title: Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty

abstract: Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single midtroposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.


title: Can you hear me now?!: it must be BGP

abstract: Industry observers expect VoIP to eventually replace most of the existing land-line telephone connections. Currently however, quality and reliability concerns largely limit VoIP usage to either personal calls on cross-domain services such as Skype and Vonage, or to single-domain services such as trunking, where a core ISP carries long-distance voice as VoIP only within its backbone, to save cost with a unified voice/data infrastructure. This paper investigates the factors that prevent cross-domain VoIP deployments from achieving the quality and reliability of existing land-line telephony (PSTN). We ran over 50,000 VoIP phone calls between 24 locations in US and Europe for a three-week period. Our results indicate that VoIP usability is hindered as much by BGP's slow convergence as network congestion. In fact, about half of the unintelligible VoIP samples in our data occur within 10 minutes of a BGP update


title: The developmental psychometrics of big five self-reports: acquiescence, factor structure, coherence, and differentiation from ages 10 to 20.

abstract: How do youths’ personality reports differ from those of adults? To identify the year-by-year timing of developmental trends from late childhood (age 10) to early adulthood (age 20), the authors examined Big Five self-report data from a large and diverse Internet sample. At younger ages within this range, there were large individual differences in acquiescent responding, and acquiescence variability had pronounced effects on psychometric characteristics. Beyond the effects of acquiescence, self-reports generally became more coherent within domains, and better differentiated across domains, at older ages. Importantly, however, different Big Five domains showed different developmental trends. Extraversion showed especially pronounced age gains in coherence but no gains in differentiation. In contrast, Agreeableness and Conscientiousness showed large age gains in differentiation but only trivial gains in coherence. Neuroticism and Openness showed moderate gains in both coherence and differentiation. Comparisons of items that were relatively easy versus difficult to comprehend indicated that these patterns were not simply due to verbal comprehension. These findings have important implications for the study of personality characteristics and other psychological attributes in childhood and adolescence.


title: Multipath Planning Based on Neural Network Optimized with Adaptive Niche in Unknown Environment

abstract: An algorithm for multipath planning in unknown environment is presented. Niche identification technology used in multimodal function problems is improved and is applied to path planning. Fitness sharing method is adopted to keep diversity of niches. The robot detects local environmental information with sensors and its movement is controlled by neural network. The neural network is optimized by the genetic algorithm based on adaptive niche. There is no need for generating feasible paths at first and clustering in every generation. The simulation results show that the algorithm is efficient.


title: Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks

abstract: Power consumption is a critical issue in many wireless sensor network scenarios where network life expectancy is measured in months or years. Communication protocols typically rely on duty-cycle mechanisms to reduce the power usage at the cost of decreased network responsiveness and increased communication latency. A low-power radio-triggered device can be used to continuously monitor the channel and activate the node for incoming communications, allowing purely asynchronous operations. To be effective, the power consumption of this wake-up receiver must be on the order of tens of microwatts since this device is always active. This paper presents the ongoing efforts to design such a low-power receiver. Initial results indicate an average power consumption below 20 uW.


title: De-Conflated Semantic Representations

abstract: One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge it derives from a semantic network. Our approach provides multiple advantages in comparison to the past work, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.


The Graph Voronoi Diagram with Applications


title: Boost DC/DC converter nonlinearity and RHP-zero: Survey of the control-to-output transfer function linearization methods

abstract: This invited paper is dedicated to both the nonlinear behavior of a step-up DC/DC converter, and to its linearization. It explains the nonlinear DC and AC control-to-output transfer function and its RHP-zero. The explanation is based on linearized models developed in the paper. These models are suitable for SPICE-like environment, and allow to obtain accurate symbolic equations in Matlab format. The linearization methods based on tri-state PWM and predistortion are also described. The description is followed by a Matlab algorithm allowing fast computation of the voltage-mode PID controller for pre-distorted PWM modulator. As a result, a linearized converter operating in wide duty-cycle range is designed with voltage mode feedback loop.


title: Patients’ perception of information about HAART: impact on treatment decisions

abstract: The aim of this study was to profile patients’ satisfaction with information they have received about HAART in relation to treatment uptake. As part of a prospective investigation into uptake and adherence to HAART, 115 participants, comprising predominantly gay men, completed validated questionnaires investigating their satisfaction with information relating to practical aspects and potential problems of HAART, perceptions of information from different sources and beliefs about HAART, following a clinically recommended treatment offer. There was a wide range of total satisfaction scores indicating variation in patients’ information requirements. Those who declined HAART were less satisfied with the information they had received than those who accepted the treatment recommendation (p<0.05). Lower levels of satisfaction were associated with stronger concerns about the potential adverse effects of HAART (p<0.05). Furthermore, concerns about HAART were related to uptake of HAART with those declining treatment being significantly more concerned about potential adverse effects than those who accepted the treatment offer (p<0.001). The most helpful sources of information were specialist HIV staff: hospital consultants, pharmacists and nurses, as well as other people with HIV. This study improves our understanding of how information about HAART is perceived by people faced with treatment decisions. It emphasizes the importance of providing information tailored to meet the needs of individual patients and address their specific concerns, in order to support informed decision making.


title: CRBT customer churn prediction: can data mining techniques work?

abstract: Coloring Ring Back Tone (CRBT) is one of the most successful Value-added (VAD) services in China telecommunication operators. Under fierce competition conditions, CRBT customer churn has significantly decreased the profits of operators. Thus churn management has become a major focus to retain subscribers via satisfying their needs under resource constraints. One of the challenges is that churn prediction specific to this business is not available in existing literature. Through empirical evaluation, this study analyse the features of CRBT, compare various data mining techniques that can assign a 'propensity to churn' to each CRBT subscriber. The results indicate that our models can achieve satisfactory prediction effectiveness by using customer demographics, billing and service usage information. At the same time, we find some new symptoms different from existing telecom churn literature, and try to explain them, and point out which predictors are needed to intensively monitor by telecom operators.


