title: A power controlled multiple access protocol for wireless packet networks

abstract: Multiple access-based collision avoidance MAC protocols have typically used fixed transmission power, and have not considered power control mechanisms based on the distance of the transmitter and receiver in order to improve spatial channel reuse. This work proposes PCMA, a power controlled multiple access wireless MAC protocol within the collision avoidance framework. PCMA generalizes the transmit-or-defer "on/off" collision avoidance model of current protocols to a more flexible "variable bounded power" collision suppression model. The algorithm is provisioned for ad hoc networks and does not require the presence of base stations to manage transmission power (i.e. it is decentralized). The advantage of implementing a power controlled protocol in an ad-hoc network is that source-destination pairs can be more tightly packed into the network allowing a greater number of simultaneous transmissions (spectral reuse). Our initial simulation results show that the PCMA can improve the throughput performance of the non-power controlled IEEE 802.11 by a factor of 2 with potential for additional scalability as source-destination pairs become more localized, thus providing a compelling reason for migrating to a new power controlled multiple access wireless MAC protocol standard.


Deep Category-Aware Semantic Edge Detection


Processing IoT Data with Cloud Computing for Smart Cities


title: Holographic Reduced Representations

abstract: Associative memories are conventionally used to represent data with very simple structure: sets of pairs of vectors. This paper describes a method for representing more complex compositional structure in distributed representations. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, simple frame-like structures, and reduced representations can be represented in a fixed width vector. These representations are items in their own right and can be used in constructing compositional structures. The noisy reconstructions extracted from convolution memories can be cleaned up by using a separate associative memory that has good reconstructive properties. >


title: DeepISP: Learning End-to-End Image Processing Pipeline

abstract: We present DeepISP, a full end-to-end deep neural model of the camera image signal processing pipeline. Our model learns a mapping from the raw low-light mosaiced image to the final visually compelling image and encompasses low-level tasks, such as demosaicing and denoising, as well as higher-level tasks, such as color correction and image adjustment. The training and evaluation of the pipeline were performed on a dedicated data set containing pairs of low-light and well-lit images captured by a Samsung S7 smartphone camera in both raw and processed JPEG formats. The proposed solution achieves the state-of-the-art performance in objective evaluation of peak signal-to-noise ratio on the subtask of joint denoising and demosaicing. For the full end-to-end pipeline, it achieves better visual quality compared to the manufacturer ISP, in both a subjective human assessment and when rated by a deep model trained for assessing image quality.


title: DFDnet: Discriminant Face Descriptor Network for Facial Age Estimation

abstract: Age estimation is an important part of biometric recognition. In this paper, we propose to use Discriminant Face Descriptor (DFD) which learns the most discriminative and related features to age variation in a data-driven way. We use it, for the first time, for facial age Age estimation is an important part of biometric recognition. In this paper, we propose a stacked structure called Discriminant Face Descriptor network (DFDnet) to learn the most discriminative and related features to age variation. We extract the multi-stage Discriminant Face Descriptor (DFD) features which are age-sensitive. We first introduce DFD for facial age estimation instead of original face recognition. Then the bag-of-features method is used so that each image can be represented by the histogram of visual words. Finally, age estimation is achieved via simple linear regression algorithm. Experiments on the publicly available MORPH and FG-NET databases validate the effectiveness of our proposed DFDnet method. To further illuminate the usefulness of our approach in unconstrained environments, we also conduct the experiment on Cross-Age Celebrity Dataset (CACD) which is collected from Internet movie database (IMDB).


Self-similarity Analysis for Motion Capture Cleaning


title: Metasurface holograms for visible light

abstract: Holographic techniques provide phase and amplitude information for images of objects, but normally the hologram thickness is comparable to the light wavelength used. Ni et al. present ultra-thin plasmonic holograms that control amplitude and phase in the visible region and are just 30 nm thick.


title: Providing all global energy with wind , water , and solar power , Part I : Technologies , energy resources , quantities and areas of infrastructure , and materials

abstract: Climate change, pollution, and energy insecurity are among the greatest problems of our time. Addressing them requires major changes in our energy infrastructure. Here, we analyze the feasibility of providing worldwide energy for all purposes (electric power, transportation, heating/cooling, etc.) from wind, water, and sunlight (WWS). In Part I, we discuss WWS energy system characteristics, current and future energy demand, availability of WWS resources, numbers of WWS devices, and area and material requirements. In Part II, we address variability, economics, and policy of WWS energy. We estimate that � 3,800,000 5 MW wind turbines, � 49,000 300 MW concentrated solar plants, � 40,000 300 MW solar PV power plants, � 1.7 billion 3 kW rooftop PV systems, � 5350 100 MW geothermal power plants, � 270 new 1300 MW hydroelectric power plants, � 720,000 0.75 MW wave devices, and � 490,000 1 MW tidal turbines can power a 2030 WWS world that uses electricity and electrolytic hydrogen for all purposes. Such a WWS infrastructure reduces world power demand by 30% and requires only � 0.41% and � 0.59% more of the world’s land for footprint and spacing, respectively. We suggest producing all new energy with WWS by 2030 and replacing the pre-existing energy by 2050. Barriers to the plan are primarily social and political, not technological or economic. The energy cost in a WWS world should be similar to that today.


title: On the (im)possibility of fairness

abstract: What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible. We present a mathematical setting in which the distinctions in previous papers can be made formal. In addition to characterizing the spaces of inputs (the "observed" space) and outputs (the "decision" space), we introduce the notion of a construct space: a space that captures unobservable, but meaningful variables for the prediction. ::: We show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space. The results in this paper imply that future treatments of algorithmic fairness should more explicitly state assumptions about the relationship between constructs and observations.


title: High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

abstract: Vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions, such as active debris removal for remediating...


title: Cognitive Test Performance and Background Music

abstract: This study examined the effects of background music on test performance. In a repeated-measures design 30 undergraduates completed two cognitive tests, one in silence and the other with background ...


title: Ultrathin Films of Single-Walled Carbon Nanotubes for Electronics and Sensors: A Review of Fundamental and Applied Aspects

abstract: Ultrathin films of single-walled carbon nanotubes (SWNTs) represent an attractive, emerging class of material, with properties that can approach the exceptional electrical, mechanical, and optical characteristics of individual SWNTs, in a format that, unlike isolated tubes, is readily suitable for scalable integration into devices. These features suggest the potential for realistic applications as conducting or semiconducting layers in diverse types of electronic, optoelectronic and sensor systems. This article reviews recent advances in assembly techniques for forming such films, modeling and experimental work that reveals their collective properties, and engineering aspects of implementation in sensors and in electronic devices and circuits with various levels of complexity. A concluding discussion provides some perspectives on possibilities for future work in fundamental and applied aspects.


title: Realtime 3D eye gaze animation using a single RGB camera

abstract: This paper presents the first realtime 3D eye gaze capture method that simultaneously captures the coordinated movement of 3D eye gaze, head poses and facial expression deformation using a single RGB camera. Our key idea is to complement a realtime 3D facial performance capture system with an efficient 3D eye gaze tracker. We start the process by automatically detecting important 2D facial features for each frame. The detected facial features are then used to reconstruct 3D head poses and large-scale facial deformation using multi-linear expression deformation models. Next, we introduce a novel user-independent classification method for extracting iris and pupil pixels in each frame. We formulate the 3D eye gaze tracker in the Maximum A Posterior (MAP) framework, which sequentially infers the most probable state of 3D eye gaze at each frame. The eye gaze tracker could fail when eye blinking occurs. We further introduce an efficient eye close detector to improve the robustness and accuracy of the eye gaze tracker. We have tested our system on both live video streams and the Internet videos, demonstrating its accuracy and robustness under a variety of uncontrolled lighting conditions and overcoming significant differences of races, genders, shapes, poses and expressions across individuals.


