title: Efficient similarity joins for near duplicate detection

abstract: With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.


title: Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

abstract: Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strongly convex problems, its convergence rate was known to be O(\log(T)/T), by running SGD for T iterations and returning the average point. However, recent results showed that using a different algorithm, one can get an optimal O(1/T) rate. This might lead one to believe that standard SGD is suboptimal, and maybe should even be replaced as a method of choice. In this paper, we investigate the optimality of SGD in a stochastic setting. We show that for smooth problems, the algorithm attains the optimal O(1/T) rate. However, for non-smooth problems, the convergence rate with averaging might really be \Omega(\log(T)/T), and this is not just an artifact of the analysis. On the flip side, we show that a simple modification of the averaging step suffices to recover the O(1/T) rate, and no other change of the algorithm is necessary. We also present experimental results which support our findings, and point out open problems.


title: Inferring Application Type Information from Tor Encrypted Traffic

abstract: Tor is a famous anonymity communication system for preserving users' online privacy. It supports TCP applications and packs application data into encrypted equal-sized cells to hide some private information of users, such as the running application type (Web, P2P, FTP, Others). The known of application types is harmful because they can be used to reduce the anonymity set and facilitate other attacks. However, unfortunately, the current Tor design cannot conceal certain application behaviors. For example, P2P applications usually upload and download files simultaneously and this behavioral feature is also kept in Tor traffic. Motivated by this observation, we investigate a new attack against Tor, traffic classification attack, which can recognize application types from Tor traffic. An attacker first carefully selects some flow features, e.g., burst volumes and directions to represent the application behaviors and takes advantage of some efficient machine learning algorithm to model different types of applications. Then these established models can be used to classify target's Tor traffic and infer its application type. We have implemented the traffic classification attack on Tor and our experiments validate the feasibility and effectiveness of the attack.


title: SELECTION OF MATERIALIZED VIEW USING QUERY OPTIMIZATION IN DATABASE MANAGEMENT : AN EFFICIENT METHODOLOGY

abstract: In large databases particularly in distributed database, query response time plays an important role as timely access to information and it is the basic requirement of successful business application. A data warehouse uses multiple materialized views to efficiently process a given set of queries. Quick response time and accuracy are important factors in the success of any database. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Selection of Materialized views is one of the most important decisions in designing a data warehouse for optimal efficiency. Selecting a suitable set of views that minimizes the total cost associated with the materialized views and is the key component in data warehousing. Materialized views are found to be very useful for fast query processing. This paper gives the results of proposed tree based materialized view selection algorithm for query processing. In distributed environment where database is distributed over the nodes on which query should get executed and also plays an important role. This paper also proposes node selection algorithm for fast materialized view selection in distributed environment. And finally it is found that the proposed methodology performs better for query processing as compared to other materialized view selection strategies.


title: Design of planar ultra-wideband antenna with polarization diversity and high isolation

abstract: This paper has presented a compact planar antenna for ultra-wideband (UWB) multiple-input-multiple-output (MIMO) applications. The polarization diversity is obtained by employing dual planar UWB antenna elements and rotating one 90 degrees with respect to the other one. The microstrip-fed element consists of a rectangular ground plane and a rectangular radiating patch. In addition, an inverted-L shaped strip is connected to the ground plane for reducing the whole size and improving the impedance matching. The results have confirmed that the proposed element occupies a size of 24×16×0.8 mm3 and achieves a wideband bandwidth from 3 to over 10 GHz. Moreover, the operating frequency band of the UWB MIMO antenna with a size of 24×44×0.8 mm3 can cover 3–10 GHz and the port-to-port isolation is around 20 dB.


title: Extracting information nuggets from disaster- Related messages in social media

abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.


title: On Adaptive Control of an Anaerobic Digestion Bioprocess

abstract: This paper presents the design and the analysis of some adaptive nonlinear control strategies for a class of depollution fermentation processes that are carried out in a continuous stirred tank bioreactor. The controllers design is based on the input-output linearization technique. The resulted control methods are applied in depollution control problem in the case of the anaerobic digestion bioprocess for which dynamical kinetics are strongly nonlinear and not exactly known, and for which not all the state variables are measurable. It must be noted that for the controlling of this very complex bioprocess it is necessary to reduce his model order. This is realized by using the singular perturbation method. Numerical simulations are included to illustrate the performances of the proposed controllers.


title: Patch-Based Near-Optimal Image Denoising

abstract: In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.


title: Dynamic CPU Resource Allocation in Containerized Cloud Environments

abstract: In recent years, lighter-weight virtualization solutions have begun to emerge as an alternative to virtual machines. Because these solutions are still in their infancy, however, several research questions remain open in terms of how to effectively manage computing resources. One important problem is the management of resources in the event of overutilization. For some applications, overutilization can severely affect performance. We provide a solution to this problem by extending the concept of timeslicing to the level of virtualization container. Through this approach we can control and mitigate some of the more detrimental performance effects oversubscription. Our results show significant improvement over standard scheduling with Docker.


IMAGE DEBLURRING BY AUGMENTED LANGRANGIAN WITH BM3D FRAME PRIOR


title: An up to 16-year prospective study of 304 porcelain veneers.

abstract: Purpose: This study aimed to prospectively analyze the outcomes of 304 feldspathic porcelain veneers prepared by the same operator, in 100 patients, that were in situ for up to 16 years. Materials and Methods: A total of 304 porcelain veneers on incisors, canines, and premolars in 100 patients completed by one prosthodontist between 1988 and 2003 were sequentially included. Preparations were designed with chamfer margins, incisal reduction, and palatal overlap. At least 80% of each preparation was in enamel. Feldspathic porcelain veneers from refractory dies were etched (hydrofluoric acid), silanated, and cemented (Vision 2, Mirage Dental Systems). Outcomes were expressed as percentages (success, survival, unknown, dead, repair, failure). The results were statistically analyzed using the chi-square test and Kaplan-Meier survival estimation. Statistical significance was set at P 2 (12.5%), caries (6%), and tooth fracture (6%). Statistically significantly fewer veneers survived as the time in situ increased. Conclusions: Feldspathic porcelain veneers, when bonded to enamel substrate, offer a predictable long-term restoration with a low failure rate. The statistical methods used to calculate the cumulative survival can markedly affect the apparent outcome and thus should be clearly defined in outcome studies.


title: Biped walking control based on hybrid position/force control

abstract: This paper describes a real-time walking control system developed for the biped robots JOHNNIE and LOLA. Walking trajectories are planned on-line using a simplified robot model and modified by a stabilizing controller. The controller uses hybrid position/force control in task space based on a resolved motion rate scheme. Inertial stabilization is achieved by modifying the contact force trajectories. The paper includes an analysis of the dynamics of controlled bipeds, which is the basis for the proposed control system. The system was tested both in forward dynamics simulations and in experiments with JOHNNIE.


title: RacerX: effective, static detection of race conditions and deadlocks

abstract: This paper describes RacerX, a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It is explicitly designed to find errors in large, complex multithreaded systems. It aggressively infers checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. It tracks a set of code features which it uses to sort errors both from most to least severe. It uses novel techniques to counter the impact of analysis mistakes. The tool is fast, requiring between 2-14 minutes to analyze a 1.8 million line system. We have applied it to Linux, FreeBSD, and a large commercial code base, finding serious errors in all of them. RacerX is a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It uses novel strategies to infer checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. We applied it to FreeBSD, Linux and a large commercial code base and found serious errors in all of them.


title: Temperature measurement in microfluidic systems using a temperature-dependent fluorescent dye.

abstract: A technique is described for the measurement of fluid temperatures in microfluidic systems based on temperature-dependent fluorescence. The technique is easy to implement with a standard fluorescence microscope and CCD camera. In addition, the method can be used to measure fluid temperatures with micrometer spatial resolution and millisecond time resolution. The efficacy of the method is demonstrated by measuring temperature distributions resulting from Joule heating in a variety of microfluidic circuits that are electrokinetically pumped. With the equipment used for these measurements, fluid temperatures ranging from room temperature to 90 degrees C were measured with a precision ranging from 0.03 to 3.5 degrees C-dependent on the amount of signal averaging done. The spatial and temporal resolutions achieved were 1 microm and 33 ms, respectively.