title: A Generic Layered Architecture for Context Aware Applications

abstract: Abstract As pervasive computing is at its preliminary stage a number of computing solutions were proposed and more are still under experimentation. In this paper a four layered generic architecture is proposed with a recommended components so as to support context awareness for pervasive applications. Detail of each layer is described sufficiently to insight the process of context generation from low level contextual data up to high level context reporting scheme in a given pervasive environment. The implementation section of this article describes how the context reasoning component employee different level of reasoning techniques such as knowledge acquisition, context based rule execution and the application of ontology in pervasive computing.


title: Learning k for kNN Classification

abstract: The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an e1-norm regularizer and an e2, 1-norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.


title: A Communication Theoretical Analysis of Multiple-Access Channel Capacity in Magneto-Inductive Wireless Networks

abstract: Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.


title: Macrocyclic and helical oligoamides as a new class of G-quadruplex ligands.

abstract: A planar trimeric macrocyclic oligoamide of 8-amino-2-quinoline carboxylic acid, bearing ammonium side chains, stabilizes the human telomeric G-quadruplex more strongly than the potent ligand telomestatin. Furthermore, a helical oligoamide tetramer also binds strongly to G-quadruplexes and is a new structural class of quadruplex ligand that appears to interact enantioselectively.


title: Identifying effective influencers based on trust for electronic word-of-mouth marketing

abstract: Because effective influencers in an online social network (OSN) can significantly affect consumers' purchasing decisions via trust among users in electronic word-of-mouth (eWOM) marketing, identifying these influencers with respect to user trust relationships has become increasingly important. However, many existing studies overlook the domain attribute of trust and the time-varying nature of social networks and only analyze a static snapshot of a user trust network (UTN). To address these issues and investigate this topic in the e-commerce context, this study proposes a research framework that takes into account the dimensions of trust, domain, and time. A time-varying hypergraph is developed to model the OSN using the time-varying features of multi-type relationships, and an algorithm is developed to extract a domain-aware UTN based on the time-varying hypergraph and user trust relationships. Reinforced by the dimensions of trust, domain, and time, a novel product review domain-aware (PRDA) approach is conceived that identifies effective influencers and categorizes them into three types, i.e., emerging influencers, holding influencers, and vanishing influencers, based on their popularity status across the life cycle. The experimental results from the Epinions dataset show that the PRDA approach outperforms both the social network-based influence-evaluating approach and the "popular author" approach.


title: Yaojia-Nenjiang Formation Hydrocarbon Accumulation Characteristics in Songliao Basin, Northeastern China

abstract: The reservoir of Late Cretaceous Yaojia-Nengjiang Formation in Songliao Basin was a large-scale delta complex formed in the transgression and regression period, which had the characteristics of sand body in distributary channel, mouth bar and sheet sand. The sand bodies extended to the lacustrine center and wedged into lacustrine mudstone and mature source rock, and formed a good combination of source, reservoir and cap rock. The large-scale delta complexes developed in the major axis direction of the material source of basin were favorable places for centrally developed oil reservoir, which is an important geological condition for mega continental petroliferous basin. Moreover, the gravity flow channel and slump fan, which are important exploration areas, are also favorable for lithological hydrocarbon reservoir.


title: High-speed visual estimation using preattentive processing

abstract: A new method is presented for performing rapid and accurate numerical estimation. The method is derived from an area of human cognitive psychology called preattentive processing. Preattentive processing refers to an initial organization of the visual field based on cognitive operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, intensity, orientation, size, and motion. We beleive that studies from preattentive vision should be used to assist in the design of visualization tools, especially those for which high-speed target detection, boundary identification, and region detection are important. In our present study, we investigated two known preattentive features (hue and orientation) in the context of a new task (numerical estimation) in order to see whether preattentive estimation was possible. Our experiments tested displays that were designed to visualize data from salmon migration simulations. The results showed that rapid and accurate estimation was indeed possible using either hue or orientation. Furthermore, random variation in one of these features resulted in no interference when subjects estimated the percentage of the other. To test the generality of our results, we varied two important display parameters—display duration and feature difference—and found boundary conditions for each. Implications of our results for application to real-world data and tasks are discussed.


title: On the Design of Practical Fault-Tolerant SDN Controllers

abstract: The increase in the number of SDN-based deployments in production networks is triggering the need to consider fault-tolerant designs of controller architectures. Commercial SDN controller solutions incorporate fault tolerance, but there has been little discussion in the SDN community on the design of such systems and the tradeoffs involved. To fill this gap, we present a by-construction design of a fault-tolerant controller, and materialize it by proposing and formalizing a practical architecture for small to medium-sized networks. A central component of our design is a replicated shared database that stores all network state. Contrary to the more common primary-backup approaches, the proposed design guarantees a smooth transition in case of failures and avoids the need of an additional coordination service. Our preliminary results show that the performance of our solution fulfils the demands of the target networks. We hope this work to be a first step in what we consider a necessary discussion on how to build robust SDNs.


