The land unit — A fundamental concept in landscape ecology, and its applications

The land unit, as an expression of landscape as a system, is a fundamental concept in landscape ecology. It is an ecologically homogeneous tract of land at the scale at issue. It provides a basis for studying topologic as well as chorologic landscape ecology relationships. A land unit survey aims at mapping such land units. This is done by simultaneously using characteristics of the most obvious (mappable) land attributes: land-form, soil and vegetation (including human alteration of these three). The land unit is the basis of the map legend but may be expressed via these three land attributes. The more dynamic land attributes, such as certain animal populations and water fluxes, are less suitable as diagnostic criteria, but often link units by characteristic information/energy fluxes. The land unit survey is related to a further development of the widely accepted physiographic soil survey see Edelman (1950). Important aspects include: by means of a systems approach, the various land data can be integrated more appropriately; geomorphology, vegetation and soil science support each other during all stages (photo-interpretation, field survey, data processing, final classification); the time and costs are considerably less compared with the execution of separate surveys; the result is directly suitable as a basis for land evaluation; the results can be expressed in separate soil, vegetation, land use and landform maps, or even single value maps. A land unit survey is therefore: a method for efficient survey of land attributes, such as soils, vegetation, landform, expressed in either separate or combined maps; a means of stimulating integration among separate land attribute sciences; an efficient basis for land evaluation. For multidisciplinary projects with applied ecologic aims (e.g., land management), it is therefore the most appropriate survey approach. Within the land unit approach there is considerable freedom in the way in which the various land attribute data are ‘integrated’. It is essential, however, that: during the photo-interpretation stage, the contributions of the various specialists are brought together to prepare a preliminary (land unit) photo-interpretation map; the fieldwork data are collected at exactly the same sample point, preferably by a team of specialists in which soil, vegetation and geomorphology are represented; the final map is prepared in close cooperation of all contributing disciplines, based on photo-interpretation and field data; the final map approach may vary from one fully-integrated land unit map to various monothematic maps.


Spanish Multicenter Normative Studies (NEURONORMA Project): norms for verbal span, visuospatial span, letter and number sequencing, trail making test, and symbol digit modalities test.

As part of the Spanish Multicenter Normative Studies (NEURONORMA project), we provide age- and education-adjusted norms for the following instruments: verbal span (digits), visuospatial span (Corsi's test), letter-number sequencing (WAIS-III), trail making test, and symbol digit modalities test. The sample consists of 354 participants who are cognitively normal, community-dwelling, and age ranging from 50 to 90 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. The current norms should provide clinically useful data for evaluating elderly Spanish people. These data may be of considerable use for comparisons with other normative studies. Limitations of these normative data are mainly related to the techniques of recruitment and stratification employed.


Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines.

Clinicians and researchers without a suitable health-related quality of life (HRQOL) measure in their own language have two choices: (1) to develop a new measure, or (2) to modify a measure previously validated in another language, known as a cross-cultural adaptation process. We propose a set of standardized guidelines for this process based on previous research in psychology and sociology and on published methodological frameworks. These guidelines include recommendations for obtaining semantic, idiomatic, experiential and conceptual equivalence in translation by using back-translation techniques and committee review, pre-testing techniques and re-examining the weight of scores. We applied these guidelines to 17 cross-cultural adaptation of HRQOL measures identified through a comprehensive literature review. The reporting standards varied across studies but agreement between raters in their ratings of the studies was substantial to almost perfect (weighted kappa = 0.66-0.93) suggesting that the guidelines are easy to apply. Further research is necessary in order to delineate essential versus optional steps in the adaptation process.


Low Power Magnetic Full-Adder Based on Spin Transfer Torque MRAM

Power issues have become a major problem of CMOS logic circuits as technology node shrinks below 90 nm. In order to overcome this limitation, emerging logic-in-memory architecture based on nonvolatile memories (NVMs) are being investigated. Spin transfer torque (STT) magnetic random access memory (MRAM) is considered one of the most promising NVMs thanks to its high speed, low power, good endurance, and 3-D back-end integration. This paper presents a novel magnetic full-adder (MFA) design based on perpendicular magnetic anisotropy (PMA) STT-MRAM. It provides advantageous power efficiency and die area compared with conventional CMOS-only full adder (FA). Transient simulations have been performed to validate this design by using an industrial CMOS 40 nm design kit and an accurate STT-MRAM compact model including physical models and experimental measurements.


A Wideband Dual-Polarized Antenna for LTE700/GSM850/GSM900 Applications

A novel resonator-loaded wideband dual-polarized antenna is proposed for LTE700/GSM850/GSM900 applications. Four single-polarized miniaturized antenna elements are placed with square arrangement upon the reflector for dual polarization. One pair of parallel elements is for +45° polarization, and the other pair is for –45° polarization. The impedance bandwidth is greatly improved loaded with a resonator. Experimentally, the antenna exhibits a wide impedance bandwidth of 37.5% ranging from 0.67 to 0.98 GHz for |<inline-formula> <tex-math notation="LaTeX">$S$</tex-math></inline-formula>11|<–15 dB, high isolation of more than 40 dB, and stable gain of around 9.5 dBi over the whole operating band.


Sex differences in mate preferences revisited: do people know what they initially desire in a romantic partner?

In paradigms in which participants state their ideal romantic-partner preferences or examine vignettes and photographs, men value physical attractiveness more than women do, and women value earning prospects more than men do. Yet it remains unclear if these preferences remain sex differentiated in predicting desire for real-life potential partners (i.e., individuals whom one has actually met). In the present study, the authors explored this possibility using speed dating and longitudinal follow-up procedures. Replicating previous research, participants exhibited traditional sex differences when stating the importance of physical attractiveness and earning prospects in an ideal partner and ideal speed date. However, data revealed no sex differences in the associations between participants' romantic interest in real-life potential partners (met during and outside of speed dating) and the attractiveness and earning prospects of those partners. Furthermore, participants' ideal preferences, assessed before the speed-dating event, failed to predict what inspired their actual desire at the event. Results are discussed within the context of R. E. Nisbett and T. D. Wilson's (1977) seminal article: Even regarding such a consequential aspect of mental life as romantic-partner preferences, people may lack introspective awareness of what influences their judgments and behavior.