title: A differential harmony search based hybrid interval type2 fuzzy EGARCH model for stock market volatility prediction

abstract: In this paper a new hybrid model integrating an interval type2 fuzzy logic system (IT2FLS) with a computationally efficient functional link artificial neural network (CEFLANN) and an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model has been proposed for accurate forecasting and modeling of financial data with changing variance over time. The proposed model denoted as IT2F-CE-EGARCH helps to enhance the ability of EGARCH model through a joint estimation of the important features of EGARCH like leverage effect, asymmetric shock by leverage effect with the secondary membership functions of interval type2 TSK FLS and the functional expansion and learning component of a CEFLANN. The secondary membership functions with upper and lower limits of IT2FLS provide a forecasting interval for handling more complicated uncertainties involved in volatility forecasting compared to type1 FLS. The performance of the proposed model has been observed with two membership functions i.e. Gaussian with fixed mean, uncertain variance and Gaussian with fixed variance and uncertain mean. The proposed model has also been compared with a few other fuzzy time series models and GARCH family models based on four performance metrics: MSFE, RMSFE, MAFE and Rel MAE. Again a differential harmony search (DHS) algorithm has been suggested for optimizing the parameters of all the fuzzy time series models. The results indicate that the proposed IT2F-CE-EGARCH model offers significant improvements in volatility forecasting performance in comparison with all other specified models over BSE Sensex and CNX Nifty dataset. A hybrid interval type2 Fuzzy-FLANN EGARCH model is proposed to forecast the volatility of three stock market indexes.The TSK-type interval type2 fuzzy inference system uses FLANN in the consequent part of the fuzzy rules for improved mapping.The leverage effect, asymmetric shock by leverage effect of EGARCH model are important for forecasting.A harmony search (HS) based learning strategy is used for EGARCH-FLANN model parameters.Statistical tests are also included for choosing the right model.


title: Domain Generalization for Object Recognition with Multi-task Autoencoders

abstract: The problem of domain generalization is to take knowledge acquired from a number of related domains, where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition. The algorithm extends the standard denoising autoencoder framework by substituting artificially induced corruption with naturally occurring inter-domain variability in the appearance of objects. Instead of reconstructing images from noisy versions, MTAE learns to transform the original image into analogs in multiple related domains. It thereby learns features that are robust to variations across domains. The learnt features are then used as inputs to a classifier. We evaluated the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets. We found that (denoising) MTAE outperforms alternative autoencoder-based models as well as the current state-of-the-art algorithms for domain generalization.


title: Physical layer deep learning of encodings for the MIMO fading channel

abstract: We introduce a novel physical layer scheme for Multiple Input Multiple Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer representation and encoding and decoding processes as a single end-to-end task by expanding the transmitter and receiver to the multi-antenna case. We introduce a domain appropriate wireless channel impairment model (the multi-input multi-output Rayleigh fading channel), into the autoencoder optimization problem in order to directly learn a system which optimizes for it. This approach demonstrates significant potential for learning schemes which achieve and exceed performance of current day methods which are widely used in existing wireless MIMO systems. We discuss how the scheme can be easily adapted for open-loop and closed-loop operation in spatial multiplexing modes as well as spatial diversity modes. Each of these modes is learned and realized using the same simple and compact approach.


title: Measurement and analytics on social groups of device-to-device sharing in mobile social networks

abstract: Recently many studies demonstrate that exploiting the Device-to-Device (D2D) content sharing in offline Mobile Social Networks (MSNs) is a promising solution to offload cellular data to local connectivities in proximity to reduce the duplicated cellular transmissions via the backbone network demanded by nearby users and hence to improve users' quality of service (QoS). However, related D2D-based social sharing and offloading proposals are based on either assumptions and theoretical models, or limited data-driven analysis caused by small scale of data sets (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts applications in practice. In this paper, we perform the world-first large-scale measurement and analytics on D2D-based content sharing groups from the perspective of social networks via the platform of Xender, a leading global D2D sharing platform in Asia. We analyze the behaviours of about 30 million users with 443 million D2D transmissions of 17 million files in 884 thousand social groups, and unveil the details of social structure properties, network motifs, cascade trees of friendship and propagation, which are helpful for improving the service of social D2D sharing. Finally we discuss challenges and opportunities for improving social D2D sharing services.


title: Minutia-based enhancement of fingerprint samples

abstract: Image enhancement is a common pre-processing step before the extraction of biometric features from a fingerprint sample. This can be essential especially for images of low image quality. An ideal fingerprint image enhancement should intend to improve the end-to-end biometric performance, i.e. the performance achieved on biometric features extracted from enhanced fingerprint samples. We use a model from Deep Learning for the task of image enhancement. This work's main contribution is a dedicated cost function which is optimized during training The cost function takes into account the biometric feature extraction. Our approach intends to improve the accuracy and reliability of the biometric feature extraction process: No feature should be missed and all features should be extracted as precise as possible. By doing so, the loss function forced the image enhancement to learn how to improve the suitability of a fingerprint sample for a biometric comparison process. The effectivity of the cost function was demonstrated for two different biometric feature extraction algorithms.


title: Evasion and causative attacks with adversarial deep learning

abstract: This paper presents a novel approach to launch and defend against the causative and evasion attacks on machine learning classifiers. As the preliminary step, the adversary starts with an exploratory attack based on deep learning (DL) and builds a functionally equivalent classifier by polling the online target classifier with input data and observing the returned labels. Using this inferred classifier, the adversary can select samples according to their DL scores and feed them to the original classifier. In an evasion attack, the adversary feeds the target classifier with test data after selecting samples with DL scores that are close to the decision boundary to increase the chance that these samples are misclassified. In a causative attack, the adversary feeds the target classifier with training data after changing the labels of samples with DL scores that are far away from the decision boundary to reduce the reliability of the training process. Results obtained for text and image classification show that the proposed evasion and causative attacks can significantly increase the error during test and training phases, respectively. A defense strategy is presented to change a small number of labels of the original classifier to prevent its reliable inference by the adversary and its effective use in evasion and causative attacks. These findings identify new vulnerabilities of machine learning and demonstrate that a proactive defense mechanism can reduce the impact of the underlying attacks.


title: Stock price movement prediction using representative prototypes of financial reports

abstract: Stock price movement prediction is an appealing topic not only for research but also for commercial applications. Most of prior research separately analyzes the meanings of the qualitative or quantitative features, and does not consider the categorical information when clustering financial reports. Since quantitative or qualitative features contain only partial information, there may be no synergy by considering them individually. It is more appropriate to predict stock price movements by simultaneously taking both quantitative and qualitative features into account. Therefore, in this study, we utilize a weighting scheme to combine both qualitative and quantitative features of financial reports together, and propose a method to predict short-term stock price movements. The proposed method employs the categorical information to localize the clusters and improve the purity of each resultant cluster. We gathered 26,255 reports of companies listed in the S&P 500 index from the EDGAR database and conducted the GICS (Global Industrial Classification System) experiments based on the industry sectors. The empirical evaluation results show that the proposed method outperforms the SVM, naïve Bayes, and PFHC methods in terms of accuracy and average profit.


title: Effect of Tuned Parameters on a LSA MCQ Answering Model

abstract: This article presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of latent semantic analysis (LSA). A difficult task, which consisted of answering (French) biology multiple choice questions, was used to test the semantic properties of the truncated singular space and to study the relative influence of the main parameters. A dedicated software was designed to fine-tune the LSA semantic space for the multiple choice questions task. With optimal parameters, the performances of our simple model were quite surprisingly equal or superior to those of seventh- and eighthgrade students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of the training data sets. In addition, we present an original entropy global weighting of the answers’ terms for each of the multiple choice questions, which was necessary to achieve the model’s success.


title: Effectiveness of PowerPoint presentations in lectures

abstract: We investigated whether students liked and learned more from PowerPoint presentations than from overhead transparencies. Students were exposed to lectures supported by transparencies and two different types of PowerPoint presentations. At the end of the semester, students preferred PowerPoint presentations but this preference was not found on ratings taken immediately after the lectures. Students performed worse on quizzes when PowerPoint presentations included non-text items such as pictures and sound effects. A second study further examined these findings. In this study participants were shown PowerPoint slides that contained only text, contained text and a relevant picture, and contained text with a picture that was not relevant. Students performed worse on recall and recognition tasks and had greater dislike for slides with pictures that were not relevant. We conclude that PowerPoint can be beneficial, but material that is not pertinent to the presentation can be harmful to students' learning.


title: A 60GHz microstrip antenna array based on PCB/Polypropylene composite substrate

abstract: We present a novel substrate material with good RF performances and low cost for the fabrication of millimeter-wave microstrip antennas. The substrate takes the copper layer of a FR-4 PCB board as its ground plane, and an Epoxy/Polypropylene/Epoxy composite layer is used as the dielectric material of the microstrip antenna. A 4×4 patch antenna array for 60GHz WPAN has been designed and fabricated on the substrate for demonstration, and the results of simulation and preliminary measurement are provided as well.