title: Binary Tree-based Generic Demosaicking Algorithm for Multispectral Filter Arrays

abstract: In this paper, we extend the idea of using mosaicked color filter array (CFA) in color imaging, which has been widely adopted in the digital color camera industry, to the use of multispectral filter array (MSFA) in multispectral imaging. The filter array technique can help reduce the cost, achieve exact registration, and improve the robustness of the imaging system. However, the extension from CFA to MSFA is not straightforward. First, most CFAs only deal with a few bands (3 or 4) within the narrow visual spectral region, while the design of MSFA needs to handle the arrangement of multiple bands (more than 3) across a much wider spectral range. Second, most existing CFA demosaicking algorithms assume the fixed Bayer CFA and are confined to properties only existed in the color domain. Therefore, they cannot be directly applied to multispectral demosaicking. The main challenges faced in multispectral demosaicking is how to design a generic algorithm that can handle the more diversified MSFA patterns, and how to improve performance with a coarser spatial resolution and a less degree of spectral correlation. In this paper, we present a binary tree based generic demosaicking method. Two metrics are used to evaluate the generic algorithm, including the root mean-square error (RMSE) for reconstruction performance and the classification accuracy for target discrimination performance. Experimental results show that the demosaicked images present low RMSE (less than 7) and comparable classification performance as original images. These results support that MSFA technique can be applied to multispectral imaging with unique advantages.


title: The role of nutrition in children's neurocognitive development, from pregnancy through childhood

abstract: This review examines the current evidence for a possible connection between nutritional intake (including micronutrients and whole diet) and neurocognitive development in childhood. Earlier studies which have investigated the association between nutrition and cognitive development have focused on individual micronutrients, including omega-3 fatty acids, vitamin B12, folic acid, choline, iron, iodine, and zinc, and single aspects of diet. The research evidence from observational studies suggests that micronutrients may play an important role in the cognitive development of children. However, the results of intervention trials utilizing single micronutrients are inconclusive. More generally, there is evidence that malnutrition can impair cognitive development, whilst breastfeeding appears to be beneficial for cognition. Eating breakfast is also beneficial for cognition. In contrast, there is currently inconclusive evidence regarding the association between obesity and cognition. Since individuals consume combinations of foods, more recently researchers have become interested in the cognitive impact of diet as a composite measure. Only a few studies to date have investigated the associations between dietary patterns and cognitive development. In future research, more well designed intervention trials are needed, with special consideration given to the interactive effects of nutrients.


title: Secure Compute-VM: Secure Big Data Processing with SGX and Compute Accelerators

abstract: This paper considers secure big data processing. With the architectural support, big data processing with compute accelerators can efficiently preserve privacy concern.


title: Experience with percutaneous internal jugular-innominate vein catheterization.

abstract: Abstract ::: A specific technique of cannulation of the internal jugular innominate vein, presented herein, is not considered an innocuous procedure, but the incidence of serious complications is very low. In this technique a needle-in-catheter unit aids greatly in advancing the catheter through the vein. More than 62 percent of the catheters were left in place for more than five days. There was an 11.4 percent incidence of positive bacterial or fungal cultures from the catheter tip. Routine follow-up chest x-ray films demonstrated all the catheters in the innominate-superior vena cava venous system.


title: Linkages between Depressive Symptomatology and Internet Harassment among Young Regular Internet Users

abstract: Recent reports indicate 97% of youth are connected to the Internet. As more young people have access to online communication, it is integrally important to identify youth who may be more vulnerable to negative experiences. Based upon accounts of traditional bullying, youth with depressive symptomatology may be especially likely to be the target of Internet harassment. The current investigation will examine the cross-sectional relationship between depressive symptomatology and Internet harassment, as well as underlying factors that may help explain the observed association. Youth between the ages of 10 and 17 (N = 1,501) participated in a telephone survey about their Internet behaviors and experiences. Subjects were required to have used the Internet at least six times in the previous 6 months to ensure a minimum level of exposure. The caregiver self-identified as most knowledgeable about the young person's Internet behaviors was also interviewed. The odds of reporting an Internet harassment experience in ...


title: Design and Characterization of Miniaturized Patch Antennas Loaded With Complementary Split-Ring Resonators

abstract: An investigation into the design of compact patch antennas loaded with complementary split-ring resonators (CSRRs) and reactive impedance surface (RIS) is presented in this study. The CSRR is incorporated on the patch as a shunt LC resonator providing a low resonance frequency and the RIS is realized using the two-dimensional metallic patches printed on a metal-grounded substrate. Both the meta-resonator (CSRR) and the meta-surface (RIS) are able to miniaturize the antenna size. By changing the configuration of the CSRRs, multi-band operation with varied polarization states can be obtained. An equivalent circuit has been developed for the CSRR-loaded patch antennas to illustrate their working principles. Six antennas with different features are designed and compared, including a circularly-polarized antenna, which validate their versatility for practical applications. These antennas are fabricated and tested. The measured results are in good agreement with the simulation.


title: 3D InfoVis is here to stay: Deal with it

abstract: 3D information visualization has existed for more than 100 years. 3D offers intrinsic attributes such as an extra dimension for encoding position and length, meshes and surfaces; lighting and separation. Further 3D can aid mental models for configuration of data within a 3D spatial framework. Perceived issues with 3D are solvable and successful, specialized information visualizations can be built.


title: Discrete cuckoo search for traveling salesman problem

abstract: Cuckoo search (CS) belongs to a class of novel nature-inspired algorithm inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species). CS has been successfully applied to solve continuous optimization problems, however, its potential in discrete problems has not been sufficiently explored. Basing on the present algorithm of CS, this paper construct an discrete cuckoo search (DCS) to solve traveling salesman problem (TSP). Two schemes are studied, i.e. discrete step size and the cuckoo's updating scheme. DCS is evaluated on the TSP problem, comparing performance to other methods. Experimental results shows that the proposed algorithm performs very well to solve some simple TSP instances, but it can be trapped into local optimum solutions for some other instances.


