.


title: Bipolar Resistive Switching Characteristics of HfO2/TiO2/HfO2 Trilayer-Structure RRAM Devices on Pt and TiN-Coated Substrates Fabricated by Atomic Layer Deposition

abstract: The HfO2/TiO2/HfO2 trilayer-structure resistive random access memory (RRAM) devices have been fabricated on Pt- and TiN-coated Si substrates with Pt top electrodes by atomic layer deposition (ALD). The effect of the bottom electrodes of Pt and TiN on the resistive switching properties of trilayer-structure units has been investigated. Both Pt/HfO2/TiO2/HfO2/Pt and Pt/HfO2/TiO2/HfO2/TiN exhibit typical bipolar resistive switching behavior. The dominant conduction mechanisms in low and high resistance states (LRS and HRS) of both memory cells are Ohmic behavior and space-charge-limited current, respectively. It is found that the bottom electrodes of Pt and TiN have great influence on the electroforming polarity preference, ratio of high and low resistance, and dispersion of the operating voltages of trilayer-structure memory cells. Compared to using symmetric Pt top/bottom electrodes, the RRAM cells using asymmetric Pt top/TiN bottom electrodes show smaller negative forming voltage of −3.7 V, relatively narrow distribution of the set/reset voltages and lower ratio of high and low resistances of 102. The electrode-dependent electroforming polarity can be interpreted by considering electrodes’ chemical activity with oxygen, the related reactions at anode, and the nonuniform distribution of oxygen vacancy concentration in trilayer-structure of HfO2/TiO2/HfO2 on Pt- and TiN-coated Si. Moreover, for Pt/HfO2/TiO2/HfO2/TiN devices, the TiN electrode as oxygen reservoir plays an important role in reducing forming voltage and improving uniformity of resistive switching parameters.


title: Why an Intelligence Explosion is Probable

abstract: The hypothesis is considered that: Once an AI system with roughly human-level general intelligence is created, an “intelligence explosion” involving the relatively rapid creation of increasingly more generally intelligent AI systems will very likely ensue, resulting in the rapid emergence of dramatically superhuman intelligences. Various arguments against this hypothesis are considered and found wanting.


title: Extracting attribute-value pairs from product specifications on the web

abstract: Comparison shopping portals integrate product offers from large numbers of e-shops in order to support consumers in their buying decisions. Product offers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the specific vendor. In addition, product offers might contain structured or semi-structured product specifications in the form of HTML tables and HTML lists. As product specifications often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these specifications is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attribute-value pairs from product specifications on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product specification or not. In order to extract attribute-value pairs from the HTML fragments identified by the specification detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attribute-value pairs from product specifications, we introduce several new features for specification detection and support the extraction of attribute-value pairs from specifications having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 different e-shops. This experiment confirms the suitability of duplicate-based schema matching for product data integration.


title: DNA Methylation and Its Basic Function

abstract: In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA. During development, the pattern of DNA methylation in the genome changes as a result of a dynamic process involving both de novo DNA methylation and demethylation. As a consequence, differentiated cells develop a stable and unique DNA methylation pattern that regulates tissue-specific gene transcription. In this chapter, we will review the process of DNA methylation and demethylation in the nervous system. We will describe the DNA (de)methylation machinery and its association with other epigenetic mechanisms such as histone modifications and noncoding RNAs. Intriguingly, postmitotic neurons still express DNA methyltransferases and components involved in DNA demethylation. Moreover, neuronal activity can modulate their pattern of DNA methylation in response to physiological and environmental stimuli. The precise regulation of DNA methylation is essential for normal cognitive function. Indeed, when DNA methylation is altered as a result of developmental mutations or environmental risk factors, such as drug exposure and neural injury, mental impairment is a common side effect. The investigation into DNA methylation continues to show a rich and complex picture about epigenetic gene regulation in the central nervous system and provides possible therapeutic targets for the treatment of neuropsychiatric disorders.


title: Psychological distress and the increased risk of falling into poverty: a longitudinal study of Australian adults

abstract: PURPOSE ::: To identify whether psychological distress is associated with an increased risk of falling into poverty, giving a more complete picture of how psychological distress affects living standards. ::: ::: ::: METHODS ::: Longitudinal analysis of the nationally representative Household Income and Labour Dynamics in Australian (HILDA) survey using Poisson regression models to estimate relative risk of falling into income poverty and multidimensional poverty between 2007 and 2012. The sample was limited to those who were not already in income poverty in 2007. Psychological distress was identified using the Kessler-10 (K10) scale. ::: ::: ::: RESULTS ::: After adjusting for confounding factors, having moderate psychological distress increased the risk of falling into income poverty by 1.62 (95% CI 1.31-2.01, p < 0.0001) and the risk of falling into multidimensional poverty by 1.85 (95% CI 1.37-2.48, p < 0.0001); having very high psychological distress increased the risk of falling into income poverty by 2.40 (95% CI 1.80-3.20, p < 0.0001) and the risk of falling into multidimensional poverty by 3.68 (95% CI 2.63-5.15, p < 0.0001), compared to those with low psychological distress. Those who did experience income poverty (RR: 1.29, 95% CI 1.04-1.61, p = 0.0210) and those who experienced multidimensional poverty (RR: 1.69, 95% CI 1.32-2.17, p < 0.0001) had an increased risk of having their level of psychological distress increase further compared to those who did not experience poverty. ::: ::: ::: CONCLUSION ::: To date, the increased risk of falling into poverty that is associated with elevated levels of psychological distress has been an overlooked burden of the condition.


title: Evolution , Neural Networks , Games , and Intelligence

abstract: Mathematical games provide a framework for studying intelligent behavior in models of real-world settings or restricted domains. The obstacle comes in choosing the appropriate representation and learning algorithm. Neural networks and evolutionary algorithms provide useful means for addressing these issues. This paper describes efforts to hybridize neural and evolutionary computation to learn appropriate strategies in zero- and nonzero-sum games, including the iterated prisoner's dilemma, tic-tac-toe, and checkers. With respect to checkers, the evolutionary algorithm was able to discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. The implications of evolutionary learning with respect to machine intelligence are also discussed. It is argued that evolution provides the framework for explaining naturally occurring intelligent entities and can be used to design machines that are also capable of intelligent behavior.


title: Cascade PID controller for quadrotor

abstract: Aiming at the attitude stabilization and angle tracking control of quadrotor aircraft, an optimized cascade double-loop PI-PID controller is proposed. It is based on strong coupling and nonlinear dynamic model in this paper. A PI controller acts as an outer loop controller, which controls primary physical parameter, namely the attitude. The other PID controller acts as an inner loop controller, which reads the output of outer loop controller as setpoint, controlling a rapid changing parameter, namely angular velocity. Hardware debugging platform is established to verify the controller. The results of simulation experiments show that the proposed controller is highly adaptable and has fast response. And the superior dynamic and stabilization performance is proved by the pole-zero maps and bode diagrams.