An examination into Australian news coverage of Papua New Guinea


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title: Count-Based Exploration with Neural Density Models

abstract: Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma's Revenge. We consider two questions left open by their work: First, how important is the quality of the density model for exploration? Second, what role does the Monte Carlo update play in exploration? We answer the first question by demonstrating the use of PixelCNN, an advanced neural density model for images, to supply a pseudo-count. In particular, we examine the intrinsic difficulties in adapting Bellemare et al.'s approach when assumptions about the model are violated. The result is a more practical and general algorithm requiring no special apparatus. We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games. One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings, including Montezuma's Revenge.


title: A randomized, controlled study of a healthy corner store initiative on the purchases of urban, low-income youth

abstract: Objective ::: Although many initiatives exist to improve the availability of healthy foods in corner stores, few randomized trials have assessed their effects. This study evaluated, in a randomized controlled trial, the effects of a first-generation healthy corner store intervention on students' food and beverage purchases over a 2-year period. ::: ::: Methods ::: Participants (n = 767) were fourth-, fifth-, and sixth-grade students. Ten schools and their nearby corner stores (n = 24) were randomly assigned to the healthy corner store intervention or an assessment-only control. Intercept surveys directly assessed the nutritional characteristics of students' corner store purchases at baseline, 1 and 2 years. Students' weight and heights were measured at baseline, 1 and 2 years. ::: ::: Results ::: There were no differences in energy content per intercept purchased from control or intervention schools at year 1 (P = 0.12) or 2 (P = 0.58). There were no differences between control and intervention students in BMI z score (year 1, P = 0.83; year 2, P = 0. 98) or obesity prevalence (year 1, P = 0.96; year 2, P = 0.58). ::: ::: Conclusions ::: A healthy corner store initiative did not result in significant changes in the energy content of corner store purchases or in continuous or categorical measures of obesity. These data will help to inform future interventions.


title: Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization

abstract: Geomagnetic field is highly applicable to indoor localization due to its pervasive spatial presence, high signal stability and infrastructure-less support. Previous work in the area often fuses it with pedometer (step counter) via particles. These approaches are computationally intensive and require strong assumptions on user behavior. To overcome the weaknesses, we propose Magil, a pedometer-free approach leveraging solely upon magnetic field for indoor localization. To the best of our knowledge, this is the first piece of work using only geomagnetism for smartphone localization. Magil is applicable to any open or complex indoor environment. In the offline phase, it continuously collects and stores geomagnetic fingerprints while a surveyor is walking in pre-defined paths covering the indoor area. In the online phase, it identifies the indoor segments whose fingerprint variations highly match with the target observations. With a modified shortest path formulation, Magil selects and connects these matched segments and obtains the target locations and paths. We have implemented Magil, and conducted extensive experiments in our university campus. Our results show that Magil outperforms many state-of-the-art schemes by a wide margin (cutting localization error by more than 30%).


Load Balancing Algorithm for Wireless Sensor Networks


title: Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting

abstract: Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result from manufacturing imperfections. Such measurements can be conducted surreptitiously in the browser and can be used to track users across applications, websites, and visits. ::: We analyze techniques to mitigate such device fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. To do this, we first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of (inaudible) audio stimulation to improve detection. We then collect measurements from a large collection of smartphones and evaluate the impact of calibration and obfuscation techniques on the classifier accuracy.


title: K-means and Hierarchical Clustering Method to Improve our Understanding of Citation Contexts

abstract: In this paper we focus of the clustering of citation contexts in scientific papers. We use two methods, k-means and hierarchical clustering to better understand the phenomenon and types of citations and to explore the multidimensional nature of the elements composing the contexts of citations in different sections of the papers. We have analyzed a data set of seven peer-reviewed academic journals published by PLOS. The obtained clusters show that the Methods section is specific in nature, regardless of the journal. A proximity between some of the journals can be observed.


title: Fully automatic generation of anatomical face simulation models

abstract: We present a fast, fully automatic morphing algorithm for creating simulatable flesh and muscle models for human and humanoid faces. Current techniques for creating such models require a significant amount of time and effort, making them infeasible or impractical. In fact, the vast majority of research papers use only a floating mask with no inner lips, teeth, tongue, eyelids, eyes, head, ears, etc.---and even those that build the full visual model would typically still lack the cranium, jaw, muscles, and other internal anatomy. Our method requires only the target surface mesh as input and can create a variety of models in only a few hours with no user interaction. We start with a symmetric, high resolution, anatomically accurate template model that includes auxiliary information such as feature points and curves. Then given a target mesh, we automatically orient it to the template, detect feature points, and use these to bootstrap the detection of corresponding feature curves. These curve correspondences are used to deform the surface mesh of the template model to match the target mesh. Then, the calculated displacements of the template surface mesh are used to drive a three-dimensional morph of the full template model including all interior anatomy. The resulting target model can be simulated to generate a large range of expressions that are consistent across characters using the same muscle activations. Full automation of this entire process makes it readily available to a wide range of users.


title: Regularized dynamic Boltzmann machine with Delay Pruning for unsupervised learning of temporal sequences

abstract: We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series. This Boltzmann machine can have infinitely many layers of units but allows exact inference and learning based on its biologically motivated structure. DyBM uses the idea of conduction delays in the form of fixed length first-in first-out (FIFO) queues, with a neuron connected to another via this FIFO queue, and spikes from a pre-synaptic neuron travel along the queue to the post-synaptic neuron with a constant period of delay. Here, we present Delay Pruning as a mechanism to prune the lengths of the FIFO queues (making them zero) by setting some delay lengths to one with a fixed probability, and finally selecting the best performing model with fixed delays. The uniqueness of structure and a non-sampling based learning rule in DyBM, make the application of previously proposed regularization techniques like Dropout or DropConnect difficult, leading to poor generalization. First, we evaluate the performance of Delay Pruning to let DyBM learn a multidimensional temporal sequence generated by a Markov chain. Finally, we show the effectiveness of delay pruning in learning high dimensional sequences using the moving MNIST dataset, and compare it with Dropout and DropConnect methods.


title: Towards User-Oriented RBAC Model

abstract: Role mining recently has attracted much attention from the role-based access control RBAC research community as it provides a machine-operated means of discovering roles from existing permission assignments. While there is a rich body of literature on role mining, we find that user experience/perception - one ultimate goal for any information system - is surprisingly ignored by the existing works. This work is the first to study role mining from the end-user perspective. Specifically, based on the observation that end-users prefer simple role assignments, we propose to incorporate to the role mining process a user-role assignment sparseness constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as two specific problems: one is user-oriented exact role mining problem RMP, which is obliged to completely reconstruct the given permission assignments, and the other is user-oriented approximate RMP, which tolerates a certain amount of deviation from the complete reconstruction. The extra sparseness constraint poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark datasets demonstrate the effectiveness of our proposed algorithm.