title: Network Traffic Anomaly Detection

abstract: This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today's social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.


title: True Random Number Generator Based on ROPUF Circuit

abstract: In this paper we propose the method of generating true random numbers utilizing the circuit primarily designed as PUF based on ring oscillators. The goal is to prove that it is possible to design the universal crypto system, that can be used for various applications - the PUF can be utilized for asymmetric cryptography and generating asymmetric keys, TRNG for symmetric cryptography (generating session and ephemeral keys), nonces and salts. In the paper the results of evaluation of such a circuit utilized for TRNG purpose are presented.


title: ProIDS: Probabilistic Data Structures Based Intrusion Detection System for Network Traffic Monitoring

abstract: Internet is an integrated platform where the data is growing at an exponential rate. Since it incorporates numerous business and personal services, we need to protect the data from illegal access or modifications. In literature, a large number of techniques have been proposed to protect the data against the malicious intent of the intruders. However, one of the most important way for monitoring and analyzing network traffic against various attacks is by the deployment of Intrusion detection systems (IDS). This paper presents a novel IDS based on probabilistic data structures named as ProIDS. In the proposed ProIDS, a popular probabilistic data structure (PDS), Bloom filter has been used to store the information about the suspicious nodes. Using Bloom filter, the number of hits on suspicious nodes per unit time has been computed using the modified version of Count min sketch, i.e., MCMS, a PDS. The work also presents a detailed theoretical analysis backed by relevant technical description. Simulation results clearly depict that the proposed system is more reliable and scalable in comparison to the existing Count-min sketch method. The results obtained show that proposed system requires comparatively less computational time and storage in comparison to the existing Count-min sketch method.


title: Preventing kernel code-reuse attacks through disclosure resistant code diversification

abstract: Software diversity has been applied to operating system kernels to protect against code-reuse attacks. However, the security of fine-grained software diversification relies on ensuring that the code layout remains secret. Unfortunately, memory disclosure vulnerabilities assist adversaries in bypassing software diversity protections by leaking the code layout. In this paper, we propose KHide, a system that thwarts kernel code-reuse attacks by combining fine-grained software diversity techniques and memory disclosure protection. First, we apply multiple fine-grained software diversity techniques to kernel code at compile time. Next, we propose a technique to protect diversified kernel code against memory disclosure at runtime. As a result, an attacker cannot predict or identify gadgets in memory to launch code-reuse attacks. We implement KHide for the Linux kernel. Our evaluation shows that KHide disclosure protection has negligible performance impact in comparison to fine-grained software diversity. We provide a security analysis of KHide calculating the survivability of gadgets across diversified versions. Our results show that KHide provides comprehensive protection against the threat of kernel code-reuse with acceptable performance impact.


title: Effects of attention and confidence on the hypothesized ERP correlates of recollection and familiarity

abstract: Dual-process theories suggest that recognition memory is determined by two separate processes: familiarity and recollection. Experiment 1 behaviorally replicated past studies using the remember/know procedure to indicate that the amount of attention devoted to study influences both recollection and familiarity, but recollection more strongly. Experiments 1 and 2 assessed the effects of attention on two ERP components that have been hypothesized to be related to familiarity (FN400 old/new effect, 300–500 ms, anterior) and recollection (parietal old/new effect, 400–800 ms, posterior). Parietal old/new effects were reduced by divided attention, but FN400 old/new effects were not. Parietal ERPs (400–800 ms) in experiment 2 increased with confidence in recognizing old items, but not new items. These results support the hypothesis that the parietal old/new effect is related to recollection.


title: OCULAR ARTIFACTS REMOVAL IN SCALP EEG: COMBINING ICA AND WAVELET DENOISING

abstract: In this communication we present the first results of a project whose goal is to remove artifacts from electroencephalographic epileptic signals. More precisely the present objective is to remove ocular (blinking) artifacts in simulated and real EEG signal using Independent Component Analysis and wavelet denoising algorithms.


title: Early language acquisition: cracking the speech code

abstract: Infants learn language with remarkable speed, but how they do it remains a mystery. New data show that infants use computational strategies to detect the statistical and prosodic patterns in language input, and that this leads to the discovery of phonemes and words. Social interaction with another human being affects speech learning in a way that resembles communicative learning in songbirds. The brain's commitment to the statistical and prosodic patterns that are experienced early in life might help to explain the long-standing puzzle of why infants are better language learners than adults. Successful learning by infants, as well as constraints on that learning, are changing theories of language acquisition.


title: FusionFS: Toward supporting data-intensive scientific applications on extreme-scale high-performance computing systems

abstract: State-of-the-art, yet decades-old, architecture of high-performance computing systems has its compute and storage resources separated. It thus is limited for modern data-intensive scientific applications because every I/O needs to be transferred via the network between the compute and storage resources. In this paper we propose an architecture that hss a distributed storage layer local to the compute nodes. This layer is responsible for most of the I/O operations and saves extreme amounts of data movement between compute and storage resources. We have designed and implemented a system prototype of this architecture — which we call the FusionFS distributed file system — to support metadata-intensive and write-intensive operations, both of which are critical to the I/O performance of scientific applications. FusionFS has been deployed and evaluated on up to 16K compute nodes of an IBM Blue Gene/P supercomputer, showing more than an order of magnitude performance improvement over other popular file systems such as GPFS, PVFS, and HDFS.


RADIO FREQUENCY HEATING AND ITS APPLICATION IN FOOD PROCESSING : A REVIEW


title: Neural correlates of nondual awareness in meditation

abstract: Dualities such as self versus other, good versus bad, and in-group versus out-group are pervasive features of human experience, structuring the majority of cognitive and affective processes. Yet, an entirely different way of experiencing, one in which such dualities are relaxed rather than fortified, is also available. It depends on recognizing, within the stream of our consciousness, the nondual awareness (NDA)—a background awareness that precedes conceptualization and intention and that can contextualize various perceptual, affective, or cognitive contents without fragmenting the field of experience into habitual dualities. This paper introduces NDA as experienced in Tibetan Buddhist meditation and reviews the results of our study on the influence of NDA on anticorrelated intrinsic and extrinsic networks in the brain. Also discussed are preliminary data from a current study of NDA with minimized phenomenal content that points to involvement of a precuneus network in NDA.


title: Correlation based dynamic time warping of multivariate time series

abstract: In recent years, dynamic time warping (DTW) has begun to become the most widely used technique for comparison of time series data where extensive a priori knowledge is not available. However, it is often expected a multivariate comparison method to consider the correlation between the variables as this correlation carries the real information in many cases. Thus, principal component analysis (PCA) based similarity measures, such as PCA similarity factor (SPCA), are used in many industrial applications. In this paper, we present a novel algorithm called correlation based dynamic time warping (CBDTW) which combines DTW and PCA based similarity measures. To preserve correlation, multivariate time series are segmented and the local dissimilarity function of DTW originated from SPCA. The segments are obtained by bottom-up segmentation using special, PCA related costs. Our novel technique qualified on two databases, the database of signature verification competition 2004 and the commonly used AUSLAN dataset. We show that CBDTW outperforms the standard SPCA and the most commonly used, Euclidean distance based multivariate DTW in case of datasets with complex correlation structure.


Detecting Phishing Web Pages Based on Image Perceptual Hashing Technology


title: User modeling with personas

abstract: User demographic and behavior data obtained from real user observation provide valuable information for designers. Yet, such information can be misinterpreted if presented as statistic figures. Personas are fictitious user representations created in order to embody behaviors and motivations that a group of real users might express, representing them during the project development process. This article describes the persona as being an effective tool to the users' descriptive model.


title: Word-level Language Identification using CRF: Code-switching Shared Task Report of MSR India System

abstract: We describe a CRF based system for word-level language identification of code-mixed text. Our method uses lexical, contextual, character n-gram, and special character features, and therefore, can easily be replicated across languages. Its performance is benchmarked against the test sets provided by the shared task on code-mixing (Solorio et al., 2014) for four language pairs, namely, EnglishSpanish (En-Es), English-Nepali (En-Ne), English-Mandarin (En-Cn), and Standard Arabic-Arabic (Ar-Ar) Dialects. The experimental results show a consistent performance across the language pairs.