Distributed active transformer-a new power-combining andimpedance-transformation technique

In this paper, we compare the performance of the newly introduced distributed active transformer (DAT) structure to that of conventional on-chip impedance-transformations methods. Their fundamental power-efficiency limitations in the design of high-power fully integrated amplifiers in standard silicon process technologies are analyzed. The DAT is demonstrated to be an efficient impedance-transformation and power-combining method, which combines several low-voltage push-pull amplifiers in series by magnetic coupling. To demonstrate the validity of the new concept, a 2.4-GHz 1.9-W 2-V fully integrated power-amplifier achieving a power-added efficiency of 41% with 50input and output matching has been fabricated using 0.35-μm CMOS transistors Item Type: Article Additional Information: © Copyright 2002 IEEE. Reprinted with permission. Manuscript received May 27, 2001. [Posted online: 2002-08-07] This work was supported by the Intel Corporation, the Army Research Office, the Jet Propulsion Laboratory, Infinion, and the National Science Foundation. The authors thank Conexant Systems for chip fabrication, particularly R. Magoon, F. In’tveld, J. Powell, A. Vo, and K. Moye. K. Potter, D. Ham, and H.Wu, all of the California Institute of Technology (Caltech), Pasadena, deserve special thanks for their assistance. The technical support for CAD tools from Agilent Technologies and Sonnet Software Inc., Liverpool, NY, are also appreciated. “Special Issue on Silicon-Based RF and Microwave Integrated Circuits”, IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 1, part 2 Subject


A simplified design approach for radial power combiners

It is known that a radial power combiner is very effective in combining a large number of power amplifiers, where high efficiency (greater than 90%) over a relatively wide band can be achieved. However, its current use is limited due to its design complexity. In this paper, we develop a step-by-step design procedure, including both the initial approximate design formulas and suitable models for final accurate design optimization purposes. Based on three-dimensional electromagnetic modeling, predicted results were in excellent agreement with those measured. Practical issues related to the radial-combiner efficiency, its graceful degradation, and the effects of higher order package resonances are discussed here in detail


A planar probe double ladder waveguide power divider

The successful demonstration of a 1:4 power divider using microstrip probes and a WR-430 rectangular waveguide is presented. The 15-dB return loss bandwidth of the nonoptimized structure is demonstrated to be 22% and its 0.5-dB insertion loss bandwidth 26%. While realized through conventional machining, such a structure is assembled in a fashion consistent with proven millimeter and submillimeter-wave micromachining techniques. Thus, the structure presents a potential power dividing and power combining architecture, which through micromachining, may be used for applications well above 100GHz.


A Ka-band power amplifier based on the traveling-wave power-dividing/combining slotted-waveguide circuit

An eight-device Ka-band solid-state power amplifier has been designed and fabricated using a traveling-wave power-dividing/combining technique. The low-profile slotted-waveguide structure employed in this design provides not only a high power-combining efficiency over a wide bandwidth, but also efficient heat sinking for the active devices. The measured maximum small-signal gain of the eight-device power amplifier is 19.4 dB at 34 GHz with a 3-dB bandwidth of 3.2 GHz (f/sub L/=31.8 GHz, f/sub H/=35 GHz). The measured maximum output power at 1-dB compression (P/sub out/ at 1 dB) from the power amplifier is 33 dBm (/spl sim/2 W) at 32.2 GHz, with a power-combining efficiency of 80%. Furthermore, performance degradation of this power amplifier due to device failures has also been simulated and measured.


Broad-Band High-Power Amplifier Using Spatial Power-Combining Technique

High power, broad bandwidth, high linearity, and low noise are among the most important features in amplifier design. The broad-band spatial power-combining technique addresses all these issues by combining the output power of a large quantity of microwave monolithic integrated circuit (MMIC) amplifiers in a broad-band coaxial waveguide environment, while maintaining good linearity and improving phase noise of the MMIC amplifiers. A coaxial waveguide was used as the host of the combining circuits for broader bandwidth and better uniformity by equally distributing the input power to each element. A new compact coaxial combiner with much smaller size is investigated. Broad-band slotline to microstrip-line transition is integrated for better compatibility with commercial MMIC amplifiers. Thermal simulations are performed and an improved thermal management scheme over previous designs is employed to improve the heat sinking in high-power application. A high-power amplifier using the compact combiner design is built and demonstrated to have a bandwidth from 6 to 17 GHz with 44-W maximum output power. Linearity measurement has shown a high third-order intercept point of 52 dBm. Analysis shows the amplifier has the ability to extend spurious-free dynamic range by 2 3 times. The amplifier also has shown a residual phase floor close to 140 dBc at 10-kHz offset from the carrier with 5–6-dB reductions compared to a single MMIC amplifier it integrates.


Ping-pong trajectory perception and prediction by a PC based High speed four-camera vision system

A high speed vision system is vital for a robot player to play table tennis successfully. The three dimensional ping-pong trajectory should be perceived in high precision at a high frame rate. What's more, with the beginning parts of the trajectory, the following trajectory should be predicted to save enough time for successful planning and control.


Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM

Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer/robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02% on the validation set of IsoGD and 98.89% on SKIG).


Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing

Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation and enhance their performance. Our approach relies on a convolutional autoencoder (CAE) with the total variation loss (TVL) for unsupervised learning. We show that, after our CAE-TVL preprocessing step, the over-segmentation effect is significantly reduced using the same unsupervised image segmentation methods. We evaluate our approach using the BSDS500 image segmentation benchmark dataset and show the performance enhancement introduced by our approach in terms of both increased segmentation accuracy and reduced computation time. We examine the robustness of the trained CAE and show that it is directly applicable to other natural scene images.


Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data

We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.


Prevention of falls and consequent injuries in elderly people

Injuries resulting from falls in elderly people are a major public-health concern, representing one of the main causes of longstanding pain, functional impairment, disability, and death in this population. The problem is going to worsen, since the rates of such injuries seem to be rising in many areas, as is the number of elderly people in both the developed and developing world. Many methods and programmes to prevent such injuries already exist, including regular exercise, vitamin D and calcium supplementation, withdrawal of psychotropic medication, cataract surgery, professional environment hazard assessment and modification, hip protectors, and multifactorial preventive programmes for simultaneous assessment and reduction of many of the predisposing and situational risk factors. To receive broader-scale effectiveness, these programmes will need systematic implementation. Care must be taken, however, to rigorously select the right actions for those people most likely to benefit, such as vitamin D and calcium supplementation and hip protectors for elderly people living in institutions.


