title: In search of methods enhancing fluency in reading: An examination of the relations between time constraints and processes of reading in readers of German

abstract: Abstract The attainment of fluency in reading is a major difficulty for reading-disabled people. Manipulations applied on the presentation of texts, leading to “on-line” effects on reading (i.e., while texts are manipulated), are one direction of examinations in search of methods affecting reading. The imposing of time constraints, by deleting one letter after the other from texts presented on a computer screen, has been established as such a method. In an attempt to further understand its nature, we tested the relations between time constraints and processes of reading: phonological decoding of small orthogrpahic units and the addressing of orthographic representations from the mental lexicon. We also examined whether the type of orthogrpahic unit deleted (lexical, sublexical, or nonlexical unit) has any additional effect. Participants were German fifth graders with ( n = 29) or without ( n = 34) reading disability. Time constraints enhanced fluency in reading in both groups, and to a similar extent, across conditions. Comprehension was unimpaired. These results place the very principle of time constraints, regardless of the orthographic unit manipulated, as a critical factor affecting fluency in reading. However, phonological decoding explained a significant amount of variance in fluency in reading across all conditions in reading-disabled children, whereas the addressing of orthographic representations was the consistent predictor of fluency in reading in regular readers. These results indicate a qualitative difference in the processes explaining the variance in fluency in reading in regular and reading-disabled readers and suggest that time constraints might not have an effect on the relations between these processes and reading performance.


title: Proposing a theory of gamification effectiveness

abstract: Gamification informally refers to making a system more game-like. More specifically, gamification denotes applying game mechanics to a non-game system. We theorize that gamification success depends on the game mechanics employed and their effects on user motivation and immersion. The proposed theory may be tested using an experiment or questionnaire study.


OPTIMIZATION OF HARD HANDOFF IN MOBILE COMMUNICATIONS USING IPv 6


title: Design of transition from WR-15 to inverted microstrip gap waveguide

abstract: Currently gap waveguide technology has revealed strong competitiveness for millimeter wave wireless applications. Specifically, this new topology of waveguide technology provides advantages of low loss, high quality factor and easy RF packaging. In this paper we present the design methodology of millimeter-wave transition from standard WR-15 rectangular waveguide to inverted microstrip gap waveguide. Furthermore, a more practical power divider from standard WR-15 rectangular waveguide to inverted microstrip gap waveguide is also introduced. Both structures are vertical and there is no additional need for any complex modification of the waveguide structure to achieve compatibility between the two structures. The proposed two transitions cover the whole unlicensed 60 GHz band from 57 to 66 GHz in order to be widely utilized as an interface between gap waveguide feed networks and equipment components with WR-15 ports. The simulated results show promising S-parameters.


title: PrIA: A Private Intelligent Assistant

abstract: Personalized services such as news recommendations are becoming an integral part of our digital lives. The problem is that they extract a steep cost in terms of privacy. The service providers collect and analyze user's personal data to provide the service, but can infer sensitive information about the user in the process. In this work we ask the question "How can we provide personalized news recommendation without sharing sensitive data with the provider?" We propose a local private intelligence assistance framework (PrIA), which collects user data and builds a profile about the user and provides recommendations, all on the user's personal device. It decouples aggregation and personalization: it uses the existing aggregation services on the cloud to obtain candidate articles but makes the personalized recommendations locally. Our proof-of-concept implementation and small scale user study shows the feasibility of a local news recommendation system. In building a private profile, PrIA avoids sharing sensitive information with the cloud-based recommendation service. However, the trade-off is that unlike cloud-based services, PrIA cannot leverage collective knowledge from large number of users. We quantify this trade-off by comparing PrIA with Google's cloud-based recommendation service. We find that the average precision of PrIA's recommendation is only 14% lower than that of Google's service. Rather than choose between privacy or personalization, this result motivates further study of systems that can provide both with acceptable trade-offs.


title: James Jurin (1684-1750): A pioneer of crowding research?

abstract: James Jurin wrote an extended essay on distinct and indistinct vision in 1738. In it, he distinguished between "perfect,'' "distinct,'' and "indistinct vision'' as perceptual categories, and his meticulous descriptions and analyses of perceptual phenomena contained observations that are akin to crowding. Remaining with the concepts of his day, however, he failed to recognize crowding as separate from spatial resolution. We present quotations from Jurin's essay and place them in the context of the contemporary concerns with visual resolution and crowding.


title: Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change

abstract: Reef corals are currently undergoing climatically driven poleward range expansions, with some evidence for equatorial range retractions. Predicting their response to future climate scenarios is cri...


title: Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs

abstract: Many time series data mining problems require subsequence similarity search as a subroutine. Dozens of similarity/distance measures have been proposed in the last decade and there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW’s usefulness and ubiquity, there has been a large community-wide effort to mitigate its relative lethargy. Proposed speedup techniques include early abandoning strategies, lower-bound based pruning, indexing and embedding. In this work we argue that we are now close to exhausting all possible speedup from software, and that we must turn to hardware-based solutions. With this motivation, we investigate both GPU (Graphics Processing Unit) and FPGA (Field Programmable Gate Array) based acceleration of subsequence similarity search under the DTW measure. As we shall show, our novel algorithms allow GPUs to achieve two orders of magnitude speedup and FPGAs to produce four orders of magnitude speedup. We conduct detailed case studies on the classification of astronomical observations and demonstrate that our ideas allow us to tackle problems that would be untenable otherwise.


title: Cuff-Less Blood Pressure Estimation Using Pulse Waveform Analysis and Pulse Arrival Time

abstract: Using the massive MIMIC physiological database, we tried to validate pulse wave analysis (PWA) based on multiparameters model whether it can continuously estimate blood pressure (BP) values on single site of one hand. In addition, to consider the limitation of insufficient data acquirement for home user, we used pulse arrival time (PAT) driven BP information to determine the individual scale factors of the PWA-BP estimation model. Experimental results indicate that the accuracy of the average regression model has error standard deviations of $10.6\pm 3.3$ mmHg (PAT), $8.7\pm 3.2$ mmHg (PWA) for SBP and $6.0\pm 2.3$ mmHg (PAT), $4.4\pm 1.6$ mmHg (PWA) for DBP on 23 subjects over a 1 day period. We defined a local-model which is extracted regression model from sparsely selected small dataset, contrast to full dataset for 24h (average-model). The limit of BP estimation accuracy from the local-model of PWA is lower than that of PAT-BP average-model. Whereas the error of the BP estimation local-model was reduced using more data for scaling, it required more than four times the 1 min data extracted over the 12 h calibration period to predict BP for 1 day. This study shows that PWA has possibility to estimate BP value and PAT-driven BP information could be used to determine the individual scale factors of the PWA-BP estimation model for home users.


title: A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal

abstract: In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.


title: A framework for computational thinking across the curriculum

abstract: We describe a framework for implementing computational thinking in a broad variety of general education courses. The framework is designed to be used by faculty without formal training in information technology in order to understand and integrate computational thinking into their own general education courses. The framework includes examples of computational thinking in a variety of general education courses, as well as sample in-class activities, assignments, and other assessments for the courses. The examples in the different courses are related and differentiated using categories taken from Denning Great Principles of Computing, so that similar types of computational thinking appearing in different contexts are brought together. This aids understanding of the computational thinking found in the courses and provides a template for future work on new course materials. Specific examples of computational thinking in the design category are provided in the context of three distinct courses.


title: An ethnopolitical approach to working with people of color.

abstract: North Americans have been expected to abdicate their ethnic backgrounds and blend into a single homogeneous identity. However, the United States President's Initiative on Race (1998) concluded that the greatest challenge facing North Americans is to accept and take pride in defining themselves as a multiracial democracy. With an ethnopolitical approach, the author studies effects of oppression, racism, and political repression on individuals, groups, and societies. She concludes that psychologists can help ameliorate racism in society by taking an antiracist stance, promoting a safe society where racial-social equity and justice prevail, and helping to formulate a collective identity that affords freedom to all members of society.