Keep Calm and Study Memes


A Syntax-Based Statistical Translation Model


title: Estimating the Extent of Parameter Bias in the Classical Twin Design: A Comparison of Parameter Estimates From Extended Twin-Family and Classical Twin Designs

abstract: The classical twin design (CTD) circumvents parameter indeterminacy by assuming (1) negligible higher-order epistasis; and (2) either nonadditive genetic or common environmental effects are nonexistent, creating two potential sources of bias (Eaves et al., 1978; Grayson, 1989). Because the extended twin-family design (ETFD) uses many more unique covariance observations to estimate parameters, common environmental and nonadditive genetic parameters can be simultaneously estimated. The ETFD thereby corrects for what is likely to be the largest of the two sources of bias in CTD parameter estimates (Keller and Coventry, 2005). In the current paper, we assess the extent of this and other potential sources of bias in the CTD by comparing all published ETFD parameter estimates to CTD parameter estimates derived from the same data. CTD estimates of the common environment were lower than ETFD estimates of the common environment for some phenotypes, but for other phenotypes (e.g., stature in females and certain social attitudes), what appeared as the common environment was resolved to be assortative mating in the ETFD. On average, CTD estimates of nonadditive genetic factors were 43% lower, and additive genetic factors 63% higher, than ETFD estimates. However, broad-sense heritability estimates from the CTD were only 18% higher than ETFD estimates, highlighting that the CTD is useful for estimating broad-sense but not narrow-sense heritability. These results suggest that CTD estimates can be misleading when interpreted literally, but useful, albeit coarse, when interpreted properly.


title: A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation

abstract: Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.


title: Consistency in Networks of Relations

abstract: Artificial intelligence tasks which can be formulated as constraint satisfaction problems, with which this paper is for the most part concerned, are usually by solved backtracking the examining the thrashing behavior that nearly always accompanies backtracking, identifying three of its causes and proposing remedies for them we are led to a class of algorithms whoch can profitably be used to eliminate local (node, arc and path) inconsistencies before any attempt is made to construct a complete solution. A more general paradigm for attacking these tasks is the altenation of constraint manipulation and case analysis producing an OR problem graph which may be searched in any of the usual ways. ::: ::: Many authors, particularly Montanari and Waltz, have contributed to the development of these ideas; a secondary aim of this paper is to trace that history. The primary aim is to provide an accessible, unified framework, within which to present the algorithms including a new path consistency algorithm, to discuss their relationships and the may applications, both realized and potential of network consistency algorithms.


Cache-Timing Techniques: Exploiting the DSA Algorithm


title: Deep Learning Techniques for Music Generation - A Survey

abstract: This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: ::: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). ::: Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. ::: Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. ::: Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. ::: Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. ::: For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.


title: System-level integrated server architectures for scale-out datacenters

abstract: A System-on-Chip (SoC) integrates multiple discrete components into a single chip, for example by placing CPU cores, network interfaces and I/O controllers on the same die. While SoCs have dominated high-end embedded products for over a decade, system-level integration is a relatively new trend in servers, and is driven by the opportunity to lower cost (by reducing the number of discrete parts) and power (by reducing the pin crossings from the cores to the I/O). Today, the mounting cost pressures in scale-out dat-acenters demand technologies that can decrease the Total Cost of Ownership (TCO). At the same time, the diminshing return of dedicating the increasing number of available transistors to more cores and caches is creating a stronger case for SoC-based servers. This paper examines system-level integration design options for the scale-out server market, specifically targeting datacenter-scale throughput computing workloads. We develop tools to model the area and power of a variety of discrete and integrated server configurations. We evaluate the benefits, trade-offs, and trends of system-level integration for warehouse-scale datacenter servers, and identify the key "uncore" components that reduce cost and power. We perform a comprehensive design space exploration at both SoC and datacenter level, identify the sweet spots, and highlight important scaling trends of performance, power, area, and cost from 45nm to 16nm. Our results show that system integration yields substantial benefits, enables novel aggregated configurations with a much higher compute density, and significantly reduces total chip area and dynamic power versus a discrete-component server. Finally, we use utilization traces and architectural profiles of real machines to evaluate the dynamic power consumption of typical scale-out cloud applications, and combine them in an overall TCO model. Our results show that, for example at 16nm, SoC-based servers can achieve more than a 26% TCO reduction at datacenter scale.


title: Speaker normalization by input space optimization for continuous density hidden Markov models

abstract: This paper proposes a novel method of speaker normalization by means of input space optimization for continuous density hidden Markov models (CDHMM). The parameters of a linear feature transformation function are so determined that, together with the previously trained CDHMM parameters, a mis-classification cost function is minimized for the normalizing data set. Preliminary experimental results on the task of sex adaptation for speaker-independent stop consonant discrimination, evaluated from the DARPA TIMIT speech database, demonstrates the effectiveness of the proposed method. >


title: Determinants of user acceptance of the e-Government services: The case of online tax filing and payment system

abstract: Abstract This paper identifies the factors that determine the publics' acceptance of e-Government services. The online tax filing and payment system (OTFPS) is a well-known e-Government service in Taiwan. Using a theoretical model based on the theory of planned behavior, this study aims to (1) identify the determinants for acceptance of the OTFPS; (2) examine the causal relationships among the variables of acceptance behavior for the OTFPS; and (3) explore the relative importance of each determinant for both those who use the OTFPS and those who do not. The article reports on a survey that collected 1,099 usable responses. The results indicate that the proposed model explained up to 72 percent of the variance in behavioral intention. In addition, the important determinants of user acceptance of the OTFPS are perceived usefulness, ease of use, perceived risk, trust, compatibility, external influences, interpersonal influence, self-efficacy, and facilitating condition. Finally, the academic and practical implications of these findings are discussed.


