title: LevelDB-Raw: Eliminating file system overhead for optimizing performance of LevelDB engine

abstract: Key-value store, a kind of NoSQL database, is data storage system for storing and retrieving key-value pairs using unique keys. In this paper, we present LevelDB-Raw, which improves LevelDB, one of the most well-known key-value store engines. LevelDB-Raw opens a raw block device file directly, bypassing the existing file system layer. Instead, it implements a simple, light-weight user-level file system to eliminate file system overhead such as redundant journaling and metadata management. We demonstrate the performance improvement of LevelDB-Raw using ForestDB-Benchmark and YCSB workloads. The results indicate that LevelDB-Raw increases the overall performance by 1.16x–3.45x in HDD and 1.05x–2.26x in SSD for updating database.


title: Advances and breakthroughs in radars and phased-arrays

abstract: Moore's Law is slowing down but the transistor density is expected to increase by ∼50 in the next 30 years while the power consumed by each transistors will decrease by ∼75. Potential further continuation of Moore's Law via: 1. Spintronics — which could revolutionize computer architecture away from von Neumann model, 2. Memristors — which potentially allows one to do what human brain does in a shoe box instead of a computer the size of a city requiring a nuclear power plant, 3. Graphene with 1 The clock speed, and 4. Quantum Computing. Advances made in meta materials. Iowa Un. demonstrated 6 dB stealthing from 8–10 GHz using thin, stretchable meta material skin that can be conformal on target. The Army Research Laboratory in Adelphi MD has developed a low profile λ/20 thick meta material 250–505 MHZ antenna. Kymeta demonstrated December 2013 transmission to satellites and back using laptop size 20 and 30 GHz antennas which use meta material resonators to effect the phase shifting. For the first time a clear explanation of how this antenna works given. PATRIOT has GaN active electronically scanned arrays (AESAs) which gives 360° coverage without mechanical rotation. AMDR handles >30 times more targets and has >30 times sensitivity of SPY-1D(V). Can now put a 32 element 60 GHz T/R array on a chip. All the RF circuitry for mm-wave radars is put on a chip. Such radars and phased arrays could cost just a few dollars. Major advances in printed flexible electronic circuits with diodes operating at 1.6 GHz and the potential for operation at 2.4GHz, the Wi-Fi band. A low profile dual polarized tightly coupled dipole antenna (TCDA) has been developed which provides a 20:1 bandwidth and has λ /40 thickness. The latest type of phased array is the MIMO radar phased array which has been shown to provide orders of magnitude better accuracy and resolution as well as better GMTI performance than conventional arrays. However, as summarized here, more recently this author has shown that conventional arrays can do just as well if properly used without suffering MIMO's signal processing load and waveform design problems.


title: A Phase II study of the safety and efficacy of teriflunomide in multiple sclerosis with relapses

abstract: Background:Teriflunomide, a dihydro-orotate dehydrogenase inhibitor, has immunomodulatory effects, including the ability to suppress experimental allergic encephalomyelitis. In this randomized, double-blind, placebo-controlled Phase II study, the authors examined the safety and efficacy of oral teriflunomide in multiple sclerosis (MS) with relapses. Methods:Patients (n = 179) with relapsing–remitting MS (n = 157) or secondary progressive MS with relapses (n = 22) were randomized to receive placebo, teriflunomide 7 mg/day, or teriflunomide 14 mg/day for 36 weeks. MRI brain scans were performed every 6 weeks. The primary endpoint was the number of combined unique active lesions per MRI scan. Secondary endpoints included MRI-defined disease burden, relapse frequency, and disability increase. Results:The median number of combined unique active lesions per scan was 0.5, 0.2, and 0.3 in the placebo, teriflunomide 7 mg/day (p < 0.03 vs placebo), and teriflunomide 14 mg/day (p < 0.01 vs placebo) groups during the 36-week double-blind treatment phase. Teriflunomide-treated patients also had significantly fewer T1 enhancing lesions per scan, new or enlarging T2 lesions per scan, and new T2 lesions. Patients receiving teriflunomide 14 mg/day had significantly reduced T2 disease burden. Teriflunomide treatment resulted in trends toward a lower annualized relapse rate and fewer relapsing patients (14 mg/day only) vs placebo. Significantly fewer patients receiving teriflunomide 14 mg/day vs placebo demonstrated disability increase. Treatment was well tolerated; numbers of adverse events and serious adverse events were similar in all treatment groups. Conclusion:Oral teriflunomide was effective in reducing MRI lesions and was well tolerated in patients with relapsing multiple sclerosis.


title: A 1–4 In-Phase Power Divider for 5G Wireless Communication System

abstract: A wide band power divider using modified Wilkinson design is presented. It is designed to operate at 5G wireless communication band of China. This power divider using wide band Wilkinson design method can be used as the feeding network of antenna array. The presented design can operate over a wide bandwidth covering 36.5GHz-44GHz which covers the 5G wireless communication band of China. The simulated and verified results show good agreement that the input return loss (S11) and the output return losses (S22, S33, S44, S55) are all under -12dB from 37GHz-42GHz. The insertion losses (including division loss) are within 7.2dB and the isolation between any two output ports are all under -10dB.


Effect of Celebrity Endorsements on Consumers ’ Decision-Making Processes: A Study of Television Advertisements for Selected FMCG Products


title: 0.6 V Voltage Shifter and Clocked Comparator for Sampling Correlation-Based Impulse Radio UWB Receiver

abstract: A 0.6-V voltage shifter and a 0.6-V clocked comparator are presented for sampling correlation-based impulse radio UWB receiver. The voltage shifter is used for a novel split swing level scheme-based CMOS transmission gate which can reduce the power consumption by four times. Compared to the conventional voltage shifter, the proposed voltage shifter can reduce the required capacitance area by half and eliminate the non-overlapping complementary clock generator. The proposed 0.6-V clocked comparator can operate at 100-MHz clock with the voltage shifter. To reduce the power consumption of the conventional continuous-time comparator based synchronization control unit, a novel clocked-comparator based control unit is presented, thereby achieving the lowest energy consumption of 3.9pJ/bit in the correlation-based UWB receiver with the 0.5ns timing step for data synchronization.


title: Linked Stream Data Processing

abstract: Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications.


A Spectrometric Method for Hydrogen Peroxide Concentration Measurement with a Reusable and Cost-Efficient Sensor


title: Variational graph auto-encoder using triplets of nodes for preserving proximity

abstract: In this paper, we present a scalable approach for unsupervised learning on graph-structured data that is based on variational auto-encoder. Most previous unsupervised methods for graph representation only consider the reconstruction errors. To train our model, we add different nodes of triplets as input, where one triplet consists of a given reference node vr, a positive node vp which is a adjacent node of vr, and a negative node vn which is a long-distance node of vr. Then, we adopt triplet-based loss function that minimize the reconstruction errors and minimize the distance between vr and vp than the distance between vr and vn. The method captures the relationships between nodes and complex features of a graph by exploiting the variational auto-encoder while still preserves both structure and text information. Experimental results in three real-world citation data show that our approach outperforms the top competitors in link prediction.


title: Understanding and Enhancing the Transferability of Adversarial Examples

abstract: State-of-the-art deep neural networks are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs. Moreover, the perturbations can \textit{transfer across models}: adversarial examples generated for a specific model will often mislead other unseen models. Consequently the adversary can leverage it to attack deployed systems without any query, which severely hinder the application of deep learning, especially in the areas where security is crucial. In this work, we systematically study how two classes of factors that might influence the transferability of adversarial examples. One is about model-specific factors, including network architecture, model capacity and test accuracy. The other is the local smoothness of loss function for constructing adversarial examples. Based on these understanding, a simple but effective strategy is proposed to enhance transferability. We call it variance-reduced attack, since it utilizes the variance-reduced gradient to generate adversarial example. The effectiveness is confirmed by a variety of experiments on both CIFAR-10 and ImageNet datasets.


