title: Toward predicting popularity of social marketing messages

abstract: Popularity of social marketing messages indicates the effectiveness of the corresponding marketing strategies. This research aims to discover the characteristics of social marketing messages that contribute to different level of popularity. Using messages posted by a sample of restaurants on Facebook as a case study, we measured the message popularity by the number of "likes" voted by fans, and examined the relationship between the message popularity and two properties of the messages: (1) content, and (2) media type. Combining a number of text mining and statistics methods, we have discovered some interesting patterns correlated to "more popular" and "less popular" social marketing messages. This work lays foundation for building computational models to predict the popularity of social marketing messages in the future.


title: Computational time-lapse video

abstract: We present methods for generating novel time-lapse videos that address the inherent sampling issues that arise with traditional photographic techniques. Starting with video-rate footage as input, our post-process downsamples the source material into a time-lapse video and provides user controls for retaining, removing, and resampling events. We employ two techniques for selecting and combining source frames to form the output. First, we present a non-uniform sampling method, based on dynamic programming, which optimizes the sampling of the input video to match the user's desired duration and visual objectives. We present multiple error metrics for this optimization, each resulting in different sampling characteristics. To complement the non-uniform sampling, we present the virtual shutter, a non-linear filtering technique that synthetically extends the exposure time of time-lapse frames.


title: Webis: An Ensemble for Twitter Sentiment Detection

abstract: We reproduce four Twitter sentiment classification approaches that participated in previous SemEval editions with diverse feature sets. The reproduced approaches are combined in an ensemble, averaging the individual classifiers’ confidence scores for the three classes (positive, neutral, negative) and deciding sentiment polarity based on these averages. The experimental evaluation on SemEval data shows our re-implementations to slightly outperform their respective originals. Moreover, not too surprisingly, the ensemble of the reproduced approaches serves as a strong baseline in the current edition where it is top-ranked on the 2015 test set.


title: Implementation Relations for the Distributed Test Architecture

abstract: Some systems interact with their environment at a number of physically distributed interfaces called ports. When testing such a system under test (SUT) it is normal to place a local tester at each port and the local testers form a local test case. If the local testers cannot interact with one another and there is no global clock then we are testing in the distributed test architecture. In this paper we explore the effect of the distributed test architecture when testing an SUT against an input output transition system, adapting the ioco implementation relation to this situation. In addition, we define what it means for a local test case to be deterministic, showing that we cannot always implement a deterministic global test case as a deterministic local test case. Finally, we show how a global test case can be mapped to a local test case.


title: Advanced Re-Engineering Approach in Humanoid Robot Development

abstract: (Sebastian Feldmann) Abstract—Based on new findings from human communications research, which show that in a social environment robots are accepted only if they are able to perform natural human movement, this contribution presents a new approach to improve the development process of humanoid robot prototypes under the term of social acceptance. Using the examples of the human shoulder- and the human hip-movement, there will be examined how the concept of re-engineering can be introduced at humanoid robot development in early stage. It is demonstrated how the inclusion of knowledge about the anatomic basis of human movement can help to overcome major problems of acceptance currently discussed by the literature under the term Uncanny Valley.


Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet


title: GUISE: a uniform sampler for constructing frequency histogram of graphlets

abstract: Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose Guise, which uses a Markov Chain Monte Carlo sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that Guise obtains the GFD with very low rate of error within few minutes, whereas the exhaustive counting-based approach takes several days.


title: Selective Hashing: Closing the Gap between Radius Search and k-NN Search

abstract: Locality Sensitive Hashing (LSH) and its variants, are generally believed to be the most effective radius search methods in high-dimensional spaces. However, many applications involve finding the k nearest neighbors (k-NN), where the k-NN distances of different query points may differ greatly and the performance of LSH suffers. We propose a novel indexing scheme called Selective Hashing, where a disjoint set of indices are built with different granularities and each point is only stored in the most effective index. Theoretically, we show that k-NN search using selective hashing can achieve the same recall as a fixed radius LSH search, using a radius equal to the distance of the c1kth nearest neighbor, with at most c2 times overhead, where c1 and c2 are small constants. Selective hashing is also easy to build and update, and outperforms all the state-of-the-art algorithms such as DSH and IsoHash.


title: Rank-revealing $QR$ factorizations and the singular value decomposition

abstract: T. Chan has noted that, even when the singular value decomposition of a matrix A is known, it is still not obvious how to find a rank-revealing QR factorization (RRQR) of A if A has numerical rank deficiency. This paper offers a constructive proof of the existence of the RRQR factorization of any matrix A of size m x n with numerical rank r . The bounds derived in this paper that guarantee the existence of RRQR are all of order f i ,in comparison with Chan's 0(2"-') . It has been known for some time that if A is only numerically rank-one deficient, then the column permutation l7 of A that guarantees a small rnn in the QR factorization of A n can be obtained by inspecting the size of the elements of the right singular vector of A corresponding to the smallest singular value of A . To some extent, our paper generalizes this well-known result. We consider the interplay between two important matrix decompositions: the singular value decomposition and the QR factorization of a matrix A . In particular, we are interested in the case when A is singular or nearly singular. It is well known that for any A E R m X n (a real matrix with rn rows and n columns, where without loss of generality we assume rn > n) there are orthogonal matrices U and V such that where C is a diagonal matrix with nonnegative diagonal elements: We assume that a, 2 a2 2 . . 2 on 2 0 . The decomposition (0.1) is the singular value decomposition (SVD) of A , and the ai are the singular values of A . The columns of V are the right singular vectors of A , and the columns of U are the left singular vectors of A . Mathematically, in terms of the singular values, Received December 1, 1990; revised February 8, 199 1. 199 1 Mathematics Subject Classification. Primary 65F30, 15A23, 15A42, 15A15.


title: Future on Power Electronics for Wind Turbine Systems

abstract: Wind power is still the most promising renewable energy in the year of 2013. The wind turbine system (WTS) started with a few tens of kilowatt power in the 1980s. Now, multimegawatt wind turbines are widely installed even up to 6-8 MW. There is a widespread use of wind turbines in the distribution networks and more and more wind power stations, acting as power plants, are connected directly to the transmission networks. As the grid penetration and power level of the wind turbines increase steadily, the wind power starts to have significant impacts to the power grid system. Therefore, more advanced generators, power electronic systems, and control solutions have to be introduced to improve the characteristics of the wind power plant and make it more suitable to be integrated into the power grid. Meanwhile, there are also some emerging technology challenges, which need to be further clarified and investigated. This paper gives an overview and discusses some development trends in the technologies used for wind power systems. First, the developments of technology and market are generally discussed. Next, several state-of-the-art wind turbine concepts, as well as the corresponding power electronic converters and control structures, are reviewed, respectively. Furthermore, grid requirements and the technology challenges for the future WTS are also addressed.


title: Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers

abstract: Brain Computer Interface (BCI) facilitates the communication with external devices (Computer) directly by the brain without involvement of any motor pathways. These systems are useful for assisting people with the impaired motor abilities. The project envisaged the development of a reliable motor imagery BCI capable of classifying right and left hand imaginary movements for controlling a computer cursor. The electroencephalogram (EEG) signals were recorded from the sensory-motor region of the brain using wireless recording device. Using the Event Related Desynchronization (ERD), movement related features were extracted from EEG with the spatial and spectral filtering. These features were classified into two classes using the non-liner Radial Basis Function based Artificial Neural Network (ANN) classifiers. A peak accuracy of 80% was achieved for classifying imaginary hand movements of 16 different subjects. Combination of the non-linear ANN classifiers with the signal processing techniques proved to be an effective method for classifying motor imagery for BCI.


title: Storage, Computation, and Communication: A Fundamental Tradeoff in Distributed Computing

abstract: We consider a MapReduce-like distributed computing system. We derive a lower bound on the communication cost for any given storage and computation costs. This lower bound matches the achievable bound we proposed recently. As a result, we completely characterize the optimal tradeoff between the storage, the computation, and the communication. Our result generalizes the previous one by Li et al. to also account for the number of computed intermediate values.


title: An effective Model Predictive Control for grid connected Packed U Cells multilevel inverter

abstract: This paper presents a Model Predictive Control (MPC) for grid-tied Packed U Cells (PUC) multilevel inverter. The system under study consists of a single-phase 3-cell PUC inverter connected to the grid through filtering inductor. The proposed topology allows the generation of 7-level output voltage with reduction of passive and active components compared to the conventional multilevel inverters. The aim of the proposed MPC technique is to achieve grid-tied current injection, low Total Harmonic Distortion (THD) of the current, unity power factor, while balancing the capacitor voltages at maximum power point (MPP). The feasibility of this strategy is validated by simulation using Matlab/Simulink environment.