title: VelociTap: Investigating Fast Mobile Text Entry using Sentence-Based Decoding of Touchscreen Keyboard Input

abstract: We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google's keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40 mm wide smartwatch-sized keyboard at a 3% character error rate.


title: Model-based testing and validation on artificial intelligence systems

abstract: In this paper, we discuss how viewing an artificial intelligence (AI) system as a model leads to certain criteria for testing methodologies. This includes a discussion of how certain mathematical techniques for testing AI systems can be used as criteria for determining the AI System's adequacy when no other models are available. We give an example of an error due to widespread rule interactions. Such errors are the keys to understanding why the independent rule assumption does not work, and therefore why AI systems must be modeled. We examine how testing can be applied both to individual system components as well as to the system as a whole. We also submit different criteria by which a set of test cases can be assembled and the problems in determining whether or not the performance of an AI system on a set of test cases is acceptable. In the end, the article shows the results of applying this model to a real case.


title: Abatacept for treatment of refractory polymyositis.

abstract: Joint Bone Spine - In Press.Proof corrected by the author Available online since dimanche 8 mai 2011


title: Income inequality and health status: a nursing issue

abstract: Objective: To review the association between income inequality and health status, and consider an appropriate nursing response. Primary Argument: Nursing has a rich heritage of advocating for a healthy society established on a foundation of social justice. The future legitimacy and success of public health nursing depends on recognising and appropriately addressing the social, economic and political determinants of health in the populations served. There is an incontrovertible association between population health status, absolute income levels and income inequality. Thus, along with other social determinants of health, income differentials within populations must be a fundamental consideration when planning and delivering nursing services. Ensuring that federal and state health policy explicitly addresses this key issue remains an important challenge for the nursing profession, the public health system and the Australian community. Conclusions: Higher mortality and worse health status occur in societies with higher income inequality. The relationship between income inequality and health appears to be determined both by relative access to resources for health gain and relative social position. The association between greater income equality and improved health may be explained by improved social cohesion. As social factors are at the root of much of health inequality, this knowledge needs to invoke political action and advocacy from the nursing profession to promote the development of healthy public policy. Including indicators of income inequality when planning and monitoring nursing services will enable services to measure to what extent they are based on the principle of social justice.


title: Direct pose estimation for planar objects

abstract: Abstract Estimating six degrees of freedom poses of a planar object from images is an important problem with numerous applications ranging from robotics to augmented reality. While the state-of-the-art Perspective- n -Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on target objects with rich texture. In this work, we propose a two-step robust direct method for six-dimensional pose estimation that performs accurately on both textured and textureless planar target objects. First, the pose of a planar target object with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Second, each object pose is refined and disambiguated using a dense alignment scheme. Extensive experiments on both synthetic and real datasets demonstrate that the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under varying conditions. Furthermore, we show that the proposed dense alignment scheme can also be used for accurate pose tracking in video sequences.


title: Anxiety and depression in the first year of medical residency training.

abstract: OBJECTIVE ::: To perform a screening, follow-up and comparative evaluation of depression and anxiety symptoms in a group of 59 first-year internal medicine residents of a teaching hospital and evaluate the influence of their rotation, nature of rotation, subgroup, sex and the time of year on these symptoms. ::: ::: ::: METHODS ::: We used the Spielberger State-Trait Anxiety Inventory (STAI) and the Beck Depression Inventory (BDI), applied seven times, in the last week of every rotation. ::: ::: ::: RESULTS AND CONCLUSION ::: We obtained two identical averages (P=0.98) of trait-anxiety, six months apart one from the other. There was a significant correlation (R=0.65, P < 0.001) between the indexes of state-anxiety and depression. Women had higher levels of symptoms of both anxiety and depression (P < 0.001) compared to men. A cluster of three subgroups of residents with higher trait-anxiety levels (P=0.001) also showed significantly higher levels of symptoms of state-anxiety and depression (P < 0.001). The time of the year and the nature of the rotation (emergency or not) did not interfere with the levels of depression (P=0.47). We detected rotations where there was greater frequency of residents with symptoms compatible with moderate and severe depression. There were 2.1% of residents with symptoms compatible with severe, 4.2% with moderate and 27% with mild depression. It was possible to graduate symptoms of anxiety and depression in residents, evaluate factors involved in their genesis and locate residents with moderate and severe depression.


title: Establishment of mouse expanded potential stem cells

abstract: Mouse embryonic stem cells derived from the epiblast contribute to the somatic lineages and the germline but are excluded from the extra-embryonic tissues that are derived from the trophectoderm and the primitive endoderm upon reintroduction to the blastocyst. Here we report that cultures of expanded potential stem cells can be established from individual eight-cell blastomeres, and by direct conversion of mouse embryonic stem cells and induced pluripotent stem cells. Remarkably, a single expanded potential stem cell can contribute both to the embryo proper and to the trophectoderm lineages in a chimaera assay. Bona fide trophoblast stem cell lines and extra-embryonic endoderm stem cells can be directly derived from expanded potential stem cells in vitro. Molecular analyses of the epigenome and single-cell transcriptome reveal enrichment for blastomere-specific signature and a dynamic DNA methylome in expanded potential stem cells. The generation of mouse expanded potential stem cells highlights the feasibility of establishing expanded potential stem cells for other mammalian species.