title: Identifying hot and cold data in main-memory databases

abstract: Main memories are becoming sufficiently large that most OLTP databases can be stored entirely in main memory, but this may not be the best solution. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). It is more economical to store the coldest records on secondary storage such as flash. As a first step towards managing cold data in databases optimized for main memory we investigate how to efficiently identify hot and cold data. We propose to log record accesses - possibly only a sample to reduce overhead - and perform offline analysis to estimate record access frequencies. We present four estimation algorithms based on exponential smoothing and experimentally evaluate their efficiency and accuracy. We find that exponential smoothing produces very accurate estimates, leading to higher hit rates than the best caching techniques. Our most efficient algorithm is able to analyze a log of 1B accesses in sub-second time on a workstation-class machine.


title: Optimus: an efficient dynamic resource scheduler for deep learning clusters

abstract: Deep learning workloads are common in today's production clusters due to the proliferation of deep learning driven AI services (e.g., speech recognition, machine translation). A deep learning training job is resource-intensive and time-consuming. Efficient resource scheduling is the key to the maximal performance of a deep learning cluster. Existing cluster schedulers are largely not tailored to deep learning jobs, and typically specifying a fixed amount of resources for each job, prohibiting high resource efficiency and job performance. This paper proposes Optimus, a customized job scheduler for deep learning clusters, which minimizes job training time based on online resource-performance models. Optimus uses online fitting to predict model convergence during training, and sets up performance models to accurately estimate training speed as a function of allocated resources in each job. Based on the models, a simple yet effective method is designed and used for dynamically allocating resources and placing deep learning tasks to minimize job completion time. We implement Optimus on top of Kubernetes, a cluster manager for container orchestration, and experiment on a deep learning cluster with 7 CPU servers and 6 GPU servers, running 9 training jobs using the MXNet framework. Results show that Optimus outperforms representative cluster schedulers by about 139% and 63% in terms of job completion time and makespan, respectively.


title: Variations of mesoscale and large-scale sea ice morphology in the 1984 Marginal Ice Zone Experiment as observed by microwave remote sensing

abstract: During the summer 1984 Marginal Ice Zone Experiment in the Fram Strait and Greenland Sea (MIZEX '84), passive and active microwave sensors on five aircraft and the Nimbus 7 scanning multichannel microwave radiometer (SMMR) acquired synoptic sequential observations which when combined give a comprehensive sequential description of the mesoscale and large-scale ice morphology variations during the period June 9 through July 16, 1984. The high-resolution ice concentration distributions in these images agree well with the low-resolution SMMR distributions. For diffuse ice edges the 30% SMMR ice concentration isopleth corresponds to the ice edge, while for compact conditions the ice edge falls within the 40 to 50% SMMR isopleths. Throughout the experiment, ice edge meanders and eddies repeatedly formed, moved, and disappeared, but the ice edge remained within a 100-km-wide zone. The ice pack behind this alternately diffuse and compact edge underwent rapid and pronounced variations in ice concentration over a 200-km-wide zone. The aircraft microwave images show the complex structures and ephemeral nature of the mesoscale sea ice morphology. The difference in oceanographic forcing between the eastern and western sectors of the experiment area generated pronounced ice morphology differences. On the Yermak Plateau, from 3°E to 10°E, the weak ocean circulation allowed the wind to be the dominant force in determining the ice morphology. To the southwest of this region, over the Molloy Deep and the Greenland continental shelf break, from 3°E to 8°W, the ice morphology was dominated by the energetic East Greenland Current with its associated eddies and meanders.


title: Eulerian emotion magnification for subtle expression recognition

abstract: Subtle emotions are expressed through tiny and brief movements of facial muscles, called micro-expressions; thus, recognition of these hidden expressions is as challenging as inspection of microscopic worlds without microscopes. In this paper, we show that through motion magnification, subtle expressions can be realistically exaggerated and become more easily recognisable. We magnify motions of facial expressions in the Eulerian perspective by manipulating their amplitudes or phases. To evaluate effects of exaggerating facial expressions, we use a common framework (LBP-TOP features and SVM classifiers) to perform 5-class subtle emotion recognition on the CASME II corpus, a spontaneous subtle emotion database. According to experimental results, significant improvements in recognition rates of magnified micro-expressions over normal ones are confirmed and measured. Furthermore, we estimate upper bounds of effective magnification factors and empirically corroborate these theoretical calculations with experimental data.


title: Advantages of high frequency PWM in AC motor drive applications

abstract: There are many advantages of using high frequency PWM (in the range of 50 to 100 kHz) in motor drive applications. High motor efficiency, fast control response, lower motor torque ripple, close to ideal sinusoidal motor current waveform, smaller filter size, lower cost filter, etc. are a few of the advantages. However, higher frequency PWM is also associated with severe voltage reflection and motor insulation breakdown issues at the motor terminals. If standard Si IGBT based inverters are employed, losses in the switches make it difficult to overcome significant drop in efficiency of converting electrical power to mechanical power. Work on SiC and GaN based inverter has progressed and variable frequency drives (VFDs) can now be operated efficiently at carrier frequencies in the 50 to 200 kHz range, using these devices. Using soft magnetic material, the overall efficiency of filtering can be improved. The switching characteristics of SiC and GaN devices are such that even at high switching frequency, the turn on and turn off losses are minimal. Hence, there is not much penalty in increasing the carrier frequency of the VFD. Losses in AC motors due to PWM waveform are significantly reduced. All the above features put together improves system efficiency. This paper presents results obtained on using a 6-in-1 GaN module for VFD application, operating at a carrier frequency of 100 kHz with an output sine wave filter. Experimental results show the improvement in motor efficiency and system efficiency on using a GaN based VFD in comparison to the standard Si IGBT based VFD.


title: gStore: a graph-based SPARQL query engine

abstract: We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.


title: Quantifying the waste reduction potential of using prefabrication in building construction in Hong Kong.

abstract: As Hong Kong is a compact city with limited available land and high land prices, the construction of high-rise buildings is prevalent. The construction industry produces a significant amount of building waste. In 2005, about 21.5 million tonnes of construction waste were generated, of which 11% was disposed of in landfills and 89% in public filling areas. At the present rate, Hong Kong will run out of both public filling areas and landfill space within the next decade. The government is taking action to tackle the problem, such as by introducing a construction waste landfill charge, and promoting prefabrication to reduce on-site waste generation. This paper reports an ongoing study on the use of prefabrication in buildings and its impact on waste reduction in Hong Kong. A questionnaire survey was administered to experienced professionals, and case studies of recently completed building projects were conducted. The results revealed that construction waste reduction is one of the major benefits when using prefabrication compared with conventional construction. The average wastage reduction level was about 52%. This implies that a wider use of prefabrication could considerably reduce construction waste generation in Hong Kong and alleviate the burdens associated with its management.


title: Safety and Liveness, Weakness and Strength, and the Underlying Topological Relations

abstract: We present a characterization that shows what it means for a formula to be a weak or strong version of another formula. We show that the weak version of a formula is not the same as Alpern and Schneider's safety component, but can be achieved by taking the closure in the Cantor topology over an augmented alphabet in which every formula is satisfiable. The resulting characterization allows us to show that the set of semantically weak formulas is exactly the set of nonpathological safety formulas. Furthermore, we use the characterization to show that the original versions of the ieee standard temporal logics psl and sva are broken, and we show that the source of the problem lies in the semantics of the sere intersection and fusion operators. Finally, we use the topological characterization to show the internal consistency of the alternative semantics adopted by the latest version of the psl standard.