Geometric Scene Parsing with Hierarchical LSTM

This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-theart methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.


Securing wireless sensor networks: a survey

The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security.


A theory of self-calibration of a moving camera

There is a close connection between the calibration of a single camera and the epipolar transformation obtained when the camera undergoes a displacement. The epipolar transformation imposes two algebraic constraints on the camera calibration. If two epipolar transformations, arising from different camera displacements, are available then the compatible camera calibrations are parameterized by an algebraic curve of genus four. The curve can be represented either by a space curve of degree seven contained in the intersection of two cubic surfaces, or by a curve of degree six in the dual of the image plane. The curve in the dual plane has one singular point of order three and three singular points of order two. If three epipolar transformations are available, then two curves of degree six can be obtained in the dual plane such that one of the real intersections of the two yields the correct camera calibration. The two curves have a common singular point of order three. Experimental results are given to demonstrate the feasibility of camera calibration based on the epipolar transformation. The real intersections of the two dual curves are found by locating the zeros of a function defined on the interval [0, 2π]. The intersection yielding the correct camera calibration is picked out by referring back to the three epipolar transformations.


Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.


A delay-tolerant network architecture for challenged internets

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.


Gimli: open source and high-performance biomedical name recognition

Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .


Using audio and visual information for single channel speaker separation

This work proposes a method to exploit both audio and visual speech information to extract a target speaker from a mixture of competing speakers. The work begins by taking an effective audio-only method of speaker separation, namely the soft mask method, and modifying its operation to allow visual speech information to improve the separation process. The audio input is taken from a single channel and includes the mixture of speakers, where as a separate set of visual features are extracted from each speaker. This allows modification of the separation process to include not only the audio speech but also visual speech from each speaker in the mixture. Experimental results are presented that compare the proposed audio-visual speaker separation with audio-only and visual-only methods using both speech quality and speech intelligibility metrics.


Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming

Research has shown that judgments tend to assimilate to irrelevant "anchors." We extend anchoring effects to show that anchors can even operate across modalities by, apparently, priming a general sense of magnitude that is not moored to any unit or scale. An initial study showed that participants drawing long "anchor" lines made higher numerical estimates of target lengths than did those drawing shorter lines. We then replicated this finding, showing that a similar pattern was obtained even when the target estimates were not in the dimension of length. A third study showed that an anchor's length relative to its context, and not its absolute length, is the key to predicting the anchor's impact on judgments. A final study demonstrated that magnitude priming (priming a sense of largeness or smallness) is a plausible mechanism underlying the reported effects. We conclude that the boundary conditions of anchoring effects may be much looser than previously thought, with anchors operating across modalities and dimensions to bias judgment.


An OpenCCG-Based Approach to Question Generation from Concepts

Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user’s expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.


mmWave phased-array with hemispheric coverage for 5th generation cellular handsets

A first-of-the-kind 28 GHz antenna solution for the upcoming 5th generation cellular communication is presented in detail. Extensive measurements and simulations ascertain the proposed 28 GHz antenna solution to be highly effective for cellular handsets operating in realistic propagating environments.


Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player’s own beliefs in evaluating how well an action will serve that player’s utility function, and (2) shift the relative priority it assigns to each player’s expected utilities over time, by a factor proportional to how well that player’s beliefs predict the machine’s inputs. Observation (2) represents a substantial divergence from näıve linear utility aggregation (as in Harsanyi’s utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.


Facilitating Object Detection and Recognition through Eye Gaze

When compared to image recognition, object detection is a much more challenging task because it requires the accurate real-time localization of an object in the target image. In interaction scenarios, this pipeline can be simplified by incorporating the users’ point of regard. Wearable eye trackers can estimate the gaze direction, but lack own processing capabilities. We enable mobile gaze-aware applications by developing an open-source platform which supports mobile eye tracking based on the Pupil headset and a smartphone running Android OS. Through our platform, we offer researchers and developers a rapid prototyping environment for gaze-enabled applications. We describe the concept, our current progress, and research implications. ACM Classification


DataVizard: Recommending Visual Presentations for Structured Data

Selecting the appropriate visual presentation of the data such that it not only preserves the semantics but also provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our DataVizard system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy.


Memorability of natural scenes: The role of attention

The image memorability consists in the faculty of an image to be recalled after a period of time. Recently, the memorability of an image database was measured and some factors responsible for this memorability were highlighted. In this paper, we investigate the role of visual attention in image memorability around two axis. The first one is experimental and uses results of eye-tracking performed on a set of images of different memorability scores. The second investigation axis is predictive and we show that attention-related features can advantageously replace low-level features in image memorability prediction. From our work it appears that the role of visual attention is important and should be more taken into account along with other low-level features.


Real-time garbage collection for flash-memory storage systems of real-time embedded systems

Flash-memory technology is becoming critical in building embedded systems applications because of its shock-resistant, power economic, and nonvolatile nature. With the recent technology breakthroughs in both capacity and reliability, flash-memory storage systems are now very popular in many types of embedded systems. However, because flash memory is a write-once and bulk-erase medium, we need a translation layer and a garbage-collection mechanism to provide applications a transparent storage service. In the past work, various techniques were introduced to improve the garbage-collection mechanism. These techniques aimed at both performance and endurance issues, but they all failed in providing applications a guaranteed performance. In this paper, we propose a real-time garbage-collection mechanism, which provides a guaranteed performance, for hard real-time systems. On the other hand, the proposed mechanism supports non-real-time tasks so that the potential bandwidth of the storage system can be fully utilized. A wear-leveling method, which is executed as a non-real-time service, is presented to resolve the endurance problem of flash memory. The capability of the proposed mechanism is demonstrated by a series of experiments over our system prototype.


Optimization of the internal grid of an offshore wind farm using Genetic algorithm

Offshore wind energy is a promising solution thanks to its best performance of production. However its development leads to many technical and especially economic challenges among them the electrical grid topology attracts a large investment. In this paper, our objective is to minimize a part of this total investment which represents the initial cost of the middle voltage cables in the internal network with different cross sections. An approach based on Genetic Algorithm is developed to find the best topology to connect all wind turbines and substations. The proposed model initially accepts all possible configurations: radial, star, ring, and tree. The results prove that the optimization model can be used for designing the electrical architecture of the internal network of an offshore wind farm.