THE IMPORTANCE OF HUMAN MENTAL WORKLOAD IN WEB DESIGN


title: The relationship of topographical memory performance to regional neurodegeneration in Alzheimer's disease

abstract: The network activated during normal route learning shares considerable homology with the network of degeneration in the earliest symptomatic stages of Alzheimer’s disease (AD). This inspired the Virtual Route Learning Test (VRLT) in which patients learn routes in a virtual reality environment. This study investigated the neural basis of VRLT performance in AD to test whether impairment was underpinned by a network or by the widely held explanation of hippocampal degeneration. VRLT score in a mild AD cohort was regressed against grey matter (GM) density and diffusion tensor metrics of white matter (WM) (n=30), and, cerebral glucose metabolism (n=26), using a mass univariate approach. GM density and cerebral metabolism were then submitted to a multivariate analysis (support vector regression) to examine whether there was a network associated with task performance. Univariate analyses of GM density, metabolism and WM axial diffusion converged on the vicinity of the retrosplenial/posterior cingulate cortex, isthmus and, possibly, hippocampal tail. The multivariate analysis revealed a significant, right hemisphere-predominant, network level correlation with cerebral metabolism; this comprised areas common to both activation in normal route learning and early degeneration in AD (retrosplenial and lateral parietal cortices). It also identified right medio-dorsal thalamus (part of the limbic-diencephalic hypometabolic network of early AD) and right caudate nucleus (activated during normal route learning). These results offer strong evidence that topographical memory impairment in AD relates to damage across a network, in turn offering complimentary lesion evidence to previous studies in healthy volunteers for the neural basis of topographical memory. The results also emphasize that structures beyond the mesial temporal lobe contribute to memory impairment in AD—it is too simplistic to view memory impairment in AD as a synonym for hippocampal degeneration.


title: Compact Hybrid Integrated 100-Gb/s Transmitter Optical Sub-Assembly Using Optical Butt-Coupling Between EADFB Lasers and Silica-Based AWG Multiplexer

abstract: A compact hybrid-integrated 100 Gb/s (4 lane × 25.78125 Gb/s) transmitter optical sub-assembly (TOSA) has been developed for a 100 Gb/s transceiver for 40-km transmission over a single-mode fiber. The TOSA has a simple configuration in which four electro-absorption modulator-integrated distributed feedback (EADFB) lasers are directly attached to the input waveguide end-face of a silica-based arrayed waveguide grating (AWG) multiplexer without bulk lenses. To achieve a high optical butt coupling efficiency between the EADFB lasers and the AWG multiplexer, we integrated a laterally tapered spot-size converter (SSC) for the EADFB laser and employed a waveguide with a high refractive index difference of 2.0% for the AWG multiplexer. By optimizing the laterally tapered SSC structure, we achieved a butt-coupling loss of less than 3 dB, which is an improvement of around 2 dB compared with a laser without an SSC structure. We also developed an ultracompact AWG multiplexer, which was 6.7 mm × 3.5 mm in size with an insertion loss of less than 1.9 dB. We achieved this by using a Mach-Zehnder interferometer-synchronized configuration to obtain a low loss and wide flat-top transmission filter spectra. The TOSA body size was 19.9 mm (L) × 6.0 mm (W) × 5.8 mm (H). Error-free operation was demonstrated for a 40-km transmission when all the lanes were driven simultaneously with a low EA modulator driving voltage of 1.5 V at an operating temperature of 55 °C.


title: Too Much of a Good Thing? Cardiac Vagal Tone's Nonlinear Relationship With Well-Being

abstract: Parasympathetic regulation of heart rate through the vagus nerve—often measured as resting respiratory sinus arrhythmia or cardiac vagal tone (CVT)—is a key biological correlate of psychological well-being. However, recent theorizing has suggested that many biological and psychological processes can become maladaptive when they reach extreme levels. This raises the possibility that CVT might not have an unmitigated positive relationship with well-being. In line with this reasoning, across 231 adult participants (Mage 40.02 years; 52% female), we found that CVT was quadratically related to multiple measures of well-being, including life satisfaction and depressive symptoms. Individuals with moderate CVT had higher well-being than those with low or high CVT. These results provide the first direct evidence of a nonlinear relationship between CVT and well-being, adding to a growing body of research that has suggested some biological processes may cease being adaptive when they reach extreme levels.


title: Generation of germline-competent induced pluripotent stem cells

abstract: We have previously shown that pluripotent stem cells can be induced from mouse fibroblasts by retroviral introduction of Oct3/4 (also called Pou5f1), Sox2, c-Myc and Klf4, and subsequent selection for Fbx15 (also called Fbxo15) expression. These induced pluripotent stem (iPS) cells (hereafter called Fbx15 iPS cells) are similar to embryonic stem (ES) cells in morphology, proliferation and teratoma formation; however, they are different with regards to gene expression and DNA methylation patterns, and fail to produce adult chimaeras. Here we show that selection for Nanog expression results in germline-competent iPS cells with increased ES-cell-like gene expression and DNA methylation patterns compared with Fbx15 iPS cells. The four transgenes (Oct3/4, Sox2, c-myc and Klf4) were strongly silenced in Nanog iPS cells. We obtained adult chimaeras from seven Nanog iPS cell clones, with one clone being transmitted through the germ line to the next generation. Approximately 20% of the offspring developed tumours attributable to reactivation of the c-myc transgene. Thus, iPS cells competent for germline chimaeras can be obtained from fibroblasts, but retroviral introduction of c-Myc should be avoided for clinical application.


Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach


title: General fuzzy min-max neural network for clustering and classification

abstract: This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given.


Material Recognition: Bayesian Inference or SVMs?


title: A multilevel relaxation algorithm for simultaneous localization and mapping

abstract: This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.


title: Unsupervised Control Through Non-Parametric Discriminative Rewards

abstract: Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.


title: A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment

abstract: Electrocardiogram (ECG) signal quality assessment (SQA) plays a vital role in significantly improving the diagnostic accuracy and reliability of unsupervised ECG analysis systems. In practice, the ECG signal is often corrupted with different kinds of noises and artifacts. Therefore, numerous SQA methods were presented based on the ECG signal and/or noise features and the machine learning classifiers and/or heuristic decision rules. This paper presents an overview of current state-of-the-art SQA methods and highlights the practical limitations of the existing SQA methods. Based upon past and our studies, it is noticed that a lightweight ECG noise analysis framework is highly demanded for real-time detection, localization, and classification of single and combined ECG noises within the context of wearable ECG monitoring devices which are often resource constrained.


title: Hierarchical Real-Time Multi-core Scheduling through Virtualization: A Survey

abstract: In recent years mechatronic systems have evolved towards a new generation of cyber-physical systems. Technology has shifted from mechanical control systems towards electronic control systems, which introduces a fast growing complexity. The use of multi-core processors and virtualization techniques on embedded systems tackles this complexity by decoupling software from hardware. Virtualization introduces a hierarchical scheduling structure, where hard real-time behavior must be guaranteed within the level of guest operating system scheduler and at the level of the hypervisor scheduler. This paper gives the current state of the art of schedulability analysis methodologies for hierarchical scheduling techniques and its application in real-time hypervisors.


title: Rule-based Korean Grapheme to Phoneme Conversion Using Sound Patterns

abstract: Grapheme-to-phoneme conversion plays an important role in text-to-speech ap- plications and other fields of computational linguistics. Although Korean uses a phonemic writing system, it must have a grapheme-to-phoneme conversion for speech synthesis because Korean writing system does not always reflect its actual pronunciations. This paper describes a grapheme-to-phoneme conversion method based on sound patterns to convert Korean text strings into phonemic representations. In the experiment with Korean news broadcasting evaluation set of 20 sentences, the accuracy of our system achieve as high as 98.70% on con- version. The performance of our rule-based system shows that the rule-based sound patterns are effective on Korean grapheme-to-phoneme conversion.


title: Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

abstract: Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were recorded. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data provides information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.