title: Job Characteristics as Antecedents of Intention to Stay and Mediating Effects of Work Family Facilitation and Family Satisfaction among Single Mothers in Malaysia

abstract: Employee turnover is crucial to be investigated since it diminishes organization effectiveness and impedes the capacity to meet its goals. This study was conducted to test the mediating effects of work-family facilitation and family satisfaction on the relationship between job characteristics, and intention to stay among 240 single mother employees in Malaysia. Data was collected using self-administered research questionnaire on simple randomized respondents whereby six out of 24 single mother associations were selected to obtain research samples. Descriptive statistical analysis was conducted to describe the respondents and Pearson Product Moment Correlation to determine the relationships among variables. Structural Equation Modeling using AMOS version 16.0 was utilized for model testing and to check the presence of mediation effects in the relationships between independent and dependent variables. The Soble’s z-test was used to test whether the mediators carry the effect of the independent variables on the dependent variable. The findings indicated that single mothers have moderate levels of job characteristics in their lives, so as their levels of work-family facilitation, family satisfaction and intention to stay. There were positive and negative significant relationships among variables. The results also established the presence of mediation effects between the independent and dependent variables. Organizations may utilize work-family facilitation and family satisfaction as mechanisms to promote longer retention among employees.


title: A novel text mining approach based on TF-IDF and Support Vector Machine for news classification

abstract: With the development of weblogs and social networks, many news providers share their news headlines on different websites and weblogs. One of the main text mining topics is how to classify news into different groups. This study aims to classify news into various groups so that users can identify the most popular news group in the desired country at any given time. Based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM), a news classification method was proposed. The proposed approach is comprised of three different steps: 1) text preprocessing, 2) feature extraction based on TF-IDF, and 3) classification based on SVM. The proposed approach was evaluated using two BBC datasets and five groups of 20Newsgroup datasets. The classification precisions were obtained as 97.84% and 94.93% for BBC and 20Newsgroup datasets respectively. These are very desirable results in comparison with other classification methods.


title: Face detection techniques: a review

abstract: people die each year in roadway departure crashes caused by driver inattention. Lane detection systems are useful in avoiding these accidents as safety is the main purpose of these systems. Such systems have the goal to detect the lane marks and to warn the driver in case the vehicle has a tendency to depart from the lane. A lane detection system is an important element of many intelligent transport systems. Lane detection is a challenging task because of the varying road conditions that one can come across while driving. In the past few years, numerous approaches for lane detection were proposed and successfully demonstrated. In this paper, a comprehensive review of the literature in lane detection techniques is presented. The main objective of this paper is to discover the limitations of the existing lane detection methods. Keywordsdetection, Lane Colorization.


title: HyComp: a hybrid cache compression method for selection of data-type-specific compression methods

abstract: Proposed cache compression schemes make design-time assumptions on value locality to reduce decompression latency. For example, some schemes assume that common values are spatially close whereas other schemes assume that null blocks are common. Most schemes, however, assume that value locality is best exploited by fixed-size data types (e.g., 32-bit integers). This assumption falls short when other data types, such as floating-point numbers, are common. This paper makes two contributions. First, HyComp -- a hybrid cache compression scheme -- selects the best-performing compression scheme, based on heuristics that predict data types. Data types considered are pointers, integers, floating-point numbers and the special (and trivial) case of null blocks. Second, this paper contributes with a compression method that exploits value locality in data types with predefined semantic value fields, e.g., as in the exponent and the mantissa in floating-point numbers. We show that HyComp, augmented with the proposed floating-point-number compression method, offers superior performance in comparison with prior art.


title: Rice disease identification using pattern recognition techniques

abstract: The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants.


title: Design of compact Ku band microstrip antenna for satellite communication

abstract: In the recent years antenna design appears as a mature field of research. It really is not the fact because as the technology grows with new ideas, fitting expectations in the antenna design are always coming up. A Ku-band patch antenna loaded with notches and slit has been designed and simulated using Ansoft HFSS 3D electromagnetic simulation tool. Multi-frequency band operation is obtained from the proposed microstrip antenna. The design was carried out using Glass PTFE as the substrate and copper as antenna material. The designed antennas resonate at 15GHz with return loss over 50dB & VSWR less than 1, on implementing different slots in the radiating patch multiple frequencies resonate at 12.2GHz & 15.00GHz (Return Loss -27.5, -37.73 respectively & VSWR 0.89, 0.24 respectively) and another resonate at 11.16 GHz, 15.64GHz & 17.73 GHz with return loss -18.99, -23.026, -18.156 dB respectively and VSWR 1.95, 1.22 & 2.1 respectively. All the above designed band are used in the satellite application for non-geostationary orbit (NGSO) and fixed-satellite services (FSS) providers to operate in various segments of the Ku-band.


title: Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

abstract: Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value. ::: ::: More fundamentally---by building on anonymity of the users' reports---we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of differential privacy. We show, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying e-local differential privacy will satisfy [MATH HERE]-central differential privacy. By this, we explain how the high noise and [MATH HERE] overhead of LDP protocols is a consequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised e would indicate---at least if reports are anonymized.


title: Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

abstract: Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We consider spatial contexts, for which we solve so-called jigsaw puzzles, i.e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration. Existing approaches formulated it as a classification task by defining a fixed mapping from a small subset of configurations to a class set, but these approaches ignore the underlying relationship between different configurations and also limit their applications to more complex scenarios. This paper presents a novel approach which applies to jigsaw puzzles with an arbitrary grid size and dimensionality. We provide a fundamental and generalized principle, that weaker cues are easier to be learned in an unsupervised manner and also transfer better. In the context of puzzle recognition, we use an iterative manner which, instead of solving the puzzle all at once, adjusts the order of the patches in each step until convergence. In each step, we combine both unary and binary features of each patch into a cost function judging the correctness of the current configuration. Our approach, by taking similarity between puzzles into consideration, enjoys a more efficient way of learning visual knowledge. We verify the effectiveness of our approach from two aspects. First, it solves arbitrarily complex puzzles, including high-dimensional puzzles, that prior methods are difficult to handle. Second, it serves as a reliable way of network initialization, which leads to better transfer performance in visual recognition tasks including classification, detection and segmentation.


title: When gene meets cloud: Enabling scalable and efficient range query on encrypted genomic data

abstract: As the cost of human full genome sequencing continues to fall, we will soon witness a prodigious amount of human genomic data in the public cloud. To protect the confidentiality of the genetic information of individuals, the data has to be encrypted at rest. On the other hand, encryption severely hinders the use of this valuable information, such as Genome-wide Range Query (GRQ), in medical/genomic research. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from practical deployment in terms of efficiency and scalability due to the data volume in human genome sequencing. In this paper, we investigate the problem of secure GRQ over human raw aligned genomic data in a third-party outsourcing model. Our solution contains a novel secure range query scheme based on multi-keyword symmetric searchable encryption (MSSE). The proposed scheme incurs minimal ciphertext expansion and computation overhead. We also present a hierarchical GRQ-oriented secure index structure tailored for efficient and large-scale genomic data lookup in the cloud while preserving the query privacy. Our experiment on real human genomic data shows that a secure GRQ request with range size 100,000 over more than 300 million encrypted short reads takes less than 3 minutes, which is orders of magnitude faster than existing solutions.


title: Effective query formulation with multiple information sources

abstract: Most standard information retrieval models use a single source of information (e.g., the retrieval corpus) for query formulation tasks such as term and phrase weighting and query expansion. In contrast, in this paper, we present a unified framework that automatically optimizes the combination of information sources used for effective query formulation. The proposed framework produces fully weighted and expanded queries that are both more effective and more compact than those produced by the current state-of-the-art query expansion and weighting methods. We conduct an empirical evaluation of our framework for both newswire and web corpora. In all cases, our combination of multiple information sources for query formulation is found to be more effective than using any single source. The proposed query formulations are especially advantageous for large scale web corpora, where they also reduce the number of terms required for effective query expansion, and improve the diversity of the retrieved results.


title: eButton: A wearable computer for health monitoring and personal assistance

abstract: Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.


title: Web services and business process management

abstract: Web services based on the service-oriented architecture framework provide a suitable technical foundation for making business processes accessible within enterprises and across enterprises. But to appropriately support dynamic business processes and their management, more is needed, namely, the ability to prescribe how Web services are used to implement activities within a business process, how business processes are represented as Web services, and also which business partners perform what parts of the actual business process. In this paper, the relationship between Web services and the management of business processes is worked out and presented in a tutorial-like manner.