title: Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files

abstract: Sonic Visualiser is a friendly and flexible end-user desktop application for analysis, visualisation, and annotation of music audio files. Its stated goal is to be "the first program you reach for when want to study a musical recording rather than simply listen to it". To this end, it has a user interface that resembles familiar audio editing applications, a set of useful standard visualisation facilities, and support for a plugin format for additional automated analysis methods.


title: Experimental validation of a frustum of cone antenna applied to Omega 360 ubiquitous 2D radar

abstract: This paper describes an ubiquitous 2D radar, called Omega 360, capable of a rose of adjacent simultaneous staring beams which allow a continuous bi-dimensional coverage of the controlled space. In particular, the attention is focused on the antenna features obtained by means of a frustum of cone. The signal received by every column is amplified, digitally down converted and combined to form independent beams. The current configuration of the Digital Antenna Evaluator (D.Ant.E) is arranged to create 8 simultaneous beams that are enough for the purpose of the study aimed at the experimental demonstration of the functionalities of an ubiquitous radar. In particular, attention is paid to verify the antenna pattern characteristics after Digital Beam Forming (DBF) downstream of pulse compression. The benches of measurement and the adopted methodology are discussed in detail. Finally, some future objectives are envisaged in order to accomplish a full chain of signal processing up to the plot extraction and track formation. In fact, the industrial aim is the realization of a complete radar to be used for civil and military applications.


title: A binary firefly algorithm for knapsack problems

abstract: Knapsack problems are one of the classical NP-hard problem and it offers many practical applications in vast field of different areas. Several traditional as well as population based metaheuristic algorithms are applied to solve this problem. In this paper, we introduce the binary version of firefly algorithm (FA) for solving knapsack problems, specially 01 knapsack problem. The modified algorithm utilizes the advantages of the variable distance move along with the opposition-based learning mechanism to solve knapsack problems efficiently. So far FA is generally applied to continuous optimization problems. In order to investigate the performance of FA on combinatorial optimization problem, an attempt is made in this paper. To demonstrate the efficiency of the proposed algorithm an extensive computational study is provided with standard bench mark problem instances and comparison with particle swarm optimization is also carried out.


title: C-BIRD: content-based image retrieval from digital libraries using illumination invariance and recognition kernel

abstract: With huge amounts of multimedia information connected to the global information network (Internet), efficient and effective image retrieval from large image and video repositories has become an imminent research issue. This article presents our research in the C-BIRD (Content-Based Image Retrieval from Digital libraries) project. In addition to the use of common features such as keywords, color texture, shape and their conjuncts, it is shown that (a) the color-channel-normalization enables search by illumination invariance, and (b) the multi-level recognition kernel facilitates search by object model in image and video databases.


title: Data Vision: Learning to See Through Algorithmic Abstraction

abstract: Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.


title: Dude, Where's That IP? Circumventing Measurement-based IP Geolocation

abstract: Many applications of IP geolocation can benefit from geolocation that is robust to adversarial clients. These include applications that limit access to online content to a specific geographic region and cloud computing, where some organizations must ensure their virtual machines stay in an appropriate geographic region. This paper studies the applicability of current IP geolocation techniques against an adversary who tries to subvert the techniques into returning a forged result. We propose and evaluate attacks on both delay-based IP geolocation techniques and more advanced topology-aware techniques. Against delay-based techniques, we find that the adversary has a clear trade-off between the accuracy and the detectability of an attack. In contrast, we observe that more sophisticated topology-aware techniques actually fare worse against an adversary because they give the adversary more inputs to manipulate through their use of topology and delay information.


title: Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

abstract: This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train.


title: Molecular Biomarkers of Pancreatic Intraepithelial Neoplasia and Their Implications in Early Diagnosis and Therapeutic Intervention of Pancreatic Cancer

abstract: Lack of early detection and effective interventions is a major reason for the poor prognosis and dismal survival rates for pancreatic cancer. Pancreatic intraepithelial neoplasia (PanIN) is the most common precursor of invasive pancreatic ductal adenocarcinoma (PDAC). Each stage in the progression from PanIN to PDAC is well characterized by multiple significant genetic alterations affecting signaling pathways. Understanding the biological behavior and molecular alterations in the progression from PanIN to PDAC is crucial to the identification of noninvasive biomarkers for early detection and diagnosis and the development of preventive and therapeutic strategies for control of pancreatic cancer progression. This review focuses on molecular biomarkers of PanIN and their important roles in early detection and treatment of pancreatic cancer.


title: Improved strategies for a zero oov rate LVCSR system

abstract: In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed hierarchical approach, a full-word language model and a context independent character-level LM (CLM) are directly used during search. The novelty of this work is to jointly model the character-level prior and the pronunciation probabilities, to introduce across-word context into the characterlevel LM, and to properly normalize the character-level LM using prefix-tree based normalization for the hierarchical approach. Significant reductions in-terms of word error rates (WER) on the best full-word Quaero Polish LVCSR system are reported.


title: Symmetry Based Chinese Ancient Architecture Reconstruction from Incomplete Point Cloud

abstract: In this paper, we propose a novel method to reconstruct Chinese ancient architecture from incomplete point cloud obtained from multi-view reconstruction approach. The proposed method utilizes both global symmetry and partial symmetry to complete the missing part in the original point cloud. First global symmetry is extracted automatically and a matching cost function based on the images associated with each point is introduced to accelerate the extraction. Then we propose a tool which allows the user to interactively fill the holes by exploring the partial symmetries in the original point set. Our experiments demonstrate the effectiveness of our algorithm.


title: Multidigit force control during unconstrained grasping in response to object perturbations

abstract: We studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistent with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.


title: Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor?