title: Recursive regularization for large-scale classification with hierarchical and graphical dependencies

abstract: The two key challenges in hierarchical classification are to leverage the hierarchical dependencies between the class-labels for improving performance, and, at the same time maintaining scalability across large hierarchies. In this paper we propose a regularization framework for large-scale hierarchical classification that addresses both the problems. Specifically, we incorporate the hierarchical dependencies between the class-labels into the regularization structure of the parameters thereby encouraging classes nearby in the hierarchy to share similar model parameters. Furthermore, we extend our approach to scenarios where the dependencies between the class-labels are encoded in the form of a graph rather than a hierarchy. To enable large-scale training, we develop a parallel-iterative optimization scheme that can handle datasets with hundreds of thousands of classes and millions of instances and learning terabytes of parameters. Our experiments showed a consistent improvement over other competing approaches and achieved state-of-the-art results on benchmark datasets.


title: Feature Selection based on Rough Sets and Particle Swarm Optimization

abstract: We propose a new feature selection strategy based on rough sets and particle swarm optimization (PSO). Rough sets have been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.


title: Fine-Grained Video Captioning for Sports Narrative

abstract: Despite recent emergence of video caption methods, how to generate fine-grained video descriptions (i.e., long and detailed commentary about individual movements of multiple subjects as well as their frequent interactions) is far from being solved, which however has great applications such as automatic sports narrative. To this end, this work makes the following contributions. First, to facilitate this novel research of fine-grained video caption, we collected a novel dataset called Fine-grained Sports Narrative dataset (FSN) that contains 2K sports videos with ground-truth narratives from YouTube.com. Second, we develop a novel performance evaluation metric named Fine-grained Captioning Evaluation (FCE) to cope with this novel task. Considered as an extension of the widely used METEOR, it measures not only the linguistic performance but also whether the action details and their temporal orders are correctly described. Third, we propose a new framework for fine-grained sports narrative task. This network features three branches: 1) a spatio-temporal entity localization and role discovering sub-network; 2) a fine-grained action modeling sub-network for local skeleton motion description; and 3) a group relationship modeling sub-network to model interactions between players. We further fuse the features and decode them into long narratives by a hierarchically recurrent structure. Extensive experiments on the FSN dataset demonstrates the validity of the proposed framework for fine-grained video caption.


title: Half-height pins - a new pin form in gap waveguide for easy manufacturing

abstract: Gap waveguide is a promising transmission structure, especially for millimeter wave and THz applications. It does not require conductive connection between the top and bottom plates, and therefore this technology gain advantages over conventional rectangular waveguides and SIWs (Substrate Integrated Waveguides) above 100 GHz. In this paper, a new pin form, the half-height pin, is proposed for the gap waveguide. This new half-height pin form has similar stop-band performance as the previous quarter-wavelength long pin form. However, there must be such half-height pins on both the upper and lower plates, in contrast to one of the two having pins in the original gap waveguide. The shorter length of the pins makes the fabrication of the pin surface much easier, such as by e.g. die-forming, molding or MEMS technology. The current work paves the way for mass production of gap waveguide devices and systems at millimeter wave and THz.


title: Preschoolers affect others' reputations through prosocial gossip

abstract: Providing evaluative information to others about absent third parties helps them to identify cooperators and avoid cheaters. Here, we show that 5-year-olds, but not 3-year-olds, reliably engage in such prosocial gossip. In an experimental setting, 5-year-old children spontaneously offered relevant reputational information to guide a peer towards a cooperative partner. Three-year-old children offered such evaluative information only rarely, although they still showed a willingness to inform in a non-evaluative manner. A follow-up study revealed that one component involved in this age difference is children's developing ability to provide justifications. The current results extend previous work on young children's tendency to manage their own reputation by showing that preschoolers also influence others' reputations via gossip.


title: A simple Python code for computing effective properties of 2D and 3D representative volume element under periodic boundary conditions

abstract: Multiscale optimization is an attractive research field recently. For the most of optimization tools, design parameters should be updated during a close loop. Therefore, a simple Python code is programmed to obtain effective properties of Representative Volume Element (RVE) under Periodic Boundary Conditions (PBCs). It can compute the mechanical properties of a composite with a periodic structure, in two or three dimensions. The computation method is based on the Asymptotic Homogenization Theory (AHT). With simple modifications, the basic Python code may be extended to the computation of the effective properties of more complex microstructure. Moreover, the code provides a convenient platform upon the optimization for the material and geometric composite design. The user may experiment with various algorithms and tackle a wide range of problems. To verify the effectiveness and reliability of the code, a three-dimensional case is employed to illuminate the code. Finally numerical results obtained by the code agree well with the available theoretical and experimental results


title: Word embeddings combination and neural networks for robustness in ASR error detection

abstract: This study focuses on error detection in Automatic Speech Recognition (ASR) output. We propose to build a confidence classifier based on a neural network architecture, which is in charge to attribute a label (error or correct) for each word within an ASR hypothesis. This classifier uses word embed-dings as inputs, in addition to ASR confidence-based, lexical and syntactic features. We propose to evaluate the impact of three different kinds of word embeddings on this error detection approach, and we present a solution to combine these three different types of word embeddings in order to take advantage of their complementarity. In our experiments, different approaches are evaluated on the automatic transcriptions generated by two different ASR systems applied on the ETAPE corpus (French broadcast news). Experimental results show that the proposed neural architectures achieve a CER reduction comprised between 4% and 5.8% in error detection, depending on test dataset, in comparison with a state-of-the-art CRF approach.


title: Use of Chlorella vulgaris for CO2 mitigation in a photobioreactor

abstract: Carbon dioxide (CO(2)) is a colorless gas that exists at a concentration of approximately 330 ppm in the atmosphere and is released in great quantities when fossil fuels are burned. The current flux of carbon out of fossil fuels is about 600 times greater than that into fossil fuels. With increased concerns about global warming and greenhouse gas emissions, there have been several approaches proposed for managing the levels of CO(2) emitted into the atmosphere. One of the most understudied methods for CO(2) mitigation is the use of biological processes in engineered systems such as photobioreactors. This research project describes the effectiveness of Chlorella vulgaris, used in a photobioreactor with a very short gas residence time, in sequestering CO(2) from an elevated CO(2) airstream. We evaluated a flow-through photobioreactor's operational parameters, as well as the growth characteristics of the C. vulgaris inoculum when exposed to an airstream with over 1850 ppm CO(2). When using dry weight, chlorophyll, and direct microscopic measurements, it was apparent that the photobioreactor's algal inoculum responded well to the elevated CO(2) levels and there was no build-up of CO(2) or carbonic acid in the photobioreactor. The photobioreactor, with a gas residence time of approximately 2 s, was able to remove up to 74% of the CO(2) in the airstream to ambient levels. This corresponded to a 63.9-g/m(3)/h bulk removal for the experimental photobioreactor. Consequently, this photobioreactor shows that biological processes may have some promise for treating point source emissions of CO(2) and deserve further study.


title: The meaning of home: An exploratory study of environmental experience

abstract: Abstract Despite a growing literature on the meaning of home, the complexity of home is, as yet, little understood. Typically (although not totally), much of the work in this area has not involved empirical research and overall the field lacks a coherent theoretical background. This paper presents a phenomenologically based study designed to reveal what home means to people through their everyday environmental experiences of home. In this exploratory study, the multiple sorting task (MST), was used as an aid to in-depth, systematic interviews which highlighted the personal, multidimensional nature of home. Findings indicate that: (1) different types of home exist; (2) different meanings of home co-exist. Relationships between the meanings of home were explored using multidimensional scaling techniques to reveal a superordinate structure which forms the first stages of a tentative model of home. This involves a tripartite division of home into three modes of experience: the personal home; the social home; the physical home. The results of the study are located within theories of place and the links between these theories and the findings are discussed.