Semiparametric Reinforcement Learning


title: Haptic coupling with augmented feedback between two KUKA Light-Weight Robots and the PR2 robot arms

abstract: This paper discusses the theoretical background and practical implementation of a large-scale, low-performance haptic remote control setup. The experimental system consists of a pair of KUKA Light Weight Robots (LWR) coupled to a Willow Garage Personal Robot (PR2) via two different robotic frameworks. The haptic “performance” is, of course, not comparable to dedicated haptic applications, but has its use as a test-bed for interaction between “legacy” service robot systems, that have not been especially designed for mutual haptic interaction. We discuss some major application problems, and the future work needed for nonuniform robot coupling. Beside haptic coupling, we provide the human operator with visual feedback. To this end, the head movements of the human operator are coupled to the head movement of the PR2 and the images of the eye cameras are displayed to the human operator using a wearable display. The presented teleoperation application is furthermore an example of the integration of two component-based robotic frameworks namely OROCOS (Open Robot Control Software)and ROS (Robot Operating System) Experimental results regarding the haptic coupling are presented using an “artistic” painting task for qualitative results, and a hard contact at the slave side for quantitative results.


title: Fabric defect classification using wavelet frames and minimum classification error training

abstract: This paper proposes a new method for fabric defect classification by incorporating the design of a wavelet frames based feature extractor with the design of a Euclidean distance based classifier. Channel variances at the outputs of the wavelet frame decomposition are used to characterize each nonoverlapping window of the fabric image. A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features. With a Euclidean distance based classifier, each nonoverlapping window of the fabric image is then assigned to its corresponding category. Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on minimum classification error (MCE) training method. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 nondefect samples, where 93.1% classification accuracy has been achieved.


title: Central pattern generators for locomotion control in animals and robots: a review

abstract: The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.


The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System


title: ADB: an efficient multihop broadcast protocol based on asynchronous duty-cycling in wireless sensor networks

abstract: The use of asynchronous duty-cycling in wireless sensor network MAC protocols is common, since it can greatly reduce energy consumption and requires no clock synchronization. However, existing systems using asynchronous duty-cycling do not efficiently support broadcast-based communication that may be used, for example, in route discovery or in network-wide queries or information dissemination. In this paper, we present the design and evaluation of ADB (Asynchronous Duty-cycle Broadcasting), a new protocol for efficient multihop broadcast in wireless sensor networks using asynchronous duty-cycling. ADB differs from traditional multihop broadcast protocols that operate above the MAC layer, in that it is integrated with the MAC layer to exploit information only available at this layer. Rather than treating the data transmission from a node to all of its neighbors as the basic unit of progress for the multihop broadcast, ADB dynamically optimizes the broadcast at the level of transmission to each individual neighbor of a node, as the neighbors asynchronously wakeup. We evaluate ADB both through ns-2 simulations and through measurements in a testbed of MICAz motes using TinyOS, and compare its performance to multihop broadcast based on X-MAC and on RI-MAC. In both evaluations, ADB substantially reduced energy consumption, network load, and delivery latency compared to other protocols, while achieving over 99% delivery ratio.


title: Dynamic personalized pagerank in entity-relation graphs

abstract: Extractors and taggers turn unstructured text into entity-relation(ER) graphs where nodes are entities (email, paper, person,conference, company) and edges are relations (wrote, cited,works-for). Typed proximity search of the form type=personNEAR company~"IBM", paper~"XML" is an increasingly usefulsearch paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, space-efficient proximity searches in ER graphs. During preprocessing, HubRank computesand indexes certain "sketchy" random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small "active" subgraph is identified, bordered bynodes with indexed fingerprints. These fingerprints are adaptively loaded to various resolutions to form approximate personalized Pagerank vectors (PPVs). PPVs at remaining active nodes are now computed iteratively. We report on experiments with CiteSeer's ER graph and millions of real Cite Seer queries. Some representative numbers follow. On our testbed, HubRank preprocesses and indexes 52 times faster than whole-vocabulary PPV computation. A text index occupies 56 MB. Whole-vocabulary PPVs would consume 102GB. If PPVs are truncated to 56 MB, precision compared to true Pagerank drops to 0.55; incontrast, HubRank has precision 0.91 at 63MB. HubRank's average querytime is 200-300 milliseconds; query-time Pagerank computation takes 11 seconds on average.


title: SME e-readiness in Malaysia : Implications for Planning and Implementation

abstract: This study hoped to answer 2 main objectives. The first objective was to assess the level of e-readiness of SMEs in Northern Malaysia. The second objective was to investigate the factors contributing to the e-readiness of SMEs in Northern Malaysia. Questionnaires were distributed using a simple random sampling method to 300 SMEs in Penang, Kedah and Perlis. The findings of this study show that SMEs in Northern Malaysia are ready to go for e-business, e-commerce and Internet in general. The findings also showed that in general top management commitment and infrastructure and technology have significant impact on SMEs’ e-readiness. However, human capital, resistance to change, and information security do not have significant impact or contribution on e-readiness in SMEs.


title: Single Image Action Recognition Using Semantic Body Part Actions

abstract: In this paper, we propose a novel single image action recognition algorithm based on the idea of semantic part actions. Unlike existing part-based methods, we argue that there exists a mid-level semantic, the semantic part action; and human action is a combination of semantic part actions and context cues. In detail, we divide human body into seven parts: head, torso, arms, hands and lower body. For each of them, we define a few semantic part actions (e.g. head: laughing). Finally, we exploit these part actions to infer the entire body action (e.g. applauding). To make the proposed idea practical, we propose a deep network-based framework which consists of two subnetworks, one for part localization and the other for action prediction. The action prediction network jointly learns part-level and body-level action semantics and combines them for the final decision. Extensive experiments demonstrate our proposal on semantic part actions as elements for entire body action. Our method reaches mAP of 93.9% and 91.2% on PASCAL VOC 2012 and Stanford-40, which outperforms the state-of-the-art by 2.3% and 8.6%.


title: PScout: analyzing the Android permission specification

abstract: Modern smartphone operating systems (OSs) have been developed with a greater emphasis on security and protecting privacy. One of the mechanisms these systems use to protect users is a permission system, which requires developers to declare what sensitive resources their applications will use, has users agree with this request when they install the application and constrains the application to the requested resources during runtime. As these permission systems become more common, questions have risen about their design and implementation. In this paper, we perform an analysis of the permission system of the Android smartphone OS in an attempt to begin answering some of these questions. Because the documentation of Android's permission system is incomplete and because we wanted to be able to analyze several versions of Android, we developed PScout, a tool that extracts the permission specification from the Android OS source code using static analysis. PScout overcomes several challenges, such as scalability due to Android's 3.4 million line code base, accounting for permission enforcement across processes due to Android's use of IPC, and abstracting Android's diverse permission checking mechanisms into a single primitive for analysis. We use PScout to analyze 4 versions of Android spanning version 2.2 up to the recently released Android 4.0. Our main findings are that while Android has over 75 permissions, there is little redundancy in the permission specification. However, if applications could be constrained to only use documented APIs, then about 22% of the non-system permissions are actually unnecessary. Finally, we find that a trade-off exists between enabling least-privilege security with fine-grained permissions and maintaining stability of the permission specification as the Android OS evolves.


title: Characterizing Smartwatch Usage in the Wild

abstract: Smartwatch has become one of the most popular wearable computers on the market. We conduct an IRB-approved measurement study involving 27 Android smartwatch users. Using a 106-day dataset collected from our participants, we perform in-depth characterization of three key aspects of smartwatch usage "in the wild": usage patterns, energy consumption, and network traffic. Based on our findings, we identify key aspects of the smartwatch ecosystem that can be further improved, propose recommendations, and point out future research directions.