Recommender Systems meet Finance: a Literature Review


title: Mobile Application Development for Quran Verse Recognition and Interpretations

abstract: Mobile learning or “m-learning” is the process of learning when learners are not at a fixed location or time and can exploit the advantage of learning opportunities using mobile technologies. Nowadays, speech recognition is being used in many mobile applications. Speech recognition helps people to interact with the device as if were they talking to another person. This technology helps people to learn anything using computers by promoting self-study over extended periods of time. The objective of this study is focusing on designing and developing a mobile application for the Arabic recognition of spoken Quranic verses. The application is suitable for Android-based devices. The application is called Say Quran and is available on Google Play Store. Moreover, this paper presents the results of a preliminary experimentation to gather feedback from students regarding the developed application


title: Big Sensor Data Systems for Smart Cities

abstract: Recent advances in large-scale networked sensor technologies and the explosive growth in big data computing have made it possible for new application deployments in smart cities ecosystems. In this paper, we define big sensor data systems, and survey progress made in the development and applications of big sensor data research. We classify the existing research based on their characteristics and smart city layer challenges. Next, we discuss several applications for big sensor data systems, and explore the potential of large-scale networked sensor technologies for smart cities in the big data era. We conclude this paper by discussing future work directions highlighting some futuristic applications. The aim of this survey paper is to be useful for researchers to get insights into this important area, and motivate the development of practical solutions toward deployment in smart cities.


A framework for identifying software project risks


title: Internet inter-domain traffic

abstract: In this paper, we examine changes in Internet inter-domain traffic demands and interconnection policies. We analyze more than 200 Exabytes of commercial Internet traffic over a two year period through the instrumentation of 110 large and geographically diverse cable operators, international transit backbones, regional networks and content providers. Our analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies. Specifically, we find the majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks. We also show significant changes in Internet application usage, including a global decline of P2P and a significant rise in video traffic. We conclude with estimates of the current size of the Internet by inter-domain traffic volume and rate of annualized inter-domain traffic growth.


title: Novelty and redundancy detection in adaptive filtering

abstract: This paper addresses the problem of extending an adaptive information filtering system to make decisions about the novelty and redundancy of relevant documents. It argues that relevance and redundance should each be modelled explicitly and separately. A set of five redundancy measures are proposed and evaluated in experiments with and without redundancy thresholds. The experimental results demonstrate that the cosine similarity metric and a redundancy measure based on a mixture of language models are both effective for identifying redundant documents.


title: Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations

abstract: The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an ongoing limitation for studies. The availability of remote sensing data has therefore helped fill a scientific need by providing high resolution temperature data of our cities. However, satellite-mounted sensors measure land surface temperatures (LST) and not canopy air temperatures with the latter being the key parameter in UHI investigations. Fortunately, such data is becoming increasingly available via urban meteorological networks, which now provide an opportunity to quantify and compare surface and canopy UHI on an unprecedented scale. For the first time, this study uses high resolution air temperature data from the Birmingham Urban Climate Laboratory urban meteorological network and MODIS LST to quantify and identify the spatial pattern of the daytime and night-time UHI in Birmingham, UK (a city with an approximate population of 1 million). This analysis is performed under a range of atmospheric stability classes and investigates the relationship between surface and canopy UHI in the city. A significant finding of this work is that it demonstrates, using observations, that the distribution of the surface UHI appears to be clearly linked to landuse, whereas for canopy UHI, advective processes appear to play an increasingly important role. Strong relationships were found between air temperatures and LST during both the day and night at a neighbourhood scale, but even with the use of higher resolution urban meteorological datasets, relationships at the city scale are still limited.


title: Ontology Learning from Incomplete Semantic Web Data by BelNet

abstract: Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand and capture the semantics of the data on the one hand, and to handle incompleteness during the learning procedure on the other hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and corresponding lypropose the ontology learning techniques in it, (ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.


title: Resource Aligning Multiple Genomic Sequences With the Threaded Blockset Aligner

abstract: We define a "threaded blockset," which is a novel generalization of the classic notion of a multiple alignment. A new computer program called TBA (for "threaded blockset aligner") builds a threaded blockset under the assumption that all matching segments occur in the same order and orientation in the given sequences; inversions and duplications are not addressed. TBA is designed to be appropriate for aligning many, but by no means all, megabase-sized regions of multiple mammalian genomes. The output of TBA can be projected onto any genome chosen as a reference, thus guaranteeing that different projections present consistent predictions of which genomic positions are orthologous. This capability is illustrated using a new visualization tool to view TBA-generated alignments of vertebrate Hox clusters from both the mammalian and fish perspectives. Experimental evaluation of alignment quality, using a program that simulates evolutionary change in genomic sequences, indicates that TBA is more accurate than earlier programs. To perform the dynamic-programming alignment step, TBA runs a stand-alone program called MULTIZ, which can be used to align highly rearranged or incompletely sequenced genomes. We describe our use of MULTIZ to produce the whole-genome multiple alignments at the Santa Cruz Genome Browser.


title: The tempotron: a neuron that learns spike timing–based decisions

abstract: The timing of action potentials in sensory neurons contains substantial information about the eliciting stimuli. Although the computational advantages of spike timing-based neuronal codes have long been recognized, it is unclear whether, and if so how, neurons can learn to read out such representations. We propose a new, biologically plausible supervised synaptic learning rule that enables neurons to efficiently learn a broad range of decision rules, even when information is embedded in the spatiotemporal structure of spike patterns rather than in mean firing rates. The number of categorizations of random spatiotemporal patterns that a neuron can implement is several times larger than the number of its synapses. The underlying nonlinear temporal computation allows neurons to access information beyond single-neuron statistics and to discriminate between inputs on the basis of multineuronal spike statistics. Our work demonstrates the high capacity of neural systems to learn to decode information embedded in distributed patterns of spike synchrony.


Overweight and Obesity in Children and Adolescents


title: What affects students’ acceptance and use of technology?

abstract: Students’ acceptance and use of technologies introduced in their academic environments is an important factor in determining the success of these technologies. This research project used the Unified Theory of Acceptance and Use of Technology (UTAUT), a prominent technology acceptance and use model, as a theoretical basis to conduct empirical research testing the factors that influence students’ acceptance and use of technology in their academic environment. ::: Although several studies applied UTAUT in various organizational and cultural contexts, very few implement the full model and examine all of its constructs. By focusing on students in higher-education and technology that is new to them, and by using the full UTAUT model with no changes or elimination of constructs, this study places itself in the area where there are no exhaustive studies. Also, this research addresses the question of whether UTAUT is applicable in an academic environment for educational technologies introduced to the students. ::: The study found a mixed support for UTAUT. Performance expectancy, effort expectancy, facilitating conditions and attitude towards using technology were significant determinants of technology acceptance. All moderating variables had a significant moderating influence except for experience, which was surprisingly not significant. The UTAUT model is found to be applicable to some extent in the educational setting, but might need a few modifications to fit the context. ::: The study provides recommendations to help prepare the appropriate environment and training before a new technology is introduced for students. It also provides recommendation for further research examining students’ acceptance and use of technology.