Simple Event Detection and Disaggregation Approach for Residential Energy Estimation


Keystones and Dominators: Framing the Operational Dynamics of Business Ecosystems


Moderating effects of online shopping experience on customer satisfaction and repurchase intentions aFatemeh


title: Metric 3D reconstruction and texture acquisition of surfaces of revolution from a single uncalibrated view

abstract: Image analysis and computer vision can be effectively employed to recover the three-dimensional structure of imaged objects, together with their surface properties. In this paper, we address the problem of metric reconstruction and texture acquisition from a single uncalibrated view of a surface of revolution (SOR). Geometric constraints induced in the image by the symmetry properties of the SOR structure are exploited to perform self-calibration of a natural camera, 3D metric reconstruction, and texture acquisition. By exploiting the analogy with the geometry of single axis motion, we demonstrate that the imaged apparent contour and the visible segments of two imaged cross sections in a single SOR view provide enough information for these tasks. Original contributions of the paper are: single view self-calibration and reconstruction based on planar rectification, previously developed for planar surfaces, has been extended to deal also with the SOR class of curved surfaces; self-calibration is obtained by estimating both camera focal length (one parameter) and principal point (two parameters) from three independent linear constraints for the SOR fixed entities; the invariant-based description of the SOR scaling function has been extended from affine to perspective projection. The solution proposed exploits both the geometric and topological properties of the transformation that relates the apparent contour to the SOR scaling function. Therefore, with this method, a metric localization of the SOR occluded parts can be made, so as to cope with them correctly. For the reconstruction of textured SORs, texture acquisition is performed without requiring the estimation of external camera calibration parameters, but only using internal camera parameters obtained from self-calibration.


title: A Driving Behaviour Model of Electrical Wheelchair Users

abstract: In spite of the presence of powered wheelchairs, some of the users still experience steering challenges and manoeuvring difficulties that limit their capacity of navigating effectively. For such users, steering support and assistive systems may be very necessary. To appreciate the assistance, there is need that the assistive control is adaptable to the user’s steering behaviour. This paper contributes to wheelchair steering improvement by modelling the steering behaviour of powered wheelchair users, for integration into the control system. More precisely, the modelling is based on the improved Directed Potential Field DPF method for trajectory planning. The method has facilitated the formulation of a simple behaviour model that is also linear in parameters. To obtain the steering data for parameter identification, seven individuals participated in driving the wheelchair in different virtual worlds on the augmented platform. The obtained data facilitated the estimation of user parameters, using the ordinary least square method, with satisfactory regression analysis results.


title: Benchmarking multidisciplinary design optimization algorithms

abstract: A comparison of algorithms for multidisciplinary design optimization (MDO) is performed with the aid of a new software framework. This framework, pyMDO, was developed in Python and is shown to be an excellent platform for comparing the performance of the various MDO methods. pyMDO eliminates the need for reformulation when solving a given problem using different MDO methods: once a problem has been described, it can automatically be cast into any method. In addition, the modular design of pyMDO allows rapid development and benchmarking of new methods. Results generated from this study provide a strong foundation for identifying the performance trends of various methods with several types of problems.


The measurement of solvent extractable matter by near infra red analysis


title: Whole page optimization: how page elements interact with the position auction

abstract: We study the trade-off between layout elements of the search results page and revenue in the real-time sponsored search auction. Using data from a randomized experiment on a major search engine, we find that having images present among the search results tends to simultaneously raise the ad click-through rate and flatten the ad click curve, reducing the premium for occupying the top slot and thus impacting bidding incentives. Theoretical analysis shows that this type of change creates an ambiguous impact on revenue in equilibrium: a steeper curve with lower total click-through rate is preferable only if the expected revenue distribution is skewed enough towards the top bidder. Empirically, we show that this is a relatively rare phenomenon, and we also find that whole page satisfaction causally raises the click-through rate of the ad block. This means search engines have a short-run incentive to boost search result quality, not just a long-run incentive based on competition between providers.


title: Multiview hierarchical bayesian regression model andapplication to online advertising

abstract: With the development of Web applications, large scale data are popular; and they are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about multi-view data with implicit structure. In this paper, we propose a novel hierarchical Bayesian mixture regression model, which discovers and then exploits the relationships among multiple views of the data to perform various machine learning tasks. A stochastic EM inference and learning algorithm is derived; and a parallel implementation in Hadoop MapReduce [9] paradigm is developed to scale up the learning. We apply the developed model and algorithm on click-through-rate (CTR) prediction and campaign targeting recommendation in online advertising to measure its effectiveness. The experiments on both synthetic data and large scale ads serving data from a real world online advertising exchange demonstrate the superior CTR prediction accuracy of our method compared to existing state-of-the-art methods. The results also show that our model can recommend high performance targeting features for online advertising campaigns.


title: Online diagnosis of hard faults in microprocessors

abstract: We develop a microprocessor design that tolerates hard faults, including fabrication defects and in-field faults, by leveraging existing microprocessor redundancy. To do this, we must: detect and correct errors, diagnose hard faults at the field deconfigurable unit (FDU) granularity, and deconfigure FDUs with hard faults. In our reliable microprocessor design, we use DIVA dynamic verification to detect and correct errors. Our new scheme for diagnosing hard faults tracks instructions' core structure occupancy from decode until commit. If a DIVA checker detects an error in an instruction, it increments a small saturating error counter for every FDU used by that instruction, including that DIVA checker. A hard fault in an FDU quickly leads to an above-threshold error counter for that FDU and thus diagnoses the fault. For deconfiguration, we use previously developed schemes for functional units and buffers and present a scheme for deconfiguring DIVA checkers. Experimental results show that our reliable microprocessor quickly and accurately diagnoses each hard fault that is injected and continues to function, albeit with somewhat degraded performance.


title: An Introduction to Deep Learning for the Physical Layer

abstract: We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded with a discussion of open challenges and areas for future investigation.


title: Sentence Alignment For Monolingual Comparable Corpora

abstract: We address the problem of sentence alignment for monolingual corpora, a phenomenon distinct from alignment in parallel corpora. Aligning large comparable corpora automatically would provide a valuable resource for learning of text-to-text rewriting rules. We incorporate context into the search for an optimal alignment in two complementary ways: learning rules for matching paragraphs using topic structure and further refining the matching through local alignment to find good sentence pairs. Evaluation shows that our alignment method outperforms state-of-the-art systems developed for the same task.


title: Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

abstract: Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.


title: General Video Game AI: competition, challenges, and opportunities

abstract: The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game-dependent heuristics. This short paper summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, and outlining our future plans.


title: Adaptive Geometry Image

abstract: We present a novel postprocessing utility called adaptive geometry image (AGIM) for global parameterization techniques that can embed a 3D surface onto a rectangular domain. This utility first converts a single rectangular parameterization into many different tessellations of square geometry images (GIMs) and then efficiently packs these GIMs into an image called AGIM. Therefore, undersampled regions of the input parameterization can be up-sampled accordingly until the local reconstruction error bound is met. The connectivity of AGIM can be quickly computed and dynamically changed at rendering time. AGIM does not have T-vertices, and therefore, no crack is generated between two neighboring GIMs at different tessellations. Experimental results show that AGIM can achieve significant PSNR gain over the input parameterization, AGIM retains the advantages of the original GIM and reduces the reconstruction error present in the original GIM technique. The AGIM is also suitable for global parameterization techniques based on quadrilateral complexes. Using the approximate sampling rates, the PolyCube-based quadrilateral complexes with AGIM can outperform state-of-the-art multichart GIM technique in terms of PSNR.


title: Developing and testing a self-assessment and tutoring system

abstract: Automated feedback on writing may be a useful complement to teacher comments in the process of learning a foreign language. This paper presents a self-assessment and tutoring system which combines an holistic score with detection and correction of frequent errors and furthermore provides a qualitative assessment of each individual sentence, thus making the language learner aware of potentially problematic areas rather than providing a panacea. The system has been tested by learners in a range of educational institutions, and their feedback has guided its development.