A flexible and low-cost tactile sensor for robotic applications

For humans, the sense of touch is essential for interactions with the environment. With robots slowly starting to emerge as a human-centric technology, tactile information becomes increasingly important. Tactile sensors enable robots to gain information about contacts with the environment, which is required for safe interaction with humans or tactile exploration. Many sensor designs for the application on robots have been presented in literature so far. However, most of them are complex in their design and require high-tech tools for their manufacturing. In this paper, we present a novel design for a tactile sensor that can be built with low-cost, widely available materials, and low effort. The sensor is flexible, may be cut to arbitrary shapes and may have a customized spatial resolution. Both pressure distribution and absolute pressure on the sensor are detected. An experimental evaluation of our design shows low detection thresholds as well as high sensor accuracy. We seek to accelerate research on tactile feedback methods with this easy to replicate design. We consider our design a starting point for the integration of multiple sensor units to a large-scale tactile skin for robots.


A Dictionary-based Approach to Racism Detection in Dutch Social Media

We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from tw o public Belgian social media sites likely to attract racist reactio ns. These comments were labeled as racist or non-racist by mu ltiple annotators. For our approach, three discourse dictionaries were create d: first, we created a dictionary by retrieving possibly raci st and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary w s created through automatic expansion using a word2vec model trained on a large corpus of general Dutch text. Finall y, third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the dist ribu ion of words over the different categories in the dictionaries as f eatures. The best-performing model used the manually clean ed dictionary and obtained an F-score of 0.46 for the racist class on a test s et consisting of unseen Dutch comments, retrieved from the s ame sites used for the training set. The automated expansion of the dic tionary only slightly boosted the model’s performance, and this increase in performance was not statistically significant. The fact t hat the coverage of the expanded dictionaries did increase i ndicates that the words that were automatically added did occur in the corpus, b t were not able to meaningfully impact performance. The di ctionaries, code, and the procedure for requesting the corpus are availa ble at:https://github.com/clips/hades.


Online anomaly detection with concept drift adaptation using recurrent neural networks

Anomaly detection in time series is an important task with several practical applications. The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. In this paper, we describe a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behavior or a change (drift) in normal behavior. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.


Self-nonself discrimination in a computer

Current commercial virus detectors are based on The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish relf from other. We describe a method for change detection which is based on the generation of T cella in the immune syetem. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses.


Towards a taxonomy of intrusion-detection systems

Intrusion-detection systems aim at detecting attacks against computer systems and networks, or against information systems in general, as it is difficult to provide provably secure information systems and maintain them in such a secure state for their entire lifetime and for every utilization. Sometimes, legacy or operational constraints do not even allow a fully secure information system to be realized at all. Therefore, the task of intrusion-detection systems is to monitor the usage of such systems and to detect the apparition of insecure states. They detect attempts and active misuse by legitimate users of the information systems or external parties to abuse their privileges or exploit security vulnerabilities. In this paper, we introduce a taxonomy of intrusion-detection systems that highlights the various aspects of this area. This taxonomy defines families of intrusion-detection systems according to their properties. It is illustrated by numerous examples from past and current projects. q 1999 Elsevier Science B.V. All rights reserved.


Artificial immune systems - a new computational intelligence paradigm

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The COPS Security Checker System

In the past several years, there have been a large number of published works that have graphically described a wide variety of security problems particular to UNIX. Without fail, the same problems have been discussed over and over again, describing the problems with sum (set user ID) programs, improper file permissions, and bad passwords (to name a few). There are two common characteristics to each of these problems: first, they are usually simple to correct, if found; second, they are fairly easy to detect. Since almost all systems have fairly equivalent problems, it seems appropriate to create a tool to detect potential security problems as an aid to system administrators. This paper describes one such tool: Cops. (Computerized Oracle and Password System) is a freely-available,


NetSTAT: A Network-Based Intrusion Detection Approach

Network-based attacks have become common and sophisticated. For this reason, intrusion detection systems are now shifting their focus from the hosts and their operating systems to the network itself. Network-based intrusion detection is challenging because network auditing produces large amounts of data, and different events related to a single intrusion may be visible in different places on the network. This paper presents NetSTAT, a new approach to network intrusion detection. By using a formal model of both the network and the attacks, NetSTAT is able to determine which network events have to be monitored and where they can be monitored.


The Impact of Indoor Lighting on Students ’ Learning Performance in Learning Environments : A knowledge internalization perspective

The purpose of this study is to identify the influence of indoor lighting on students’ learning performance within learning environments from knowledge internalization perspective. This study is a comprehensive review of literatures base on the influence of indoor lighting on people’s productivity and performance especially students’ learning performance. The result that comes from this study shows that it is essential to improve lighting in learning environments to enhance students’ learning performance and also motivate them to learn more. In this study the researchers utilized Pulay (2010) survey and measured the influence of lighting on students’ learning performance. Utilizing survey data collected from 150 students from Alpha course in Malaysia. This study found significant impact between lighting quality and students’ learning performance this finding is also supported by interview from two experts.


Schema Theory for Semantic Link Network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.


Design considerations on low voltage synchronous power MOSFETs with monolithically integrated gate voltage pull-down circuitry

In this paper, a monolithically integrated gate voltage pull-down circuitry is presented to avoid the unintentional C·dV/dt induced turn-on. The concept of a low threshold voltage MOSFET with this integrated gate voltage pull-down circuitry is introduced as a contributing factor to the next generation high frequency DC-DC converter efficiency improvement. Design considerations on this new device and influences of critical design parameters on device/circuit performance will be fully discussed. In synchronous buck application, this integrated power module achieves more than 2% efficiency improvement over reference solution at high operation frequency (1MHz) under 19V input and 1.3V output condition.


A Framework for Dynamic Image Sampling Based on Supervised Learning

Sparse sampling schemes can broadly be classified into two main categories: static sampling where the sampling pattern is predetermined, and dynamic sampling where each new measurement location is selected based on information obtained from previous measurements. Dynamic sampling methods are particularly appropriate for pointwise imaging methods, in which pixels are measured sequentially in arbitrary order. Examples of pointwise imaging schemes include certain implementations of atomic force microscopy, electron back scatter diffraction, and synchrotron X-ray imaging. In these pointwise imaging applications, dynamic sparse sampling methods have the potential to dramatically reduce the number of measurements required to achieve a desired level of fidelity. However, the existing dynamic sampling methods tend to be computationally expensive and are, therefore, too slow for many practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using datasets that are representative of the specific application. In addition, we introduce an approximate method to terminate dynamic sampling at a desired level of distortion. We then extend our algorithm to incorporate multiple measurements at each step, which we call groupwise SLADS. Finally, we present results on computationally generated synthetic data and experimentally collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods


High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study

Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.