title: The diagnosis and management of rhinitis: An updated practice parameter

abstract: These parameters were developed by the Joint Task Force on Practice Parameters, representing the American Academy of Allergy, Asthma & Immunology; the American College of Allergy, Asthma and Immunology; and the Joint Council of Allergy, Asthma and Immunology. The American Academy of Allergy, Asthma & Immunology (AAAAI) and the American College of Allergy, Asthma and Immunology (ACAAI) have jointly accepted responsibility for establishing "The diagnosis and Management of Rhinitis: An Updated Practice Parameter." This is a complete and comprehensive document at the current time. The medical environment is a changing environment, and not all recommendations will be appropriate for all patients. Because this document incorporated the efforts of many participants, no single individual, including those who served on the Joint Task Force, is authorized to provide an official AAAAI or ACAAI interpretation of these practice parameters. Any request for information about or an interpretation of these practice parameters by the AAAAI or ACAAI should be directed to the Executive Offices of the AAAAI, the ACAAI, and the Joint Council of Allergy, Asthma and Immunology. These parameters are not designed for use by pharmaceutical companies in drug promotion.


title: The role of Kaizen (continuous improvement) in improving companies' performance: A case study

abstract: Kaizen refers to the philosophy or practices that focus upon continuous improvement of processes in manufacturing, engineering and business management. The improvements are usually accomplished at little or no expense and without sophisticated techniques. The purpose of this paper is to present the results of implementing the Kaizen principles at Port Installed Options Center in Toyota Saudi Arabia. Using relevant Kaizen tools including Toyota Production System (TPS), 5S and the seven Muda enabled us to understand the system and identify the most critical problem areas. The Kaizen target was set after analyzing the problems and identifying the gap between the existing system and the TPS. The main results of the study can be summarized as: using the same facilities it was possible to be more efficient by reducing the manpower needed by 26.9% (from 349 to 275 installers) and more effective by increasing the annual output by 13% (from 188000 to 212400 vehicles); improve the Associates Engagement Index (Q12) by 6.4% (from 2.91 to 3.11); potential for inventory reduction due to the use of Kanban system with just-in-time production; avoid investing in new facilities needed to meet the increase in the demand in the market. All these resulted in improving the production line productivity which were appreciated and acknowledged by Toyota Motor Corporation.


title: Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning

abstract: Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors amid challenging cases of clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after only a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at http://vpg.cs.princeton.edu


title: High-quality computational imaging through simple lenses

abstract: Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing. In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.


title: Supporting ITIL processes implementation using business process management systems

abstract: ITIL is one of the most used frameworks for IT service management. Implementing ITIL processes through an organization is not an easy task and present many difficulties. This paper explores the ITIL implementation's challenges and tries to experiment how Business Process Management Systems can help organization overtake those challenges.


title: The design and implementation of arbiters for Network-on-chips

abstract: Round robin arbiter and matrix arbiter mechanism are widely used in Network-on-chips. These two mechanisms are implemented in this paper. The performances in 2D-mesh topology are tested in a FPGA platform. The resource consumption and throughput between Round-robin arbiter and Matrix-arbiter are compared. Through the experiment result, we found that the Matrix-arbiter has higher throughput than the Round-robin arbiter. However the Round-robin arbiter can save much more resources than Matrix arbiter. Thus a tradeoff between the two mechanisms should be considered when design networks-on-chip arbiters.


title: Circular-polarized compact low-profile omni-directional antenna

abstract: A circular-polarized low-profile antenna with omnidirectional characteristics is proposed by utilizing an arrangement of zero-order resonator (ZOR) antennas. The proposed CP antenna is low-profile and has a nearly omnidirectional pattern with good CP characteristics (with an axial ratio around 1.2 dB).


title: Estimating the eye gaze from one eye

abstract: Eye gaze estimation via images has been well investigated and all methods worked on images with the full face. This makes the eye details small and accuracy is affected. We address this problem by zooming in on a single eye. In this paper, we will show the validity of the method and investigate the performance with controlled synthetic data and also with real images. The principle is to rely on the fact that the outer boundary of the iris is a circle. With a fully calibrated camera, its elliptical image can be back-projected into the 3D space yielding two possible circles. To disambiguate, the solution is found by making use of anthropomorphic knowledge of the structure of the eyeball. Hence, getting a larger eye image with a zoom camera enabled us to achieve higher resolutions and thereby higher accuracies. The robustness of the algorithm was verified by extensive statistical trials conducted on synthetic data and real images. The two key contributions in this paper are to show that it is possible to estimate eye gaze with only one eye image and that consequently this achieves higher accuracy of eye gaze estimation.


title: The Decline in Child Sexual Abuse Cases

abstract: The analysis of child sexual abuse cases presented in this Bulletin revealed that substantiated cases of child sexual abuse decreased from a national estimated peak of 149,800 cases in 1992 to 103,600 cases in 1998, a decline of 31 percent. This decrease occurred in the majority of States, with no clear regional pattern. The average decline for all States was 37 percent and for most the decline was gradual, rather than abrupt, and occurred over several years. Although cases of other types of child maltreatment have also declined in recent years, the decrease in child sexual abuse cases has been more marked. Substantiated cases of physical abuse declined 16 percent from a 1995 peak, compared with a 31-percent decline in child sexual abuse cases. In addition to the decline in the number of substantiated cases, child sexual abuse reports also decreased from an estimated 429,000 in 1991 to 315,400 in 1998, a 26-percent decline. Possible explanations for the decline include a real underlying decline in the incidence of child sexual abuse or changes in attitudes, policies, and standards that have reduced the amount of child sexual abuse being reported and substantiated. It is possible that both of these processes are affecting trends in child sexual abuse.


title: Biobanking 3.0: Evidence based and customer focused biobanking

abstract: Abstract Biobanking is a new and very dynamic field. To achieve long term financial sustainability of biobank infrastructures we propose that a new focus is needed on activities, products and services provided by the biobank that relate to the external stakeholder: biobanking 3.0. Earlier stages of biobanking are biobanking 1.0 (primary focus on the number of biospecimens and data) and biobanking 2.0 (primary focus on the quality of biospecimens and data). Both stages 1.0 and 2.0 are predominantly product oriented areas and have required a mostly internal focus on operational development within the biobank itself. In this paper we will introduce our concept of biobanking 3.0 which capitalizes on the earlier stages but dictates a shift in focus to enhancing the value and impact for the three major sets of external stakeholders (people/patients, funders, and research customers) and creating a path to balanced and planned investment in biobank infrastructure and the sustainability of biobanking. Biobanking 3.0 will improve real understanding as well as perceptions of value across different stakeholders. Patients and donors will appreciate seeing how their biospecimens and data are effectively used for research. Funders will value the ability to plan efficient targeting of funding and to monitor the impact of their support. Researchers will capitalize on the ability to translate their ideas into effective knowledge. Ultimately adoption of biobanking 3.0 will impact on the sustainability in the three main dimensions relevant to biobanking: social sustainability (acceptability), operational sustainability (efficiency), and financial sustainability (accomplishment).