title: Empowering Low-Power Wide Area Networks in Urban Settings

abstract: Low-Power Wide Area Networks (LP-WANs) are an attractive emerging platform to connect the Internet-of-things. LP-WANs enable low-cost devices with a 10-year battery to communicate at few kbps to a base station, kilometers away. But deploying LP-WANs in large urban environments is challenging, given the sheer density of nodes that causes interference, coupled with attenuation from buildings that limits signal range. Yet, state-of-the-art techniques to address these limitations demand inordinate hardware complexity at the base stations or clients, increasing their size and cost. This paper presents Choir, a system that overcomes challenges pertaining to density and range of urban LP-WANs despite the limited capabilities of base station and client hardware. First, Choir proposes a novel technique that aims to disentangle and decode large numbers of interfering transmissions at a simple, single-antenna LP-WAN base station. It does so, perhaps counter-intuitively, by taking the hardware imperfections of low-cost LP-WAN clients to its advantage. Second, Choir exploits the correlation of sensed data collected by LP-WAN nodes to collaboratively reach a faraway base station, even if individual clients are beyond its range. We implement and evaluate Choir on USRP N210 base stations serving a 10 square kilometer area surrounding Carnegie Mellon University campus. Our results reveal that Choir improves network throughput of commodity LP-WAN clients by 6.84 x and expands communication range by 2.65 x.


title: Abstract Highly Automatic Approach to Architectural Floorplan Image Understanding & Model Generation

abstract: Authoring of a 3D model using computers typically involves tedious manual efforts. In this paper, we propose a highly automated approach to generate the 3D model of a building from its 2D floorplan image. The proposed system treats the floorplan as a vector image in that the lines in the plan are converted into vector outlines. Building wall identification is thus reduced to a matching problem of the outlines in the plan. A number of advantages are resulted from using this approach. For example, special constructs such as doors can be recognized through the geometric characteristics of the line signatures. The concept of interior space can also be explored through examining the line loop within the plan. Our system allows the generation of building interior as well as the exterior model. The presented algorithm is useful for data generation in architectural walkthrough of city scenes as well as 3D games development.


title: Software metrics : usability and evaluation of software quality

abstract: It is difficult to understand, let alone improve, the quality of software without the knowledge of its software development process and software products. There must be some measurement process to predict the software development, and to evaluate the software products. This thesis provides a brief view on Software Quality, Software Metrics, and Software Metrics methods that will predict and measure the specified quality factors of software. It further discusses about the Quality as given by the standards such as ISO, principal elements required for the Software Quality and Software Metrics as the measurement technique to predict the Software Quality. This thesis was performed by evaluating a source code developed in Java, using Software Metrics, such as Size Metrics, Complexity Metrics, and Defect Metrics. Results show that, the quality of software can be analyzed, studied and improved by the usage of software metrics.


title: A framework for evaluating customer satisfaction

abstract: Along with the activity of human on social media platform increasing, social media analysis has been widely used in various fields' research, including sentiment analysis. In this paper, we propose a framework to evaluate customer satisfaction on the basis of the data from social media platform and the technology of sentiment analysis. Evaluating customer satisfaction based on the comments from social media platform is very authentic and reliable for the reason that people prefer to tell the true feeling in social media rather than in business survey. Thus our analysis result can help potential customer pick out the most suitable one according to their demands when facing so many choices. And furthermore, the result can provide great valuable insight for companies to improve their services. For better demonstrate our approach, we take six American airlines as example to explain the process minutely and our source data is collected from Twitter.


title: Analysis and suppression of conducted EMI emissions for front-end LLC resonant DC/DC converters

abstract: LLC resonant DC/DC converters are becoming popular in computing applications, such as telecom, server systems. For these applications, it is required to meet the EMI standard. In this paper, novel EMI noise transferring path and EMI model for LLC resonant DC/DC converters are proposed. DM and CM noise of LLC resonant converter are analyzed. Several EMI noise reduction approaches are proposed. Shield layers are applied to reduce CM noise. By properly choosing the ground point of shield layer, significant noise reduction can be obtained. With extra EMI balance capacitor, CM noise can be reduced further. Two channel interleaving LLC resonant converters are proposed to cancel the CM current. Conceptually, when two channels operate with 180 degree phase shift, CM current can be canceled. Therefore, the significant EMI noise reduction can be achieved.


title: Advances of Scene Text Datasets

abstract: This article introduces publicly available datasets in scene text detection and recognition. The information is as of 2017.


title: TOWARD OPTIMAL FEATURE SELECTION USING RANKING METHODS AND CLASSIFICATION ALGORITHMS

abstract: We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.


title: Spontaneous speech emotion recognition using prior knowledge

abstract: Automatic and spontaneous speech emotion recognition is an important part of a human-computer interactive system. However, emotion identification in spontaneous speech is difficult because most often the emotion expressed by the speaker are not necessarily as prominent as in acted speech. In this paper, we propose a spontaneous speech emotion recognition framework that makes use of the associated knowledge. The framework is motivated by the observation that there is significant disagreement amongst human annotators when they annotate spontaneous speech; the disagreement largely reduces when they are provided with additional knowledge related to the conversation. The proposed framework makes use of the contexts (derived from linguistic contents) and the knowledge regarding the time lapse of the spoken utterances in the context of an audio call to reliably recognize the current emotion of the speaker in spontaneous audio conversations. Our experimental results demonstrate that there is a significant improvement in the performance of spontaneous speech emotion recognition using the proposed framework.


title: Trends, challenges and needs for lattice-based cryptography implementations: special session

abstract: Advances in computing steadily erode computer security at its foundation, calling for fundamental innovations to strengthen the weakening cryptographic primitives and security protocols. At the same time, the emergence of new computing paradigms, such as Cloud Computing and Internet of Everything, demand that innovations in security extend beyond their foundational aspects, to the actual design and deployment of these primitives and protocols while satisfying emerging design constraints such as latency, compactness, energy efficiency, and agility. While many alternatives have been proposed for symmetric key cryptography and related protocols (e.g., lightweight ciphers and authenticated encryption), the alternatives for public key cryptography are limited to post-quantum cryptography primitives and their protocols. In particular, lattice-based cryptography is a promising candidate, both in terms of foundational properties, as well as its application to traditional security problems such as key exchange, digital signature, and encryption/decryption. We summarize trends in lattice-based cryptographic schemes, some fundamental recent proposals for the use of lattices in computer security, challenges for their implementation in software and hardware, and emerging needs.


title: Adaptive Dispatching of Tasks in the Cloud

abstract: The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.


title: A Surface Wave Holographic Antenna for Broadside Radiation Excited by a Traveling Wave Patch Array

abstract: A surface wave based hologram fed by a 1-D traveling wave (series-fed) microstrip patch array to achieve broadside radiation is presented. This antenna may alternatively be described as a periodic surface wave based leaky-wave antenna (LWA) excited by a fundamental mode periodic microstrip LWA. Broadside beam in the E-plane is formed by the symmetric holographic surface. The radiation beam scans with frequency in the H-plane from backward to forward direction through broadside. A reflection compensation technique that facilitates the broadside beam for the series-fed array is introduced. Measured maximum peak radiation at broadside in both E and H-planes is achieved with 17.7 dBi antenna gain at approximately 7.7 GHz.


title: Using ontologies to improve semantic interoperability in health data

abstract: The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.


title: A New Phase Correction Method in NMR Imaging Based on Autocorrelation and Histogram Analysis

abstract: A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogram of phase distribution of the first-order corrected image. Since all the correction procedures are performed on the spatial domain after completion of data acquisition, no prior adjustments or additional measurements are required. The algorithm can be applicable to most of the phase-involved NMR imaging techniques including inversion recovery imaging, quadrature modulated imaging, spectroscopic imaging, and flow imaging, etc. Some experimental results with inversion recovery imaging as well as quadrature spectroscopic imaging are shown to demonstrate the usefulness of the algorithm.