Physicochemical changes in contact lenses and their interactions with the cornea and tears: a review and personal observations.


title: 3D surface registration using Z-SIFT

abstract: We present a Z-SIFT based 3D surface registration algorithm that utilizes the depth information enhanced SIFT features to make initial alignment and the 2D feature weighted Iterative Closest Point (ICP) algorithm to realize accurate registration. The combination of SIFT features and depth information extracts faithful corresponding points between the 2D images and provides good coarse alignment for the 3D surfaces. The 2D feature weighted ICP also outperforms the naive ICP algorithm in terms of speed and accuracy. We use this approach in the context of multiple view alignment for 3D scanners. Experimental results with real objects and human faces indicate the effectiveness of the proposed approach.


title: Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

abstract: Preface 1. Introduction 2. Bayesian networks 3. Belief updating and cluster graphs 4. Junction tree representation 5. Belief updating with junction trees 6. Multiply sectioned Bayesian networks 7. Linked junction forests 8. Distributed multi-agent inference 9. Model construction and verification 10. Looking into the future Bibliography Index.


title: The transport sector as a source of air pollution, Atmos

abstract: Transport first became a significant source of air pollution after the problems of sooty smog from coal combustion had largely been solved in western European and North American cities. Since then, emissions from road, air, rail and water transport have been partly responsible for acid deposition, stratospheric ozone depletion and climate change. Most recently, road traffic exhaust emissions have been the cause of much concern about the effects of urban air quality on human health and tropospheric ozone production. This article considers the variety of transport impacts on the atmospheric environment by reviewing three examples: urban road traffic and human health, aircraft emissions and global atmospheric change, and the contribution of sulphur emissions from ships to acid deposition. Each example has associated with it a different level of uncertainty, such that a variety of policy responses to the problems are appropriate, from adaptation through precautionary emissions abatement to cost-benefit analysis and optimised abatement. There is some evidence that the current concern for road transport contribution to urban air pollution is justified, but aircraft emissions should also give cause for concern given that air traffic is projected to continue to increase. Emissions from road traffic are being reduced substantially by the introduction of technology especially three-way catalysts and also, most recently, by local traffic reduction measures especially in western European cities. In developing countries and Eastern Europe, however, there remains the possibility of great increase in car ownership and use, and it remains to be seen whether these countries will adopt measures now to prevent transport-related air pollution problems becoming severe later in the 21st Century. (C) 2001 Elsevier Science Ltd. All rights reserved.


title: Moral psychology is relationship regulation: Moral motives for unity, hierarchy, equality, and proportionality.

abstract: Genuine moral disagreement exists and is widespread. To understand such disagreement, we must examine the basic kinds of social relationships people construct across cultures and the distinct moral obligations and prohibitions these relationships entail. We extend relational models theory (Fiske, 1991) to identify 4 fundamental and distinct moral motives. Unity is the motive to care for and support the integrity of in-groups by avoiding or eliminating threats of contamination and providing aid and protection based on need or empathic compassion. Hierarchy is the motive to respect rank in social groups where superiors are entitled to deference and respect but must also lead, guide, direct, and protect subordinates. Equality is the motive for balanced, in-kind reciprocity, equal treatment, equal say, and equal opportunity. Proportionality is the motive for rewards and punishments to be proportionate to merit, benefits to be calibrated to contributions, and judgments to be based on a utilitarian calculus of costs and benefits. The 4 moral motives are universal, but cultures, ideologies, and individuals differ in where they activate these motives and how they implement them. Unlike existing theories (Haidt, 2007; Hauser, 2006; Turiel, 1983), relationship regulation theory predicts that any action, including violence, unequal treatment, and “impure” acts, may be perceived as morally correct depending on the moral motive employed and how the relevant social relationship is construed. This approach facilitates clearer understanding of moral perspectives we disagree with and provides a template for how to influence moral motives and practices in the world.


title: Predicting risk of software changes

abstract: Reducing the number of software failures is one of the most challenging problems of software production. We assume that software development proceeds as a series of changes and model the probability that a change to software will cause a failure. We use predictors based on the properties of a change itself. Such predictors include size in lines of code added, deleted, and unmodified; diffusion of the change and its component subchanges, as reflected in the number of files, modules, and subsystems touched, or changed; several measures of developer experience; and the type of change and its subchanges (fault fixes or new code). The model is built on historic information and is used to predict the risk of new changes. In this paper we apply the model to 5ESS® software updates and find that change diffusion and developer experience are essential to predicting failures. The predictive model is implemented as a Web-based tool to allow timely prediction of change quality. The ability to predict the quality of change enables us to make appropriate decisions regarding inspection, testing, and delivery. Historic information on software changes is recorded in many commercial software projects, suggesting that our results can be easily and widely applied in practice.


The Positive and Negative Effects of Video Game Play


title: Cyber-Physical Security of a Chemical Plant

abstract: The increasing number of cyber-attacks on industriesdemands immediate attention for providing more securemechanisms to safeguard industries and minimize risks. Cyberphysical systems (CPS) being used in industries such as oil and gas, chemical process plants and the like are termed Industrial Control Systems (ICS). Control system security is aimed at preventing intentional or unintentional interference with the proper operation of industrial automation and control systems (ICS). This paper proposes a process-aware approach with the use of some invariant equations based on the physical and chemical properties of the process and a Multiple Security Domain Nondeducibility (MSDND) framework to detect when a sensor signal is being maliciously manipulated. We have taken benzene production plant as case study to illustrate our approach and its effectiveness in determining the state of the system.


title: Geometry-based shading for shape depiction enhancement

abstract: Recent works on Non-Photorealistic Rendering (NPR) show that object shape enhancement requires sophisticated effects such as: surface details detection and stylized shading. To date, some rendering techniques have been proposed to overcome this issue, but most of which are limited to correlate shape enhancement functionalities to surface feature variations. Therefore, this problem still persists especially in NPR. This paper is an attempt to address this problem by presenting a new approach for enhancing shape depiction of 3D objects in NPR. We first introduce a tweakable shape descriptor that offers versatile functionalities for describing the salient features of 3D objects. Then to enhance the classical shading models, we propose a new technique called Geometry-based Shading. This technique controls reflected lighting intensities based on local geometry. Our approach works without any constraint on the choice of material or illumination. We demonstrate results obtained with Blinn-Phong shading, Gooch shading, and cartoon shading. These results prove that our approach produces more satisfying results compared with the results of previous shape depiction techniques. Finally, our approach runs on modern graphics hardware in real time, which works efficiently with interactive 3D visualization.


title: A neural network approach to complete coverage path planning

abstract: Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.


title: Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

abstract: This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver-road-vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi-Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.


title: BQAS: A bilingual question answering system

abstract: In this paper, we present a bilingual question answering system, BQAS that can generate questions and provide their answers from electronic Bangla and English documents. The system first performs the analysis of the contents of the documents to generate questions based on user provided query. It then generates answers of the questions. The system has been evaluated with real documents. We have found that our system can generate questions and related answers up to a satisfactory level from the documents of two different languages as well as from the documents of the same language.


title: The Associations Between Aggressive Behaviors and Internet Addiction and Online Activities in Adolescents

abstract: Abstract Purpose To evaluate (a) the association between Internet addiction and aggressive behaviors, as well as the moderating effects of gender, school, and depression on this association; and (b) to evaluate the association between Internet activities and aggressive behaviors. Methods A total of 9405 adolescents were recruited into this study and completed the questionnaires. Their aggressive behaviors, with or without Internet addiction, Internet activities, demographic data, with or without depression, self-esteem, family function, and the watching of violent TV were assessed. Results The results demonstrated that after controlling for the effects of shared associated factors and watching violent TV programs, adolescents with Internet addiction were more likely to have aggressive behaviors during the previous year. The association was more significant among adolescents in junior high schools than in senior high/vocational schools. Online chatting, adult sex Web viewing, online gaming, online gambling, and Bulletin Board System were all associated with aggressive behaviors. Conclusions The results suggest that preventive programs for aggressive behaviors should pay attention to Internet addiction among adolescents. Also, intervention to prevent the effects of Internet addiction on aggressive behaviors should be conducted as early as possible.