title: Sub-Sampled Newton Methods I: Globally Convergent Algorithms

abstract: Large scale optimization problems are ubiquitous in machine learning and data analysis and there is a plethora of algorithms for solving such problems. Many of these algorithms employ sub-sampling, as a way to either speed up the computations and/or to implicitly implement a form of statistical regularization. In this paper, we consider second-order iterative optimization algorithms and we provide bounds on the convergence of the variants of Newton's method that incorporate uniform sub-sampling as a means to estimate the gradient and/or Hessian. Our bounds are non-asymptotic and quantitative. Our algorithms are global and are guaranteed to converge from any initial iterate. ::: Using random matrix concentration inequalities, one can sub-sample the Hessian to preserve the curvature information. Our first algorithm incorporates Hessian sub-sampling while using the full gradient. We also give additional convergence results for when the sub-sampled Hessian is regularized by modifying its spectrum or ridge-type regularization. Next, in addition to Hessian sub-sampling, we also consider sub-sampling the gradient as a way to further reduce the computational complexity per iteration. We use approximate matrix multiplication results from randomized numerical linear algebra to obtain the proper sampling strategy. In all these algorithms, computing the update boils down to solving a large scale linear system, which can be computationally expensive. As a remedy, for all of our algorithms, we also give global convergence results for the case of inexact updates where such linear system is solved only approximately. ::: This paper has a more advanced companion paper, [42], in which we demonstrate that, by doing a finer-grained analysis, we can get problem-independent bounds for local convergence of these algorithms and explore trade-offs to improve upon the basic results of the present paper.


Can we learn to gamble efficiently


title: Dependable Structural Health Monitoring Using Wireless Sensor Networks

abstract: As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming an increasingly compelling platform for engineering structural health monitoring (SHM) due to relatively low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called DependSHM ) and then examining its ability to cope with sensor faults and constraints. We find evidence that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by one or more faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. In DependSHM , we present a distributed automated algorithm to detect such types of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through comprehensive simulations and a WSN prototype system implementation, we evaluate the effectiveness of DependSHM .


title: PSO Based PI Controller Design for a Solar Charger System

abstract: Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).


title: Current-fed DC/DC converters for high voltage gain and low voltage high current applications: An overview of topologies and modulation techniques

abstract: Emerging applications such as solar cells, fuel cells, energy storage, UPS, etc. require voltage gain and are low voltage high current specifications. This paper thoroughly studies and reviews high-frequency current-fed converters for low voltage high current applications. Hard-switching, dissipative or passive snubber based, active-clamped, naturally-clamped, impulse commutated, and parasitic assisted current-fed topologies are studied, compared, and evaluated. Individual merits, demerits, unique attributes and features of the various categories of the current-fed topologies are reported. Detailed comparison and critical evaluation are presented for the selection and design of a topology for the given application and specifications.


title: Object Oriented Modelling with Object Petri Nets

abstract: This paper informally introduces Object Petri Nets (OPNs) with a number of examples. OPNs support a thorough integration of object-oriented concepts into Petri Nets, including inheritance and the associated polymorphism and dynamic binding. They have a single class hierarchy which includes both token types and subnet types, thereby allowing multiple levels of activity in the net. The paper discusses some theoretical issues pertinent to the analysis of OPNs, and compares the provisions of OPNs with those of other Concurrent Object-Oriented Programming Languages. ::: ::: The paper then considers a case study of using OPNs to model a cooperative editor for hierarchical diagrams. This extended example demonstrates the applicability of OPNs to the modelling of non-trivial concurrent systems. The methodology for deriving a Petri Net model is to adapt an object-oriented design methodology: the Object Model is prepared in Rumbaugh's OMT notation; the Dynamic Model is then prepared in the form of lifecycles, following the Shlaer-Mellor methodology; and finally these models are mapped into an OPN model. This approach has the advantage of guiding the development with well-accepted methodologies, and demonstrates the generality and flexibility of the OPN formalism.


title: Bidirectional Single Power-Conversion DC–AC Converter With Noncomplementary Active-Clamp Circuits

abstract: This paper presents a bidirectional single power-conversion dc–ac converter with noncomplementary active-clamp circuits. The proposed converter comprises a bidirectional flyback converter and an unfolding bridge. In order to interface the grid with a low-voltage energy storage through only single power-conversion, the bidirectional flyback converter transforms the low voltage directly into the folded grid voltage and regulates the folded grid current. The proposed converter adopts noncomplementary operation strategy for the active-clamp circuits. By using this strategy, the bidirectional flyback converter not only avoids the voltage spike but also minimizes the power losses by the circulating energy. Thus, with single power-conversion and noncomplementary active-clamp circuits, the proposed converter obtains high power efficiency. To facilitate the bidirectional single power-conversion, a novel control algorithm is developed. With this control algorithm, the proposed converter ensures high grid power quality and seamless mode transition. The proposed bidirectional dc–ac converter is theoretically analyzed in detail. The experimental results based on a 250-W prototype are provided to evaluate its performance.