title: Real-time human action recognition based on depth motion maps

abstract: This paper presents a human action recognition method by using depth motion maps (DMMs). Each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Under each projection view, the absolute difference between two consecutive projected maps is accumulated through an entire depth video sequence forming a DMM. An l2-regularized collaborative representation classifier with a distance-weighted Tikhonov matrix is then employed for action recognition. The developed method is shown to be computationally efficient allowing it to run in real-time. The recognition results applied to the Microsoft Research Action3D dataset indicate superior performance of our method over the existing methods.


title: DDoS-Shield: DDoS-Resilient Scheduling to Counter Application Layer Attacks

abstract: Countering distributed denial of service (DDoS) attacks is becoming ever more challenging with the vast resources and techniques increasingly available to attackers. In this paper, we consider sophisticated attacks that are protocol-compliant, non-intrusive, and utilize legitimate application-layer requests to overwhelm system resources. We characterize application-layer resource attacks as either request flooding, asymmetric, or repeated one-shot, on the basis of the application workload parameters that they exploit. To protect servers from these attacks, we propose a counter-mechanism namely DDoS Shield that consists of a suspicion assignment mechanism and a DDoS-resilient scheduler. In contrast to prior work, our suspicion mechanism assigns a continuous value as opposed to a binary measure to each client session, and the scheduler utilizes these values to determine if and when to schedule a session's requests. Using testbed experiments on a web application, we demonstrate the potency of these resource attacks and evaluate the efficacy of our counter-mechanism. For instance, we mount an asymmetric attack which overwhelms the server resources, increasing the response time of legitimate clients from 0.3 seconds to 40 seconds. Under the same attack scenario, DDoS Shield improves the victims' performance to 1.5 seconds.


title: Force sensor in simulated skin and neural model mimic tactile SAI afferent spiking response to ramp and hold stimuli

abstract: Background ::: The next generation of prosthetic limbs will restore sensory feedback to the nervous system by mimicking how skin mechanoreceptors, innervated by afferents, produce trains of action potentials in response to compressive stimuli. Prior work has addressed building sensors within skin substitutes for robotics, modeling skin mechanics and neural dynamics of mechanotransduction, and predicting response timing of action potentials for vibration. The effort here is unique because it accounts for skin elasticity by measuring force within simulated skin, utilizes few free model parameters for parsimony, and separates parameter fitting and model validation. Additionally, the ramp-and-hold, sustained stimuli used in this work capture the essential features of the everyday task of contacting and holding an object.


title: Anomaly Detection in Dataset for Improved Model Accuracy Using DBSCAN Clustering Algorithm

abstract: The purity of the dataset used for model construction plays important roles in the accuracy and reliability of model building; outliers are often caused by noisy data as a result of mechanical faults, changes in system behaviour, or due to human error. This is why it is essential to pre-process dataset prior to modelling, in order to differentiate between data that appears normal or abnormal within the sample space. One important reason for removing outliers is to prevent contaminating effect on the dataset which can lead to bad consequences and serious disaster if not removed. An effective measure that automatically clusters outliers in the dataset using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique is proposed in this paper. Rapidminer, an open source software tool is used to experiment on some sample dataset and based on the characteristics of these data objects, some clusters are formed which filter out outliers from the dataset being explored. The experimental results from this study show that, the DBSCAN algorithm is a suitable technique for outliers detection and capable of filtering the abnormal data from a combination of noise and normal dataset.


title: Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks

abstract: As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.


title: Quantum perceptron over a field and neural network architecture selection in a quantum computer

abstract: In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.


title: Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

abstract: GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles and strategies that allow efficient mapping of computation to graphics hardware. In this work we discuss the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies. Key to performance on this platform is using massive multithreading to utilize the large number of cores and hide global memory latency. To achieve this, developers face the challenge of striking the right balance between each thread's resource usage and the number of simultaneously active threads. The resources to manage include the number of registers and the amount of on-chip memory used per thread, number of threads per multiprocessor, and global memory bandwidth. We also obtain increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations. We apply these strategies across a variety of applications and domains and achieve between a 10.5X to 457X speedup in kernel codes and between 1.16X to 431X total application speedup.


title: Robust subspace clustering via thresholding ridge regression

abstract: Given a data set from a union of multiple linear sub-spaces, a robust subspace clustering algorithm fits each group of data points with a low-dimensional subspace and then clusters these data even though they are grossly corrupted or sampled from the union of dependent sub-spaces. Under the framework of spectral clustering, recent works using sparse representation, low rank representation and their extensions achieve robust clustering results by formulating the errors (e.g., corruptions) into their objective functions so that the errors can be removed from the inputs. However, these approaches have suffered from the limitation that the structure of the errors should be known as the prior knowledge. In this paper, we present a new method of robust sub-space clustering by eliminating the effect of the errors from the projection space (representation) rather than from the input space. We firstly prove that l1-, l2-, and l∞ -norm-based linear projection spaces share the property of intra-subspace projection dominance, i.e., the coefficients over intra-subspace data points are larger than those over inter-subspace data points. Based on this property, we propose a robust and efficient sub-space clustering algorithm, called Thresholding Ridge Regression (TRR). TRR calculates the ∞2 -norm-based coefficients of a given data set and performs a hard thresholding operator; and then the coefficients are used to build a similarity graph for clustering. Experimental studies show that TRR outperforms the state-of-the-art methods with respect to clustering quality, robustness, and time-saving.


title: An Analysis of Unsupervised Pre-training in Light of Recent Advances

abstract: Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to improve performance. Unfortunately, unsupervised pre-training is not used by state-of-the-art methods leading to the following question: Is unsupervised pre-training still useful given recent advances? If so, when? We answer this in three parts: we 1) develop an unsupervised method that incorporates ReLUs and recent unsupervised regularization techniques, 2) analyze the benefits of unsupervised pre-training compared to data augmentation and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised samples, 3) verify our findings on STL-10. We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low. We also use unsupervised pre-training with additional color augmentation to achieve near state-of-the-art performance on STL-10.


title: An Advanced Tabu Search Algorithm for the Job Shop Problem

abstract: The job shop scheduling problem with the makespan criterion is a certain NP-hard case from OR theory having excellent practical applications. This problem, having been examined for years, is also regarded as an indicator of the quality of advanced scheduling algorithms. In this paper we provide a new approximate algorithm that is based on the big valley phenomenon, and uses some elements of so-called path relinking technique as well as new theoretical properties of neighbourhoods. The proposed algorithm owns, unprecedented up to now, accuracy, obtainable in a quick time on a PC, which has been confirmed after wide computer tests.


title: Rethinking Teaching for the Knowledge Society

abstract: For over a decade now, universities have been aware of the pressures to expand access to higher education.1 The knowledge society needs more graduates, and those graduates will keep returning to study as lifelong learning takes its place in both work and leisure time. These are the positive pressures for expansion. But the knowledge society, fueled by the expanding higher education sector, is in turn generating more knowledge industries, producing additional, competitive pressures for traditional institutions of higher education. Those involved in university teaching in this digital age must cope with the fact that the knowledge industries are creating the means by which individuals can acquire the immediate skills and knowledge those industries need. As a result, many individuals are questioning the true benefit of a university education, given its cost.


title: Efficient architecture for soft-output massive MIMO detection with Gauss-Seidel method

abstract: In massive multiple-input multiple-output (MIMO) uplink, the minimum mean square error (MMSE) algorithm is near-optimal and linear, but still suffers from high-complexity of matrix inversion. Based on Gauss-Seidel (GS) method, an efficient architecture for massive MIMO soft-output detection is proposed in this paper. To further accelerate the convergence rate of the conventional GS method with acceptable overhead complexity, a truncated Neumann series of the first 2 terms, is employed for initialization. The architecture can meet various application requirements by flexibly adjusting the number of iterations. FPGA implementation for a 128 × 8 MIMO demonstrates its advantages in both hardware efficiency and flexibility.