title: Wiener structure based model identification for an electronic throttle body

abstract: A model of Wiener structure is presented in this work to represent the characteristics of an electronic throttle body. The proposed model consists of a linear submodel, which is used to capture the electric motor dynamics of the throttle body, and a nonlinear submodel, which is used to represent the nonlinearities in friction and spring torques. The relationship between the proposed model and the physics based model is analyzed. Model parameter identification and validation are demonstrated using open-loop measurement data.


title: Methodologies to Optimize Automated Guided Vehicle Scheduling and Routing Problems: A Review Study

abstract: Automated guided vehicles (AGVs) are used as a material handling device in flexible manufacturing systems. Traditionally, AGVs were mostly used at manufacturing systems, but currently other applications of AGVs are extensively developed in other areas, such as warehouses, container terminals and transportation systems. This paper discusses literature related to different methodologies to optimize AGV systems for the two significant problems of scheduling and routing at manufacturing, distribution, transshipment and transportation systems. We categorized the methodologies into mathematical methods (exact and heuristics), simulation studies, meta-heuristic techniques and artificial intelligent based approaches.


title: Evaluation of machine translation and its evaluation

abstract: Abstract : Evaluation of MT evaluation measures is limited by inconsistent human judgment data. Nonetheless, machine translation can be evaluated using the well-known measures precision, recall, and their average, the F-measure. The unigrambased F-measure has significantly higher correlation with human judgments than recently proposed alternatives. More importantly, this standard measure has an intuitive graphical interpretation, which can facilitate insight into how MT systems might be improved.


title: Bike-Share Systems: Accessibility and Availability

abstract: The cities of Paris, London, Chicago, and New York (among many others) have set up largescale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact on bike-share ridership of two facets of system performance: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). Our analysis is based on a structural demand model for spatially differentiated products that includes distinct mechanisms for the short and long-term effects of bike-availability (via lost sales and increased user-interest, respectively). The bike-share context, and the distinct mechanisms require us to go beyond past work in incorporating real time changes in product (bike)-availability information, and including much finer data on potential demand sources. These enhancements render traditional estimation methods computationally infeasible; we transform our estimation from the time domain to the “local-stockout- state” domain to address this. Our estimates for the Velib’ bike-share system in Paris suggest that a 10% increase in station density would increase ridership by 5.09% (±0.45%), while a 10% increase in bike-availability would increase ridership by 12.29% (±0.39%), three-fourths of which comes from fewer lost-sales, and the rest from increased user interest. We illustrate the use of our estimates in identifying neighborhoods and times to target for improvements, and in comparing alternate operational improvements and station networks.


title: A Tutorial on White-box AES

abstract: White-box cryptography concerns the design and analysis of implementations of cryptographic algorithms engineered to execute on untrusted platforms. Such implementations are said to operate in a white-box attack context. This is an attack model where all details of the implementation are completely visible to an attacker: not only do they see input and output, they see every intermediate computation that happens along the way. The goal of a white-box attacker when targeting an implementation of a cipher is typically to extract the cryptographic key; thus, white-box implementations have been designed to thwart this goal (i.e., to make key extraction difficult/infeasible). The academic study of white-box cryptography was initiated in 2002 in the seminal work of Chow et al. (White-box cryptography and an AES implementation. In: Selected areas in cryptography: 9th annual international workshop, SAC 2002. Lecture notes in computer science, vol 2595, pp 250–270, 2003). Here, we review the first white-box AES implementation proposed by Chow et al. and give detailed information on how to construct it. We provide a number of diagrams that summarize the flow of data through the various look-up tables in the implementation, which helps clarify the overall design. We then briefly review the impressive 2004 cryptanalysis by Billet et al. (Cryptanalysis of a white box AES implementation. In: Selected areas in cryptography: 11th international workshop, SAC 2004. Lecture notes in computer science, vol 3357, pp 227–240, 2005). The BGE attack can used to extract an AES key from Chow et al.’s original white-box AES implementation with a work factor of about 230, and this fact has motivated subsequent work on improved AES implementations.


title: Comparative Study of Authenticated Encryption Targeting Lightweight IoT Applications

abstract: This article focuses on detecting attacks to power system with the help of cyber-physical co-modeling. The foundational algorithm used to detect attacks is a new dynamic state estimator that can provide real-tie models of the system improving over legacy state estimators and three-phase linear state estimators. This new system can help operators of the power grid detect when device settings have been tampered, and help identified the context of a command (i.e., under which conditions of the system are specific commands allowed). —Alvaro Cardenas, University of Texas at Dallas


title: Revisit Word Embeddings with Semantic Lexicons for Modeling Lexical Contrast

abstract: It is widely accepted that traditional word embedding models, which rely on distributional semantics hypothesis, are relatively limited for contrast meaning problem. Distributional semantics hypothesis indicates that words lying in similar contexts have similar representations in vector space. Nevertheless, synonyms and antonyms often locate in similar contexts, which means they appear close to each other in vector space. Hence, it is of great difficulty to distinguish antonyms from synonyms. To address this challenge, we propose an optimization model, named Lexicon-based Word Embedding Tuning (LWET) model. The goal of LWET is to incorporate reliable semantic lexicons to tune the distributions of pre-trained word embeddings in the vector space so as to improve their ability of distinguishing antonyms from synonyms. To speed up the training process of LWET, we propose two approximation algorithms, including positive sampling and quasi-hierarchical softmax. Compared with quasi-hierarchical softmax, positive sampling is faster, however, at the cost of worse performance. In experiments, LWET and other state-of-the-art models are tested on antonyms recognition, distinguishing antonyms from synonyms and word similarity. The results of the first two experiments show that LWET significantly improves the ability of word embeddings to detect antonyms, thus achieving the state-of-the-art performance. On word similarity, LWET gets slightly better performance than the state-of-the-art models. It means that LWET can remain and strengthen the semantic structure rather than destroy it when tuning word distributions in vector space. In general, compared with related work, LWET can not only achieve similar or even better performance, but also speed up the training process.


title: A 0.03mm2, 40nm CMOS 1.5GS/s all-digital complementary PWM-GRO

abstract: Nonlinearity is one of the main problems associated with VCO based noise shaping ADCs. Their open loop architecture does not permit correction of the nonlinear voltage to frequency response of the VCO by feedback. Recently, linearization of a VCO ADC by Pulse Width Modulation (PWM) pre-coding has been proposed. This work presents an area- and power-efficient realization of a new complementary PWM Gated Ring Oscillator (GRO) that can be used as input stage of an all-digital multi-stage ADC. Here, the analog input signal is encoded by two PWM modulators to drive two GRO's with a 2-level signal in a complementary way, thus eliminating the nonlinearity of the VCO's. In order to reduce power consumption the PWM modulator is completely self-biased thus reducing influence of PVT variations and eliminating the need for a voltage reference. In order to show the efficiency of this new architecture a prototype has been fabricated and measured. The differential output of the PWM-GRO shows first order noise shaping and clocked at 1.5GHz consumes only 2.15mW from a single 1.1V supply. Integrating the output noise in a 20MHz bandwidth the equivalent FoM is 105fJ/conversion-step. The design occupies 0.03mm2 in a 40nm CMOS process.