title: Discriminative Feature Selection for Uncertain Graph Classification

abstract: Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presence" of linkages among the nodes. In many real-world applications, however, the linkage structure of the graphs is inherently uncertain. Therefore, existing measurements of interestingness based upon certain graphs are unable to capture the structural uncertainty in these applications effectively. In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from conventional subgraph mining problems because both the structure of the graph objects and the discrimination score of each subgraph feature are uncertain. To address these challenges, we propose a novel discriminative subgraph feature selection method, DUG, which can find discriminative subgraph features in uncertain graphs based upon different statistical measures including expectation, median, mode and phi-probability. We first compute the probability distribution of the discrimination scores for each subgraph feature based on dynamic programming. Then a branch-and-bound algorithm is proposed to search for discriminative subgraphs efficiently. Extensive experiments on various neuroimaging applications (i.e., Alzheimer's Disease, ADHD and HIV) have been performed to analyze the gain in performance by taking into account structural uncertainties in identifying discriminative subgraph features for graph classification.


title: Affect in web interfaces: a study of the impacts of web page visual complexity and order

abstract: This research concentrates on visual complexity and order as central factors in the design of webpages that enhance users' positive emotional reactions and facilitate desirable psychological states and behaviors. Drawing on existing theories and empirical findings in the environmental psychology, human-computer interaction, and marketing research literatures, a research model is developed to explain the relationships among visual complexity and order design features of a webpage, induced emotional responses in users, and users' approach behaviors toward the website as moderated by users' metamotivational states. A laboratory experiment was conducted to test the model and its associated hypotheses. The results of the study suggested that a web user's initial emotional responses (i.e., pleasantness and arousal), evoked by the visual complexity and order design features of a webpage when first encountered, will have carry-over effects on subsequent approach behavior toward the website. The results also revealed how webpage visual complexity and order influence users' emotions and behaviors differently when users are in different metamotivational states. The salience and importance of webpage visual complexity and order for users' feelings of pleasantness were largely dependent on users' metamotivational states.


title: A Linear Algorithm for Tracing Magnet Position and Orientation by Using Three-Axis Magnetic Sensors

abstract: For medical diagnoses and treatments, it is often desirable to wirelessly trace an object that moves inside the human body. A magnetic tracing technique suggested for such applications uses a small magnet as the excitation source, which does not require the power supply and connection wire. It provides good tracing accuracy and can be easily implemented. As the magnet moves, it establishes around the human body a static magnetic field, whose intensity is related to the magnet's 3-D position and 2-D orientation parameters. With magnetic sensors, these magnetic intensities can be detected in some predetermined spatial points, and the position and orientation parameters can be computed. Typically, a nonlinear optimization algorithm is applied to such a problem, but a linear algorithm is preferable for faster, more reliable computation, and lower complexity. In this paper, we propose a linear algorithm to determine the 5-D magnet's position and orientation parameters. With the data from five (or more) three-axis magnetic sensors, this algorithm results in a solution by the matrix and algebra computations. We applied this linear algorithm on the real localization system, and the results of simulations and real experiments show that satisfactory tracing accuracy can be achieved by using a sensor array with enough three-axis magnetic sensors.


Speed Optimizations in Bitcoin Key Recovery Attacks.


title: Division Algorithms and Implementations

abstract: Many algorithms have been developed for implementing division in hardware. These algorithms differ in many aspects, including quotient convergence rate, fundamental hardware primitives, and mathematical formulations. The paper presents a taxonomy of division algorithms which classifies the algorithms based upon their hardware implementations and impact on system design. Division algorithms can be divided into five classes: digit recurrence, functional iteration, very high radix, table look-up, and variable latency. Many practical division algorithms are hybrids of several of these classes. These algorithms are explained and compared. It is found that, for low-cost implementations where chip area must be minimized, digit recurrence algorithms are suitable. An implementation of division by functional iteration can provide the lowest latency for typical multiplier latencies. Variable latency algorithms show promise for simultaneously minimizing average latency while also minimizing area.


title: A Maturity Model for Implementing ITIL v3

abstract: Information Technology Infra-structure Library (ITIL) is the most popular “best practices” framework for managing Information Technology (IT) services. However, implementing ITIL not only is very difficult but there also are no best practices for implementing ITIL. As a result, ITIL implementations are usually long, expensive, and risky. In this paper, we propose a maturity model to assess an ITIL implementation and provide a roadmap for improvement based on priorities, dependencies, and guidelines. We then demonstrate a practical application of the proposed model with a questionnaire to assess the ITIL Incident Management process that was evaluated in two real-world organizations.


title: An incremental data stream clustering algorithm based on dense units detection

abstract: The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream's efficiency and efficacy.


title: Rule based Network Intrusion Detection using Genetic Algorithm

abstract: The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development of consistent, extensible, easily manageable and have low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various type of attack. DoS or Probing attack are relatively more common and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.


title: An Adaptive Large Neighborhood Search for relocating vehicles in electric carsharing services

abstract: Abstract We propose an Adaptive Large Neighborhood Search metaheuristic to solve a vehicle relocation problem arising in the management of electric carsharing systems. The solution approach, aimed to optimize the total profit, is tested on three real-like benchmark sets of instances. It is compared with a Tabu Search, ad hoc designed for this work, with a previous Ruin and Recreate metaheuristic and with the optimal results obtained via Mixed Integer Linear Programming. We also develop bounding procedures to evaluate the solution quality when the optimal solution is not available.


title: TextFlow: Towards Better Understanding of Evolving Topics in Text

abstract: Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.


title: 3-Axis Magnetic Sensor Array System for Tracking Magnet's Position and Orientation

abstract: In medical diagnoses and treatments, e.g., the endoscopy, the dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we present a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source. It does not require the connection wire and power supply for excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be calculated based on an appropriate algorithm. Here, we propose a real-time tracking system built by Honeywell 3-axis magnetic sensors, HMC1053, as well as the computer sampling circuit. The results show that satisfactory tracking accuracy (average localization error is 3.3 mm) can be achieved using a sensor array with enough number of the 3-axis magnetic sensors.