title: Few Shot Learning with Simplex

abstract: Deep learning has made remarkable achievement in many fields. However, learning the parameters of neural networks usually demands a large amount of labeled data. The algorithms of deep learning, therefore, encounter difficulties when applied to supervised learning where only little data are available. This specific task is called few-shot learning. To address it, we propose a novel algorithm for fewshot learning using discrete geometry, in the sense that the samples in a class are modeled as a reduced simplex. The volume of the simplex is used for the measurement of class scatter. During testing, combined with the test sample and the points in the class, a new simplex is formed. Then the similarity between the test sample and the class can be quantized with the ratio of volumes of the new simplex to the original class simplex. Moreover, we present an approach to constructing simplices using local regions of feature maps yielded by convolutional neural networks. Experiments on Omniglot and miniImageNet verify the effectiveness of our simplex algorithm on few-shot learning.


title: On the Perturb-and-Observe and Incremental Conductance MPPT Methods for PV Systems

abstract: This paper presents a detailed analysis of the two most well-known hill-climbing maximum power point tracking (MPPT) algorithms: the perturb-and-observe (P&O) and incremental conductance (INC). The purpose of the analysis is to clarify some common misconceptions in the literature regarding these two trackers, therefore helping the selection process for a suitable MPPT for both researchers and industry. The two methods are thoroughly analyzed both from a mathematical and practical implementation point of view. Their mathematical analysis reveals that there is no difference between the two. This has been confirmed by experimental tests according to the EN 50530 standard, resulting in a deviation between their efficiencies of 0.13% in dynamic and as low as 0.02% under static conditions. The results show that despite the common opinion in the literature, the P&O and INC are equivalent.


title: Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements

abstract: Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with the web. It also presents various opportunities for the intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper, we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Prior literature has covered various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are a relatively new concept, being widely adopted due to significant progress in the development of platforms and frameworks. The novelty of this paper lies in the specific integration of Chatbots in the IoT scenario. We analyzed the shortcomings of existing IoT systems and put forward ways to tackle them by incorporating chatbots. A general architecture is proposed for implementing such a system, as well as platforms and frameworks – both commercial and open source – which allow for the implementation of such systems. Identification of the newer challenges and possible future research directions with this new integration have also been addressed.


title: Enemy of the State: a State-aware Black-box Web Vulnerability Scanner

abstract: Black-box web vulnerability scanners are a popular choice for finding security vulnerabilities in web applications in an automated fashion. These tools operate in a point-and-shootmanner, testing any web application-- regardless of the server-side language--for common security vulnerabilities. Unfortunately, black-box tools suffer from a number of limitations, particularly when interacting with complex applications that have multiple actions that can change the application's state. If a vulnerability analysis tool does not take into account changes in the web application's state, it might overlook vulnerabilities or completely miss entire portions of the web application. ::: ::: We propose a novel way of inferring the web application's internal state machine from the outside--that is, by navigating through the web application, observing differences in output, and incrementally producing a model representing the web application's state. ::: ::: We utilize the inferred state machine to drive a black-box web application vulnerability scanner. Our scanner traverses a web application's state machine to find and fuzz user-input vectors and discover security flaws. We implemented our technique in a prototype crawler and linked it to the fuzzing component from an open-source web vulnerability scanner. ::: ::: We show that our state-aware black-box web vulnerability scanner is able to not only exercise more code of the web application, but also discover vulnerabilities that other vulnerability scanners miss.


Learning and Thinking Strategy for Training Sequence Generation Models


title: Self-esteem, study skills, self-concept, social support, psychological distress, and coping mechanism effects on test anxiety and academic performance

abstract: The purpose of this article is to assess the association of test anxiety and academic performance with study skills, coping mechanism, self-concept, self-esteem, and psychological distress among un...


title: Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision

abstract: Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are train on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving. The proposed generative framework, coined Generative One-Shot Learning (GOL), takes as input single one-shot objects, or generic patterns, and a small set of so-called regularization samples used to drive the generative process. New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator. GOL has been evaluated on environment perception challenges encountered in autonomous vision.


title: Collapsed Gibbs sampling for latent Dirichlet allocation on spark

abstract: In this paper we implement a collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model on Spark. Spark is a fast in-memory cluster computing framework for large-scale data processing, which has been the talk of the Big Data town for a while. It is suitable for iterative and interactive algorithm. Our approach splits the dataset into P * P partitions, shuffles and recombines these partitions into P sub-datasets using rules to avoid conflicts of sampling, where each of P sub-datasets only contains P partitions, and then parallel processes each sub-dataset one by one. Despite increasing the number of iterations, this method reduces data communication overhead, makes good use of Spark's efficient iterative execution and results in significant speedup on large-scale datasets in our experiments.


title: Design of SIW Cavity-Backed Circular-Polarized Antennas Using Two Different Feeding Transitions

abstract: Two circular-polarized circular patch antennas which have novel feeding structures such as a substrate integrated waveguide (SIW), a cavity-backed resonator and two different feeding transitions, are proposed and experimentally investigated in terms of electrical performances, including reflection coefficients, optimized parameter values, circular polarized antenna gain, axial ratios and radiation patterns. By inserting asymmetrical inductive via arrays into the interface region between the circular patch and SIW feeding structure, it is found that an enhancement of input impedance bandwidth has been achieved. In addition, in order to check the effects of feeding transition types on the electrical performances of the main radiator, two different feeding transitions, namely microstrip-to-SIW and coax-to-SIW, have been studied by considering reflection coefficients, gain, axial ratios and radiation patterns. As a result, it is experimentally proved that a broadband impedance bandwidth of 17.32% and 14.42% under the criteria of less than VSWR 2:1 and 1.5:1, respectively, have been obtained and an RHCP axial ratio of 2.34% with a maximum gain of 7.79 dBic has been accomplished by using the proposed antenna with coax-to-SIW transition operating at the X-band of 10 GHz center frequency.


title: Adaptive Resource Management and Control in Software Defined Networks

abstract: The heterogeneous nature of the applications, technologies and equipment that today's networks have to support has made the management of such infrastructures a complex task. The Software-Defined Networking (SDN) paradigm has emerged as a promising solution to reduce this complexity through the creation of a unified control plane independent of specific vendor equipment. However, designing a SDN-based solution for network resource management raises several challenges as it should exhibit flexibility, scalability and adaptability. In this paper, we present a new SDN-based management and control framework for fixed backbone networks, which provides support for both static and dynamic resource management applications. The framework consists of three layers which interact with each other through a set of interfaces. We develop a placement algorithm to determine the allocation of managers and controllers in the proposed distributed management and control layer. We then show how this layer can satisfy the requirements of two specific applications for adaptive load-balancing and energy management purposes.


title: Automatic Video Classification: A Survey of the Literature

abstract: There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research.


title: Continuum Robot Dynamics Utilizing the Principle of Virtual Power

abstract: Efficient formulations for the dynamics of continuum robots are necessary to enable accurate modeling of the robot's shape during operation. Previous work in continuum robotics has focused on low-fidelity lumped parameter models, in which actuated segments are modeled as circular arcs, or computationally intensive high-fidelity distributed parameter models, in which continuum robots are modeled as a parameterized spatial curve. In this paper, a novel dynamic modeling methodology is studied that captures curvature variations along a segment using a finite set of kinematic variables. This dynamic model is implemented using the principle of virtual power (also called Kane's method) for a continuum robot. The model is derived to account for inertial, actuation, friction, elastic, and gravitational effects. The model is inherently adaptable for including any type of external force or moment, including dissipative effects and external loading. Three case studies are simulated on a cable-driven continuum robot structure to study the dynamic properties of the numerical model. Cross validation is performed in comparison to both experimental results and finite-element analysis.