Urban computing: enabling urban intelligence with big data


title: A sub-100µW 2GHz differential colpitts CMOS/FBAR VCO

abstract: We present a thin film bulk acoustic wave resonator (FBAR)-based Colpitts VCO with a cross-coupled gain boosting transistor pair. The combination of the cross-coupled pair and capacitive feedback in the Colpitts oscillator increases the effective gm to reduce the start-up current requirement, increases the output swing at a low supply voltage, and reduces the phase noise. The differential Colpitts VCO was fabricated in a 0.13µm CMOS process and oscillates at 2GHz under a wide range of supply voltage (0.51V–1.5V). The minimum power consumption is 67µW with −141 dBc/Hz phase noise at a 1 MHz offset and a −220dB Figure-of-Merit. At nominal 0.6V supply voltage, the VCO consumes 126 µW, achieves −149 dBc/Hz phase noise at 1MHz offset and shows a FOM of −224dB.


title: Using a model of social dynamics to predict popularity of news

abstract: Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity a priori, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.


title: VibSense: Sensing Touches on Ubiquitous Surfaces through Vibration

abstract: VibSense pushes the limits of vibration-based sensing to determine the location of a touch on extended surface areas as well as identify the object touching the surface leveraging a single sensor. Unlike capacitive sensing, it does not require conductive materials and compared to audio sensing it is more robust to acoustic noise. It supports a broad array of applications through either passive or active sensing using only a single sensor. In VibSense's passive sensing, the received vibration signals are determined by the location of the touch impact. This allows location discrimination of touches precise enough to enable emerging applications such as virtual keyboards on ubiquitous surfaces for mobile devices. Moreover, in the active mode, the received vibration signals carry richer information of the touching object's characteristics (e.g., weight, size, location and material). This further enables VibSense to match the signals to the trained profiles and allows it to differentiate personal objects in contact with any surface. VibSense is evaluated extensively in the use cases of localizing touches (i.e., virtual keyboards), object localization and identification. Our experimental results demonstrate that VibSense can achieve high accuracy, over 95%, in all these use cases.


title: Psychosexual outcome of gender-dysphoric children

abstract: Abstract Objective To establish the psychosexual outcome of gender-dysphoric children at 16 years or older and to examine childhood characteristics related to psychosexual outcome. Method We studied 77 children who had been referred in childhood to our clinic because of gender dysphoria (59 boys, 18 girls; mean age 8.4 years, age range 5–12 years). In childhood, we measured the children's cross-gender identification and discomfort with their own sex and gender roles. At follow-up 10.4 ± 3.4 years later, 54 children (mean age 18.9 years, age range 16–28 years) agreed to participate. In this group, we assessed gender dysphoria and sexual orientation. Results At follow-up, 30% of the 77 participants (19 boys and 4 girls) did not respond to our recruiting letter or were not traceable; 27% (12 boys and 9 girls) were still gender dysphoric (persistence group), and 43% (desistance group: 28 boys and 5 girls) were no longer gender dysphoric. Both boys and girls in the persistence group were more extremely cross-gendered in behavior and feelings and were more likely to fulfill gender identity disorder (GID) criteria in childhood than the children in the other two groups. At follow-up, nearly all male and female participants in the persistence group reported having a homosexual or bisexual sexual orientation. In the desistance group, all of the girls and half of the boys reported having a heterosexual orientation. The other half of the boys in the desistance group had a homosexual or bisexual sexual orientation. Conclusions Most children with gender dysphoria will not remain gender dysphoric after puberty. Children with persistent GID are characterized by more extreme gender dysphoria in childhood than children with desisting gender dysphoria. With regard to sexual orientation, the most likely outcome of childhood GID is homosexuality or bisexuality. J. Am. Acad. Child and Adolesc. Psychiatry , 2008;47(12):1413–1423.


Big Data Research in Information Systems: Toward an Inclusive Research Agenda


title: Closing the loop in cortically-coupled computer vision : a brain – computer interface for searching image databases

abstract: We describe a closed-loop brain–computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.


title: The natural non-protein amino acid N-β-methylamino-l-alanine (BMAA) is incorporated into protein during synthesis

abstract: N-β-methylamino-L-alanine (BMAA) is an amino acid produced by cyanobacteria and accumulated through trophic levels in the environment and natural food webs. Human exposure to BMAA has been linked to progressive neurodegenerative diseases, potentially due to incorporation of BMAA into protein. The insertion of BMAA and other non-protein amino acids into proteins may trigger protein misfunction, misfolding and/or aggregation. However, the specific mechanism by which BMAA is associated with proteins remained unidentified. Such studies are challenging because of the complexity of biological systems and samples. A cell-free in vitro protein synthesis system offers an excellent approach for investigation of changing amino acid composition in protein. In this study, we report that BMAA incorporates into protein as an error in synthesis when a template DNA sequence is used. Bicinchoninic acid assay of total protein synthesis determined that BMAA effectively substituted for alanine and serine in protein product. LC-MS/MS confirmed that BMAA was selectively inserted into proteins in place of other amino acids, but isomers N-(2-aminoethyl)glycine (AEG) and 2,4-diaminobutyric acid (DAB) did not share this characteristic. Incorporation of BMAA into proteins was significantly higher when genomic DNA from post-mortem brain was the template. About half of BMAA in the synthetic proteins was released with denaturation with sodium dodecylsulfonate and dithiothreitol, but the remaining BMAA could only be released by acid hydrolysis. Together these data demonstrate that BMAA is incorporated into the amino acid backbone of proteins during synthesis and also associated with proteins through non-covalent bonding.


title: On the performance of ConvNet features for place recognition

abstract: After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different. This paper comprehensively evaluates and compares the utility of three state-of-the-art ConvNets on the problems of particular relevance to navigation for robots; viewpoint-invariance and condition-invariance, and for the first time enables real-time place recognition performance using ConvNets with large maps by integrating a variety of existing (locality-sensitive hashing) and novel (semantic search space partitioning) optimization techniques. We present extensive experiments on four real world datasets cultivated to evaluate each of the specific challenges in place recognition. The results demonstrate that speed-ups of two orders of magnitude can be achieved with minimal accuracy degradation, enabling real-time performance. We confirm that networks trained for semantic place categorization also perform better at (specific) place recognition when faced with severe appearance changes and provide a reference for which networks and layers are optimal for different aspects of the place recognition problem.


title: Diversity measure minimization based method for computing sparse solutions to linear inverse problems with multiple measurement vectors

abstract: We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in many different applications. Here, we consider in depth the extension of two classes of algorithms-Matching Pursuit (MP) and FOCal Underdetermined System Solver (FOCUSS)-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed. Cost functions appropriate to the multiple measurement problem are developed, and algorithms are derived based on their minimization. A simulation study is conducted on a test-case dictionary to show how the utilization of more than one measurement vector improves the performance of the MP and FOCUSS classes of algorithm, and their performances are compared.