Open-Domain Neural Dialogue Systems

Until recently, the goal of developing opendomain dialogue systems that not only emulate human conversation but fulfill complex tasks, such as travel planning, seemed elusive. However, we start to observe promising results in the last few years as the large amount of conversation data is available for training and the breakthroughs in deep learning and reinforcement learning are applied to dialogue. In this tutorial, we start with a brief introduction to the history of dialogue research. Then, we describe in detail the deep learning and reinforcement learning technologies that have been developed for two types of dialogue systems. First is a task-oriented dialogue system that can help users accomplish tasks, ranging from meeting scheduling to vacation planning. Second is a social bot that can converse seamlessly and appropriately with humans. In the final part of the tutorial, we review attempts to developing opendomain neural dialogue systems by combining the strengths of task-oriented dialogue systems and social bots. The tutorial material is available at http://opendialogue.miulab.tw.


Rationalizing Neural Predictions

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.1


Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments

Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.


Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.


Coupling CCG and Hybrid Logic Dependency Semantics

Categorial grammar has traditionally used the λ-calculus to represent meaning. We present an alternative, dependency-based perspective on linguistic meaning and situate it in the computational setting. This perspective is formalized in terms of hybrid logic and has a rich yet perspicuous propositional ontology that enables a wide variety of semantic phenomena to be represented in a single meaning formalism. Finally, we show how we can couple this formalization to Combinatory Categorial Grammar to produce interpretations compositionally.


Secure data deduplication

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. By identifying common chunks of data both within and between files and storing them only once, deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication exploits identical content, while encryption attempts to make all content appear random; the same content encrypted with two different keys results in very different ciphertext. Thus, combining the space efficiency of deduplication with the secrecy aspects of encryption is problematic.
 We have developed a solution that provides both data security and space efficiency in single-server storage and distributed storage systems. Encryption keys are generated in a consistent manner from the chunk data; thus, identical chunks will always encrypt to the same ciphertext. Furthermore, the keys cannot be deduced from the encrypted chunk data. Since the information each user needs to access and decrypt the chunks that make up a file is encrypted using a key known only to the user, even a full compromise of the system cannot reveal which chunks are used by which users.


Robust Median Reversion Strategy for Online Portfolio Selection

Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust <inline-formula><tex-math notation="LaTeX"> $L_1$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="huang-ieq1-2563433.gif"/></alternatives> </inline-formula>-median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.


Activities and well-being in older age: effects of self-concept and educational attainment.

The positive effect of activities on well-being is proposed to be mediated by self-conceptualizations and facilitated by socioeconomic status. The hypothesized processes were estimated with LISREL VIII using data from a large cross-sectional survey with a sample of 679 adults aged 65 and older who were representative of older adults living in the Detroit area. Findings indicate that the frequency of performing both leisure and productive activities yields an effect on physical health and depression and that these effects are mediated in part by a sense of self as agentic, but less clearly by a sense of self as social. Furthermore, socioeconomic status, operationalized as formal educational attainment, facilitates the effect of leisure to a greater extent than that of productive activities.


A Hybrid Algorithm for LTL Games

In the game theoretic approach to the synthesis of reactive systems, specifications are often given in linear time logic (LTL). Computing a winning strategy to an infinite game whose winning condition is the set of LTL properties is the main step in obtaining an implementation. We present a practical hybrid algorithm—a combination of symbolic and explicit algorithm—for the computation of winning strategies for unrestricted LTL games that we have successfully applied to synthesize reactive systems with up to 10 states.


An IoT-Oriented Data Storage Framework in Cloud Computing Platform

The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.


Maintaining Integrity: How Nurses Navigate Boundaries in Pediatric Palliative Care.

PURPOSE
To explore how nurses manage personal and professional boundaries in caring for seriously ill children and their families.

DESIGN AND METHODS
Using a constructivist grounded theory approach, a convenience sample of 18 registered nurses from four practice sites was interviewed using a semi-structured interview guide.

RESULTS
Nurses across the sites engaged in a process of maintaining integrity whereby they integrated two competing, yet essential, aspects of their nursing role - behaving professionally and connecting personally. When skillful in both aspects, nurses were satisfied that they provided high-quality, family-centered care to children and families within a clearly defined therapeutic relationship. At times, tension existed between these two aspects and nurses attempted to mitigate the tension. Unsuccessful mitigation attempts led to compromised integrity characterized by specific behavioral and emotional indicators. Successfully mitigating the tension with strategies that prioritized their own needs and healing, nurses eventually restored integrity. Maintaining integrity involved a continuous effort to preserve completeness of both oneself and one's nursing practice.

CONCLUSIONS
Study findings provide a theoretical conceptualization to describe the process nurses use in navigating boundaries and contribute to an understanding for how this specialized area of care impacts health care providers.

PRACTICE IMPLICATIONS
Work environments can better address the challenges of navigating boundaries through offering resources and support for nurses' emotional responses to caring for seriously ill children. Future research can further refine and expand the theoretical conceptualization of maintaining integrity presented in this paper and its potential applicability to other nursing specialties.


Comparing Two IRT Models for Conjunctive Skills

A step in ITS often involve multiple skills. Thus a step requiring a conjunction of skills is harder than steps that require requiring each individual skill only. We developed two Item-Response Models – Additive Factor Model (AFM) and Conjunctive Factor Model (CFM) – to model the conjunctive skills in the student data sets. Both models are compared on simulated data sets and a real assessment data set. We showed that CFM was as good as or better than AFM in the mean cross validation errors on the simulated data. In the real data set CFM is not clearly better. However, AFM is essentially performing as a conjunctive model.