title: Tagging Grammatical Functions

abstract: This paper addresses issues in automated treebank construction. We show how standard part-of-speech tagging techniques extend to the more general problem of structural annotation, especially for determining grammatical functions and syntactic categories. Annotation is viewed as an interactive process where manual and automatic processing alternate. Efficiency and accuracy results are presented. We also discuss further automation steps.


title: Complete Anytime Beam Search

abstract: Beam search executes a state-space search, but may abandon nonpromising search avenues in order to reduce complexity. Although it has existed for more than two decades and has been applied to many real-world problems, beam search still suffers from the drawback of possible termination with no solution or a solution of unsatisfactory quality. In this paper, we first propose a domain-independent heuristic for node pruning, and a method to reduce the possibility that beam search will fail. We then develop a complete beam search algorithm. The new algorithm can not only find an optimal solution, but can also reach better solutions sooner than its underlying search method. We apply complete beam search to the maximum boolean satisfiability and the symmetric and asymmetric Traveling Salesman Problems. Our experimental results show that the domain-independent pruning heuristic is effective and the new algorithm significantly improves the performance of its underlying search algorithm.


openSMILE:): the Munich open-source large-scale multimedia feature extractor


title: Efficient resource scheduling for eMTC/NB-IoT communications in LTE Rel. 13

abstract: Next generation cellular networks (5G) have to deal with deployment of massive machine-type-communication (MTC) devices as the internet of things (IoT) becomes pervasive. To cater to such demand, 3GPP has finalized specifications for enhanced machine type communication (eMTC) and narrowband IoT (NB-IoT) as part of LTE Release 131. To avoid resource congestion to legacy users, eMTC communication can be limited to predefined eMTC narrowband(s) with receiver bandwidth of 1.08MHz, whereas in NB-IoT all communications are limited to a single resource block2. Both eMTC and NB-IoT are designed to cater for bandwidth limited low complexity/coverage enhancement (BL/CE) devices, with MTC messages repeated across several BL/CE subframes lasting up to several seconds, to accomodate for large maximum coupling losses (MCL). To avoid MTC users from hogging, collide with legacy resources during lengthy repetitions, 3GPP defines BL/CE subframes and has made provisions for valid and invalid BL/CE subframes to be configured. With large number of MTC devices sharing limited resources and message repetitions lasting several SFs, the LTE medium access control (MAC) scheduler processing burden will be significantly increased. This is because the scheduler operates on a subframe (SF) basis and has to find the MTC users who can be scheduled either in the current SF, or at a future SF after ongoing repetitions finish, or within a gap between previously scheduled repetitions. Another complexity is that the control messages(e.g., MPDCCH) can only be scheduled at certain start intervals and within predefined boundaries. In this paper we address these problems with a low complexity constant time scheduling scheme for MTC users3 when invalid BL/CE SFs are configured. The proposed algorithms are generic and applicable to both eNB and UE implementation. With detailed system level simulations we demonstrate the usefulness of the proposed scheme when large number of MTC users are active in the system.


title: Comparison of noninvasive pulse transit time estimates as markers of blood pressure using invasive pulse transit time measurements as a reference

abstract: Pulse transit time (PTT) measured as the time delay between invasive proximal and distal blood pressure (BP) or flow waveforms (invasive PTT [I‐PTT]) tightly correlates with BP. PTT estimated as the time delay between noninvasive proximal and distal arterial waveforms could therefore permit cuff‐less BP monitoring. A popular noninvasive PTT estimate for this application is the time delay between ECG and photoplethysmography (PPG) waveforms (pulse arrival time [PAT]). Another estimate is the time delay between proximal and distal PPG waveforms (PPG‐PTT). PAT and PPG‐PTT were assessed as markers of BP over a wide physiologic range using I‐PTT as a reference. Waveforms for determining I‐PTT, PAT, and PPG‐PTT through central arteries were measured from swine during baseline conditions and infusions of various hemodynamic drugs. Diastolic, mean, and systolic BP varied widely in each subject (group average (mean ± SE) standard deviation between 25 ± 2 and 36 ± 2 mmHg). I‐PTT correlated well with all BP levels (group average R 2 values between 0.86 ± 0.03 and 0.91 ± 0.03). PPG‐PTT also correlated well with all BP levels (group average R 2 values between 0.81 ± 0.03 and 0.85 ± 0.02), and its R 2 values were not significantly different from those of I‐PTT. PAT correlated best with systolic BP (group average R 2 value of 0.70 ± 0.04), but its R 2 values for all BP levels were significantly lower than those of I‐PTT ( P < 0.005) and PPG‐PTT ( P < 0.02). The pre‐ejection period component of PAT was responsible for its inferior correlation with BP. In sum, PPG‐PTT was not different from I‐PTT and superior to the popular PAT as a marker of BP.


title: Mining Student Data Using Decision Trees

abstract: Student performance in university courses is of great concern to the higher education managements where several factors may affect the performance. This paper is an attempt to use the data mining processes, particularly classification, to help in enhancing the quality of the higher educational system by evaluating student data to study the main attributes that may affect the student performance in courses. For this purpose, the CRISP framework for data mining is used for mining student related academic data. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. A system that facilitates the use of the generated rules is built which allows students to predict the final grade in a course under study.


title: Automatic Detection of Accretion of Glaucoma in Eye

abstract: Glaucoma arises due to the inadequate fluid flow from the drainage canals of the eye, leading to the crystallization of the fluid in the cornea and iris regions. We propose an algorithm to automatically compute this accretion from the ultrasound images of the eye. The existing techniques of glaucoma detection involve manual/computer aided strategies which confronts major challenges in the form of speed of processing and precision angle detection. Apart from improving the contrast of the low resolution ultrasound image, the algorithm aims to determine the exact location of the apex point of the anterior chamber region for efficient angle calculation. We realize that it is highly imperative to detect glaucoma in its early stages for diagnosis and hence the algorithm also addresses the importance of precise results with effective immunity towards speckle noise. The algorithm was tested with many samples of ultrasound images from diseased and normal eyes of a number of patients, which provided promising results in most cases.


title: Cryptographically Enforced Control Flow Integrity

abstract: Recent Pwn2Own competitions have demonstrated the continued effectiveness of control hijacking attacks despite deployed countermeasures including stack canaries and ASLR. A powerful defense called Control flow Integrity (CFI) offers a principled approach to preventing such attacks. However, prior CFI implementations use static analysis and must limit protection to remain practical. These limitations have enabled attacks against all known CFI systems, as demonstrated in recent work. This paper presents a cryptographic approach to control flow integrity (CCFI) that is both fine-grain and practical: using message authentication codes (MAC) to protect control flow elements such as return addresses, function pointers, and vtable pointers. MACs on these elements prevent even powerful attackers with random read/write access to memory from tampering with program control flow. We implemented CCFI in Clang/LLVM, taking advantage of recently available cryptographic CPU instructions. We evaluate our system on several large software packages (including nginx, Apache and memcache) as well as all their dependencies. The cost of protection ranges from a 3-18% decrease in request rate.


title: A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range

abstract: In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.


title: Three-layer graph framework with the sumD feature for alpha matting

abstract: Abstract Alpha matting, the process of extracting opacity mask of the foreground in an image, is an important task in image and video editing. All of the matting methods need exploit the relationships between pixels. The traditional propagation-based methods construct constrains based on nonlocal principle and color line model to reflect the relationships. However, these methods would produce artifacts if the constrains are not reliable. So we improve this problem in three points. Firstly, we design a novel feature called sumD feature to increase the pixel discrimination. This feature is simple and could encourage pixels with similar texture to have similar feature values. Secondly, we design a three-layer graph framework to construct nonlocal constrains. This framework finds constrains in multi-scale range and selects reliable constrains, then unifies nonlocal constrains according to their reliabilities. Thirdly, we develop a new label extension method to add hard constrains. Experimental results confirm that the effectiveness of the three changes, and the proposed method achieves high rank on the benchmark dataset.