abstract: PURPOSE ::: Data mining on electronic health records (EHRs) has emerged as a promising complementary method for post-marketing drug safety surveillance. The EU-ADR project, funded by the European Commission, is developing techniques that allow mining of EHRs for adverse drug events across different countries in Europe. Since mining on all possible events was considered to unduly increase the number of spurious signals, we wanted to create a ranked list of high-priority events. ::: ::: ::: METHODS ::: Scientific literature, medical textbooks, and websites of regulatory agencies were reviewed to create a preliminary list of events that are deemed important in pharmacovigilance. Two teams of pharmacovigilance experts independently rated each event on five criteria: 'trigger for drug withdrawal', 'trigger for black box warning', 'leading to emergency department visit or hospital admission', 'probability of event to be drug-related', and 'likelihood of death'. In case of disagreement, a consensus score was obtained. Ordinal scales between 0 and 3 were used for rating the criteria, and an overall score was computed to rank the events. ::: ::: ::: RESULTS ::: An initial list comprising 23 adverse events was identified. After rating all the events and calculation of overall scores, a ranked list was established. The top-ranking events were: cutaneous bullous eruptions, acute renal failure, anaphylactic shock, acute myocardial infarction, and rhabdomyolysis. ::: ::: ::: CONCLUSIONS ::: A ranked list of 23 adverse drug events judged as important in pharmacovigilance was created to permit focused data mining. The list will need to be updated periodically as knowledge on drug safety evolves and new issues in drug safety arise.


title: Privometer: Privacy protection in social networks

abstract: The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.


title: Development of a Low-Profile Circularly Polarized Cavity-Backed Antenna Using HMSIW Technique

abstract: In this paper, two single fed low-profile cavity-backed planar slot antennas for circular polarization (CP) applications are first introduced by half mode substrate integrated waveguide (HMSIW) technique. One of the structures presents right handed CP (RHCP), while the other one offers left handed CP (LHCP). A single layer of low cost printed circuit board (PCB) is employed for both antennas providing low-cost, lightweight, and also easy integration with planar circuits. An inset microstrip line is used to excite two orthogonal quarter-wave length patch modes with required phase difference for generating CP wave. The new proposed antennas are successfully designed and fabricated. Measured results are in good agreement with those obtained by numerical investigation using HFSS. Results exhibit that both antennas present the advantages of conventional cavity backed antennas including high gain and high front to back ratio (FTBR).


abstract: Ideal for graduate and senior undergraduate courses in computer arithmetic and advanced digital design, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, provides a balanced, comprehensive treatment of computer arithmetic. It covers topics in arithmetic unit design and circuit implementation that complement the architectural and algorithmic speedup techniques used in high-performance computer architecture and parallel processing. Using a unified and consistent framework, the text begins with number representation and proceeds through basic arithmetic operations, floating-point arithmetic, and function evaluation methods. Later chapters cover broad design and implementation topics-including techniques for high-throughput, low-power, fault-tolerant, and reconfigurable arithmetic. An appendix provides a historical view of the field and speculates on its future.An indispensable resource for instruction, professional development, and research, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, combines broad coverage of the underlying theories of computer arithmetic with numerous examples of practical designs, worked-out examples, and a large collection of meaningful problems. This second edition includes a new chapter on reconfigurable arithmetic, in order to address the fact that arithmetic functions are increasingly being implemented on field-programmable gate arrays (FPGAs) and FPGA-like configurable devices. Updated and thoroughly revised, the book offers new and expanded coverage of saturating adders and multipliers, truncated multipliers, fused multiply-add units, overlapped quotient digit selection, bipartite and multipartite tables, reversible logic, dot notation, modular arithmetic, Montgomery modular reduction, division by constants, IEEE floating-point standard formats, and interval arithmetic.Features:* Divided into 28 lecture-size chapters * Emphasizes both the underlying theories of computer arithmetic and actual hardware designs * Carefully links computer arithmetic to other subfields of computer engineering * Includes 717 end-of-chapter problems ranging in complexity from simple exercises to mini-projects * Incorporates many examples of practical designs * Uses consistent standardized notation throughout * Instructor's manual includes solutions to text problems * An author-maintained website http://www.ece.ucsb.edu/~parhami/text_comp_arit.htm contains instructor resources, including complete lecture slides


title: Folded Recurrent Neural Networks for Future Video Prediction

abstract: Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.


title: FRAUD DETECTION IN MOBILE TELECOMMUNICATION

abstract: Fraud has been very common in our society, and it affects private enterprises as well as public entities. However, in recent years, the development of new technologies has also provided criminals more sophisticated way to commit fraud and it therefore requires more advanced techniques to detect and prevent such events. The types of fraud in Telecommunication industry includes: Subscription Fraud, Clip on Fraud, Call Forwarding, Cloning Fraud, Roaming Fraud, and Calling Card. Thus, detection and prevention of these frauds is one of the main objectives of the telecommunication industry. In this research, we developed a model that detects fraud in Telecommunication sector in which a random rough subspace based neural network ensemble method was employed in the development of the model to detect subscription fraud in mobile telecoms. This study therefore presents the development of patterns that illustrate the customers' subscription's behaviour focusing on the identification of non-payment events. This information interrelated with other features produces the rules that lead to the predictions as earlier as possible to prevent the revenue loss for the company by deployment of the appropriate actions.