title: UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

abstract: Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do.


title: Impact Sourcing: Employing Prison Inmates to Perform Digitally Enabled Business Services

abstract: This chapter explores the practice of employing prisoners to perform business services for the public and private sectors. The International Centre for Prison Studies reports that there are 6,291,179 prisoners worldwide. The United States (US) has the greatest number of inmates with 2,193,789 prisoners and the highest incarceration rate at 737 inmates per 100,000 people compared to all other countries. Given that 95% of US inmates will one day be released and given that 70% of released inmates become repeat offenders (FPI 2012), any intervention that can help released inmates re-enter society successfully must have positive impacts on the individuals, their families and communities. Prison employment programs have been one such intervention method. In this chapter, we aim to study a special type of prison employment program: the hiring and training of prisoners to perform business services using a computer, otherwise known as “digitally enabled” business services (Carmel et al. 2013).


title: Missing Data Imputation using Genetic Algorithm for Supervised Learning

abstract: Data is an important asset for any organization to ::: successfully run its business. When we collect data, it contains ::: data with low qualities such as noise, incomplete, missing values ::: etc. If the quality of data is low then mining results of any ::: data mining algorithm will also below. In this paper, we propose ::: a technique to deal with missing values. Genetic algorithm ::: (GA) is used for the estimation of missing values in datasets. ::: GA is introduced to generate optimal sets of missing values ::: and information gain (IG) is used as the fitness function to ::: measure the performance of an individual solution. Our goal ::: is to impute missing values in a dataset for better classification ::: results. This technique works even better when there is a higher ::: rate of missing values or incomplete information along with a ::: greater number of distinct values in attributes/features having ::: missing values. We compare our proposed technique with single ::: imputation techniques and multiple imputations (MI) statistically ::: based approaches on various benchmark classification techniques ::: on different performance measures. We show that our proposed ::: methods outperform when compare with another state of the art ::: missing data imputation techniques.


title: A dynamic load balancing for massive multiplayer online game server

abstract: On-line games are becoming more popular lately as the Internet becomes popular, game platforms become diverse and a ubiquitous game environment is supported. Therefore, distributed game server technology is required to support large numbers of concurrent game users simultaneously. Especially, while game users are playing games, many unpredictable problems can arise, such as a certain server handles more server loads than recommended because many game users crowded into a specific region of a game world. These kinds of situations can lead to whole game server instability. In this paper, global dynamic load balancing model and distributed MMOG(Massive Multiplayer Online Game) server architecture are proposed to apply our load balancing algorithm. Many different experiments were carried out to test for efficiency. Also an example of applying real MMOG application to our research work is shown.


title: ALS Clinical Trials Review: 20 Years of Failure. Are We Any Closer to Registering a New Treatment?

abstract: Amyotrophic lateral sclerosis (ALS) is a devastating condition with an estimated mortality of 30 000 patients a year worldwide. A median reported survival time since onset ranges from 24 to 48 months. Riluzole is the only currently approved mildly efficacious treatment. Riluzole received marketing authorization in 1995 in the USA and in 1996 in Europe. In the years that followed, over 60 molecules have been investigated as a possible treatment for ALS. Despite significant research efforts, the overwhelming majority of human clinical trials (CTs) have failed to demonstrate clinical efficacy. In the past year, oral masitinib and intravenous edaravone have emerged as promising new therapeutics with claimed efficacy in CTs in ALS patients. Given their advanced phase of clinical development one may consider these drugs as the most likely near-term additions to the therapeutic arsenal available for patients with ALS. In terms of patient inclusion, CT with masitinib recruited a wider, more representative, less restrictive patient population in comparison to the only successful edaravone CT (edaravone eligibility criteria represents only 18% of masitinib study patients). The present manuscript reviews >50 CTs conducted in the last 20 years since riluzole was first approved. A special emphasis is put on the analysis of existing evidence in support of the clinical efficacy of edaravone and masitinib and the possible implications of an eventual marketing authorisation in the treatment of ALS.


title: Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework

abstract: Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool.


title: Implementation of fully isolated Low Vgs nLDMOS with low specific on-resistance

abstract: In this paper, we present a new isolated Low Vgs NLDMOS in 0.35um BCDMOS process. The proposed LDMOS is fully isolated from substrate and has very lower Rsp(specific on-resistance) than other competitors. This device can apply a negative bias to drain and it can be used in AMOLED application. The proposed LDMOS devices in 30–40V ranges have the lowest Rsp with other competitors in 0.13–0.35um BCDMOS technologies. And the Rsp of the proposed LDMOS in 40V range is 46.3% lower than Low Vgs LDMOS last reported. And the isolation efficiency of the proposed LDMOS has very good performance. Furthermore, a logic CMOS and all the other components are compatible in the proposed process.


title: Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification

abstract: This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.


title: 3D Object Localization using Forward Looking Sonar (FLS) and Optical Camera via particle filter based calibration and fusion

abstract: Underwater Object Localization is widely used in the industry in Autonomous Underwater Vehicles (AUV), both in sea and lake environments for various applications. Sonars and Cameras are popular choices for this, but each sensor alone poses several problems. Data extraction from Optical Cameras underwater is a challenge due to poor lighting conditions, hazing over large distances and spatio-temporal irradiate (flickering), while Sonars tend to have coarser sensor resolution and a lower signal-to-noise ratio (SNR) making it difficult to extract data. This makes false positives more likely. In this paper, we present a robust method to localize objects in front of an AUV in 3D space, using camera imagery, sonar imagery and odometry information from onboard sensors. This is done through various image processing techniques, and a hybrid sonar/camera particle filter based calibration step and fusion step.


title: Self-compassion and social anxiety disorder

abstract: Abstract Self-compassion refers to having an accepting and caring orientation towards oneself. Although self-compassion has been studied primarily in healthy populations, one particularly compelling clinical context in which to examine self-compassion is social anxiety disorder (SAD). SAD is characterized by high levels of negative self-criticism as well as an abiding concern about others’ evaluation of one's performance. In the present study, we tested the hypotheses that: (1) people with SAD would demonstrate less self-compassion than healthy controls (HCs), (2) self-compassion would relate to severity of social anxiety and fear of evaluation among people with SAD, and (3) age would be negatively correlated with self-compassion for people with SAD, but not for HC. As expected, people with SAD reported less self-compassion than HCs on the Self-Compassion Scale and its subscales. Within the SAD group, lesser self-compassion was not generally associated with severity of social anxiety, but it was associate...


title: Dispositional factors predicting use of online dating sites and behaviors related to online dating

abstract: Although prior research has examined how individual difference factors are related to relationship initiation and formation over the Internet (e.g., online dating sites, social networking sites), little research has examined how dispositional factors are related to other aspects of online dating. The present research therefore sought to examine the relationship between several dispositional factors, such as Big-Five personality traits, self-esteem, rejection sensitivity, and attachment styles, and the use of online dating sites and online dating behaviors. Rejection sensitivity was the only dispositional variable predictive of use of online dating sites whereby those higher in rejection sensitivity are more likely to use online dating sites than those lower in rejection sensitivity. We also found that those higher in rejection sensitivity, those lower in conscientiousness, and men indicated being more likely to engage in potentially risky behaviors related to meeting an online dating partner face-to-face. Further research is needed to further explore the relationships between these dispositional factors and online dating behaviors.


title: Refining Non-Taxonomic Relation Labels with External Structured Data to Support Ontology Learning

abstract: This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base improves the quality of suggested relation labels. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.