title: LightProbe: A 64-channel programmable ultrasound transducer head with an integrated front-end and a 26.4 Gb/s optical link

abstract: Medical ultrasound processing features two main components: A transducer head to generate the ultrasound wave and acquire the reflected signals and a processing system that will generate the final image. The connection between these two components is established using digital communication over a USB link for smaller mobile systems whereas large stationary systems operating with 4–16x more channels use analog signals over micro-coaxial cables to avoid link rates of 16–100 Gb/s. In this paper, we present LightProbe, a programmable ultrasound transducer head with an integrated 64-channel frontend and operating on an estimated 12 W worst-case power budget. LightProbe is the first transducer head equipped with a 26.4 Gb/s optical link. Moreover, it features a configurable FPGA that can be configured to pre-process the data on the transducer head and allows a flexible, inexpensive, light digital optical link that is immune to interference and can be tailored to fit a variety of devices from small mobile devices all the way to large stationary devices with high throughput requirements.


title: Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time

abstract: Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model known as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNN-RSM shows better generalization, topic interpretation, evolution and trends. We also propose to quantify the capability of dynamic topic model to capture word evolution in topics over time.


title: Rain drop detection and removal using K-Means clustering

abstract: A novel hybrid algorithm has been proposed in this paper to detect raindrops, remove and then restore the image back ground in a single image. This new hybrid algorithm is framed based on K-Means clustering and Median filtering for the fast retrieval of Rain droplets from the single image. K-Means clustering algorithm is an efficient algorithm for image clustering. The algorithm proposed in this paper has different approach from other established numerical schemes. The hybrid algorithm is framed in order to identify the rain droplets using clustering and shape modeling of raindrops. The proposed system is fast compared with alternate droplet identification schemes. The process allows rapid evaluation of the contour of the raindrops and convergence to its final resultant with very little iteration. The experiments demonstrate the efficiency and accuracy of the method.


title: Increasing marijuana use for black adolescents in the United States: A test of competing explanations

abstract: Abstract Aims In the last decade the relatively lower levels of marijuana use for black relative to non-black high school seniors has grown smaller and disappeared, drawing to a close a unique disparity that actually favored a disadvantaged group for at least thirty years. In this study we test trends in cigarette smoking and religiosity as possible explanations for this closing disparity. The study also examines whether increasing marijuana levels for black adolescents is better characterized as a cohort effect or an historical period effect. Design Analyses use relative risk regression and focus on data from yearly, cross-sectional surveys from the time period 2008–2017. Setting and participants Data comes from the nationally-representative Monitoring the Future survey, which conducts in-school surveys of secondary school students. The analysis uses data from 114,552 high school seniors (in 12th grade), 123,594 in 10th grade, and 136,741 in 8th grade. Findings Past 12-month marijuana prevalence significantly increased for black as compared to non-black adolescents from 2008 to 2017 in 12th grade, 10th grade, and 8th grade. The increase attenuated by more than half and was not statistically significant after adjusting for cigarette smoking. In contrast, the increase was little changed after adjusting adolescent levels of religiosity. The increase is better characterized as a cohort effect than a period effect. Conclusions These results support the increase in marijuana use for black relative to non-black adolescents as an unexpected consequence of the great decline in adolescent cigarette smoking, which has occurred slower for black adolescents.


Designing An Automatic Web-Based Certificate Verification System For Institutions ( CASE STUDY : MICHAEL OKPARA UNIVERSITY OF AGRICULTURE , UMUDIKE )


title: Reading the mind's eye: Decoding category information during mental imagery

abstract: article i nfo Category information for visually presented objects can be read out from multi-voxel patterns of fMRI activity in ventral-temporal cortex. What is the nature and reliability of these patterns in the absence of any bottom-up visualinput,forexample,duringvisualimagery?Here,we firstaskhowwellcategoryinformationcanbedecoded forimaginedobjectsandthencomparetherepresentationsevokedduringimageryandactualviewing.InanfMRI study, four object categories(food, tools,faces, buildings) wereeithervisuallypresented tosubjects, or imagined by them. Using pattern classification techniques, we could reliably decode category information (including for non-special categories, i.e., food and tools) from ventral-temporal cortex in both conditions, but only during actual viewing from retinotopic areas. Interestingly, in temporal cortex when the classifier was trained on the viewed condition and tested on the imagery condition, or vice versa, classification performance was comparable to within the imagery condition. The above results held even when we did not use information in the specialized category-selective areas. Thus, the patterns of representation during imagery and actual viewing are in fact surprisingly similar to each other. Consistent with this observation, the maps of "diagnostic voxels" (i.e., the classifier weights) for the perception and imagery classifiers were more similar in ventral-temporal cortex than in retinotopic cortex. These results suggest that in the absence of any bottom-up input, cortical back projections can selectively re-activate specific patterns of neural activity.


title: A First Course in Database Systems

abstract: Written by well-known computer scientists, this accessible and succinct introduction to database systems focuses on database design and use. It provides in-depth coverage of databases from the point of view of the database designer, user, and application programmer. The authors provide an overview of important programming systems (e.g., SQL, JDBC, PSM, CLI, PHP, XQuery, etc.) and the intellectual framework to put them into context. For software engineers, database engineers, and programmers.


title: An Internet of Things-Based Model for Smart Water Management

abstract: Water is a vital resource for life, and for the economy. Nowadays, one of the most serious challenges to solve is to manage the water scarcity. Current water management ICT systems are supported by specific vendor equipment, without considering any interoperability standards. The lack of standardization among producer's water ICT equipment hinders proper monitoring and control systems, resulting in low efficiency in water distribution and consumption, system's maintenance and improvement, and failure identification. In this paper we propose a smart water management model integrating Internet of Things technologies for decoupling decision support systems and monitoring from business processes coordination and subsystem implementation. The proposed smart water management model makes specific vendor equipment interoperable and manageable in a water management domain in a homogeneous way.


title: A novel mixed-mode adaptive equalization system for high-speed 2-level PAM signals

abstract: A novel mixed-mode adaptive equalization system for a high-speed 2-level pulse-amplitude nodulated (PAM) signal is presented. The intended application is in the area of digital serial interface, such as a non-return-to-zero-inverted (NRZI) serial video signal with a data rate of 270 Mbits/s. The adaptive system tunes the zeros of a finite-impulse-response (FIR) analog programmable filter. Programmable differentiators and transconductors are designed to increase the tuning range of the filter. An average error, obtained by calculating the accumulated area of the eye-diagram, and average gradients are proposed as inputs to a digital least-mean-squared (LMS) algorithm. This approach allows a more relaxed speed requirement for analog-to-digital converters (ADCs). A third-order analog filter, along with some essential circuit blocks, are designed for a 0.35 /spl mu/m CMOS process.


title: Visual Imagery Can Impede Reasoning

abstract: Although it is natural to suppose that visual mental imagery is important in human deductive reasoning, the evidence is equivocal. This article argues that reasoning studies have not distinguished between ease of visualization and ease of constructing spatial models. Rating studies show that these factors can be separated. Their results yielded four sorts of relations: (1)visuospatial relations that are easy to envisage visually and spatially, (2)visual relations that are easy to envisage visually but hard to envisage spatially, (3)spatial relations that are hard to envisage visually but easy to envisage spatially, and (4)control relations that are hard to envisage both visually and spatially. Three experiments showed that visual relations slow down the process of reasoning in comparison with control relations, whereas visuospatial and spatial relations yield inferences comparable with those of control relations. We conclude that irrelevant visual detail can be a nuisance in reasoning and can impede the process.


title: A space-efficient parallel algorithm for computing betweenness centrality in distributed memory

abstract: Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large to fit in the memory of a single processing unit, making parallel execution a necessity. Par-allelization over the vertex set of the standard algorithm, with a final reduction of the centrality for each vertex, is straightforward but requires Ω(|V|2) storage. In this paper we present a new parallelizable algorithm with low spatial complexity that is based on the best known sequential algorithm. Our algorithm requires O(|V| + |E|) storage and enables efficient parallel execution. Our algorithm is especially well suited to distributed memory processing because it can be implemented using coarse-grained parallelism. The presented time bounds for parallel execution of our algorithm on CRCW PRAM and on distributed memory systems both show good asymptotic performance. Experimental results with a distributed memory computer show the practical applicability of our algorithm.