title: Omnidirectional Dielectric Resonator Antenna With a Planar Feed for Circular Polarization Diversity Design

abstract: The cylindrical dielectric resonator (DR) antenna (DRA) is excited in its omnidirectional TM $_{01\delta }$ mode by a planar shorted microstrip cross. With this nonintrusive feed, the DRA can be fabricated without the need of drilling a hole in the DR as required in the probe feed method. This DRA is applied to the first omnidirectional circularly polarized (CP) diversity DRA. To generate omnidirectional CP fields, the TM $_{01\delta }$ and TE $_{011+\delta }$ modes are excited simultaneously. The TE $_{011+\delta }$ mode is excited by four microstrip arcs. They provide a pair of equivalent magnetic dipoles that generate fields that are orthogonal to those of the TM $_{01\delta }$ mode. Omnidirectional CP fields can be obtained when the (orthogonal) fields of the TM $_{01\delta }$ and TE $_{011+\delta }$ modes are equal in amplitude but in phase quadrature. In our two-port CP diversity design, phase differences of +90° and −90° are obtained in ports 1 and 2 to generate right- and left-hand CP fields, respectively. Prototypes at ~2.4 GHz were designed, fabricated, and measured for WLAN applications. The S-parameters, radiation patterns, antenna gains, and efficiencies are studied. For the diversity design, the axial ratio, envelope correlation coefficient, and mean effective gain are also obtained. The measured and simulation results are in reasonable agreement.


title: Safety and Efficacy of a Fish-Oil-Based Fat Emulsion in the Treatment of Parenteral Nutrition-Associated Liver Disease

abstract: BACKGROUND ::: Parenteral nutrition-associated liver disease can be a progressive and fatal entity in children with short-bowel syndrome. Soybean-fat emulsions provided as part of standard parenteral nutrition may contribute to its pathophysiology. ::: ::: ::: METHODS ::: We compared safety and efficacy outcomes of a fish-oil-based fat emulsion in 18 infants with short-bowel syndrome who developed cholestasis (serum direct bilirubin level of > 2 mg/dL) while receiving soybean emulsions with those from a historical cohort of 21 infants with short-bowel syndrome who also developed cholestasis while receiving soybean emulsions. The primary end point was time to reversal of cholestasis (3 consecutive measurements of serum direct bilirubin level of < or = 2 mg/dL). ::: ::: ::: RESULTS ::: Among survivors, the median time to reversal of cholestasis was 9.4 and 44.1 weeks in the fish-oil and historical cohorts, respectively. Subjects who received fish-oil-based emulsion experienced reversal of cholestasis 4.8 times faster than those who received soybean emulsions and 6.8 times faster in analysis adjusted for baseline bilirubin concentration, gestational age, and the diagnosis of necrotizing enterocolitis. A total of 2 deaths and 0 liver transplantations were recorded in the fish-oil cohort and 7 deaths and 2 transplantations in the historical cohort. The provision of fish-oil-based fat emulsion was not associated with essential fatty acid deficiency, hypertriglyceridemia, coagulopathy, infections, or growth delay. ::: ::: ::: CONCLUSIONS ::: Parenteral fish-oil-based fat emulsions are safe and may be effective in the treatment of parenteral nutrition-associated liver disease.


title: Single cell analysis of cancer genomes

abstract: Genomic studies have provided key insights into how cancers develop, evolve, metastasize and respond to treatment. Cancers result from an interplay between mutation, selection and clonal expansions. In solid tumours, this Darwinian competition between subclones is also influenced by topological factors. Recent advances have made it possible to study cancers at the single cell level. These methods represent important tools to dissect cancer evolution and provide the potential to considerably change both cancer research and clinical practice. Here we discuss state-of-the-art methods for the isolation of a single cell, whole-genome and whole-transcriptome amplification of the cell's nucleic acids, as well as microarray and massively parallel sequencing analysis of such amplification products. We discuss the strengths and the limitations of the techniques, and explore single-cell methodologies for future cancer research, as well as diagnosis and treatment of the disease.


title: Single-Ended Forward Switching Power Supply Design Based on UCC2800 PWM Controller

abstract: Switching Power Supply which has a lot of advantages compared with Linear Power Supply is used very widely in the modern life. This paper discussed a kind of DC/DC switching power supply based on the UCC2800 PWM controller chip, the basic technical indicators is DC 28V rated input voltage ranging from 16V to 40V, 5V30W output, and analyzed its working principle、soft start and short-circuit protection, finally, briefly described switching power supply design methods in the use of UCC2800 PWM controller chip.


EssayCritic: Writing to Learn with a Knowledge-Based Design Critiquing System


title: Improvement of a genetic algorithm approach for the solution of vehicle routing problem with time windows

abstract: In this study, Vehicle Routing Problem with Time Windows (VRPTW) with known customer demands, a single depot and identical vehicles, is considered. Minimizing the total distance and the total waiting time of the vehicles are determined as objective functions for VRPTW which is capable to serve the customers in a prespecified time interval. A hybridized version of genetic algorithm which is a metaheuristic solution technique with constructive heuristic methods is proposed to produce effective solutions for VRPTW. By using sweep algorithm in initial population generation phase of genetic algorithm, it is planned to begin the search with high quality solution sets and in this way, get more feasible solutions faster. A benchmark problem in the literature is solved and obtained results are compared with the results of genetic algorithm with the nearest neighbor algorithm based algorithm. It is observed that the proposed genetic algorithm beginning with sweep based initial population generation algorithm reaches more effective solutions.


title: Intelligent Biofeedback using an Immersive Competitive Environment

abstract: In this paper we introduce the idea of enhancing biofeedback by placing it within a competitive virtual gaming environment. This is an example of a new concept to be called ‘Affective Feedback’. In essence this term means that the computer is an active intelligent participant in the biofeedback loop. The relationship between human and machine is now two way the computer is affected by the state of the human and vice versa. We demonstrate the concept through a simple video game where two people compete in a race based on the relaxation levels of the players. The competitive virtual environment affects the players’ level of relaxation while the levels of relaxation determine the outcome of the game. The person most relaxed wins the game. INTRODUCTION BIOFEEDBACK TO AFFECTIVE FEEDBACK: If a person is provided with real-time information about a certain aspect of their physiology, they can determine the way that their mental changes affect this state. Over time, the person can learn, consciously or subconsciously, how to control this aspect of their “bio-state.” This process is known as biofeedback and can be used to gain mental control over brainwaves, blood flow, muscular action, and many other physiological events. For example, many studies have shown that, over time, people can gain control of their brainstate (as measured by an electroencephalogram [EEG]) when given a graphical or auditory representation of their EEG. Biofeedback has many clinical applications and has been used to treat a wide range of conditions, from attention deficit disorder(ADD) to addiction. Affective Feedback is a hybrid of traditional biofeedback and “affective computing,” a term coined by Rosalind Picard meaning “computing that...arises from or deliberately influences emotion.” The basic idea is to make the framework in which the biofeedback takes place directly influence and affect the “bio-state” of the user. In other words, the person is not only affected by the feedback information about their bio-state, but also by the way that it is presented. An example of a simple way of achieving this goal will be presented in this paper. We have created a two-player competitive racing game which is based on the players’ level of relaxation, as measured by their galvanic skin response. The basic idea is simple: As a player relaxes, their character moves faster. Therefore, the person who relaxes most wins the race. In some sense, this is traditional biofeedback: The person is given feedback information on their level of relaxation by the action of their dragon. However, the key difference is that the environment is designed to affect the user. First of all, the game takes place in a visually stunning virtual 3-D world. This type of environment has been shown to draw more of the user's attention and make the feedback more effective. In addition, the game provides a much more appealing and effective “reward” for achieving the correct “bio-state.” Studies have shown that more “meaningful” reinforcement makes biofeedback more effective, especially in children. Finally, and most important, the competitive framework has a profound affect on the mental state of the user. Competitive games are normally associated with tension and stress, so the player must learn how to override this tendency and learn not only how to relax, but how to relax in a tense environment. This game demonstrates a small but important step into the next age of intelligent, ubiquitous computers: Computers with the ability to recognize, react to, and affect our physiological state in a meaningful way. Currently, we are working to expand the concept by adding other bio-state measurements and creating a more “intelligent” interface. The hope is that, in the future, we will be able to create a game in which the computer actually “learns” which environments foster different mental states in individual users, and uses this information to affect and reinforce the user’s target “bio-state.” In some sense, the computer would be performing its own biofeedback as it is changing the environment and recording how the user reacts. Traditional biofeedback is a standard feedback loop: The user learns to control their state based on information presented on a computer. Affective feedback creates another loop: The computer learns how different environments and frameworks affect the user and adjusts accordingly. It is important to note that, just as biofeedback can be administered without this new loop, affective feedback is possible without traditional biofeedback. If the computer “knows” how different situations and environments can affect the user’s state, it can change based on the needs of the user. This research has large potential for interesting expansion and, given the clinical applications of biofeedback, a good chance of genuinely improving peoples’ lives.