title: Investigating the Use of Code Analysis and NLP to Promote a Consistent Usage of Identifiers

abstract: Meaningless identifiers as well as inconsistent use of identifiers in the source code might hinder code readability and result in increased software maintenance efforts. Over the past years, effort has been devoted to promoting a consistent usage of identifiers across different parts of a system through approaches exploiting static code analysis and Natural Language Processing (NLP). These techniques have been evaluated in small-scale studies, but it is unclear how they compare to each other and how they complement each other. Furthermore, a full-fledged larger empirical evaluation is still missing.,,We aim at bridging this gap. We asked developers of five projects to assess the meaningfulness of the recommendations generated by three techniques, two already existing in the literature (one exploiting static analysis, one using NLP) and a novel one we propose. With a total of 922 rename refactorings evaluated, this is, to the best of our knowledge, the largest empirical study conducted to assess and compare rename refactoring tools promoting a consistent use of identifiers. Our study sheds light on the current state-of-the-art in rename refactoring recommenders, and indicates directions for future work.


title: Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer

abstract: The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use. In addition, two different CNN based NAO robot detectors that are able to run in real-time while playing soccer are proposed. One of the detectors is based on the XNOR-Net and the other on the SqueezeNet. Each detector is able to process a robot object-proposal in ~1ms, with an average number of 1.5 proposals per frame obtained by the upper camera of the NAO. The obtained detection rate is ~97%.


title: Learning Word Importance with the Neural Bag-of-Words Model

abstract: The Neural Bag-of-Words (NBOW) model ::: performs classification with an average of ::: the input word vectors and achieves an impressive ::: performance. While the NBOW ::: model learns word vectors targeted for ::: the classification task it does not explicitly ::: model which words are important for ::: given task. In this paper we propose an ::: improved NBOW model with this ability ::: to learn task specific word importance ::: weights. The word importance weights ::: are learned by introducing a new weighted ::: sum composition of the word vectors. ::: With experiments on standard topic and ::: sentiment classification tasks, we show ::: that (a) our proposed model learns meaningful ::: word importance for a given task (b) ::: our model gives best accuracies among the ::: BOW approaches. We also show that the ::: learned word importance weights are comparable ::: to tf-idf based word weights when ::: used as features in a BOWSVM classifier.


title: Abacus: A Processor Family for Education

abstract: We present the Abacus processor family and its compiler framework for the MiniC language that we have developed for teaching processor architectures. Besides typical RISC instructions, Abacus also offers instructions for vector processing and thread synchronization, but it is still small enough to be discussed completely in a class. With reasonable effort, students can therefore modify given implementations of micro-architectures and code generators to deepen their understanding of the theoretical concepts. Moreover, using benchmark examples, they can explore the quantitative aspects of their optimizations. In contrast to commercial and other educational processors, we provide many micro-architectures that are based on a pure concept only rather than on a combination of concepts, and we provide code generators which contain the core ideas of some architectures.


LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression


A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials


Oxidative stress inhibits distant metastasis by human melanoma cells


title: A review of channel selection algorithms for EEG signal processing

abstract: Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.


title: Semantics based service orchestration in IoT

abstract: Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual "things" communicate and share information. There arises a need to allow heterogeneous "things" to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the "things". In IoT context, service orchestration refers to identifying which devices or software components are required to formulate the requested service. IoT devices can offer different services based on the context and semantics. Service orchestration provides an approach to integration and interoperability which decouples applications from each other, and enhances capabilities to centrally manage and monitor components. This paper investigates requirements for semantic interoperability and exposes current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The paper proposes a semantic platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The proposed platform provides a common ontology-based framework for representing semantics allowing for a consistent information exchange format. The approach used combines domain-specific ontologies to obtain device and service abstraction for the purposes of process mining and analysis of service orchestration in heterogonous IoT environments. This approach was evaluated using a prototype IoT environment and mining techniques developed in the Process Mining Framework (ProM). The results revealed that semantically enriching service annotation and device descriptions through ontologies can successfully enable interoperability in heterogeneous IoT environments. Semantically enriched descriptions enable multiple perspectives of analysis on service orchestration processes and expose the steps involved in service request and provision in an a typical IoT setup.


title: A Single-Chip 64-Channel Ultrasound RX-Beamformer Including Analog Front-End and an LUT for Non-Uniform ADC-Sample-Clock Generation

abstract: A 64-channel RX digital beamformer was implemented in a single chip for 3-D ultrasound medical imaging using 2-D phased-array transducers. The RX beamformer chip includes 64 analog front-end branches including 64 non-uniform sampling ADCs, a FIFO/Adder, and an on-chip look-up table (LUT). The LUT stores the information on the rising edge timing of the non-uniform ADC sampling clocks. To include the LUT inside the beamformer chip, the LUT size was reduced by around 240 times by approximating an ADC-sample-time profile w.r.t. focal points (FP) along a scanline (SL) for a channel into a piece-wise linear form. The maximum error between the approximated and accurate sample times of ADC is eight times the sample time resolution (Ts) that is 1/32 of the ultrasound signal period in this work. The non-uniform sampling reduces the FIFO size required for digital beamforming by around 20 times. By applying a 9-dot image from Field-II program and 2-D ultrasound phantom images to the fabricated RX beamformer chip, the original images were successfully reconstructed from the measured output. The chip in a 0.13-um CMOS occupies 30.25 mm2 and consumes 605 mW.


title: Heart disease prediction using data mining techniques

abstract: The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease prediction. This paper has analyzed prediction systems for Heart disease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart disease.


title: Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions

abstract: Abstract Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable “networks” or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on “rest” and task human functional MRI data by employing a mixed-membership Bayesian model that allows each brain region to belong to all communities simultaneously with varying membership strengths. The approach allowed us to 1) compare the structure of disjoint and overlapping communities; 2) determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities); 3) characterize overlapping community structure; 4) characterize the degree of non-modularity in brain networks; 5) study the distribution of “bridges”, including bottleneck and hub bridges. Our findings revealed the existence of dense community overlap that was not limited to “special” hubs. Furthermore, the findings revealed important differences between community organization during rest and during specific task states. Overall, we suggest that dense overlapping communities are well suited to capture the flexible and task dependent mapping between brain regions and their functions.


title: Integrating perceived playfulness into expectation-confirmation model for web portal context

abstract: This paper investigated the value of including ''playfulness'' in expectation-confirmation theory (ECT) when studying continued use of a web site. Original models examined cognitive beliefs and effects that influence a person's intention to continue to use an information system. Here, an extended ECT model (with an additional relationship between perceived playfulness and satisfaction) was shown to provide a better fit than a simple path from perceived usefulness to satisfaction. The results indicated that perceived playfulness, confirmation to satisfaction, and perceived usefulness all contributed significantly to the users' intent to reuse a web site. Thus, we believe that the extended ECT model is an appropriate tool for the study of web site effects.


title: Long-Term prediction of discharges in manwan reservoir using artificial neural network models

abstract: Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide better accuracy in forecasting river flow than does the auto-regression time series model. In particular, the scaled conjugate gradient algorithm furnishes the highest correlation coefficient and the smallest root mean square error. This ANN model is finally employed in the advanced water resource project of Yunnan Power Group.


Social Media Competition: Differentiation with User-Generated Content


Rendered by www.RenderX.com MusicXML: An Internet-Friendly Format for Sheet Music


title: Using Multi-armed Bandit to Solve Cold-Start Problems in Recommender Systems at Telco

abstract: Recommending best-fit rate-plans for new users is a challenge for the Telco industry. Rate-plans differ from most traditional products in the way that a user normally only have one product at any given time. This, combined with no background knowledge on new users hinders traditional recommender systems. Many Telcos today use either trivial approaches, such as picking a random plan or the most common plan in use. The work presented here shows that these methods perform poorly. We propose a new approach based on the multi-armed bandit algorithms to automatically recommend rate-plans for new users. An experiment is conducted on two different real-world datasets from two brands of a major international Telco operator showing promising results.