title: A pre-clinical framework for neural control of a therapeutic upper-limb exoskeleton

abstract: In this paper, we summarize a novel approach to robotic rehabilitation that capitalizes on the benefits of patient intent and real-time assessment of impairment. Specifically, an upper-limb, physical human-robot interface (the MAHI EXO-II robotic exoskeleton) is augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy `active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. Robotic measures of motor impairment are derived from real-time sensor data from the MAHI EXO-II and the BMI. These measures can be validated through correlation with widely used clinical measures and used to drive patient-specific therapy sessions adapted to the capabilities of the individual, with the MAHI EXO-II providing assistance or challenging the participant as appropriate to maximize rehabilitation outcomes. This approach to robotic rehabilitation takes a step towards the seamless integration of BMIs and intelligent exoskeletons to create systems that can monitor and interface with brain activity and movement. Such systems will enable more focused study of various issues in development of devices and rehabilitation strategies, including interpretation of measurement data from a variety of sources, exploration of hypotheses regarding large scale brain function during robotic rehabilitation, and optimization of device design and training programs for restoring upper limb function after stroke.


title: Pose-Invariant Face Alignment via CNN-Based Dense 3D Model Fitting

abstract: Pose-invariant face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many facial analysis tasks, e.g., face recognition, expression recognition, and 3D face reconstruction. Recently, there have been a few attempts to tackle this problem, but still more research is needed to achieve higher accuracy. In this paper, we propose a face alignment method that aligns an image with arbitrary poses, by combining the powerful cascaded CNN regressors, 3D Morphable Model (3DMM), and mirrorability constraint. The core of our proposed method is a novel 3DMM fitting algorithm, where the camera projection matrix parameters and 3D shape parameters are estimated by a cascade of CNN-based regressors. Furthermore, we impose the mirrorability constraint during the CNN learning by employing a novel loss function inside the siamese network. The dense 3D shape enables us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging large-pose face databases (AFLW and AFW), with comparison to the state of the art.


title: Functional MRI studies of spatial and nonspatial working memory.

abstract: Abstract Single-unit recordings in monkeys have revealed neurons in the lateral prefrontal cortex that increase their firing during a delay between the presentation of information and its later use in behavior. Based on monkey lesion and neurophysiology studies, it has been proposed that a dorsal region of lateral prefrontal cortex is necessary for temporary storage of spatial information whereas a more ventral region is necessary for the maintenance of nonspatial information. Functional neuroimaging studies, however, have not clearly demonstrated such a division in humans. We present here an analysis of all reported human functional neuroimaging studies plotted onto a standardized brain. This analysis did not find evidence for a dorsal/ventral subdivision of prefrontal cortex depending on the type of material held in working memory, but a hemispheric organization was suggested (i.e., left—nonspatial; right—spatial). We also performed functional MRI studies in 16 normal subjects during two tasks designed to probe either nonspatial or spatial working memory, respectively. A group and subgroup analysis revealed similarly located activation in right middle frontal gyrus (Brodmann's area 46) in both spatial and nonspatial [working memory-control] subtractions. Based on another model of prefrontal organization [M. Petrides, Frontal lobes and behavior, Cur. Opin. Neurobiol., 4 (1994) 207–211], a reconsideration of the previous imaging literature data suggested that a dorsal/ventral subdivision of prefrontal cortex may depend upon the type of processing performed upon the information held in working memory.


title: On the Value of Reminders within E-Commerce Recommendations

abstract: Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely to remind users of items that they have viewed or consumed in the past. In this work, we aim to quantify the value of such reminders in recommendation lists ("recominders"), which has to our knowledge not been done in the past. We first report the results of a live experiment in which we applied a naive reminding strategy on an online platform and compare them with results obtained through different offline analyses. We then propose more elaborate reminding techniques, which aim to avoid reminders of too obvious or of already outdated items. Overall, our results show that although reminders do not lead to new item discoveries, they can be valuable both for users and service providers.


AN INTELLIGENT CONVERSATION AGENT FOR HEALTH CARE DOMAIN


title: Detecting rumors from microblogs with recurrent neural networks

abstract: Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.


title: Efficiency improvement in charge pump circuits

abstract: Conventional charge pump circuits use a fixed switching frequency that leads to power efficiency degradation for loading less than the rated loading. This paper proposes a level shifter design that also functions as a frequency converter to automatically vary the switching frequency of a dual charge pump circuit according to the loading. The switching frequency is designed to be 25 kHz with 12 mA loading on both inverting and noninverting outputs. The switching frequency is automatically reduced when loading is lighter to improve the power efficiency. The frequency tuning range of this circuit is designed to be from 100 Hz to 25 kHz. A start-up circuit is included to ensure proper pumping action and avoid latch-up during power-up. A slow turn-on, fast turn-off driving scheme is used in the clock buffer to reduce power dissipation. The new dual charge pump circuit was fabricated in a 3-/spl mu/m p-well double-poly single-metal CMOS technology with breakdown voltage of 18 V, the die size is 4.7/spl times/4.5 mm/sup 2/. For comparison, a charge pump circuit with conventional level shifter and clock buffer was also fabricated. The measured results show that the new charge pump has two advantages: (1) the power dissipation of the charge pump is improved by a factor of 32 at no load and by 2% at rated loading of 500 /spl Omega/ and (2) the breakdown voltage requirement is reduced from 19.2 to 17 V.


title: An approach to web service discovery based on the semantics

abstract: The research work in this paper focuses on solving the critical problems in Web Service discovery such as how to locate functionality-desired Web Services and how to select the best one from large numbers of functionality-similar Web Services. The semantic description and quality description of Web Service based on ontology proposed in this paper, provides a consistent description of different kinds of Web Service, which is used as the basis of service matching. Then the matching model of Web Service is discussed in detail, which consists of semantic similarity matching according to functionality of Web Service, and semantic filtering according to quality of Web Service. Using the descriptions and matching approach, the performance of Web Service discovery can be improved by increasing the precision and recall of Web Service searching.


title: Fine-grained accelerators for sparse machine learning workloads

abstract: Text analytics applications using machine learning techniques have grown in importance with ever increasing amount of data being generated from web-scale applications, social media and digital repositories. Apart from being large in size, these generated data are often unstructured and are heavily sparse in nature. The performance of these applications on current systems is hampered by hard to predict branches and low compute-per-byte ratio. This paper proposes a set of fine-grained accelerators that improve the performance and energy-envelope of these applications by an order of magnitude.


title: Deep Metric Learning for Person Re-identification

abstract: Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network has a symmetry structure with two sub-networks which are connected by a cosine layer. Each sub network includes two convolutional layers and a full connected layer. To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers. Experiments on VIPeR illustrate the superior performance of our method and a cross database experiment also shows its good generalization.