title: Road-sign detection and tracking

abstract: In a visual driver-assistance system, road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, traffic signs located in the previous phase are tracked through image sequences using a Kalman filter. The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.


title: Agile Model Driven Development: An Intelligent Compromise

abstract: Both the model-driven and agile development approaches have significantly enhanced productivity and predictability of software development in practice. Agile Model Driven Development or AMDD is an attempt to effectively bring together the fast pace of agile development and the guaranteed quality of model-driven development. Despite the proliferation of processes claim to comply with AMDD principles, there has been little academic research examining merits and demerits of such an approach. This paper first presents the underlying theoretical foundations of AMDD and then reviews, classifies and compares existing AMDD processes. The results indicate significant disparity between compared processes that implies the AMDD concepts are still in their infancy.


title: Using a Single RGB Frame for Real Time 3D Hand Pose Estimation in the Wild

abstract: We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild.


title: Locality-aware dynamic VM reconfiguration on MapReduce clouds

abstract: Cloud computing based on system virtualization, has been expanding its services to distributed data-intensive platforms such as MapReduce and Hadoop. Such a distributed platform on clouds runs in a virtual cluster consisting of a number of virtual machines. In the virtual cluster, demands on computing resources for each node may fluctuate, due to data locality and task behavior. However, current cloud services use a static cluster configuration, fixing or manually adjusting the computing capability of each virtual machine (VM). The fixed homogeneous VM configuration may not adapt to changing resource demands in individual nodes. In this paper, we propose a dynamic VM reconfiguration technique for data-intensive computing on clouds, called Dynamic Resource Reconfiguration (DRR). DRR can adjust the computing capability of individual VMs to maximize the utilization of resources. Among several factors causing resource imbalance in the Hadoop platforms, this paper focuses on data locality. Although assigning tasks on the nodes containing their input data can improve the overall performance of a job significantly, the fixed computing capability of each node may not allow such locality-aware scheduling. DRR dynamically increases or decreases the computing capability of each node to enhance locality-aware task scheduling. We evaluate the potential performance improvement of DRR on a 100-node cluster, and its detailed behavior on a small scale cluster with constrained network bandwidth. On the 100-node cluster, DRR can improve the throughput of Hadoop jobs by 15% on average, and 41% on the private cluster with the constrained network connection.


title: Dynamics and vibrational control of an underwater inverted pendulum

abstract: This paper discusses the dynamics and vibrational control problem of an underwater inverted pendulum system. The system's equations of motion are obtained by using the Lagrange equation. The vibrational control technique is applied to stabilize the inverted pendulum in water. Furthermore, stability analyses in terms of stable-unstable region for the underwater inverted pendulum system is presented. While a number of control techniques for the stabilization problem of an inverted pendulum in air by oscillating the support point have been developed, but a similar technique for the system in water has not yet been investigated. The dynamics of any system in liquid is strongly influenced by the density of the liquid and the relative velocity of the surrounding liquid. Also, it becomes a challenging task to stabilize the system in the presence of the hydrodynamic forces acting on it. Simulation results reveal that the application of the vibrational control technique newly developed in this paper can successfully stabilize the system in water under the influence of hydrodynamic forces.


title: Social media in China: Rising Weibo in government

abstract: The development of social media infuses the modern relationships with vitalities and brings a series of information dissemination with subjective consciousness. This is one of the general characteristics of the information age after a widespread wave of Social Network Services(SNS), arousing the unprecedented management pressure for the Chinese government. Considering the difficulties in managing with technologies, human resources and information, the government has to adopt SNS immediately and combine with the existing network resources, netizens and more channels to build a better electronic government for the rapid expansion of SNS in the future. This paper will present a special introduction of the social media in China and meaningful experiences the Chinese government have gained step by step, especially in the extraordinary practices in Weibo, i.e. microblogging pronounced in Chinese. The Chinese government is playing a leading role in the SNS evolution and enjoying the updating progress of government electronization.


title: Genital Cutting and Western Discourses on Sexuality

abstract: This article explores dominant discourses surrounding male and female genital cutting. Over a similar period of time, these genital operations have separately been subjected to scrutiny and criticism. However, although critiques of female circumcision have been widely taken up, general public opinion toward male circumcision remains indifferent. This difference cannot merely be explained by the natural attributes and effects of these practices. Rather, attitudes toward genital cutting reflect historically and culturally specific understandings of the human body. In particular, I suggest that certain problematic understandings of male and female sexuality are deeply implicated in the dominant Western discourses on genital surgery.


title: Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

abstract: The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.


title: Evaluating English to Arabic Machine Translation Using BLEU

abstract: This study aims to compare the effectiveness of two popular machine translation systems (Google Translate and Babylon machine translation system) used to translate English sentences into Arabic relative to the effectiveness of English to Arabic human translation. There are many automatic methods used to evaluate different machine translators, one of these methods; Bilingual Evaluation Understudy (BLEU) method, which was adopted and implemented to achieve the main goal of this study. BLEU method is based on the assumptions of automated measures that depend on matching machine translators' output to human reference translations; the higher the score, the closer the translation to the human translation will be. Well known English sayings in addition to manually collected sentences from different Internet web sites were used for evaluation purposes. The results of this study have showed that Google machine translation system is better than Babylon machine translation system in terms of precision of translation from English to Arabic.


title: A Data Mining Approach for Coronary Heart Disease Prediction using HRV Features and Carotid Arterial Wall Thickness

abstract: The main objective of our work has been to develop and then propose a new and unique methodology useful in developing the various features of heart rate variability (HRV) and carotid arterial wall thickness helpful in diagnosing cardiovascular disease. We also propose a suitable prediction model to enhance the reliability of medical examinations and treatments for cardiovascular disease. We analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on HRV indexes. We also measured intima-media of carotid arteries and used measurements of arterial wall thickness as other features. Patients underwent carotid artery scanning using high-resolution ultrasound devised in a previous study. In order to extract various features, we tested six classification methods. As a result, CPAR and SVM (gave about 85%-90% goodness of fit) outperforming the other classifiers.


title: Pick-by-Vision comes on age: evaluation of an augmented reality supported picking system in a real storage environment

abstract: Order picking is one of the most important process steps in logistics. Because of their flexibility human beings cannot be replaced by machines. But if workers in order picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization. In this paper the development of such a system -- called Pick-by-Vision - is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as subjective figures were measured. The results show that a Pick-by-Vision system can improve considerably industrial order picking processes.


title: A Simple Model Predictive Power Control Strategy for Single-Phase PWM Converters With Modulation Function Optimization

abstract: This paper proposes a simple model predictive direct power control (MP-DPC) of single-phase pulsewidth-modulated rectifiers with constant switching frequency using modulation function optimization. The instantaneous active and reactive power theory for single-phase converters is discussed, on the basis of a second-order generalized integrator. The modulation function is resolved from the cost function of the MP-DPC scheme. The relationship between the instantaneous powers and ac-side inductance mismatch is discussed. The inductance mismatch has an effect on the reactive power, but not effect on active power. The quantitative expression of reactive power caused by inductance mismatch is shown and verified in the experimental test. An inductance parameter online estimation scheme is proposed to eliminate its effect on reactive power. Compared with the conventional proportional-integral-based instantaneous current control scheme and finite-control-set MP-DPC scheme, the proposed MP-DPC can provide these advantageous features: lower current harmonics and total harmonic distortion components, faster dynamic response, fewer adjusted parameters, and zero steady-state error. In addition, the proposed MP-DPC is free of the weighting factor selection in cost function. The effectiveness of the proposed MP-DPC scheme is verified by experimental results.