title: An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network

abstract: Detection of helmeted and non-helmeted motorcyclist is mandatory now-a-days in order to ensure the safety of riders on the road. However, due to many constraints such as poor video quality, occlusion, illumination, and other varying factors it becomes very difficult to detect them accurately. In this paper, we introduce an approach for automatic detection of helmeted and non-helmeted motorcyclist using convolutional neural network (CNN). During the past several years, the advancements in deep learning models have drastically improved the performance of object detection. One such model is YOLOv2 [1] which combines both classification and object detection in a single architecture. Here, we use YOLOv2 at two different stages one after another in order to improve the helmet detection accuracy. At the first stage, YOLOv2 model is used to detect different objects in the test image. Since this model is trained on COCO dataset, it can detect all classes of the COCO dataset. In the proposed approach, we use detection of person class instead of motorcycle in order to increase the accuracy of helmet detection in the input image. The cropped images of detected persons are used as input to second YOLOv2 stage which was trained on our dataset of helmeted images. The non-helmeted images are processed further to extract license plate by using OpenALPR. In the proposed approach, we use two different datasets i.e., COCO and helmet datasets. We tested the potential of our approach on different helmeted and non-helmeted images. Experimental results show that the proposed method performs better when compared to other existing approaches with 94.70% helmet detection accuracy.


title: Mining Causality of Network Events in Log Data

abstract: Network log messages (e.g., syslog) are expected to be valuable and useful information to detect unexpected or anomalous behavior in large scale networks. However, because of the huge amount of system log data collected in daily operation, it is not easy to extract pinpoint system failures or to identify their causes. In this paper, we propose a method for extracting the pinpoint failures and identifying their causes from network syslog data. The methodology proposed in this paper relies on causal inference that reconstructs causality of network events from a set of time series of events. Causal inference can filter out accidentally correlated events, thus it outputs more plausible causal events than traditional cross-correlation-based approaches can. We apply our method to 15 months’ worth of network syslog data obtained from a nationwide academic network in Japan. The proposed method significantly reduces the number of pseudo correlated events compared with the traditional methods. Also, through three case studies and comparison with trouble ticket data, we demonstrate the effectiveness of the proposed method for practical network operation.


title: The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

abstract: The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect here is the management of robot errors, including fast and accurate online robot-error detection and correction. Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement. In this paper we evaluate whether a novel framework based on deep convolutional neural networks (deep ConvNets) could improve the accuracy of decoding robot errors from the EEG of a human observer, both during an object grasping and a pouring task. We show that deep ConvNets reached significantly higher accuracies than both regularized Linear Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA, both widely used EEG classifiers. Deep ConvNets reached mean accuracies of 75% ± 9 %, rLDA 65% ± 10% and FB-CSP + rLDA 63% ± 6% for decoding of erroneous vs. correct trials. Visualization of the time-domain EEG features learned by the ConvNets to decode errors revealed spatiotemporal patterns that reflected differences between the two experimental paradigms. Across subjects, ConvNet decoding accuracies were significantly correlated with those obtained with rLDA, but not CSP, indicating that in the present context ConvNets behaved more “rLDA-like” (but consistently better), while in a previous decoding study with another task but the same ConvNet architecture, it was found to behave more “CSP-like”. Our findings thus provide further support for the assumption that deep ConvNets are a versatile addition to the existing toolbox of EEG decoding techniques, and we discuss steps how ConvNet EEG decoding performance could be further optimized.


title: Unilateral hearing loss in children: a retrospective study and a review of the current literature

abstract: Despite the introduction of universal newborn hearing screening (UNHS), unilateral hearing loss (UHL) is sometimes recognized late. This diagnostic delay has adverse repercussions, given the importance of binaural hearing for the development of normal auditory processing. It is incorrect to maintain that unilateral hearing is the minimum requirement for adequate speech development and that hearing aid provision is consequently unnecessary. In our retrospective study, hearing aid provision resulted in improved directional and selective hearing (quiet and noisy environments) and, compared with their chronically ill counterparts, the children in our study displayed superior health-related quality of life (HRQoL) scores in all areas. On the basis of the results, the authors conclude that even mild hearing losses (from an auditory threshold of 30 to 40 dB) should have the opportunity for hearing aid provision. A selective literature review was conducted in PubMed and textbooks and with reference to national and international guidelines. Early diagnosis and treatment of UHL have a positive effect on verbal-cognitive, linguistic, communicative, and socio-emotional development, as demonstrated by neurophysiological studies. Among the treatment modalities with differing effects on the quality of binaural hearing, cochlear implants are now used increasingly in children with hearing loss bordering on deafness.


title: Good genes, complementary genes and human mate preferences

abstract: The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.


title: Towards semantic context-aware drones for aerial scenes understanding

abstract: Visual object tracking with unmanned aerial vehicles (UAVs) plays a central role in the aerial surveillance. Reliable object detection depends on many factors such as large displacements, occlusions, image noise, illumination and pose changes or image blur that may compromise the object labeling. The paper presents a proposal for a hybrid solution that adds semantic information to the video tracking processing: along with the tracked objects, the scene is completely depicted by data from places, natural features, or in general Points of Interest (POIs). Each scene from a video sequence is semantically described by ontological statements which, by inference, support the object identification which often suffers from some weakness in the object tracking methods. The synergy between the tracking methods and semantic technologies seems to bridge the object labeling gap, enhance the understanding of the situation awareness, as well as critical alarming situations.


title: Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming

abstract: While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge is needed to correctly answer the questions. Proposed alternatives involve translating the question and the natural language text to a logical representation and then use logical reasoning. However, this alternative falters when the size of the text gets bigger. To address this we propose an approach that does logical reasoning over premises written in natural language text. The proposed method uses recent features of Answer Set Programming (ASP) to call external NLP modules (which may be based on ML) which perform simple textual entailment. To test our approach we develop a corpus based on the life cycle questions and showed that Our system achieves up to $18\%$ performance gain when compared to standard MCQ solvers.


title: GALS systems prototyping using multiclock FPGAs and asynchronous network-on-chips

abstract: This paper presents an innovating methodology for network-centric globally-asynchronous locally-synchronous (GALS) system prototyping. High-performance multiclock FPGAs are exploited for easy and fast prototyping of GALS systems based of an asynchronous network-on-chip (ANoC) interfacing synchronous standard IP cores. Modularity property of asynchronous circuits is fully exploited to design regular distributed interconnect topologies by the means of basic topology-free building blocks, with a focus and special design effort to solve arbitration and synchronization problems. A case-study is implemented on an up-to-date FPGA which includes two independently clocked processors, memory banks, serial and parallel communication links and an asynchronous DES (data encryption standard) module connected through an asynchronous 5/spl times/5 crossbar. The clock-less modules are implemented using a quasidelay insensitive logic on the FPGA by the means of a dedicated library. Performance figures are reported on the FPGA platform, especially for communication costs, speed and latency of the ANoC.


title: SCANRAW: A Database Meta-Operator for Parallel In-Situ Processing and Loading

abstract: In this article, we propose SCANRAW, a novel database meta-operator for in-situ processing over raw files that integrates data loading and external tables seamlessly, while preserving their advantages: optimal performance across a query workload and zero time-to-query. We decompose loading and external table processing into atomic stages in order to identify common functionality. We analyze alternative implementations and discuss possible optimizations for each stage. Our major contribution is a parallel superscalar pipeline implementation that allows SCANRAW to take advantage of the current many- and multicore processors by overlapping the execution of independent stages. Moreover, SCANRAW overlaps query processing with loading by speculatively using the additional I/O bandwidth arising during the conversion process for storing data into the database, such that subsequent queries execute faster. As a result, SCANRAW makes intelligent use of the available system resources—CPU cycles and I/O bandwidth—by switching dynamically between tasks to ensure that optimal performance is achieved. We implement SCANRAW in a state-of-the-art database system and evaluate its performance across a variety of synthetic and real-world datasets. Our results show that SCANRAW with speculative loading achieves the best-possible performance for a query sequence at any point in the processing. Moreover, SCANRAW maximizes resource utilization for the entire workload execution while speculatively loading data and without interfering with normal query processing.


title: Sarcasm Detection on Czech and English Twitter

abstract: This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582).