title: Spiral angle of elementary cellulose fibrils in cell walls ofPicea abies determined by small-angle x-ray scattering

abstract: The spiral angle of the elementary cellulose fibril in the wood cell wall, often called microfibril angle, is of primary importance for the mechanical properties of wood. While there are a number of methods to estimate this angle, x-ray diffraction (XRD) techniques have recently obtained a lot of attention because of their ability to provide information averaged over a significantly large specimen volume. Here, we present results from a related method, small-angle x-ray scattering (SAXS). The advantage of SAXS is that, unlike XRD, it does not require any assumption on the orientation of the cellulose crystal axis with respect to the fibril axis. Full three-dimensional scattering patterns were collected using an area detector by rotating the sample around one axis. The distribution of fibrillar orientations was seen to reflect the typical cross-sectional shape of the tracheids (square or circular). In the stem, the spiral angle was found <5° in earlywood and ≈20° in latewood. In branches the angle was ≈30° in the upper part and ≈40° in the lower part, which strongly supports the idea that the spiral angle has primarily mechanical function.


title: Fabric: a platform for secure distributed computation and storage

abstract: Fabric is a new system and language for building secure distributed information systems. It is a decentralized system that allows heterogeneous network nodes to securely share both information and computation resources despite mutual distrust. Its high-level programming language makes distribution and persistence largely transparent to programmers. Fabric supports data-shipping and function-shipping styles of computation: both computation and information can move between nodes to meet security requirements or to improve performance. Fabric provides a rich, Java-like object model, but data resources are labeled with confidentiality and integrity policies that are enforced through a combination of compile-time and run-time mechanisms. Optimistic, nested transactions ensure consistency across all objects and nodes. A peer-to-peer dissemination layer helps to increase availability and to balance load. Results from applications built using Fabric suggest that Fabric has a clean, concise programming model, offers good performance, and enforces security.


title: Chancroid, lymphogranuloma venereum, granuloma inguinale, genital herpes simplex infection, and molluscum contagiosum.

abstract: Chancroid, lymphogranuloma venereum, and granuloma inguinale may be considered as tropical venereal diseases. These diseases were a major diagnostic and therapeutic challenge in past centuries. Currently, patients with these bacterial infections that are endemic to the tropics occasionally consult with dermatologists in temperate climates. Due to the increasing frequency of travel to the tropics for tourism and work, as well as the increasing number of immigrants from these areas, it is important for dermatologists practicing in temperate climates to be familiar with the dermatologic manifestations of such infections, to be prepared to diagnose these diseases, and to treat these patients. All three "tropical" infections respond well to prompt and appropriate antimicrobial treatment, although herpes progenitalis still cannot be cured, and the number of people infected keeps growing; moreover, genital herpes can be transmitted by viral shedding before and after the visual signs or symptoms. Acyclovir, valacyclovir, and famciclovir can shorten outbreaks and make them less severe or even stop them from happening. There is currently no etiologic treatment for molluscum contagiosum, and the majority of treatment options are mechanical, causing a certain degree of discomfort. The molluscum contagiosum virus, unlike the other infectious agents mentioned, does not invade the skin.


title: Remotely Controllable Outlet System for Home Power Management

abstract: In this paper, we describe a remotely controllable outlet system with mechanism for home power management. The system consists of four blocks: the Bluetooth Power-Controlled Outlet Module (BPCOM), the home server, the remote control and the GSM channel. Bluetooth, wireless system can control the home electric appliances wirelessly at home without new wiring. The BPCOM integrates the multiply AC power sockets and a simple plug-in low-power microcontroller performs the power on/off switching of the sockets. In addition, the power detection circuits are used by the Hall current transducer to verify home electric appliance power on/off status. For the power management module of the home server, we designed the software module by Visual Basic .NET, which supports the Graphic User Interface (GUI) to let users easily monitor the home electric appliances power on/off. We also integrate Bluetooth to receive/transmit between the home server and the microcontroller. The remote control also provides the users with the ability to control home appliances power on/off through the Internet and the GSM channel linked to the home server in order to receive the SMS (Short Message Service) for the power management of the home electric appliances power on/off anytime and anywhere.


title: Design of Multiple-Level Hybrid Classifier for Intrusion Detection System

abstract: With increasing connectivity between computers, the need to keep networks secure progressively becomes more vital. Intrusion detection systems (IDS) have become an essential component of computer security to supplement existing defenses. This paper proposes a multiple-level hybrid classifier, a novel intrusion detection system, which combines the supervised tree classifiers and unsupervised Bayesian clustering to detect intrusions. Performance of this new approach is measured using the KDDCUP99 dataset and is shown to have high detection and low false alarm rates.


title: Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks

abstract: With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data (big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network’s performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete (or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4G LTE-A to detect network’s anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.


title: A comparison of the cost‐effectiveness of intramedullary nail fixation and locking plate fixation in the treatment of adult patients with an extra‐articular fracture of the distal tibia: ECONOMIC EVALUATION BASED ON THE FixDT TRIAL

abstract: Aims ::: The aim of this study was to compare the cost-effectiveness of intramedullary nail fixation and 'locking' plate fixation in the treatment of extra-articular fractures of the distal tibia. ::: ::: ::: Patients and Methods ::: An economic evaluation was conducted from the perspective of the United Kingdom National Health Service (NHS) and personal social services (PSS), based on evidence from the Fixation of Distal Tibia Fractures (UK FixDT) multicentre parallel trial. Data from 321 patients were available for analysis. Costs were collected prospectively over the 12-month follow-up period using trial case report forms and participant-completed questionnaires. Cost-effectiveness was reported in terms of incremental cost per quality adjusted life year (QALY) gained, and net monetary benefit. Sensitivity analyses were conducted to test the robustness of cost-effectiveness estimates. ::: ::: ::: Results ::: Mean NHS and PSS costs were significantly lower for patients treated with an intramedullary nail than for those treated with a locking plate (-£970, 95% confidence interval (CI) -1685 to -256; p = 0.05). There was a small increase in QALYs gained in the nail fixation group (0.01, 95% CI -0.03 to 0.06; p = 0.52). The probability of cost-effectiveness for nail fixation exceeded 90% at cost-effectiveness thresholds as low as £15 000 per additional QALY. The cost-effectiveness results remained robust to several sensitivity analyses. ::: ::: ::: Conclusion ::: This trial-based economic evaluation suggests that nail fixation is a cost-effective alternative to locking plate fixation. Cite this article: Bone Joint J 2018;100-B:624-33.


title: Lattice Long Short-Term Memory for Human Action Recognition

abstract: Human actions captured in video sequences are three-dimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and RNNs). CNN based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. RNNs, especially Long Short-Term Memory (LSTM), are able to learn temporal motion dynamics. However, naively applying RNNs to video sequences in a convolutional manner implicitly assumes that motions in videos are stationary across different spatial locations. This assumption is valid for short-term motions but invalid when the duration of the motion is long. ::: In this work, we propose Lattice-LSTM (L2STM), which extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations. This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity. Additionally, we introduce a novel multi-modal training procedure for training our network. Unlike traditional two-stream architectures which use RGB and optical flow information as input, our two-stream model leverages both modalities to jointly train both input gates and both forget gates in the network rather than treating the two streams as separate entities with no information about the other. We apply this end-to-end system to benchmark datasets (UCF-101 and HMDB-51) of human action recognition. Experiments show that on both datasets, our proposed method outperforms all existing ones that are based on LSTM and/or CNNs of similar model complexities.


title: 100 000 Frames/s 64 × 32 Single-Photon Detector Array for 2-D Imaging and 3-D Ranging

abstract: We report on the design and characterization of a multipurpose 64 × 32 CMOS single-photon avalanche diode (SPAD) array. The chip is fabricated in a high-voltage 0.35-μm CMOS technology and consists of 2048 pixels, each combining a very low noise (100 cps at 5-V excess bias) 30-μm SPAD, a prompt avalanche sensing circuit, and digital processing electronics. The array not only delivers two-dimensional intensity information through photon counting in either free-running (down to 10-μs integration time) or time-gated mode, but can also perform smart light demodulation with in-pixel background suppression. The latter feature enables phase-resolved imaging for extracting either three-dimensional depth-resolved images or decay lifetime maps, by measuring the phase shift between a modulated excitation light and the reflected photons. Pixel-level memories enable fully parallel processing and global-shutter readout, preventing motion artifacts (e.g., skew, wobble, motion blur) and partial exposure effects. The array is able to acquire very fast optical events at high frame-rate (up to 100 000 fps) and at single-photon level. Low-noise SPADs ensure high dynamic range (up to 110 dB at 100 fps) with peak photon detection efficiency of almost 50% at 410 nm. The SPAD imager provides different operating modes, thus, enabling both time-domain applications, like fluorescence lifetime imaging (FLIM) and fluorescence correlation spectroscopy, as well as frequency-domain FLIM and lock-in 3-D ranging for automotive vision and lidar.