title: PID vs LQ control techniques applied to an indoor micro quadrotor

abstract: The development of miniature flying robots has become a reachable dream, thanks to the new sensing and actuating technologies. Micro VTOL systems represent a useful class of flying robots because of their strong abilities for small-area monitoring and building exploration. In this paper, we present the results of two model-based control techniques applied to an autonomous four-rotor micro helicopter called quadrotor. A classical approach (PID) assumed a simplified dynamics and a modern technique (LQ) based on a more complete model. Various simulations were performed and several tests on the bench validate the control laws. Finally, we present the results of the first test in flight with the helicopter released. These developments are part of the OS4 project in our lab.


title: A Rule-Based Classification Algorithm for Uncertain Data

abstract: Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain data. This algorithm introduces new measures for generating, pruning and optimizing rules. These new measures are computed considering uncertain data interval and probability distribution function. Based on the new measures, the optimal splitting attribute and splitting value can be identified and used for classification and prediction. The proposed uRule algorithm can process uncertainty in both numerical and categorical data. Our experimental results show that uRule has excellent performance even when data is highly uncertain.


title: Motion Estimation for Self-Driving Cars with a Generalized Camera

abstract: In this paper, we present a visual ego-motion estimation algorithm for a self-driving car equipped with a close-to-market multi-camera system. By modeling the multi-camera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the generalized essential matrix where the full relative motion including metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera correspondences. We show that up to a maximum of 6 solutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the special case with only intra-camera correspondences where the scale becomes unobservable and provide a practical alternative solution. Our formulation can be efficiently implemented within RANSAC for robust estimation. We verify the validity of our assumptions on the motion model by comparing our results on a large real-world dataset collected by a car equipped with 4 cameras with minimal overlapping field-of-views against the GPS/INS ground truth.


title: Dual port UWB diversity/MIMO antenna with dual band-notch characteristics

abstract: A compact two-port diversity/MIMO antenna with dual band-notch characteristics for ultra wideband (UWB) applications is presented in this paper. The antennas exhibit a good impedance match over frequency band of 3.1 GHz–12 GHz, while exhibiting high isolation. Enhanced isolation is achieved with a decoupling structure having circular slots. The decoupling structure provides isolation of more than 20 dB for whole UWB spectrum. By introducing U-shaped slot in the main radiator and horizontal stubs in ground plane, band-notch characteristic is achieved in proposed MIMO design. Two notches are achieved at 5.150–5.825 GHz (WLAN) and 7.25–7.75 GHz (downlink of X-band satellite). The antenna is miniaturized having dimensions of 22mm × 36mm. The results suggests that the proposed diversity/MIMO antenna design can be suitable for portable UWB applications.


title: Toward Robust Distance Metric Analysis for Similarity Estimation

abstract: In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.


An Unsolvable Problem of Elementary Number Theory


title: Interactive gaze driven animation of the eye region

abstract: We propose a system for real-time animation of eyes that can be interactively controlled in a WebGL enabled device using a small number of animation parameters, including gaze. These animation parameters can be obtained using traditional keyframed animation curves, measured from an actor's performance using off-the-shelf eye tracking methods, or estimated from the scene observed by the character, using behavioral models of human vision. We present a model of eye movement, that includes not only movement of the globes, but also of the eyelids and other soft tissues in the eye region. The model includes formation of expression wrinkles in soft tissues. To our knowledge this is the first system for real-time animation of soft tissue movement around the eyes based on gaze input.


THE MOTIVATIONS FOR CHANGE TOWARDS E- GOVERNMENT ADOPTION: CASE STUDIES FROM SAUDI


title: Impact of Inner Pickup on ESD Robustness of Multifinger MOSFET in 28-nm High- $k$/Metal Gate CMOS Process

abstract: The impact of pickup structure on electrostatic discharge (ESD) robustness of multifinger MOSFET in 28-nm high- $k$ /metal gate CMOS process was investigated in this paper. Verified in silicon, the multifinger MOSFET without the pickup structure inserted into its source region can sustain the higher ESD level and more compact layout area.


title: The World of Connections and Information Flow in Twitter

abstract: Information propagation in online social networks like Twitter is unique in that word-of-mouth propagation and traditional media sources coexist. We collect a large amount of data from Twitter to compare the relative roles different types of users play in information flow. Using empirical data on the spread of news about major international headlines as well as minor topics, we investigate the relative roles of three types of information spreaders: 1) mass media sources like BBC; 2) grassroots, consisting of ordinary users; and 3) evangelists, consisting of opinion leaders, politicians, celebrities, and local businesses. Mass media sources play a vital role in reaching the majority of the audience in any major topics. Evangelists, however, introduce both major and minor topics to audiences who are further away from the core of the network and would otherwise be unreachable. Grassroots users are relatively passive in helping spread the news, although they account for the 98% of the network. Our results bring insights into what contributes to rapid information propagation at different levels of topic popularity, which we believe are useful to the designers of social search and recommendation engines.


Advances in Swarm and Computational Intelligence


title: Short-term and Long-term Effects of Violent Media on Aggression in Children and Adults

abstract: Objectives: To test whether the results of the accumulated studies on media violence and aggressive behavior are consistent with the theories that have evolved to explain the effects. We tested for the existence of both shortterm and long-term effects for aggressive behavior. We also tested the theory-driven hypothesis that short-term effects should be greater for adults and long-term effects should be greater for children. Design: Meta-analysis. Participants: Children younger than 18 years and adults. Main Exposures: Violent media, including TV, movies, video games, music, and comic books. Main Outcome Measures: Measures of aggressive behavior, aggressive thoughts, angry feelings, physiological arousal (eg, heart rate, blood pressure), and helping behavior. Results: Effect size estimates were combined using metaanalytic procedures. As expected, the short-term effects of violent media were greater for adults than for children whereas the long-term effects were greater for children than for adults. The results also showed that there were overall modest but significant effect sizes for exposure to media violence on aggressive behaviors, aggressive thoughts, angry feelings, arousal levels, and helping behavior. Conclusions: The results are consistent with the theory that short-term effects are mostly due to the priming of existing well-encoded scripts, schemas, or beliefs, which adults have had more time to encode. In contrast, longterm effects require the learning (encoding) of scripts, schemas, or beliefs. Children can encode new scripts, schemas, and beliefs via observational learning with less interference and effort than adults. Arch Pediatr Adolesc Med. 2006;160:348-352


The Social Impact of Natural Language Processing


title: The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes

abstract: Abstract The emergence of novel and powerful digital technologies, digital platforms and digital infrastructures has transformed innovation and entrepreneurship in significant ways. Beyond simply opening new opportunities for innovators and entrepreneurs, digital technologies have broader implications for value creation and value capture. Research aimed at understanding the digital transformation of the economy needs to incorporate multiple and cross-levels of analysis, embrace ideas and concepts from multiple fields/disciplines, and explicitly acknowledge the role of digital technologies in transforming organizations and social relationships. To help realize this research agenda, we identify three key themes related to digitization—openness, affordances, and generativity—and, outline broad research issues relating to each. We suggest that such themes that are innate to digital technologies could serve as a common conceptual platform that allows for connections between issues at different levels as well as the integration of ideas from different disciplines/areas. We then summarize the contributions of the 11 papers presented in this Special Issue relating them to one or more of these themes and outlining their implications for future research.


title: Accelerated deep neural networks for enhanced Intrusion Detection System

abstract: Network based communication is more vulnerable to outsider and insider attacks in recent days due to its wide spread applications in many fields. Intrusion Detection System (IDS) a software application or a hardware is a security mechanism that is able to monitor network traffic and find abnormal activities in the network. Machine learning techniques which have an important role in detecting the attacks were mostly used in the development of IDS. Due to huge increase in network traffic and different types of attacks, monitoring each and every packet in the network traffic is time consuming and computational intensive. Deep learning acts as a powerful tool by which thorough packet inspection and attack identification is possible. The parallel computing capabilities of the neural network make the Deep Neural Network (DNN) to effectively look through the network traffic with an accelerated performance. In this paper an accelerated DNN architecture is developed to identify the abnormalities in the network data. NSL-KDD dataset is used to compute the training time and to analyze the effectiveness of the detection mechanism.


title: Model Based Vehicle Detection and Tracking for Autonomous Urban Driving

abstract: Situational awareness is crucial for autonomous driving in urban environments. This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. Furthermore, we show how to build consistent and efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the Urban Grand Challenge as well as other urban settings.