Decoding EEG Brain Signals using Recurrent Neural Networks


A Dependency Grammar for Amharic Michael Gasser


title: Design and Characterization of a Robotized Gearbox System Based on Voice Coil Actuators for a Formula SAE Race Car

abstract: This paper discusses the use of voice coil actuators to enhance the performance of shift-by-wire systems. This innovative purely electric actuation approach was implemented and applied to a Formula SAE race car. The result was more compact and faster than conventional solutions, which usually employ pneumatic actuators. The designed shift-by-wire system incorporates a control unit based on a digital signal processor, which runs the control algorithms developed for both gear shifting and launching the car. The system was successfully validated through laboratory and on-track tests. In addition, a comparative test with an equivalent pneumatic counterpart was carried out. This showed that an effective use of voice coil actuators enabled the upshift time to be almost halved, thus proving that these actuators are a viable solution to improving shift-by-wire system performance.


The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations


title: The special role of rimes in the description, use, and acquisition of English orthography.

abstract: The links between spellings and sounds in a large set of English words with consonantvowel-consonant phonological structure were examined. Orthographic rimes, or units consisting of a vowel grapheme and a final consonant grapheme, had more stable pronunciations than either individual vowels or initial consonant-plus-vowel units. In 2 large-scale studies of word pronunciation, the consistency of pronunciation of the orthographic rime accounted for variance in latencies and errors beyond that contributed by the consistency of pronunciation of the individual graphemes and by other factors. In 3 experiments, as well, children and adults made more errors on words with less consistently pronounced orthographic rimes than on words with more consistently pronounced orthographic rimes. Relations between spellings and sounds in the simple monomorphemic words of English are more predictable when the level of onsets and rimes is taken into account than when only graphemes and phonemes are considered.


Position Control of a 3-DOF 3RRR Planar Parallel Manipulator Using Model Predictive Control


Clustering Items for Collaborative Filtering


title: Competition and Selection Among Conventions

abstract: In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the on-line domain, where the ease of information transmission makes them particularly forceful, and where the available data offers the potential to study competition among conventions at a fine-grained level. ::: ::: In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis of the overall dynamics must take place in the presence of these two issues. ::: ::: In this work we study a setting in which we can cleanly track the competition among conventions while explicitly taking these sources of complexity into account. Our analysis is based on the spread of low-level authoring conventions in the e-print arXiv over 24 years and roughly a million posted papers: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of conventions; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time --- and at the individual level about the outcome of ``fights'' between people over convention choices.


title: TorFlow: Tor Network Analysis

abstract: The Tor Network is a low-latency anonymity, privacy, and censorship resistance network whose servers are run by volunteers around the Internet. This distribution of trust creates resilience in the face of compromise and censorship; but it also creates performance, security, and usability issues. The TorFlow suite attempts to address this by providing a library and associated tools for measuring Tor nodes for reliability, capacity and integrity, with the ultimate goal of feeding these measurements back into the Tor directory authorities.


title: Stability analysis of open-loop learning in CMAC neural networks

abstract: This paper presents the results of a CMAC neural network stability analysis in which it is postulated that the function being learned by the CMAC is itself adequately represented by a fully trained CMAC of identical form. It is shown, using Lyapunov techniques, that the CMAC weights being trained converge to the corresponding target weights. Results are presented for single-input single-output, multiple-input single-output, and multiple-input multiple output systems.


title: Osmotic Message-Oriented Middleware for the Internet of Things

abstract: Message-oriented middleware is a key technology in todays Internet of Things (IoT). Centralized message brokers facilitate decoupled device-to-device communication and can transparently scale to handle many millions of messages per second. However, Cloud-based solutions, such as AWS IoT or Azure IoT Hub, cannot satisfy the stringent QoS and privacy requirements of many modern IoT scenarios. Instead, distributed middleware needs to leverage the ever- increasing amount of resources at the edge of the network to provide reliable, ultra-low-latency, and privacy-aware message routing. But the heterogeneity and volatility inherent to Edge resources, and the unpredictability of mobile clients, make it extremely challenging to provide resilient coordination mechanisms and guaranteed message delivery. Applying Osmotic Computing principles to message-oriented middleware opens new opportunities for solving these challenges.


title: NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

abstract: Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables. A neural variational inference framework is proposed for training, where gradients are directly back-propagated through the discrete latent variable to optimize the hash function. We also draw connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of the proposed framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both unsupervised and supervised scenarios.


AClick SequenceModel forWeb Search


title: Novel approach employing buck-boost converter as DC-link modulator and inverter as AC-chopper for induction motor drive applications: An alternative to conventional AC-DC-AC scheme

abstract: Induction motor (IM) is the workhorse of the industries. Amongst various speed control schemes for IM, variable-voltage variable-frequency (VVVF) is popularly used. Inverters are broadly used to produce variable/controlled frequency and variable/controlled output voltage for various applications like ac machine drives, switched mode power supply (SMPS), uninterruptible power supplies (UPS), etc. This paper presents the two-fold solution of control for such loads. In this novel solution, rms values of output voltage is varied by controlling the inverter duty ratio which operates as an ac-chopper, while the fundamental frequency of output voltage is varied by controlling the buck-boost converter according to the reference frequency given to it. The buck-boost converter shuffles between buck-mode and boost-mode to produce required frequency by generating the modulated dc-link for the inverter, unlike conventional fixed dc-link in case of ac-dc-ac converters. The proposed technique eliminates overmodulation (as in conventional pulse width modulated inverters) and hence the non-linearity, and lower order harmonics are absent. Further, it reduces dv/dt in the output voltage resulting less stress on the insulation of machine winding, and electromagnetic interference. However, the proposed scheme demands more number of power semiconductor devices as compared to their conventional ac-dc-ac counterparts. Simulation studies of proposed single-phase as well as three-phase topologies are carried out in MATLAB/Simulink. Hardware implementation of proposed single-phase topology is done using dSPACE DS1104 R&D controller board and results are presented.


Design of Z-Shape Compact Antenna for Next Generation Wireless Network


title: IIR digital filter design implemented on FPGA for myoelectric signals

abstract: In order to attenuate the added noise by electric network used by myoelectric signals acquisition equipment, in this research work, it is developed an IIR digital filter implemented on FPGA. This filter removes a specific spectra frequencies without adding noise to the signal, which allows a better performance in the usage that is given to the signals. The filter coefficients are taken and proved from MATLAB functions. Then, those are transferred to the filter design in the FPGA. For this purpose, it was used a Basys 3 of Xilinx Artix-7 family and the design was implemented in Vivado Design Suite. The filtered signal does not present additional noise and it was eliminated the desired frequency.


title: MODELING CHORD AND KEY STRUCTURE WITH MARKOV LOGIC

abstract: We propose the use of Markov Logic Networks (MLNs) as a highly flexible and expressive formalism for the harmonic analysis of audio signals. Using MLNs information about the physical and semantic content of the signal can be intuitively and compactly encoded and expert knowledge can be easily expressed and combined using a single unified formal model that combines probabilities and logic. In particular, we propose a new approach for joint estimation of chord and global key The proposed model is evaluated on a set of popular music songs. The results show that it can achieve similar performance to a state of the art Hidden Markov Model for chord estimation while at the same time estimating global key. In addition when prior information about global key is used it shows a small but statistically significant improvement in chord estimation performance. Our results demonstrate the potential of MLNs for music analysis as they can express both structured relational knowledge as well as uncertainty.