End-User Programming and the Advent of Sharable, Social Machines


title: Artificial neural networks as a tool in ecological modelling, an introduction

abstract: Abstract Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. In this reason, the international workshop on the applications of ANNs to ecological modelling was organized in Toulouse, France (December 1998). During this meeting, we discussed different methods, and their reliability to deal with ecological data. The special issue of this ecological modelling journal begins with the state-of-the-art with emphasis on the development of structural dynamic models presented by S.E. Jorgensen (DK). Then, to illustrate the ecological applications of ANNs, examples are drawn from several fields, e.g. terrestrial and aquatic ecosystems, remote sensing and evolutionary ecology. In this paper, we present some of the most important papers of the first workshop about ANNs in ecological modelling. We briefly introduce here two algorithms frequently used; (i) one supervised network, the backpropagation algorithm; and (ii) one unsupervised network, the Kohonen self-organizing mapping algorithm. The future development of ANNs is discussed in the present work. Several examples of modelling of ANNs in various areas of ecology are presented in this special issue.


title: Scalable distributed algorithm for approximate nearest neighbor search problem in high dimensional general metric spaces

abstract: We propose a novel approach for solving the approximate nearest neighbor search problem in arbitrary metric spaces. The distinctive feature of our approach is that we can incrementally build a non-hierarchical distributed structure for given metric space data with a logarithmic complexity scaling on the size of the structure and adjustable accuracy probabilistic nearest neighbor queries. The structure is based on a small world graph with vertices corresponding to the stored elements, edges for links between them and the greedy algorithm as base algorithm for searching. Both search and addition algorithms require only local information from the structure. The performed simulation for data in the Euclidian space shows that the structure built using the proposed algorithm has navigable small world properties with logarithmic search complexity at fixed accuracy and has weak (power law) scalability with the dimensionality of the stored data.


title: Multimodal Deep Boltzmann Machines for feature selection on gene expression data

abstract: In this paper, multimodal Deep Boltzmann Machines (DBM) is employed to learn important genes (biomarkers) on gene expression data from human carcinoma colorectal. The learning process involves gene expression data and several patient phenotypes such as lymph node and distant metastasis occurrence. The proposed framework in this paper uses multimodal DBM to train records with metastasis occurrence. Later, the trained model is tested using records with no metastasis occurrence. After that, Mean Squared Error (MSE) is measured from the reconstructed and the original gene expression data. Genes are ranked based on the MSE value. The first gene has the highest MSE value. After that, k-means clustering is performed using various number of genes. Features that give the highest purity index are considered as the important genes. The important genes obtained from the proposed framework and two sample t-test are being compared. From the accuracy of metastasis classification, the proposed framework gives higher results compared to the top genes from two sample t-test.


title: Induction by the Guanosine Analogue Oxanosine of Reversion toward the Normal Phenotype of K-ras-transformed Rat Kidney Cells

abstract: Oxanosine, a guanosine analogue antibiotic, altered the “transformed” morphology of rat kidney cells integrating a temperature-sensitive K- ras gene into “normal” morphology at 34°C, a permissive temperature. Oxanosine also increased the cellular content of fibronectin to the normal level. The cells under these conditions had lower levels of guanine nucleotides and unstable and less palmitylated M r 21,000 protein (K- ras gene product, referred to as p21). These changes in p21 were the same as those which occurred to p21 in the cells cultured in the absence of oxanosine at 39.5°C, a nonpermissive temperature.


title: Lesions and Neoplasms of the Penis: A Review.

abstract: In addition to practitioners who care for male patients, with the increased use of high-resolution anoscopy, practitioners who care for women are seeing more men in their practices as well. Some diseases affecting the penis can impact on their sexual partners. Many of the lesions and neoplasms of the penis occur on the vulva as well. In addition, there are common and rare lesions unique to the penis. A review of the scope of penile lesions and neoplasms that may present in a primary care setting is presented to assist in developing a differential diagnosis if such a patient is encountered, as well as for practitioners who care for their sexual partners. A familiarity will assist with recognition, as well as when consultation is needed.


title: Spatial filter selection for EEG-based communication

abstract: Abstract Individuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.


title: Scalable server and storage architectures for video streaming

abstract: Internet-based video streaming has significantly picked up in the market place, followed by almost a decade of intensive research and standard efforts. Not only are we interested in the current status of video streaming, but also the future that it is heading towards. The paper discusses scalable server and storage architectures for video streaming.


title: A 3.1 to 4.6 GHz 3-bit reconfigurable bandpass filter using λ/4 microstrip resonators and chip inductor coupling

abstract: For the efficient use of frequency spectrum resources, RF devices which can flexibly change frequency bands are needed. In the fifth-generation (5G) mobile communication systems, a reconfigurable BPF will be necessary that covers higher frequency bands such as low-SHF bands and enables concurrent dual access by using a high-SHF band. In this paper, a 3-bit/eight center-frequency switchable low-SHF reconfigurable BPF has been designed and fabricated with lumped-element coupling technique. The design and measurement results of the prototype BPF using 03015-type chip inductors are presented as the initial study. The center frequency range of prototype BPF is from 3.1 to 4.6 GHz with insertion loss range from 1.0 to 2.1 dB.


title: NeuFlow: Dataflow vision processing system-on-a-chip

abstract: This paper presents a bio-inspired vision system-on-a-chip - neuFlow SoC implemented in the IBM 45 nm SOI process. The neuFlow SoC was designed to accelerate neural networks and other complex vision algorithms based on large numbers of convolutions and matrix-to-matrix operations. Post-layout characterization shows that the system delivers up to 320 GOPS with an average power consumption of 0.6 W. The power-efficiency and portability of this system is ideal for embedded vision-based devices, such as driver assistance, and robotic vision.


title: Byzantine generals in action: implementing fail-stop processors

abstract: A fail-stop processor halts instead of performing an erroneous state transformation that might be visible to other processors, can detect whether another fail-stop processor has halted (due to a failure), and has a predefined portion of its storage that will remain unaffected by failures and accessible to any other fail-stop processor. Fail-stop processors can simplify the construction of fault-tolerant computing systems. In this paper, the problem of approximating fail-stop processors is discussed. Use of fail-stop processors is compared with the state machine approach, another general paradigm for constructing fault-tolerant systems.


title: Driving Pressure during Thoracic Surgery: A Randomized Clinical Trial

abstract: Editor’s PerspectiveWhat We Already Know about This TopicDriving pressure (plateau minus end-expiratory airway pressure) is a target in patients with acute respiratory distress syndrome, and is proposed as a target during general anesthesia for patients with normal lungs. It has not been reported fo


title: Framing and Deliberation: How Citizens' Conversations Limit Elite Influence

abstract: Public opinion research demonstrates that citizens' opinions depend on elite rhetoric and interpersonal conversations. Yet, we continue to have little idea about how these two forces interact with one another. In this article, we address this issue by experimentally examining how interpersonal conversations affect (prior) elite framing effects. We find that conversations that include only common perspectives have no effect on elite framing, but conversations that include conflicting perspectives eliminate elite framing effects. We also introduce a new individual level moderator of framing effects—called “need to evaluate”—and we show that framing effects, in general, tend to be short-lived phenomena. In the end, we clarify when elites can and cannot use framing to influence public opinion and how interpersonal conversations affect this process.