Lean Configuration Management Contents


title: A brief review of current scientific evidence involving aromatherapy use for nausea and vomiting.

abstract: OBJECTIVES ::: The objective of this study was to compile existing scientific evidence regarding the effects of essential oils (EOs) administered via inhalation for the alleviation of nausea and vomiting. ::: ::: ::: METHODS ::: CINAHL, PubMed, and EBSCO Host and Science Direct databases were searched for articles related to the use of EOs and/or aromatherapy for nausea and vomiting. Only articles using English as a language of publication were included. Eligible articles included all forms of evidence (nonexperimental, experimental, case report). Interventions were limited to the use of EOs by inhalation of their vapors to treat symptoms of nausea and vomiting in various conditions regardless of age group. Studies where the intervention did not utilize EOs or were concerned with only alcohol inhalation and trials that combined the use of aromatherapy with other treatments (massage, relaxations, or acupressure) were excluded. ::: ::: ::: RESULTS ::: Five (5) articles met the inclusion criteria encompassing trials with 328 respondents. Their results suggest that the inhaled vapor of peppermint or ginger essential oils not only reduced the incidence and severity of nausea and vomiting but also decreased antiemetic requirements and consequently improved patient satisfaction. However, a definitive conclusion could not be drawn due to methodological flaws in the existing research articles and an acute lack of additional research in this area. ::: ::: ::: CONCLUSIONS ::: The existing evidence is encouraging but yet not compelling. Hence, further well-designed large trials are needed before confirmation of EOs effectiveness in treating nausea and vomiting can be strongly substantiated.


Marčenko-Pastur law for Kendall’s tau


title: A Novel Deep Neural Network Architecture for Mars Visual Navigation

abstract: In this paper, emerging deep learning techniques are leveraged to deal with Mars visual navigation problem. Specifically, to achieve precise landing and autonomous navigation, a novel deep neural network architecture with double branches and non-recurrent structure is designed, which can represent both global and local deep features of Martian environment images effectively. By employing this architecture, Mars rover can determine the optimal navigation policy to the target point directly from original Martian environment images. Moreover, compared with the existing state-of-the-art algorithm, the training time is reduced by 45.8%. Finally, experiment results demonstrate that the proposed deep neural network architecture achieves better performance and faster convergence than the existing ones and generalizes well to unknown environment.


title: Structured-Light-Based System for Shape Measurement of the Human Body in Motion

abstract: The existing methods for measuring the shape of the human body in motion are limited in their practical application owing to immaturity, complexity, and/or high price. Therefore, we propose a method based on structured light supported by multispectral separation to achieve multidirectional and parallel acquisition. Single-frame fringe projection is employed in this method for detailed geometry reconstruction. An extended phase unwrapping method adapted for measurement of the human body is also proposed. This method utilizes local fringe parameter information to identify the optimal unwrapping path for reconstruction. Subsequently, we present a prototype 4DBODY system with a working volume of 2.0 × 1.5 × 1.5 m³, a measurement uncertainty less than 0.5 mm and an average spatial resolution of 1.0 mm for three-dimensional (3D) points. The system consists of eight directional 3D scanners functioning synchronously with an acquisition frequency of 120 Hz. The efficacy of the proposed system is demonstrated by presenting the measurement results obtained for known geometrical objects moving at various speeds as well actual human movements.


title: Multiphase high-voltage high-frequency sawtooth wave generator

abstract: The generator of sawtooth wave with voltage of 1 kV and frequency of 300 kHz intended to power the pumping region of the electrohydrodynamic flow systems is presented in the work. By varying the storage inductor the range of the operating frequencies can be expanded from dozens of kilohertz to 1 megahertz. The generator also can operate at the resonance frequency, creating a quasi-sinusoidal signal form. The three-phase scheme are implemented. The phase shift can be regulated by the driving generator.


title: Approximate Area-to-Point Regression Kriging for Fast Hyperspectral Image Sharpening

abstract: Area-to-point regression kriging (ATPRK) is an advanced image fusion approach in remote sensing In this paper, ATPRK is considered for sharpening hyperspectral images (HSIs), based on the availability of a fine spatial resolution panchromatic or multispectral image. ATPRK can be used straightforwardly to sharpen each coarse hyperspectral band in turn. This scheme, however, is computationally expensive due to the large number of bands in HSIs, and this problem is exacerbated for multiscene or multitemporal analysis. Thus, we extend ATPRK for fast HSI sharpening with a new approach, called approximate ATPRK (AATPRK), which transforms the original HSI to a new feature space and image fusion is performed for only the first few components before back transformation. Experiments on two HSIs show that AATPRK greatly expedites ATPRK, but inherits the advantages of ATPRK, including maintaining a very similar performance in sharpening (both ATPRK and AATPRK can produce more accurate results than seven benchmark methods) and precisely conserving the spectral properties of coarse HSIs.


title: LTE evolution for vehicle-to-everything services

abstract: Wireless communication has become a key technology for competitiveness of next generation vehicles. Recently, the 3GPP has initiated standardization activities for LTE-based V2X services composed of vehicle-to-vehicle, vehicle- to-pedestrian, and vehicle-to-infrastructure/network. The goal of these 3GPP activities is to enhance LTE systems to enable vehicles to communicate with other vehicles, pedestrians, and infrastructure in order to exchange messages for aiding in road safety, controlling traffic flow, and providing various traffic notifications. In this article, we provide an overview of the service flow and requirements of the V2X services LTE systems are targeting. This article also discusses the scenarios suitable for operating LTE-based V2X services, and addresses the main challenges of high mobility and densely populated vehicle environments in designing technical solutions to fulfill the requirements of V2X services. Leveraging the spectral-efficient air interface, the cost-effective network deployment, and the versatile nature of supporting different communication types, LTE systems along with proper enhancements can be the key enabler of V2X services.


title: Personality assessment website using DISC: A case study in information technology school

abstract: One of factors to determine success in educational is the ability of the lecturer to deliver teaching material and assess students according to their personalities. This becomes a problem if the lecturer does not have ability to assess students' personalities. Therefore, the issue mentioned above makes this causes the lecturers to generalize teaching methods for all students. Another issue occurs is about academic counselors, they also often provide motivation or suggestion that does not suit to the student's personality. This paper presents a design of web-based application to obtain student's personality profile using DISC assessment method. The DISC method considered to be simple, need less time to assess, and enable to adequate information for lecturer and academic counselor in compact way for a non-psychological lecturer. The system was tested in information technology school, where almost all lecturers and academic counselors do not have the ability to assess student's personality. The application provides online assessment for students, map students' personality and provide feedback for lecturers and counselors.


title: Using Data Type Based Security Alert Dialogs to Raise Online Security Awareness

abstract: When browsing the Internet, users are likely to be exposed to security and privacy threats -- like fraudulent websites. Automatic browser mechanisms can protect them only to some extent. In other situations it is still important to raise the users' security awareness at the right moment. Passive indicators are mostly overlooked and blocking warnings are quickly dismissed by habituated users. In this work, we present a new concept of warnings that appear in-context, right next to data the user has just entered. Those dialogs are displayed whenever critical data types -- e.g. credit card data -- are entered by the users into online forms. Since they do not immediately interrupt the users' interaction but appear right in the users' focus, it is possible to place important security information in a way that it can be easily seen. We implemented the concept as a Firefox plugin and evaluated it in a row of studies including two lab studies, one focus group and one real world study. Results show that the concept is very well accepted by the users and that with the plugin, especially non-expert participants were more likely to identify fraudulent (or phishing) websites than using the standard browser warnings. Besides this, we were able to gather interesting findings on warning usage.