title: Score test variable screening.

abstract: Variable screening has emerged as a crucial first step in the analysis of high-throughput data, but existing procedures can be computationally cumbersome, difficult to justify theoretically, or inapplicable to certain types of analyses. Motivated by a high-dimensional censored quantile regression problem in multiple myeloma genomics, this article makes three contributions. First, we establish a score test-based screening framework, which is widely applicable, extremely computationally efficient, and relatively simple to justify. Secondly, we propose a resampling-based procedure for selecting the number of variables to retain after screening according to the principle of reproducibility. Finally, we propose a new iterative score test screening method which is closely related to sparse regression. In simulations we apply our methods to four different regression models and show that they can outperform existing procedures. We also apply score test screening to an analysis of gene expression data from multiple myeloma patients using a censored quantile regression model to identify high-risk genes.


title: Holistic, Instance-Level Human Parsing

abstract: Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.


title: Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

abstract: The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures-interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored.


title: A multi-method simulation of a high-frequency bus line

abstract: In this work a mixed agent-based and discrete event simulation model is developed for a high frequency bus route in the Netherlands. With this model, different passenger growth scenarios can be easily evaluated. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. The model is validated using several performance indicators, showing that under some model assumptions, it can realistically simulate real-life situations. The simulation's workings are illustrated by two use cases.


title: The jMetal framework for multi-objective optimization: Design and architecture

abstract: jMetal is a Java-based framework for multi-objective optimization using metaheuristics. It is a flexible, extensible, and easy-to-use software package that has been used in a wide range of applications. In this paper, we describe the design issues underlying jMetal, focusing mainly on its internal architecture, with the aim of offering a comprehensive view of its main features to interested researchers. Among the covered topics, we detail the basic components facilitating the implementation of multi-objective metaheuristics (solution representations, operators, problems, density estimators, archives), the included quality indicators to assess the performance of the algorithms, and jMetal's support to carry out full experimental studies.


title: Automatic number plate recognition

abstract: The main focus in this research paper is to experiment deeply with, and find alternative solutions to the image segmentation and character recognition problems within the License Plate Recognition framework. Three main stages are identified in such applications. First, it is necessary to locate and extract the license plate region from a larger scene image. Second, having a license plate region to work with, the alphanumeric characters in the plate need to be extracted from the background. Third, deliver them to an character system (BOX APPROACH)for recognition. In order to identify a vehicle by reading its license plate successfully, it is obviously necessary to locate the plate in the scene image provided by some acquisition system (e.g. video or still camera). Locating the region of interest helps in dramatically reducing both the computational expense and algorithm complexity. For example, a currently common1024x768 resolution image contains a total of 786,432pixels, while the region of interest (in this case a license plate) may account for only 10% of the image area. Also, the input to the following segmentation and recognition stages is simplified, resulting in easier algorithm design and shorter computation times. The paper mainly work with the standard license plates but the techniques, algorithms and parameters that is be used can be adjusted easily for any similar number plates even with other alpha-numeric set.


title: Low power and low voltage VT extractor circuit and MOSFET radiation dosimeter

abstract: This work discusses two fundamental blocks of an in vivo MOS dosimeter, namely the radiation sensor and the V T -extractor circuit. It is shown that threshold extractor circuits based on an all-region MOSFET model are very appropriate for low power design. The accuracy of the extractor circuits allows using the PMOS transistors of the integrated circuit CD4007 as the radiation sensor in a dosimeter for radiotherapy applications.


title: Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval

abstract: Recently, promising results have been shown on face recognition researches. However, face recognition and retrieval across age is still challenging. Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC is able to encode the low-level feature of a face image with an age-invariant reference space. In the testing phase, the proposed method only requires a linear projection to encode the feature and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD). The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62. To the best of our knowledge, it is by far the largest publicly available cross-age face dataset. Experimental results show that the proposed method can achieve state-of-the-art performance on both our dataset as well as the other widely used dataset for face recognition across age, MORPH dataset.


title: A Fully Automated Framework for Control of Linear Systems from Temporal Logic Specifications

abstract: We consider the following problem: given a linear system and a linear temporal logic (LTL) formula over a set of linear predicates in its state variables, find a feedback control law with polyhedral bounds and a set of initial states so that all trajectories of the closed loop system satisfy the formula. Our solution to this problem consists of three main steps. First, we partition the state space in accordance with the predicates in the formula, and construct a transition system over the partition quotient, which captures our capability of designing controllers. Second, using a procedure resembling model checking, we determine runs of the transition system satisfying the formula. Third, we generate the control strategy. Illustrative examples are included.


title: Mobile services modeling and assurance

abstract: It discusses some generic issues for mobile service modeling, and the requirements stemming from terminals and new services. After that, the end-to-end service assurance is analyzed with KQIs and KPIs, and a sample of service model is defined accordingly. Finally, it concludes with a summary to say that modeling mobile service is important for facilitating flexible service creation processes with multi-party participation as well as for workflow management.


title: Learning of inverse-dynamics for SCARA robot

abstract: In this paper, we describe a positioning control for a SCARA robot using a recurrent neural network. The simultaneous perturbation optimization method is used for the learning rule of the recurrent neural network. Then the recurrent neural network learns inverse dynamics of the SCARA robot. We present details of the control scheme using the simultaneous perturbation. Moreover, we consider an example for two target positions using an actual SCARA robot. The result is shown.


title: Scalable Graph Exploration on Multicore Processors

abstract: Many important problems in computational sciences, social network analysis, security, and business analytics, are data-intensive and lend themselves to graph-theoretical analyses. In this paper we investigate the challenges involved in exploring very large graphs by designing a breadth-first search (BFS) algorithm for advanced multi-core processors that are likely to become the building blocks of future exascale systems. Our new methodology for large-scale graph analytics combines a highlevel algorithmic design that captures the machine-independent aspects, to guarantee portability with performance to future processors, with an implementation that embeds processorspecific optimizations. We present an experimental study that uses state-of-the-art Intel Nehalem EP and EX processors and up to 64 threads in a single system. Our performance on several benchmark problems representative of the power-law graphs found in real-world problems reaches processing rates that are competitive with supercomputing results in the recent literature. In the experimental evaluation we prove that our graph exploration algorithm running on a 4-socket Nehalem EX is (1) 2.4 times faster than a Cray XMT with 128 processors when exploring a random graph with 64 million vertices and 512 millions edges, (2) capable of processing 550 million edges per second with an R-MAT graph with 200 million vertices and 1 billion edges, comparable to the performance of a similar graph on a Cray MTA-2 with 40 processors and (3) 5 times faster than 256 BlueGene/L processors on a graph with average degree 50.


title: True Filterless Class-D Audio Amplifier

abstract: The design of a fully integrated, filterless, class-D audio amplifier in standard 0.25- CMOS technology is described: a novel class-D amplifier architecture, where uniform pulsewidth modulation is introduced. The architecture attenuates residual clock signals around the loop allowing very low harmonic distortion, , to be achieved in conjunction with high PSRR, at 217 Hz. When driving 1.2 W into an 8- load, it achieves an SNR of 103 dB (A-weighted) with an efficiency of . The maximum output power at 1% THD is 3.1 W. Figures of merit are defined to establish that the amplifier exceeds the performance of alternative designs. The amplifier occupied a chip area 1.44 and was packaged as a WLCSP.


title: Aligning Gaussian-Topic with Embedding Network for Summarization Ranking

abstract: Query-oriented summarization addresses the problem of information overload and help people get the main ideas within a short time. Summaries are composed by sentences. So, the basic idea of composing a salient summary is to construct quality sentences both for user specific queries and multiple documents. Sentence embedding has been shown effective in summarization tasks. However, these methods lack of the latent topic structure of contents. Hence, the summary lies only on vector space can hardly capture multi-topical content. In this paper, our proposed model incorporates the topical aspects and continuous vector representations, which jointly learns semantic rich representations encoded by vectors. Then, leveraged by topic filtering and embedding ranking model, the summarization can select desirable salient sentences. Experiments demonstrate outstanding performance of our proposed model from the perspectives of prominent topics and semantic coherence.