Scholarly Journal Prices: Selected Trends and Comparisons


title: Stuff : Automatic Identification of Discourse Structure in Student Essays

abstract: An essay-based discourse analysis system can help students improve their writing by identifying relevant essay-based discourse elements in their essays. Our discourse analysis software, which is embedded in Criterion, an online essay evaluation application, uses machine learning to identify discourse elements in student essays. The system makes decisions that exemplify how teachers perform this task. For instance, when grading student essays, teachers comment on the discourse structure. Teachers might explicitly state that the essay lacks a thesis statement or that an essay's single main idea has insufficient support. Training the systems to model this behavior requires human judges to annotate a data sample of student essays. The annotation schema reflects the highly structured discourse of genres such as persuasive writing. Our discourse analysis system uses a voting algorithm that takes into account the discourse labeling decisions of three independent systems.


title: Video Google: a text retrieval approach to object matching in videos

abstract: We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films.


Abstract Virtual Grasping Assessment Using 3D Digital Hand Model


title: Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator

abstract: A two-link robotic manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) controller for a two-link planar rigid robotic manipulator for trajectory tracking problem is investigated. Robustness testing of FOFPID controller for model uncertainties, disturbance rejection and noise suppression is also investigated. To study the effectiveness of FOFPID controller, its performance is compared with other three controllers namely Fuzzy PID (FPID), Fractional Order PID (FOPID) and conventional PID. For tuning of parameters of all the controllers, Cuckoo Search Algorithm (CSA) optimization technique was used. Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Controller Output (IACCO) having equal weightage for both the links are considered for minimization. Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.


title: Ambiguity as a resource for design

abstract: Ambiguity is usually considered anathema in Human Computer Interaction. We argue, in contrast, that it is a resource for design that can be used to encourage close personal engagement with systems. We illustrate this with examples from contemporary arts and design practice, and distinguish three broad classes of ambiguity according to where uncertainty is located in the interpretative relationship linking person and artefact. Ambiguity of information finds its source in the artefact itself, ambiguity of context in the sociocultural discourses that are used to interpret it, and ambiguity of relationship in the interpretative and evaluative stance of the individual. For each of these categories, we describe tactics for emphasising ambiguity that may help designers and other practitioners understand and craft its use.


title: A 'G' chromosome banding study of three cupped oyster species: Crassostrea gigas, Crassostrea angulata and Crassostrea virginica (Mollusca: Bivalvia)

abstract: The G-banding technique was performed on chromosomes from gill tissue of three cupped oyster species: Orassostrea gigas, Cmssostrea angulata and Cmssostrea virginiea. Identification of the ten individual chromosome pairs was obtained. Comparative analysis of G-banded karyotypes of the three species showed that their banding patterns generally resembled each other, with chromosome pair 3 being similar in ail three species. However, differences from one species to another were also observed. The G-banding pattern highlighted greater similarities between C. gigas and C. ang'l!lata than between these two species and C. virginiea, thus providing an additional argument for genetic divergence between these two evolutionary lineages. C. gigas and C. angulata showed a different G-banding patterns on the two arms of chromosome pair 7, which agrees with their taxonomic separation. The application of this banding technique offers a new approach to specifie problems in oyster taxonorny and genetics. © Inra/Elsevier, Paris chI'omosome / G-banding / Crassostrea gigas / Crassostrea angulata / Crassostrea virginica


SECURE COMPUTER SYSTEMS : MATHEMATICAL FOUNDATIONS


title: The Dropout Learning Algorithm.

abstract: Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations.


title: Using Ontologies for Semantic Data Integration

abstract: While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed.


A scored AUC Metric for Classifier Evaluation and Selection


title: Mobile robot ego-motion estimation by proprioceptive sensor fusion

abstract: For any mobile robot it is a major issue that of estimating its position into the working environment. Although this task is partly carried out through external sensors, incrementally computing the ego-motion of the robot using proprioceptive sensors still is a fundamental step to obtain an estimation of the robot displacement. In this work we deal with the sensor fusion problem for the case of a mobile robot equipped with an odometer and an inertial sensor (a gyroscope). We address this problem rigorously through its formulation as a probabilistic estimation problem, developing an efficient solution in the form of an Extended Kalman Filter (EKF), which can be easily implemented in the low-level firmware of a real mobile robot. Experimental results reveal a qualitative improvement in the robot pose estimation for our sensor fusion system when compared with odometry only, which is the most wide spread technique in commercial robots.


title: Remembering that big things sound big: Sound symbolism and associative memory

abstract: According to sound symbolism theory, individual sounds or clusters of sounds can convey meaning. To examine the role of sound symbolic effects on processing and memory for nonwords, we developed a novel set of 100 nonwords to convey largeness (nonwords containing plosive consonants and back vowels) and smallness (nonwords containing fricative consonants and front vowels). In Experiments 1A and 1B, participants rated the size of the 100 nonwords and provided definitions to them as if they were products. Nonwords composed of fricative/front vowels were rated as smaller than those composed of plosive/back vowels. In Experiment 2, participants studied sound symbolic congruent and incongruent nonword and participant-generated definition pairings. Definitions paired with nonwords that matched the size and participant-generated meanings were recalled better than those that did not match. When the participant-generated definitions were re-paired with other nonwords, this mnemonic advantage was reduced, although still reliable. In a final free association study, the possibility that plosive/back vowel and fricative/front vowel nonwords elicit sound symbolic size effects due to mediation from word neighbors was ruled out. Together, these results suggest that definitions that are sound symbolically congruent with a nonword are more memorable than incongruent definition-nonword pairings. This work has implications for the creation of brand names and how to create brand names that not only convey desired product characteristics, but also are memorable for consumers.


title: The Neural Basis of Financial Risk Taking

abstract: Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk- neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk- seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices, and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision-making.


title: Parallel progression of primary tumours and metastases

abstract: When, in tumour progression, might metastasis be initiated? This Perspective argues that tumour cells disseminate early in malignant progression of the primary tumour so that disseminated tumour cells evolve the traits to allow growth within the metastatic site independently from the primary tumour.