Using temporal IDF for efficient novelty detection in text streams

Novelty detection in text streams is a challenging task that emerges in quite a few different scenarios, ranging from email thread filtering to RSS news feed recommendation on a smartphone. An efficient novelty detection algorithm can save the user a great deal of time and resources when browsing through relevant yet usually previously-seen content. Most of the recent research on detection of novel documents in text streams has been building upon either geometric distances or distributional similarities, with the former typically performing better but being much slower due to the need of comparing an incoming document with all the previously-seen ones. In this paper, we propose a new approach to novelty detection in text streams. We describe a resource-aware mechanism that is able to handle massive text streams such as the ones present today thanks to the burst of social media and the emergence of the Web as the main source of information. We capitalize on the historical Inverse Document Frequency (IDF) that was known for capturing well term specificity and we show that it can be used successfully at the document level as a measure of document novelty. This enables us to avoid similarity comparisons with previous documents in the text stream, thus scaling better and leading to faster execution times. Moreover, as the collection of documents evolves over time, we use a temporal variant of IDF not only to maintain an efficient representation of what has already been seen but also to decay the document frequencies as the time goes by. We evaluate the performance of the proposed approach on a real-world news articles dataset created for this task. We examine an exhaustive number of variants of the model and compare them to several commonly used baselines that rely on geometric distances. The results show that the proposed method outperforms all of the baselines while managing to operate efficiently in terms of time complexity and memory usage, which are of great importance in a mobile setting scenario.


Applications of SAT Solvers to AES Key Recovery from Decayed Key Schedule Images

Cold boot attack is a side channel attack which exploits the data remanence property of random access memory (RAM) to retrieve its contents which remain readable shortly after its power has been removed. Given the nature of the cold boot attack, only a corrupted image of the memory contents will be available to the attacker. In this paper, we investigate the use of an off-the-shelf SAT solver, CryptoMinSat, to improve the key recovery of the AES-128 key schedules from its corresponding decayed memory images. By exploiting the asymmetric decay of the memory images and the redundancy of key material inherent in the AES key schedule, rectifying the faults in the corrupted memory images of the AES-128 key schedule is formulated as a Boolean satisfiability problem which can be solved efficiently for relatively very large decay factors. Our experimental results show that this approach improves upon the previously known results.


Improved Shortest Path Maps with GPU Shaders

We present in this paper several improvements for computing shortest path maps using OpenGL shaders [1]. The approach explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing shortest path maps which can efficiently be queried at run-time. Our improved method relies on Compute Shaders for improved performance, does not require any CPU pre-computation, and handles shortest path maps both with source points and with line segment sources. The produced path maps partition the input environment into regions sharing a same parent point along the shortest path to the closest source point or segment source. Our method produces paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.


The Cyclic Towers of Hanoi

The famous Towers of Hanoi puzzle consists of 3 pegs (A, B, C) on one of which (A) are stacked n rings of different sizes, each ring resting on a larger ring. The objective is to move the n rings one by one until they are all stacked on another peg (B) in such a way that no ring is ever placed on a smaller ring; the other peg (C) can be used as workspace. The problem has tong been a favourite iir programming courses as one which admits a concise recursive solution. This solution hinges on the observation that, when the largest ring is moved from A to B, the n 1 remaining rings must all be on peg C. This immediately leads to the recursive procedure


Shakeout: A New Regularized Deep Neural Network Training Scheme

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice. We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR10 show that Shakeout deals with over-fitting effectively.


Big Data Security: Survey on Frameworks and Algorithms

Technology today has progressed to an extent wherein collection of data is possible for every granular aspect of a business, in real time. Electronic devices, power grids and modern software all generate huge volumes of data, which is in the form of petabytes, exabytes and zettabytes. It is important to secure existing big data environments due to increasing threats of breaches and leaks from confidential data and increased adoption of cloud technologies due to the ability of buying processing power and storage on-demand. This is exposing traditional and new data warehouses and repositories to the outside world and at the risk of being compromised to hackers and malicious outsiders and insiders. In this paper the current big data scenario has been summarized along with challenges faced and security issues that need attention. Also some existing approaches have been described to illustrate current and standard directions to solving the issues.


Scribbles to Vectors: Preparation of Scribble Drawings for CAD Interpretation

This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.


On the Robust Control of Buck-Converter DC-Motor Combinations

The concepts of active disturbance rejection control and flatness-based control are used in this paper to regulate the response of a dc-to-dc buck power converter affected by unknown, exogenous, time-varying load current demands. The generalized proportional integral observer is used to estimate and cancel the time-varying disturbance signals. A key element in the proposed control for the buck converter-dc motor combination is that even if the control input gain is imprecisely known, the control strategy still provides proper regulation and tracking. The robustness of this method is further extended to the case of a double buck topology driving two different dc motors affected by different load torque disturbances. Simulation results are provided.


Comparative study of Proportional Integral and Backstepping controller for Buck converter

This paper describes the comparative study of Proportional Integral (PI) and Backstepping controller for Buck converter with R-load and DC motor. Backstepping approach is an efficient control design procedure for both regulation and tracking problems. This approach is based upon a systematic procedure which guarantees global regulation and tracking. The proposed control scheme is to stabilize the output (voltage or speed) and tracking error to converge zero asymptotically. Buck converter system is simulated in MATLAB, using state reconstruction techniques. Simulation results of buck converter with R-load and PMDC motor reveals that, settling time of Backstepping controller is less than PI controller


Robust Passivity-Based Control of a Buck–Boost-Converter/DC-Motor System: An Active Disturbance Rejection Approach

This paper presents an active disturbance rejection (ADR) approach for the control of a buck-boost-converter feeding a dc motor. The presence of arbitrary, time-varying, load torque inputs on the dc motor and the lack of direct measurability of the motor's angular velocity variable prompts a generalized proportional integral (GPI) observer-based ADR controller which is synthesized on the basis of passivity considerations. The GPI observer simultaneously estimates the angular velocity and the exogenous disturbance torque input in an on-line cancellation scheme, known as the ADR control. The proposed control scheme is thus a sensorless one with robustness features added to the traditional energy shaping plus damping injection methodology. The discrete switching control realization of the designed continuous feedback control law is accomplished by means of a traditional PWM-modulation scheme. Additionally, an input to state stability property of the closed-loop system is established. Experimental and simulation results are provided.