title: One in the Jungle: Downbeat Detection in Hardcore, Jungle, and Drum and Bass

abstract: Hardcore, jungle, and drum and bass (HJDB) are fastpaced electronic dance music genres that often employ resequenced breakbeats or drum samples from jazz and funk percussionist solos. We present a style-specific method for downbeat detection specifically designed for HJDB. The presented method combines three forms of metrical information in the prediction of downbeats: lowlevel onset event information; periodicity information from beat tracking; and high-level information from a regression model trained with classic breakbeats. In an evaluation using 206 HJDB pieces, we demonstrate superior accuracy of our style specific method over four general downbeat detection algorithms. We present this result to motivate the need for style-specific knowledge and techniques for improved downbeat detection.


title: Hybrid SDN Networks: A Survey of Existing Approaches

abstract: Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.


title: Lumipen: Projection-Based Mixed Reality for Dynamic Objects

abstract: Recently, mixed reality (MR) involving the use of a projector, which is often called projection-based MR, has attracted much attention and is being widely studied. However, most MR research is mainly focused on static objects or situations. In contrast, we propose "Lumipen," which is a projection-based MR system for high-speed or high-frequency objects, using a high-speed vision sensor and a projector with a high-speed optical gaze controller. The high-speed vision sensor detects the dynamics of the object, and the high-speed optical gaze controller controls the projection direction in a few milliseconds. Lumipen is expected to enable addition of sophisticated and diverse visual information even in dynamic live content (e.g., a play, a sports game, a music concert, etc.), like computer graphics added to video content.


title: State-aware anomaly detection for industrial control systems

abstract: Anomaly detection for industrial control systems (ICS) can leverage process data to detect malicious derivations from expected process behavior. We propose state-aware anomaly detection that uses state dependent detection thresholds, which provide tighter constraints for an attacker trying to manipulate the process. In particular, our system provides: (i) estimation of system state from the knowledge of the network and the physical process (ii) a state-aware cumulative sum of residuals for monitoring the industrial control system (iii) and a novel state-aware anomaly detection technique. We implement and evaluate our anomaly detection technique on a real-world ICS. We pre-compute the process-state parameters using a big data framework for ICS and train the detector leveraging more than 120 GB of historical data from the ICS. The results show that the proposed method improves prior works by providing less time-to-detect of attacks while generating fewer false alarms.


title: Benchmarking evolutionary computation approaches to insider threat detection

abstract: Insider threat detection represents a challenging problem to companies and organizations where malicious actions are performed by authorized users. This is a highly skewed data problem, where the huge class imbalance makes the adaptation of learning algorithms to the real world context very difficult. In this work, applications of genetic programming (GP) and stream active learning are evaluated for insider threat detection. Linear GP with lexicase/multi-objective selection is employed to address the problem under a stationary data assumption. Moreover, streaming GP is employed to address the problem under a non-stationary data assumption. Experiments conducted on a publicly available corporate data set show the capability of the approaches in dealing with extreme class imbalance, stream learning and adaptation to the real world context.


title: Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors

abstract: The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) has been shown to find much smaller solutions of equally high quality compared to other state-of-the-art GP approaches. This is an interesting aspect as small solutions better enable human interpretation. In this paper, an adaptation of GP-GOMEA to tackle real-world symbolic regression is proposed, in order to find small yet accurate mathematical expressions, and with an application to a problem of clinical interest. For radiotherapy dose reconstruction, a model is sought that captures anatomical patient similarity. This problem is particularly interesting because while features are patient-specific, the variable to regress is a distance, and is defined over patient pairs. We show that on benchmark problems as well as on the application, GP-GOMEA outperforms variants of standard GP. To find even more accurate models, we further consider an evolutionary meta learning approach, where GP-GOMEA is used to construct small, yet effective features for a different machine learning algorithm. Experimental results show how this approach significantly improves the performance of linear regression, support vector machines, and random forest, while providing meaningful and interpretable features.


title: A web-based approach using reactive programming for complex event processing in internet of things applications

abstract: The Internet of things (IoT) has expanded at a fantastic pace in the last years. It will be only a matter of years before every single daily object will contain embedded sensors and actuators connected through the Internet, producing massive amounts of data. Meanwhile, efforts are being carried out to enable IoT objects to be accessible through the Web as well. Thanks to those efforts, a new class of graphical tools, namely physical mashups, that explores data flow programming to connect Web components and Internet of things' devices easily, can now be used to build applications that mix the physical world with the virtual one. In parallel, the concept of Complex Event processing has emerged as a response to the increase of data from different types of distributed applications due to the need for (quasi) real-time processing of data. In this paper we propose the combination of both approaches, CEP and WoT mashups, to build a simple approach to tackle this new avalanche of data in the context of IoT applications. We apply Reactive Programming to implement the CEP operators that we provide as extensions of an existing WoT platform. A use case scenario is presented to demonstrate the approach.


title: The Evolving Nature of Disaster Management in the Internet and Social Media Era

abstract: Traditional means for contacting emergency responders depend critically on the availability of the 911 service to request help. Large-scale natural disasters such as hurricanes and earthquakes often result in overloading and sometimes failure of communication facilities. Affected citizens are increasingly using social media to obtain and disseminate information. Social media is not only being used to communicate with first responders but also for people to organically volunteer and seek help from each other, complementing the role of first responders. In this paper, we examine the use of Twitter during two major hurricanes in the U.S. in 2017. We find that there exists a sizable number of people with access to the Internet even in areas where 911 services were down, and they tweet disaster-related information including requests for help. Our analysis indicates that social media can potentially help in disaster management and improve outcomes.


title: A taxonomy and platform for anomaly detection

abstract: There are hundreds of anomaly detection methods developed for different purposes and using a wide range of theoretical backgrounds, from system theory and signal processing towards artificial intelligence techniques. Therefore, it is very difficult for a specialist in a given domain (e.g. finance, industrial engineering, networking, environment monitoring, etc.) to select an anomaly detection method that fits best for a given application. The goal of this paper is to propose a taxonomy for anomaly detection methods and also to present a platform that allows a developer to find and tune a given anomaly detection method that is optimal for an application. The platform provides the basic functionalities needed to acquire, process and visualize multidimensional data collected from different sources, as well as the means to test and compare different anomaly detection techniques.


title: CDM ESD protection design with initial-on concept in nanoscale CMOS process

abstract: Integrated circuits (ICs) have been fabricated with thinner gate oxides to achieve higher speed and lower power consumption in nanoscale CMOS processes. However, the charged-device-model (CDM) electrostatic discharge (ESD) events became more critical because of the thinner gate oxide in nanoscale CMOS transistors and the larger die size for the system-on-chip (SoC) applications. Thus, effective on-chip ESD protection design against CDM ESD stresses has become more challenging to be implemented. A novel on-chip ESD protection design against CDM ESD events was proposed in this work, and its performance has been verified by the silicon chip fabricated in 55-nm CMOS process.


title: Design and Analysis of the synchronization control method for BCM/DCM current-mode flyback micro-inverter

abstract: A micro-inverter could be directly integrated with a PV panel to generate grid-compatible AC power. Placing two flyback converters in parallel as the DC-DC stage and utilizing interleaving extends the power range of flyback micro-inverters. In this paper, open-loop interleaving technique for flyback micro-inverter operating at the boundary conduction mode (BCM) and discontinuous conduction mode (DCM) with a master-slave relationship are analyzed first. Then, a novel interleaving phase synchronization control method is proposed to achieve high power conversion efficiency and stable operation at the same time. In DCM operation region the open-loop phase regulation will be utilized while in BCM a closed-loop technique will dominate. A 200 W interleaved flyback micro-inverter prototype is built to verify the proposed interleaving synchronization control method. Simulation and Experimental results are provided to show that the proposed closed-loop control method could achieve stable interleaving phase synchronization while maintain good efficiency and low THD performance.


title: A Taxonomy and Survey of Dynamic Graph Visualization

abstract: Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and development of the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.


title: What You Get Is What You See: A Visual Markup Decompiler.