title: Unsupervised Adaptation for Deep Stereo

abstract: Recent ground-breaking works have shown that deep neural networks can be trained end-to-end to regress dense disparity maps directly from image pairs. Computer generated imagery is deployed to gather the large data corpus required to train such networks, an additional fine-tuning allowing to adapt the model to work well also on real and possibly diverse environments. Yet, besides a few public datasets such as Kitti, the ground-truth needed to adapt the network to a new scenario is hardly available in practice. In this paper we propose a novel unsupervised adaptation approach that enables to fine-tune a deep learning stereo model without any ground-truth information. We rely on off-the-shelf stereo algorithms together with state-of-the-art confidence measures, the latter able to ascertain upon correctness of the measurements yielded by former. Thus, we train the network based on a novel loss-function that penalizes predictions disagreeing with the highly confident disparities provided by the algorithm and enforces a smoothness constraint. Experiments on popular datasets (KITTI 2012, KITTI 2015 and Middlebury 2014) and other challenging test images demonstrate the effectiveness of our proposal.


title: Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

abstract: In this paper, we propose a novel object detection algorithm named"Deep Regionlets"by integrating deep neural networks and a conventional detection schema for accurate generic object detection. Motivated by the effectiveness of regionlets for modeling object deformations and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select sub-regions from which features can be learned from. An object proposal typically contains 3-16 sub-regions. The regionlet learning module focuses on local feature selection and transformations to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a"gating network"within the regionlet leaning module to enable instance dependent soft feature selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present ablation studies and extensive experiments on the PASCAL VOC dataset and the Microsoft COCO dataset. The proposed method yields competitive performance over state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.


title: Fine-grained Visual Categorization using PAIRS: Pose and Appearance Integration for Recognizing Subcategories.

abstract: In Fine-grained Visual Categorization (FGVC), the differences between similar categories are often highly localized to a small number of object parts, and significant pose variation therefore constitutes a great challenge for identification. To address this, we propose extracting image patches using pairs of predicted keypoint locations as anchor points. The benefits of this approach are two-fold: (1) it achieves explicit top-down visual attention on object parts, and (2) the extracted patches are pose-aligned and thus contain stable appearance features. We employ the popular Stacked Hourglass Network to predict keypoint locations, reporting state-of-the-art keypoint localization results on the challenging CUB-200-2011 dataset. Anchored by these predicted keypoints, an overcomplete basis of pose-aligned patches is extracted and a specialized appearance classification network is trained for each patch. An aggregating network is then applied to combine the patch networks' individual predictions, producing a final classification score. Our PAIRS algorithm attains an accuracy of 88.6%, an increase of 1.1% over the current state-of-the-art. Enhancing the base PAIRS model with single-keypoint patches produces a further improvement, yielding a new state-of-the-art accuracy of 89.2% on the CUB dataset and clearly demonstrating the power of integrating pose and appearance features.


title: Fixed-Point CORDIC-Based QR Decomposition by Givens Rotations on FPGA

abstract: This paper presents a parallel architecture of an QR decomposition systolic array based on the Givens rotations algorithm on FPGA. The proposed architecture adopts a direct mapping by 21 fixed-point CORDIC-based process units that can compute the QR decomposition for an 4×4 real matrix. In order to achieve a comprehensive resource and performance evaluation, the computational error analysis, the resource utilized, and speed achieved on Virtex5 XC5VTX150T FPGA, are evaluated with the different precision of the intermediate word lengthes. The evaluation results show that 1) the proposed systolic array satisfies 99.9% correct 4×4 QR decomposition for the 2-13 accuracy requirement when the word length of the data path is lager than 25-bit, 2) occupies about 2, 810 (13%) slices, and achieves about 2.06 M/sec updates by running at the maximum frequency 111 MHz.


title: Advanced social engineering attacks

abstract: Social engineering has emerged as a serious threat in virtual communities and is an effective means to attack information systems. The services used by today's knowledge workers prepare the ground for sophisticated social engineering attacks. The growing trend towards BYOD (bring your own device) policies and the use of online communication and collaboration tools in private and business environments aggravate the problem. In globally acting companies, teams are no longer geographically co-located, but staffed just-in-time. The decrease in personal interaction combined with a plethora of tools used for communication (e-mail, IM, Skype, Dropbox, LinkedIn, Lync, etc.) create new attack vectors for social engineering attacks. Recent attacks on companies such as the New York Times and RSA have shown that targeted spear-phishing attacks are an effective, evolutionary step of social engineering attacks. Combined with zero-day-exploits, they become a dangerous weapon that is often used by advanced persistent threats. This paper provides a taxonomy of well-known social engineering attacks as well as a comprehensive overview of advanced social engineering attacks on the knowledge worker.


Far-Field ASR Without Parallel Data.


title: Vehicular Ad hoc Network (VANETs): A Review

abstract: A Vehicular Ad-Hoc Network or VANET is a sub form of Mobile Ad-Hoc Network or MANET that provides communication between vehicles and between vehicles and road-side base stations with an aim of providing efficient and safe transportation. A vehicle in VANET is considered to be an intelligent mobile node capable of communicating with its neighbours and other vehicles in the network. VANET introduces more challenges aspects as compare to MANET because of high mobility of nodes and fast topology changes in VANET. Various routing protocols have been designed and presented by researchers after considering the major challenges involved in VANETs This paper provides a survey of routing protocols for VANET . It covers application areas, challenges and security issues prevailing in VANETs.