title: The Yale Pharyngeal Residue Severity Rating Scale: An Anatomically Defined and Image-Based Tool

abstract: The Yale Pharyngeal Residue Severity Rating Scale was developed, standardized, and validated to provide reliable, anatomically defined, and image-based assessment of post-swallow pharyngeal residue severity as observed during fiberoptic endoscopic evaluation of swallowing (FEES). It is a five-point ordinal rating scale based on residue location (vallecula and pyriform sinus) and amount (none, trace, mild, moderate, and severe). Two expert judges reviewed a total of 261 FEES evaluations and selected a no residue exemplar and three exemplars each of trace, mild, moderate, and severe vallecula and pyriform sinus residue. Hard-copy color images of the no residue, 12 vallecula, and 12 pyriform sinus exemplars were randomized by residue location for hierarchical categorization by 20 raters with a mean of 8.3 years of experience (range 2–27 years) performing and interpreting FEES. Severity ratings for all images were performed by the same 20 raters, 2 weeks apart, and with the order of image presentations randomized. Intra-rater test–retest reliability, inter-rater reliability, and construct validity were determined by pooled multi-category multi-rater kappa statistics. Residue ratings were excellent for intra-rater reliability for vallecula (kappa = 0.957 ± 0.014) and pyriform sinus (kappa = 0.854 ± 0.021); very good to excellent for inter-rater reliability for vallecula (kappa = 0.868 ± 0.011) and pyriform sinus (kappa = 0.751 ± 0.011); and excellent for validity for vallecula (kappa = 0.951 ± 0.014) and pyriform sinus (kappa = 0.908 ± 0.017). Clinical uses include accurate classification of vallecula and pyriform sinus residue severity patterns as none, trace, mild, moderate, or severe for diagnostic purposes, determination of functional therapeutic change, and precise dissemination of shared information. Scientific uses include tracking outcome measures, demonstrating efficacy of interventions to reduce pharyngeal residue, investigating morbidity and mortality in relation to pharyngeal residue severity, and improving training and accuracy of FEES interpretation by students and clinicians. The Yale Pharyngeal Residue Severity Rating Scale is a reliable, validated, anatomically defined, and image-based tool to determine residue location and severity based on FEES.


title: Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use

abstract: This research investigated the associations between personality traits and both normal and problematic massively multiplayer online role playing game (MMORPGs) play, as measured by a self report scale. A total of 225 participants completed the online questionnaire, 66 of whom played MMORPGs. Correlational analyses indicated that low levels of functional impulsivity and agreeableness alongside high levels of verbal aggression and video game dependency were associated with greater amount of time spent playing MMORPGs. When comparing problematic and non-problematic MMORPG players directly, problematic players were found to be lower in self regulation, dysfunctional impulsivity and agreeableness, suggesting that these traits may be important in the development and maintenance of problematic MMORPG use.


title: ExpertEyes: Open-source, high-definition eyetracking

abstract: ExpertEyes is a low-cost, open-source package of hardware and software that is designed to provide portable high-definition eyetracking. The project involves several technological innovations, including portability, high-definition video recording, and multiplatform software support. It was designed for challenging recording environments, and all processing is done offline to allow for optimization of parameter estimation. The pupil and corneal reflection are estimated using a novel forward eye model that simultaneously fits both the pupil and the corneal reflection with full ellipses, addressing a common situation in which the corneal reflection sits at the edge of the pupil and therefore breaks the contour of the ellipse. The accuracy and precision of the system are comparable to or better than what is available in commercial eyetracking systems, with a typical accuracy of less than 0.4° and best accuracy below 0.3°, and with a typical precision (SD method) around 0.3° and best precision below 0.2°. Part of the success of the system comes from a high-resolution eye image. The high image quality results from uncasing common digital camcorders and recording directly to SD cards, which avoids the limitations of the analog NTSC format. The software is freely downloadable, and complete hardware plans are available, along with sources for custom parts.


title: Toward Intelligent Traffic Light Control with Quality-of-Service Provisioning

abstract: Today's fixed-cycle traffic signaling is highly suboptimal and aggravates traffic congestion and waste of energy in urban areas. In addition, it offers no quality-of-service guarantee and makes travel time prediction extremely hard. While existing traffic light control research primarily focuses on improving the average wait time of cars, we study in this paper how traffic light scheduling affects the worst-case wait time. In particular, we derive the time a car spends at an intersection in the best-case and the worst-case, respectively. Using the theoretical results, we propose a simple but effective controller and run simulation to verify its performance. The result shows that it works much better than fixed-cycle controllers in both light and heavy traffic scenarios.


title: A first look at mobile cloud storage services: architecture, experimentation, and challenges

abstract: Mobile cloud storage services provide users a convenient and reliable way to store and share data on mobile devices. Despite increasing popularity, little work has focused on their architecture and internal sync protocol. In this article, we present a thorough architecture of mobile cloud storage services including sync protocols and key capabilities for speeding up transmission. Furthermore, we conduct a series of experiments to evaluate the sync performance of four popular commercial mobile cloud storage services. Our results show that the concrete deployment of capabilities has a strong impact on the performance of mobile cloud storage services. There is no clear winner, with all services suffering from some limitations or having potential for improvement. We pose issues and challenges to advance the topic area, and hope to pave a way for the forthcoming.


title: Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification

abstract: Person re-identification is generally divided into two part: first how to represent a pedestrian by discriminative visual descriptors and second how to compare them by suitable distance metrics. Conventional methods isolate these two parts, the first part usually unsupervised and the second part supervised. The Bag-of-Words (BoW) model is a widely used image representing descriptor in part one. Its codebook is simply generated by clustering visual features in Euclidian space. In this paper, we propose to use part two metric learning techniques in the codebook generation phase of BoW. In particular, the proposed codebook is clustered under Mahalanobis distance which is learned supervised. Extensive experiments prove that our proposed method is effective. With several low level features extracted on superpixel and fused together, our method outperforms state-of-the-art on person re-identification benchmarks including VIPeR, PRID450S, and Market1501.


title: The road to SDN: an intellectual history of programmable networks

abstract: Software Defined Networking (SDN) is an exciting technology that enables innovation in how we design and manage networks. Although this technology seems to have appeared suddenly, SDN is part of a long history of efforts to make computer networks more programmable. In this paper, we trace the intellectual history of programmable networks, including active networks, early efforts to separate the control and data plane, and more recent work on OpenFlow and network operating systems. We highlight key concepts, as well as the technology pushes and application pulls that spurred each innovation. Along the way, we debunk common myths and misconceptions about the technologies and clarify the relationship between SDN and related technologies such as network virtualization.


title: Measuring Values With the Short Schwartz's Value Survey

abstract: The reliability and validity of the Short Schwartz's Value Survey (SSVS) was examined in 4 studies. In Study 1 (N = 670), we examined whether value scores obtained with the SSVS correlate with those obtained with Schwartz's Value Survey (SVS; Schwartz, 1992, 1996) and the Portrait Values Questionnaire (Schwartz et al., 2001) and whether the quasi-circular structure of values can be found with the SSVS. In Study 2 (N = 3,261), we replicated the quasi-circular structure in a more heterogeneous sample and assessed whether the SSVS can differentiate appropriately between gender, religiosity, students from different fields, and supporters of left- and right-wing political parties. In Study 3 (N = 112), we examined the test–retest reliability of the SSVS and in Study 4 (N = 38), time saving gained by the SSVS compared to the SVS. The results show that the new scale had good reliability and validity and that the values measured by the SSVS were arrayed on a circle identical to the theoretical structure of values...


title: A reconfigurable patch antenna using switchable slots for circular polarization diversity

abstract: A novel design of a microstrip patch antenna with switchable slots (PASS) is proposed to achieve circular polarization diversity. Two orthogonal slots are incorporated into the patch and two pin diodes are utilized to switch the slots on and off. By turning the diodes on or off, this antenna can radiate with either right hand circular polarization (RHCP) or left hand circular polarization (LHCP) using the same feeding probe. Experimental results validate this concept. This design demonstrates useful features for wireless communication applications and future planetary missions.