Closed-Loop Analysis and Cascade Control of a Nonminimum Phase Boost Converter

In this paper, a cascade controller is designed and analyzed for a boost converter. The fast inner current loop uses sliding-mode control. The slow outer voltage loop uses the proportional-integral (PI) control. Stability analysis and selection of PI gains are based on the nonlinear closed-loop error dynamics. It is proven that the closed-loop system has a nonminimum phase behavior. The voltage transients and reference voltage are predictable. The current ripple and system sensitivity are studied. The controller is validated by a simulation circuit with nonideal circuit parameters, different circuit parameters, and various maximum switching frequencies. The simulation results show that the reference output voltage is well tracked under parametric changes, system uncertainties, or external disturbances with fast dynamic transients, confirming the validity of the proposed controller.


A general approach to control a Positive Buck-Boost converter to achieve robustness against input voltage fluctuations and load changes

A positive buck-boost converter is a known DC- DC converter which may be controlled to act as buck or boost converter with same polarity of the input voltage. This converter has four switching states which include all the switching states of the above mentioned DC-DC converters. In addition there is one switching state which provides a degree of freedom for the positive buck-boost converter in comparison to the buck, boost, and inverting buck-boost converters. In other words the positive buck- boost converter shows a higher level of flexibility for its inductor current control compared to the other DC-DC converters. In this paper this extra degree of freedom is utilised to increase the robustness against input voltage fluctuations and load changes. To address this capacity of the positive buck-boost converter, two different control strategies are proposed which control the inductor current and output voltage against any fluctuations in input voltage and load changes. Mathematical analysis for dynamic and steady state conditions are presented in this paper and simulation results verify the proposed method.


Wideband Millimeter-Wave Surface Micromachined Tapered Slot Antenna

A millimeter-wave surface micromachined tapered slot antenna (TSA) fed by an air-filled rectangular coaxial line is proposed. Detailed parametric study is conducted to determine the effects of the TSA's key structural features on its VSWR and gain. Selected exponential taper with determined growth rate and corrugation length resulted in an antenna with VSWR <; 2.5, gain > 2.75 dBi, and stable patterns over a 43-140-GHz range. The TSA is fabricated, and good correlation between modeling and W-band measurements confirms its wideband performance.


The genomic landscapes of human breast and colorectal cancers.

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.


Incorporating partitioning and parallel plans into the SCOPE optimizer

Massive data analysis on large clusters presents new opportunities and challenges for query optimization. Data partitioning is crucial to performance in this environment. However, data repartitioning is a very expensive operation so minimizing the number of such operations can yield very significant performance improvements. A query optimizer for this environment must therefore be able to reason about data partitioning including its interaction with sorting and grouping. SCOPE is a SQL-like scripting language used at Microsoft for massive data analysis. A transformation-based optimizer is responsible for converting scripts into efficient execution plans for the Cosmos distributed computing platform. In this paper, we describe how reasoning about data partitioning is incorporated into the SCOPE optimizer. We show how relational operators affect partitioning, sorting and grouping properties and describe how the optimizer reasons about and exploits such properties to avoid unnecessary operations. In most optimizers, consideration of parallel plans is an afterthought done in a postprocessing step. Reasoning about partitioning enables the SCOPE optimizer to fully integrate consideration of parallel, serial and mixed plans into the cost-based optimization. The benefits are illustrated by showing the variety of plans enabled by our approach.


Sentiment classification of Hinglish text

In order to determine the sentiment polarity of Hinglish text written in Roman script, we experimented with different combinations of feature selection methods and a host of classifiers using term frequency-inverse document frequency feature representation. We carried out in total 840 experiments in order to determine the best classifiers for sentiment expressed in the news and Facebook comments written in Hinglish. We concluded that a triumvirate of term frequency-inverse document frequency-based feature representation, gain ratio based feature selection, and Radial Basis Function Neural Network as the best combination to classify sentiment expressed in the Hinglish text.


Multi words quran and hadith searching based on news using TF-IDF

Each week religious leaders need to give advice to their community. Religious advice matters ideally contain discussion and solution of the problem that arising in society. But the lot of religious resources that must be considered agains many arising problems make this religious task is not easy. Especially in moslem community, the religious resources are Quran and Kutubus Sitah, the six most referenced collection of Muhammad (pbuh) news (hadith). The problem that arising in society can be read from various online mass media. Doing manually, they must know the Arabic word of the problem, and make searching manually from Mu'jam, Quran and Hadith index, then write out the found verses or hadith. TF-IDF method is often used in the weighting informational retrieval and text mining. This research want to make tools that get input from mass media news, make multi words searching from database using TF-IDF (Term Frequency - Inverse Document Frequency), and give relevan verse of Quran and hadith. Top five the most relevan verse of Quran and hadith will be displayed. Justified by religious leader, application give 60% precision for Quranic verses, and 53% for hadith, with the average query time 2.706 seconds.


GALE: Geometric Active Learning for Search-Based Software Engineering

Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.


Local force cues for strength and stability in a distributed robotic construction system

Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability during the building process. Physical forces present throughout a structure may be usable as a cue to inform agent actions as well as an indirect communication mechanism (stigmergy) to coordinate their behavior, as adding material leads to redistribution of forces which then informs the addition of further material. Here we consider in simulation a system of decentralized climbing robots capable of traversing and extending a two-dimensional truss structure, and explore the use of feedback based on force sensing as a way for the swarm to anticipate and prevent structural failures. We consider a scenario in which robots are tasked with building an unsupported cantilever across a gap, as for a bridge, where the goal is for the swarm to build any stable spanning structure rather than to construct a specific predetermined blueprint. We show that access to local force measurements enables robots to build cantilevers that span significantly farther than those built by robots without access to such information. This improvement is achieved by taking measures to maintain both strength and stability, where strength is ensured by paying attention to forces during locomotion to prevent joints from breaking, and stability is maintained by looking at how loads transfer to the ground to ensure against toppling. We show that swarms that take both kinds of forces into account have improved building performance, in both structured settings with flat ground and unpredictable environments with rough terrain.


Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.


Smart Cars on Smart Roads: An IEEE Intelligent Transportation Systems Society Update

To promote tighter collaboration between the IEEE Intelligent Transportation Systems Society and the pervasive computing research community, the authors introduce the ITS Society and present several pervasive computing-related research topics that ITS Society researchers are working on. This department is part of a special issue on Intelligent Transportation.


Towards Multi-Agent Communication-Based Language Learning

We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an adhoc communication code only effective for the game they are playing.


A region-based image caption generator with refined descriptions

Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoderdecoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance, and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images.