title: Improvements in Resilience, Stress, and Somatic Symptoms Following Online Resilience Training: A Dose–Response Effect

abstract: OBJECTIVE ::: To determine if participation in an online resilience program impacts resilience, stress, and somatic symptoms. ::: ::: ::: METHODS ::: Approximately 600 enrollees in the meQuilibrium resilience program received a series of brief, individually prescribed video, and text training modules in a user-friendly format. Regression models tested how time in the program affected change in resilience from baseline and how changes in resilience affected change in stress and reported symptoms. ::: ::: ::: RESULTS ::: A significant dose-response was detected, where increases in the time spent in training corresponded to greater improvements in resilience. Degree of change in resilience predicted the magnitude of reduction in stress and symptoms. Participants with the lowest resilience level at baseline experienced greater improvements. ::: ::: ::: CONCLUSION ::: Interaction with the online resilience training program had a positive effect on resilience, stress, and symptoms in proportion to the time of use.


title: BigBench: towards an industry standard benchmark for big data analytics

abstract: There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems. In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques. We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.


title: Walk detection and step counting on unconstrained smartphones

abstract: Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use. This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.


title: Big graph mining for the web and social media: algorithms, anomaly detection, and applications

abstract: Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success of social networking websites and Web 2.0 applications, and the high availability of data sources lead to graphs being generated at unprecedented size. They are now measured in terabytes or even petabytes, with more than billions of nodes and edges. Finding patterns on large graphs have a lot of applica- tions including cyber security on the Web, social media min- ing (Facebook, Twitter), and fraud detection, among others. This tutorial will cover topics related to finding patterns and anomalies and sensemaking in large-scale graphs with appli- cations to real-world problems in social media and the Web. Specifically, we aim to answer the following questions: How can we scale up graph mining algorithms for massive graphs with billions of edges? How can we find anomalies in such large-scale graphs? How can we make sense of disk-resident large graphs, what and how can we do visual analytics? How can we use the algorithms and anomaly detection techniques to solve challenging real-world problems that play key role in social media and the Web? Our tutorial consists of three main parts. We start with scalable graph mining algorithms for billion-scale graphs, in- cluding structure analysis, eigensolvers, storage and index- ing, and graph layout and graph compression. Next we de- scribe anomaly detection techniques for large scale graphs with applications on social media. Finally, we discuss vi- sual analytics techniques which leverage these algorithms and anomaly detection techniques in the previous parts.


title: Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

abstract: Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer’s disease. Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: ("random forest" OR "random forests") AND neuroimaging AND ("alzheimer's disease" OR alzheimer's OR alzheimer) AND (prediction OR classification). The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science. Results: Twelve articles – published between the 2007 and 2017 - have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer’s disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as MRI morphometric, diffusion tensor imaging and PET images. Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.


title: Bandpass filter (BPF) design using waveguide iris conformation over Substrate Integrated Waveguide (SIW) Bend

abstract: In this paper a novel Substrate Integrated Waveguide(SIW) filter has been proposed, bended with 150° angle and loaded with cylindrical iris for obtaining the filtering characteristic in the range of Ku band. 150°bend within the SIW structure is provided to facilitate its integration ability over planar Microwave Integrated Circuits (MICs). Several configurations of iris are analysed for obtaining the pass band near about 1 GHz (15.5GHz to 16.5GHz) with minimum insertion loss (IL< 1.2dB). The proposed filters are designed over a material with dielectric constant of 3.2 and thickness of 0.8 mm. The effect on transmission bandwidth as well as stopband attenuation due to different iris configurations are also presented with parametric analysis.


title: Atom2Vec: learning atoms for materials discovery

abstract: Exciting advances have been made in artificial intelligence (AI) during the past decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition and natural language understanding. Even in Go, the ancient game of profound complexity, the AI player already beat human world champions convincingly with and without learning from human. In this work, we show that our unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials. These learned properties are represented in terms of high dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups in consistent with human knowledge. We use the atom vectors as basic input units for neural networks and other ML models designed and trained to predict materials properties, which demonstrate significant accuracy.


title: Power Factor Correction in Cuk–SEPIC-Based Dual-Output-Converter-Fed SRM Drive

abstract: In this paper, a power factor correction (PFC)-based switched reluctance motor (SRM) drive is proposed. Generally, the SRM drive is fed with a simple diode bridge rectifier followed by a bulky capacitor, which draws peaky current at low power factor and high-input-current total harmonic distortion (THD). Here, a dual output converter is proposed to control the SRM and to provide power quality improvement as well. This converter is a combination of Cuk and SEPIC converters, which produces two equal output voltages with neutral point N, to feed a midpoint converter of the SRM drive. The converter is designed to operate in discontinuous conduction mode of operation to obtain inherent PFC at supply side. The motor control is attained by regulating dc link voltage over a wide range. Test results on a prototype of the SRM drive demonstrate the excellent performance with high power factor and reduced input current THD. The observed power quality indices depict the improved power quality as per the standard IEC 61000-3-2.


title: A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging

abstract: Abstract In this paper, a novel online and non-destructive algorithm is proposed to detect inter-turn short circuit faults in the stator windings of an induction motor. This fault detection algorithm is based on the features extracted from infrared images taken from the hottest region of the motor surface. Feature extraction method from infrared thermal images has been developed based on histogram of temperature profile, correlation between fault severity and the hottest region on the motor body, and comparison with healthy motor features. Experiments are conducted to demonstrate the effectiveness of the proposed method using a 2-hp induction motor.


title: Cancellation of common-mode output signal in Class-BD audio amplifiers

abstract: In the paper new topologies of the Class-BD amplifiers with Common-Mode (CM) free outputs have been presented. They are composed of the typical H-bridge output stage with four MOSFETs controlled by the optimal NBDD PWM signals and two additional switches separating the H-bridge from the power supply during the time intervals in which either high-side or low-side MOSFETs of the H-bridge are closed at the same time. The proposed amplifiers have been compared to the Class-BD configuration using the optimal NBDD modulation with respect to audio performance of the differential and CM outputs. Experimental models of the presented in the paper Class-BD audio amplifiers have been realized using digital audio MOSFETs IRF6775MTRPbF, single-output gate drivers with self-boost charge pump bias supply and with capacitor-based signal isolation for floating single-output gate drivers. Intensive SPICE simulations of the real output stage circuits of the proposed Class-BD amplifiers have pointed out that proposed new configurations have similar audio performance as the prototype using optimal NBDD modulation, besides keep the CM outputs constant, thereby removing a major contributor to radiated emissions.


title: Black start experiences for 400 kV hydro power plant in western regional grid of India

abstract: Black start exercises are planned exercises carried out with the black start capable generating stations to test their readiness for the unforeseen emergency situation. The Western Regional grid in India is having a number of such units, which are either hydro or gas based power stations. Since 2011, various such exercises are being carried out on a regular basis in Western Regional Grid of India to bring out the challenges associated with grid restoration with black start of hydro & gas generators. This has helped the system operators in the upgrading the methodology of restoration and identifying other corrective actions for improving upon the existing practices for a successful black start. This paper is an attempt at sharing the experiences gained during various black start mock drills conducted in western region on a hydropower plant connected at 400 kV EHV grid and bring forth the challenges faced during this controlled black start activity followed by remedial actions. These exercises have helped in boosting the confidence of system operators to deal with real blackout scenarios while improving the performance of the black start capable generators over the years.


title: Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach

abstract: We consider the problem of learning Boltzmann machine classifiers from relational data. Our goal is to extend the deep belief framework of RBMs to statistical relational models. This allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL). Specifically, we use lifted random walks to generate features for predicates that are then used to construct the observed features in the RBM in a manner similar to Markov Logic Networks. We show empirically that this method of constructing an RBM is comparable or better than the state-of-the-art probabilistic relational learning algorithms on six relational domains.


title: Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks

abstract: We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned through back propagation. The original one-hot represented loss function is converted into a new loss function with soft distributions, such that the originally unrelated labels have continuous interactions with each other during the training process. As a result, the trained model can achieve substantially higher accuracy and with faster convergence speed. Experimental results based on competitive tasks demonstrate the effectiveness of the proposed method, and the learned label embedding is reasonable and interpretable. The proposed method achieves comparable or even better results than the state-of-the-art systems.