A Review on Customer Churn Prediction in Telecommunication Using Data Mining Techniques


title: Image repairing: robust image synthesis by adaptive ND tensor voting

abstract: We present a robust image synthesis method to automatically infer missing information from a damaged 2D image by tensor voting. Our method translates image color and texture information into an adaptive ND tensor, followed by a voting process that infers non-iteratively the optimal color values in the ND texture space for each defective pixel. ND tensor voting can be applied to images consisting of roughly homogeneous and periodic textures (e.g. a brick wall), as well as difficult images of natural scenes, which contain complex color and texture information. To effectively tackle the latter type of difficult images, a two-step method is proposed. First, we perform texture-based segmentation in the input image, and extrapolate partitioning curves to generate a complete segmentation for the image. Then, missing colors are synthesized using ND tensor voting. Automatic tensor scale analysis is used to adapt to different feature scales inherent in the input. We demonstrate the effectiveness of our approach using a difficult set of real images.


title: Multi-UAV Simulator Utilizing X-Plane

abstract: This paper describes the development of a simulator for multiple Unmanned Aerial Vehicles (UAVs) utilizing the commercially available simulator X-Plane and Matlab. Coordinated control of unmanned systems is currently being researched for a wide range of applications, including search and rescue, convoy protection, and building clearing to name a few. Although coordination and control of Unmanned Ground Vehicles (UGVs) has been a heavily researched area, the extension towards controlling multiple UAVs has seen minimal attention. This lack of development is due to numerous issues including the difficulty in realistically modeling and simulating multiple UAVs. This work attempts to overcome these limitations by creating an environment that can simultaneously simulate multiple air vehicles as well as provide state data and control input for the individual vehicles using a heavily developed and commercially available flight simulator (X-Plane). This framework will allow researchers to study multi-UAV control algorithms using realistic unmanned and manned aircraft models in real-world modeled environments. Validation of the system’s ability is shown through the demonstration of formation control algorithms implemented on four UAV helicopters with formation and navigation controllers built in Matlab/Simulink.


title: How to communicate the scientific consensus on climate change: plain facts, pie charts or metaphors?

abstract: Previous research has identified public perceptions of the scientific consensus on climate change as an important gateway belief. Yet, little research to date has examined how to effectively communicate the scientific consensus on climate change. In this study, we conducted an online experiment using a national quota sample to compare three approaches to communicating the scientific consensus, namely: (a) descriptive text, (b) a pie chart and (c) metaphorical representations. Results indicate that while all three approaches can significantly increase public understanding of the degree of scientific consensus, the pie chart and simple text have superior recall and are most effective across political party lines. We conclude that the scientific consensus on climate change is most effectively communicated as a short, simple message that is easy to comprehend and remember. Representing the consensus visually in the form of a pie chart appears to be particularly useful.


title: Decentralized web of trust and authentication for the internet of things

abstract: As the Internet of Thing (IoT) matures, a lot of concerns are being raised about security, privacy and interoperability. The Web of Things (WoT) model leverages web technologies to improve interoperability. Due to its distributed components, the web scaled well beyond initial expectations. Still, secure authentication and communication across organization boundaries rely on the Public Key Infrastructure (PKI) which is a non-transparent, centralized single point of failure. We can improve transparency and reduce the chain of trust---thus significantly improving the IoT security---by empowering blockchain technology and web security standards. In this paper, we build a scalable, decentralized IoT-centric PKI and discuss how we can combine it with the emerging web authentication and authorization framework for constrained environments.


title: Fast Warehouse Management System (WMS) using RFID based goods locator system

abstract: In rapidly growing e-commerce businesses, searching purchased items in the warehouse and delivering them within a very short time becomes a crucial problem. In order to solve this problem, we propose a complete RFID based goods locator system which consists of specially designed RFID reader and an integrated cloud based Warehouse Management System (WMS). The RFID reader is able to detect and sense the distance to the location of the purchased item which is attached with a RFID tag based on its RSSI information. The RFID reader is connected with the WMS application installed in a smartphone, through a Bluetooth connection. To update the inventory item information in real time, the Android based WMS application is connected to cloud based logistic and commerce database. Hence, by using this system, the time needed to search the purchased item and track the item delivery status can be shortened. As the result, it increases the efficiency of WMS system.


title: Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation

abstract: A state estimator for a quadrocopter is presented, using measurements from an accelerometer, angular rate gyroscope, and a set of ultra-wideband ranging radios. The estimator uses an extended aerodynamic model for the quadrocopter, where the full 3D airspeed is observable through accelerometer measurements. The remaining quadrocopter states, including the yaw orientation, are rendered observable by fusing ultra-wideband range measurements, under the assumption of no wind. The estimator is implemented on a standard microcontroller using readily-available, low-cost sensors. Performance is experimentally investigated in a variety of scenarios, where the quadrocopter is flown under feedback control using the estimator output.


title: Data Provenance in the Internet of Things

abstract: There is a need to create a trusted and secure IoT environment to share information, create knowledge and perform digital transactions. Trustworthy data collection, mining and fusion is vital for the successful widespread acceptance of IoT applications. This requires not only accurate, secure, and precise data collection; but also provisioning of data provenance throughout the whole life-cycle of the IoT device. To this end, this paper introduces a provenance-based trust management solution which helps in establishing a trust relationship among communicating devices in the IoT. This work extends the Internet of Things Management Platform (IoT-MP) by assuring data provenance. Thus complementing the previous IoT-MP capabilities in preserving the privacy of users in the IoT.


title: Link prediction and classification in social networks and its application in healthcare and systems biology

abstract: Prediction is one of the most attractive aspects in data mining. Link prediction has recently attracted the attention of many researchers as an effective technique to be used in graph based models in general and in particular for social network analysis due to the recent popularity of the field. Link prediction helps to understand associations between nodes in social communities. Existing link prediction-related approaches described in the literature are limited to predict links that are anticipated to exist in the future. To the best of our knowledge, none of the previous works in this area has explored the prediction of links that could disappear in the future. We argue that the latter set of links are important to know about; they are at least equally important as and do complement the positive link prediction process in order to plan better for the future. In this paper, we propose a link prediction model which is capable of predicting both links that might exist and links that may disappear in the future. The model has been successfully applied in two different though very related domains, namely health care and gene expression networks. The former application concentrates on physicians and their interactions while the second application covers genes and their interactions. We have tested our model using different classifiers and the reported results are encouraging. Finally, we compare our approach with the internal links approach and we reached the conclusion that our approach performs very well in both bipartite and non-bipartite graphs.


title: Mobile Assisted Language Learning: Review of the Recent Applications of Emerging Mobile Technologies

abstract: As mobile computing technologies have been more powerful and inclusive in people’s daily life, the issue of mobile assisted language learning (MALL) has also been widely explored in CALL research. Many researches on MALL consider the emerging mobile technologies have considerable potentials for the effective language learning. This review study focuses on the investigation of newly emerging mobile technologies and their pedagogical applications for language teachers and learners. Recent research or review on mobile assisted language learning tends to focus on more detailed applications of newly emerging mobile technology, rather than has given a broader point focusing on types of mobile device itself. In this paper, I thus reviewed recent research and conference papers for the last decade, which utilized newly emerging and integrated mobile technology. Its pedagogical benefits and challenges are discussed.


title: Cloud service oriented architecture (CSoA) for agriculture through internet of things (IoT) and big data

abstract: In the present backdrop of agriculture scenario the fruits of farming are not being enjoyed by the producer due to various obstacles that come up in the process. Hence in order to get rid off these obstacles and to see that farming becomes smart and friendly, by using the technological advancements, the present work proposed has been prepared. The proposed work which makes use of various technologies like Big Data, Internet of Things (IoT), Cloud Computing, etc is going to be a big boon to the farmer who otherwise is made to undergo a tough time in view of lack of the technology that he/she should have been adapted by this time. The proposed one will provide a number of services to the farmers that include crop management, marketing, finance management, e-commerce, web services through cloud etc. which also will reduce the unemployment problem in the youth. It also makes agriculture not only a profession for living but also a profitable sector in the globe which further enhances the GDP.


title: Rule-Based Named Entity Recognition in Urdu

abstract: Named Entity Recognition or Extraction (NER) is an important task for automated text processing for industries and academia engaged in the field of language processing, intelligence gathering and Bioinformatics. In this paper we discuss the general problem of Named Entity Recognition, more specifically the challenges in NER in languages that do not have language resources e.g. large annotated corpora. We specifically address the challenges for Urdu NER and differentiate it from other South Asian (Indic) languages. We discuss the differences between Hindi and Urdu and conclude that the NER computational models for Hindi cannot be applied to Urdu. A rule-based Urdu NER algorithm is presented that outperforms the models that use statistical learning.