abstract: Building on recent advances in image caption generation and optical character recognition (OCR), we present a general-purpose, deep learning-based system to decompile an image into presentational markup. While this task is a well-studied problem in OCR, our method takes an inherently different, data-driven approach. Our model does not require any knowledge of the underlying markup language, and is simply trained end-to-end on real-world example data. The model employs a convolutional network for text and layout recognition in tandem with an attention-based neural machine translation system. To train and evaluate the model, we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup, as well as a synthetic dataset of web pages paired with HTML snippets. Experimental results show that the system is surprisingly effective at generating accurate markup for both datasets. While a standard domain-specific LaTeX OCR system achieves around 25% accuracy, our model reproduces the exact rendered image on 75% of examples.


title: Conveying shape with texture: experimental investigations of texture's effects on shape categorization judgments

abstract: We describe the results of two comprehensive controlled observer experiments intended to yield insight into the following question: If we could design the ideal texture pattern to apply to an arbitrary smoothly curving surface in order to enable its 3D shape to be most accurately and effectively perceived, what would the characteristics of that texture pattern be? We begin by reviewing the results of our initial study in this series, which were presented at the 2003 IEEE Symposium on Information Visualization, and offer an expanded analysis of those findings. We continue by presenting the results of a follow-on study in which we sought to more specifically investigate the separate and combined influences on shape perception of particular texture components, with the goal of obtaining a clearer view of their potential information carrying capacities. In each study, we investigated the observers' ability to identify the intrinsic shape category of a surface patch (elliptical, hyperbolic, cylindrical, or flat) and its extrinsic surface orientation (convex, concave, both, or neither). In our first study, we compared performance under eight different texture type conditions, plus two projection conditions (perspective or orthographic) and two viewing conditions (head-on or oblique). We found that: 1) shape perception was better facilitated, in general, by the bidirectional "principal direction grid" pattern than by any of the seven other patterns tested; 2) shape type classification accuracy remained high under the orthographic projection condition for some texture types when the viewpoint was oblique; 3) perspective projection was required for accurate surface orientation classification; and 4) shape classification accuracy was higher when the surface patches were oriented at a (generic) oblique angle to the line of sight than when they were oriented (in a nongeneric pose) to face the viewpoint straight on. In our second study, we compared performance under eight new texture type conditions, redesigned to facilitate gathering insight into the cumulative effects of specific individual directional components in a wider variety of multidirectional texture patterns. We found that shape classification accuracy was equivalently good under a variety of test patterns that included components following either the first or first and second principal directions, in addition to other directions, suggesting that a principal direction grid texture is not the only possible "best option" for enhancing shape representation.


title: Chronic treatment with rivastigmine in patients with Alzheimer’s disease: A study on primary motor cortex excitability tested by 5Hz-repetitive transcranial magnetic stimulation

abstract: Abstract Objective To investigate changes in cortical excitability and short-term synaptic plasticity we delivered 5 Hz repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex in 11 patients with mild-to-moderate Alzheimer’s disease (AD) before and after chronic therapy with rivastigmine. Methods Resting motor threshold (RMT), motor evoked potential (MEP), cortical silent period (CSP) after single stimulus and MEP facilitation during rTMS trains were tested three times during treatment. All patients underwent neuropsychological tests before and after receiving rivastigmine. rTMS data in patients were compared with those from age-matched healthy controls. Results At baseline, RMT was significantly lower in patients than in controls whereas CSP duration and single MEP amplitude were similar in both groups. In patients, rTMS failed to induce the normal MEP facilitation during the trains. Chronic rivastigmine intake significantly increased MEP amplitude after a single stimulus, whereas it left the other neurophysiological variables studied unchanged. No significant correlation was found between patients’ neuropsychological test scores and TMS measures. Conclusions Chronic treatment with rivastigmine has no influence on altered cortical excitability and short-term synaptic plasticity as tested by 5 Hz-rTMS. Significance The limited clinical benefits related to cholinesterase inhibitor therapy in patients with AD depend on factors other than improved plasticity within the cortical glutamatergic circuits.


title: Optimal Priority Assignment And Feasibility Of Static Priority Tasks With Arbitrary Start Times

abstract: Within the hard real-time community, static priority pre-emptive scheduling is receiving increased attention. Current optimal priority assignment schemes require that at some point in the system lifetime all tasks must be released simultaneously. Two main optimal priority assignment schemes have been proposed: rate-monotonic, where task period equals deadline, and deadlinemonotonic where task deadline maybe less than period. When tasks are permitted to have arbitrary start times, a common release time between all tasks in a task set may not occur. In this eventuality, both rate-monotonic and deadline-monotonic priority assignments cease to be optimal. This paper presents an method of determining if the tasks with arbitrary release times will ever share a common release time. This has complexity O(m loge m) in the longest task period. Also, an optimal priority assignment method is given, of complexity O(n 2 + n) in the number of tasks. Finally, an efficient feasibility test is presented, for those task sets whose tasks do not share a common release time.


title: Job satisfaction and employee turnover: A firm-level perspective

abstract: In this paper, I study an employment situation where the employer and the employees cooperate about the implementation of a job satisfaction survey. Cooperation is valuable because it improves the firm's ability to predict employee quits, but it is only an equilibrium outcome because the employer-employee relation is repeated and long-term. Using a unique combination of firm-level data and information from job satisfaction surveys, the empirical analysis reveals that the cooperation reduces the firm's employee turnover costs significantly by improving its ability to predict quits. This cost reduction may easily exceed the cost of conducting the survey. The analysis also reveals that the firm is willing to sacrifice profits in a given year to be able to sustain the cooperative relationship with the employees.


Self-Driving Database Management Systems


title: SDN based Evolved Packet Core architecture for efficient user mobility support

abstract: In current generation LTE networks, user mobility leads to high levels of signaling traffic from the eNodeB (Evolved Node B) to the Evolved Packet Core (EPC) for maintaining the GPRS Tunneling Protocol (GTP) and Proxy Mobile IPv6 (PMIPv6) tunnel. Further, the presence of Packet Gateway (PGW) at the network edge introduces additional delay for every signaling procedure. In this paper, we propose an improved EPC architecture based on Software Defined Networking (SDN) concepts. This architecture (logically) centralizes the control plane functionality of EPC thereby eliminating the use of mobility management protocols and reducing mobility related signaling costs. The architecture utilizes the global network view feature of SDN for mobility management. The proposed architecture has been implemented in the ns-3 simulator framework. The results quantify the performance of the proposed architecture in terms of signaling cost, tunneling cost, handover latency and scalability.


title: Network Anomaly Detection System: The State of Art of Network Behaviour Analysis

abstract: This paper presents a taxonomy of anomaly detection techniques that is then used to survey and classify a number of research prototypes and commercial products. Commercial products and solutions based anomaly detection techniques are beginning to establish themselves in mainstream security solutions alongside firewalls, intrusion prevention systems and network monitoring solutions. These solutions are focused mainly on network-based anomaly detection, thus creating a new industry buzzword that describes it: Network Behavior Analysis. This classification is used predictably, pointing towards a number of areas of future research in the field of anomaly detection.


title: Language support for Morton-order matrices

abstract: The uniform representation of 2-dimensional arrays serially in Morton order (or {\eee} order) supports both their iterative scan with cartesian indices and their divide-and-conquer manipulation as quaternary trees. This data structure is important because it relaxes serious problems of locality and latency, and the tree helps to schedule multi-processing. Results here show how it facilitates algorithms that avoid cache misses and page faults at all levels in hierarchical memory, independently of a specific runtime environment. We have built a rudimentary C-to-C translator that implements matrices in Morton-order from source that presumes a row-major implementation. Early performance from LAPACK's reference implementation of \texttt{dgesv} (linear solver), and all its supporting routines (including \texttt{dgemm} matrix-multiplication) form a successful research demonstration. Its performance predicts improvements from new algebra in back-end optimizers. We also present results from a more stylish \texttt{dgemm} algorithm that takes better advantage of this representation. With only routine back-end optimizations inserted by hand (unfolding the base case and passing arguments in registers), we achieve machine performance exceeding that of the manufacturer-crafted {\tt dgemm} running at 67% of peak flops. And the same code performs similarly on several machines. Together, these results show how existing codes and future block-recursive algorithms can work well together on this matrix representation. Locality is key to future performance, and the new representation has a remarkable impact.