title: Retrieving Smith-Waterman Alignments with Optimizations for Megabase Biological Sequences Using GPU

abstract: In Genome Projects, biological sequences are aligned thousands of times, in a daily basis. The Smith-Waterman algorithm is able to retrieve the optimal local alignment with quadratic time and space complexity. So far, aligning huge sequences, such as whole chromosomes, with the Smith-Waterman algorithm has been regarded as unfeasible, due to huge computing and memory requirements. However, high-performance computing platforms such as GPUs are making it possible to obtain the optimal result for huge sequences in reasonable time. In this paper, we propose and evaluate CUDAlign 2.1, a parallel algorithm that uses GPU to align huge sequences, executing the Smith-Waterman algorithm combined with Myers-Miller, with linear space complexity. In order to achieve that, we propose optimizations which are able to reduce significantly the amount of data processed, while enforcing full parallelism most of the time. Using the NVIDIA GTX 560 Ti board and comparing real DNA sequences that range from 162 KBP (Thousand Base Pairs) to 59 MBP (Million Base Pairs), we show that CUDAlign 2.1 is scalable. Also, we show that CUDAlign 2.1 is able to produce the optimal alignment between the chimpanzee chromosome 22 (33 MBP) and the human chromosome 21 (47 MBP) in 8.4 hours and the optimal alignment between the chimpanzee chromosome Y (24 MBP) and the human chromosome Y (59 MBP) in 13.1 hours.


title: Hindi Part-of-Speech Tagging and Chunking : A Maximum Entropy Approach

abstract: The present invention describes a class of dyes for use in staining cell samples and methods of making such dyes. A preferred class of dyes known as detergent dyes which possess the ability to stain cells in whole blood and are only slowly leached or lost from the stained cells over time are described. The present invention has application, for example, to blood typing for the determination of the presence of blood group antigens A, B, AB, O, and D (Rho) and antibodies to such antigens.


title: A Comprehensive Survey on Cross-modal Retrieval

abstract: In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation learning. Real-valued representation learning methods aim to learn real-valued common representations for different modalities of data. To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space. Then, we introduce several multimodal datasets in the community, and show the experimental results on two commonly used multimodal datasets. The comparison reveals the characteristic of different kinds of cross-modal retrieval methods, which is expected to benefit both practical applications and future research. Finally, we discuss open problems and future research directions.


title: Theoretical and Empirical Analysis of a GPU Based Parallel Bayesian Optimization Algorithm

abstract: General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges and design choices involved in parallelization of Bayesian Optimization Algorithm (BOA) to solve complex combinatorial optimization problems over nVidia commodity graphics hardware using Compute Unified Device Architecture (CUDA). BOA is a well-known multivariate Estimation of Distribution Algorithm (EDA) that incorporates methods for learning Bayesian Network (BN). It then uses BN to sample new promising solutions. Our implementation is fully compatible with modern commodity GPUs and therefore we call it gBOA (BOA on GPU). In the results section, we show several numerical tests and performance measurements obtained by running gBOA over an nVidia Tesla C1060 GPU. We show that in the best case we can obtain a speedup of up to 13x.


title: Analysis of Financial News with NewsStream

abstract: Unstructured data, such as news and blogs, can provide valuable insights into the financial world. We present the NewsStream portal, an intuitive and easy-to-use tool for news analytics, which supports interactive querying and visualizations of the documents at different levels of detail. It relies on a scalable architecture for real-time processing of a continuous stream of textual data, which incorporates data acquisition, cleaning, natural-language preprocessing and semantic annotation components. It has been running for over two years and collected over 18 million news articles and blog posts. The NewsStream portal can be used to answer the questions when, how often, in what context, and with what sentiment was a financial entity or term mentioned in a continuous stream of news and blogs, and therefore providing a complement to news aggregators. We illustrate some features of our system in three use cases: relations between the rating agencies and the PIIGS countries, reflection of financial news on credit default swap (CDS) prices, the emergence of the Bitcoin digital currency, and visualizing how the world is connected through news.


Multitemporal Cloud Masking in the Google Earth Engine


title: Fulfilling the promise of massive MIMO with 2D active antenna array

abstract: In this paper, we study practical 2-Dimensional (2D) active antenna array configurations for Massive MIMO systems and evaluate the system-level performance with the form factor at the base stations operating in cellular frequency band. To this end, we develop a 3-Dimensional Spatial Channel Model (3D SCM) to facilitate accurate performance evaluation and to build insights for system design. System-level simulation is conducted for various 2D antenna array configurations (8Hx8V, 8Hx4V) by using the 3D SCM developed. Simulation results show that 2–5 times cell average throughput gain and 1.5–9 times cell edge throughput gain can be achieved compared to the 4G LTE system.


Learning strategies and general cognitive ability as predictors of gender- specific academic achievement


title: Detecting cheaters for multiplayer games: theory, design and implementation[1]

abstract: This paper presents a system to detect cheaters through statistical analysis using a Baysean Network. Implementation is based on aimbot and score distribution.


title: A 5.8-Gb/s Adaptive Integrating Duobinary DFE Receiver for Multi-Drop Memory Interface

abstract: This paper describes a 5.8 Gb/s adaptive integrating duobinary decision-feedback equalizer (DFE) for use in next-generation multi-drop memory interface. The proposed receiver combines traditional interface techniques like the integrated signaling and the duobinary signaling, in which the duobinary signal is generated by current integration in the receiver. It can address issues such as input data dependence during integration, need for precursor equalization, high equalizer gain boosting, and sensitivity to high-frequency noise. The proposed receiver also alleviates DFE critical timing to provide gain in speed, and embed DFE taps in duobinary decoding to provide gain in power and area. The adaptation for adjusting the equalizer common-mode level, duobinary zero level, tap coefficient values, and timing recovery is incorporated. The proposed DFE receiver was fabricated in a 45 nm CMOS process, whose measurement results indicated that it worked at 5.8 Gb/s speed in a four-drop channel configuration with seven slave ICs, and the bathtub curve shows 36% open for $10^{-10}$ bit error rate.


title: Time-Domain CMOS Temperature Sensors With Dual Delay-Locked Loops for Microprocessor Thermal Monitoring

abstract: We report on CMOS temperature sensors that work by measuring temperature-dependent delays in CMOS inverters. Two new features distinguish this work from the prior delay-based temperature sensors. First, our sensor operates with simple, low-cost one-point calibration. Second, it uses delay-locked loops (DLLs) to convert inverter delays to digital temperature outputs: the use of DLLs enables low energy (0.24 μJ/sample) and high bandwidth (5 kilo-samples/s), facilitating fast thermal monitoring. After calibration, measurement errors for 15 chips fabricated in digital CMOS 0.13 μm fall within -4.0~4.0 °C in a temperature range of 0~100 °C, where the temperature chamber used has a control uncertainty of ±1.1 °C. Microprocessor thermal profiling can be a potential application.


title: Psychometric validation of the physician global assessment scale for assessing severity of psoriasis disease activity

abstract: PURPOSE ::: The Physician Global Assessment (PGA) is a key measure of psoriasis frequently used in clinical trials. A psychometric validation of a three-item (erythema, induration, and scaling) PGA scale was performed using Phase 2 data. ::: ::: ::: METHODS ::: Confirmatory factor analysis (CFA) tested the PGA measurement model and appropriateness of equal weighting of the items. PGA test-retest reliability was assessed by estimating the intraclass correlation coefficient (ICC). Internal consistency reliability was gauged by calculating Cronbach's coefficient α (CCα). Clinically important difference (CID) was defined using the repeated measures model to estimate the relationship between PGA and Patient Global Assessment (PtGA). Known-group, convergent, and divergent validity for the PGA were also assessed. ::: ::: ::: RESULTS ::: 197 patients with psoriasis were randomized to tofacitinib 2, 5, 15 mg twice daily, or placebo. CFA demonstrated that the PGA measurement model fitted the data using equal weighting of the PGA items. The PGA scale demonstrated good test-retest reliability (ICC > 0.7) and internal consistency reliability (CCα > 0.8). The CID for PGA was estimated at 0.52 (95 % confidence interval: 0.47, 0.56). A robust monotonic relationship between PGA and Psoriasis Area and Severity Index (PASI) data substantiated known-group validity. Relatively high correlations of PGA with PASI and PtGA data (all correlations >0.5 except at baseline) supported convergent validity; relatively low correlations of PGA with the Pain/Discomfort Assessment and the Ocular Comfort Index supported divergent validity. ::: ::: ::: CONCLUSIONS ::: The three-item PGA scale has sound psychometric properties with respect to reliability and validity, with equal weighting of the items being appropriate.