Aphid performance changes with plant defense mediated by Cucumber mosaic virus titer


title: How an Artificially Intelligent Virtual Assistant Helps Students Navigate the Road to College

abstract: Deep reinforcement learning using convolutional neural networks is the technology behind autonomous vehicles. Could this same technology facilitate the road to college? During the summer between high school and college, college-related tasks that students must navigate can hinder successful matriculation. We employ conversational artificial intelligence (AI) to efficiently support thousands of would-be college freshmen by providing personalized, text message–based outreach and guidance for each task where they needed support. We implemented and tested this system through a field experiment with Georgia State University (GSU). GSU-committed students assigned to treatment exhibited greater success with pre-enrollment requirements and were 3.3 percentage points more likely to enroll on time. Enrollment impacts are comparable to those in prior interventions but with substantially reduced burden on university staff. Given the capacity for AI to learn over time, this intervention has promise for scaling persona...


title: Discriminating Gender on Twitter

abstract: Accurate prediction of demographic attributes from social media and other informal online content is valuable for marketing, personalization, and legal investigation. This paper describes the construction of a large, multilingual dataset labeled with gender, and investigates statistical models for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large-scale human assessment using Amazon Mechanical Turk. Our methods significantly out-perform both baseline models and almost all humans on the same task.


title: Knee rehabilitation robot control by Sliding-Backstepping and Admittance control

abstract: In this paper, a knee rehabilitation robot will be developed and controlled. The structure of the system containing the robot and some parts of patient body will be described and modeled using Newton-Euler approach. Two controllers containing Admittance and Sliding-Backstepping are composed to control the system. In this hybrid control scheme, the Sliding-Backstepping controller is considered to track the prescribed trajectory commanded by the expert physiotherapist, and the admittance control is considered to produce a soft and smooth interaction between the robot and human leg. The simulation results show the performance of the designed hybrid controller with suggested structure, in presence of involuntary movement of the leg and noisy measured signals.


title: Achieving near-capacity on a multiple-antenna channel

abstract: Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve near-capacity on a single-antenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve near-capacity on a multiple-antenna system where the receiver knows the channel. Combining iterative processing with multiple-antenna channels is particularly challenging because the channel capacities can be a factor of ten or more higher than their single-antenna counterparts. Using a "list" version of the sphere decoder, we provide a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs. We exemplify our technique by directly transmitting symbols that are coded with a channel code; we show that iterative processing with even this simple scheme can achieve near-capacity. We consider both simple convolutional and powerful turbo channel codes and show that excellent performance at very high data rates can be attained with either. We compare our simulation results with Shannon capacity limits for ergodic multiple-antenna channel.


title: Cloud Computing and Robotics for Disaster Management

abstract: Nowadays, IT community is experiencing great shift in computing and information storage infrastructures by using powerful, flexible and reliable alternative of cloud computing. The power of cloud computing may also be realized for mankind if some dedicated disaster management clouds will be developed at various countries cooperating each other on some common standards. The experimentation and deployment of cloud computing by governments of various countries for mankind may be the justified use of IT at social level. It is possible to realize a real-time disaster management cloud where applications in cloud will respond within a specified time frame. If a Real-Time Cloud (RTC) is available then for intelligent machines like robots the complex processing may be done on RTC via request and response model. The complex processing is more desirable as level of intelligence increases in robots towards humans even more. Therefore, it may be possible to manage disaster sites more efficiently with more intelligent cloud robots without great lose of human lives waiting for various assistance at disaster site. Real-time garbage collector, real-time specification for Java, multicore CPU architecture with network-on-chip, parallel algorithms, distributed algorithms, high performance database systems, high performance web servers and gigabit networking can be used to develop real-time applications in cloud.


title: Double stator winding induction generator for wind and hydro applications: 2D-FEM analysis and optimal design

abstract: In an effort to introduce a low cost (PM less), low power electric wind or hydro generators, this paper reports on preliminary optimal design aspects and FEM analysis of a 3kW, 250 rpm, dual stator winding asynchronous (DSWA) generator. At the first stage the single winding, three phase asynchronous rotor is optimally designed for low speed and low frequency considering the influence of the poles pairs on the main dimensions, weight, active material cost and efficiency. Than, the rated power and cost reduction of the inverter is analyzed considering dual stator winding asynchronous generators in two topologies: with the inverter placed on the excitation windings and with the inverter placed on the main winding. The analytical model used for optimal design and the model of the double stator winding is validated trough finite element method. Finally the proposed solutions are compared with the single winding induction generators.


title: A Text Mining-Based Anomaly Detection Model in Network Security

abstract: Anomaly detection systems are extensively used security tools to detect cyber-threats and attack activities in computer systems and networks. In this paper, we present Text Mining-Based Anomaly Detection (TMAD) model. We discuss n-gram text categorization and focus our attention on a main contribution of method TF-IDF (Term frequency, inverse document frequency), which enhance the performance commonly term weighting schemes are used, where the weights reflect the importance of a word in a specific document of the considered collection. Mahalanobis Distances Map (MDM) and Support Vector Machine (SVM) are used to discover hidden correlations between the features and among the packet payloads. Experiments have been accomplished to estimate the performance of TMAD against ISCX dataset 2012 intrusion detection evaluation dataset. The results show TMAD has good accuracy.