3D printing: Basic concepts mathematics and technologies

3D printing is the process of being able to print any object layer by layer. But if we question this proposition, can we find any three dimensional objects that can't be printed layer by layer? To banish any disbeliefs we walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally through series of case studies we show that 3d printers in medicine have made a massive breakthrough lately.


3D strip meander delay line structure for multilayer LTCC-based SiP applications

Recently, the timing control of high-frequency signals is strongly demanded due to the high integration density in three-dimensional (3D) LTCC-based SiP applications. Therefore, to control the skew or timing delay, new 3D delay lines will be proposed. For frailty of the signal via, we adopt the concept of coaxial line and proposed an advanced signal via structure with quasi coaxial ground (QCOX-GND) vias. We will show the simulated results using EM and circuit simulator.


Visual secret sharing scheme for (k, n) threshold based on QR code with multiple decryptions

In this paper, a novel visual secret sharing (VSS) scheme based on QR code (VSSQR) with (k, n) threshold is investigated. Our VSSQR exploits the error correction mechanism in the QR code structure, to generate the bits corresponding to shares (shadow images) by VSS from a secret bit in the processing of encoding QR. Each output share is a valid QR code that can be scanned and decoded utilizing a QR code reader, which may reduce the likelihood of attracting the attention of potential attackers. Due to different application scenarios, two different recovered ways of the secret image are given. The proposed VSS scheme based on QR code can visually reveal secret image with the abilities of stacking and XOR decryptions as well as scan every shadow image, i.e., a QR code, by a QR code reader. The secret image could be revealed by human visual system without any computation based on stacking when no lightweight computation device. On the other hand, if the lightweight computation device is available, the secret image can be revealed with better visual quality based on XOR operation and could be lossless revealed when sufficient shares are collected. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.


Use of Web 2 . 0 technologies in K-12 and higher education : The search for evidence-based practice

Evidence-based practice in education entails making pedagogical decisions that are informed by relevant empirical research evidence. The main purpose of this paper is to discuss evidence-based pedagogical approaches related to the use of Web 2.0 technologies in both K-12 and higher education settings. The use of such evidence-based practice would be useful to educators interested in fostering student learning through Web 2.0 tools. A comprehensive literature search across the Academic Search Premier, Education Research Complete, ERIC, and PsycINFO databases was conducted. Empirical studies were included for review if they specifically examined the impact of Web 2.0 technologies on student learning. Articles that merely described anecdotal studies such as student perception or feeling toward learning using Web 2.0, or studies that relied on student self-report data such as student questionnaire survey and interview were excluded. Overall, the results of our review suggested that actual evidence regarding the impact of Web 2.0 technologies on student learning is as yet fairly weak. Nevertheless, the use of Web 2.0 technologies appears to have a general positive impact on student learning. None of the studies reported a detrimental or inferior effect on learning. The positive effects are not necessarily attributed to the technologies per se but to how the technologies are used, and how one conceptualizes learning. It may be tentatively concluded that a dialogic, constructionist, or coconstructive pedagogy supported by activities such as Socratic questioning, peer review and self-reflection appeared to increase student achievement in blog-, wiki-, and 3-D immersive virtual world environments, while a transmissive pedagogy supported by review activities appeared to enhance student learning using podcast. 2012 Elsevier Ltd. All rights reserved.


Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

Abstract Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variable that serves as an additional unobserved covariate for the regression. This model (which we call GPLC) allows for heteroscedasticity since it allows the function to have a changing partial derivative with respect to this unobserved covariate. With a suitable covariance function, our GPLC model can handle (a) Gaussian residuals with input-dependent variance, or (b) nonGaussian residuals with input-dependent variance, or (c) Gaussian residuals with constant variance. We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c). Markov Chain Monte Carlo methods are developed for both modelsl. Experiments show that when the data is heteroscedastic, both GPLC and GPLV give better results (smaller mean squared error and negative log-probability density) than standard GP regression. In addition, when the residual are Gaussian, our GPLC model is generally nearly as good as GPLV, while when the residuals are non-Gaussian, our GPLC model is better than GPLV.


Atrous Convolutional Neural Network (ACNN) for Semantic Image Segmentation with full-scale Feature Maps

Deep Convolutional Neural Networks (DCNNs) are used extensively in biomedical image segmentation. However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to semantic image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, different atrous blocks, shortcut connections and normalization methods are explored to select the optimal network structure setting. These lead to a new and full-scale DCNN Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Fine Group Normalization (FGN). Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve comparable segmentation Dice Similarity Coefficients (DSCs) to U-Net, optimized U-Net and hybrid network, but with significantly reduced trainable parameters due to the use of fullscale feature maps and therefore computationally is much more efficient for both the training and inference.


Orthogonal AMP

Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for linear system models. When the system transform matrix has independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scalar recursion called state evolution (SE). However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. This imposes limits on the applications of AMP. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for general unitarily-invariant matrices, including IID Gaussian matrices and partial orthogonal matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method. Our numerical results demonstrate that OAMP can be advantageous over AMP, especially for ill-conditioned matrices.


Learning Discrete Hashing Towards Efficient Fashion Recommendation

In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.


Improving malware detection by applying multi-inducer ensemble

Detection of malicious software (malware) using ma chine learning methods has been explored extensively to enable fas t detection of new released malware. The performance of these classifiers depen ds on the induction algorithms being used. In order to benefit from mul tiple different classifiers, and exploit their strengths we suggest using an ens emble method that will combine the results of the individual classifiers i nto one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Dec ision Tree, Naïve Bayes, KNN, VFI and OneR) on five malware datasets. The mai n goal is to find the best combining method for the task of detecting mal icious files in terms of accuracy, AUC and execution time.


Unikernel-based approach for software-defined security in cloud infrastructures

The heterogeneity of cloud resources implies sub-stantial overhead to deploy and configure adequate security mechanisms. In that context, we propose a software-defined security strategy based on unikernels to support the protection of cloud infrastructures. This approach permits to address management issues by uncoupling security policy from their enforcement through programmable security interfaces. It also takes benefits from unikernel virtualization properties to support this enforcement and provide resources with low attack surface. These resources correspond to highly constrained configurations with the strict minimum for a given period. We describe the management framework supporting this software-defined security strategy, formalizing the generation of unikernel images that are dynamically built to comply with security requirements over time. Through an implementation based on MirageOS, and extensive experiments, we show that the cost induced by our security integration mechanisms is small while the gains in limiting the security exposure are high.