title: SceneSeer: 3D Scene Design with Natural Language

abstract: Designing 3D scenes is currently a creative task that requires significant expertise and effort in using complex 3D design interfaces. This effortful design process starts in stark contrast to the easiness with which people can use language to describe real and imaginary environments. We present SceneSeer: an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language. A user provides input text from which we extract explicit constraints on the objects that should appear in the scene. Given these explicit constraints, the system then uses a spatial knowledge base learned from an existing database of 3D scenes and 3D object models to infer an arrangement of the objects forming a natural scene matching the input description. Using textual commands the user can then iteratively refine the created scene by adding, removing, replacing, and manipulating objects. We evaluate the quality of 3D scenes generated by SceneSeer in a perceptual evaluation experiment where we compare against manually designed scenes and simpler baselines for 3D scene generation. We demonstrate how the generated scenes can be iteratively refined through simple natural language commands.


title: A fast pruned-extreme learning machine for classification problem

abstract: Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification. In particular, we describe the proposed pruned-ELM (P-ELM) algorithm as a systematic and automated approach for designing ELM classifier network. P-ELM uses statistical methods to measure the relevance of hidden nodes. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned by considering their relevance to the class labels. As a result, the architectural design of ELM network classifier can be automated. Empirical study of P-ELM on several commonly used classification benchmark problems and with diverse forms of hidden node functions show that the proposed approach leads to compact network classifiers that generate fast response and robust prediction accuracy on unseen data, comparing with traditional ELM and other popular machine learning approaches.


title: Adas on Cots with OpenCL: A Case Study with Lane Detection

abstract: The concept of autonomous cars is driving a boost for car electronics and the size of automotive electronics market is foreseen to double by 2025. How to benefit from this boost is an interesting question. This article presents a case study to test the feasibility of using OpenCL as the programming language and Cots components as the underlying computing platforms for Adas development. For representative Adas applications, a scalable lane detection is developed that can tune the trade-off between detection accuracy and speed. Our OpenCL implementation is tested on 14 video streams from different data-sets with different road scenarios on 5 Cots platforms. We demonstrate that the Cots platforms can provide more than sufficient computing power for the lane detection in the meanwhile our OpenCL implementation can exploit the massive parallelism provided by the Cots platforms.


title: Collision avoidance using a model of the locust LGMD neuron

abstract: Abstract The lobula giant movement detector (LGMD) system in the locust responds selectively to objects approaching the animal on a collision course. In earlier work we have presented a neural network model based on the LGMD system which shared this preference for approaching objects. We have extended this model in order to evaluate its responses in a real-world environment using a miniature mobile robot. This extended model shows reliable obstacle detection over an eight-fold range of speeds, and raises interesting questions about basic properties of the biological system.


title: Reaction of amines on 3-ureidomethylenecoumarins. A new route toN-(methylene-4-oxocoumarinyl)amines

abstract: N-(Methylene-4-oxocoumarinyl)amines were prepared in good yield by reacting aliphatic and aromatic amines bearing functional groups with the relevant 3-ureidomethylenecoumarins.


title: The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

abstract: We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.


title: A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars

abstract: Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.


title: Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification

abstract: This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.


Active Learning of Link Specifications using Decision Tree Learning


title: Low-power high-speed level shifter design for block-level dynamic voltage scaling environment

abstract: Two novel level shifters that are suitable for block-level dynamic voltage scaling environment (namely, V/sub DD/-hopping) are proposed. In order to achieve reduction in power consumption and delay, the first proposed level shifter which is called contention mitigated level shifter (CMLS) uses a contention-reduction technique. The simulation results with 65-nm CMOS model show 24% reduction in power and 50% decrease in delay with 4% area increase compared with the conventional level shifter. The second proposed level shifter which is called bypassing enabled level shifter (BELS) implements a bypass function and it is fabricated using 0.35/spl mu/m CMOS technology. The measurement results show that the power and delay of the proposed BELS are reduced by 50% and 65%, respectively with 60% area overhead over the conventional level shifter.


title: An Image Processing Application for the Localization and Segmentation of Lymphoblast Cell Using Peripheral Blood Images

abstract: An important preliminary step in the diagnosis of leukemia is the visual examination of the patient’s peripheral blood smear under the microscope. Morphological changes in the white blood cells can be an indicator of the nature and severity of the disease. Manual techniques are labor intensive, slow, error prone and costly. A computerized system can be used as a supportive tool for the specialist in order to enhance and accelerate the morphological analysis process. This research present a new method that integrates color features with the morphological reconstruction to localize and isolate lymphoblast cells from a microscope image that contains many cells. The localization and segmentation are conducted using a proposed method that consists of an integration of several digital image processing techniques. 180 microscopic blood images were tested, and the proposed framework managed to obtain 100% accuracy for the localization of the lymphoblast cells and separate it from the image scene. The results obtained indicate that the proposed method can be safely used for the purpose of lymphoblast cells localization and segmentation and subsequently, aiding the diagnosis of leukemia.


Compliance with a structured bedside handover protocol : An observational , multicentred study ☆


title: Speeding Up Set Intersections in Graph Algorithms using SIMD Instructions

abstract: In this paper, we focus on accelerating a widely employed computing pattern --- set intersection, to boost a group of graph algorithms. Graph's adjacency-lists can be naturally considered as node sets, thus set intersection is a primitive operation in many graph algorithms. We propose QFilter, a set intersection algorithm using SIMD instructions. QFilter adopts a merge-based framework and compares two blocks of elements iteratively by SIMD instructions. The key insight for our improvement is that we quickly filter out most of unnecessary comparisons in one byte-checking step. We also present a binary representation called BSR that encodes sets in a compact layout. By combining QFilter and BSR, we achieve data-parallelism in two levels --- inter-chunk and intra-chunk parallelism. Moreover, we find that node ordering impacts the performance of intersection by affecting the compactness of BSR. We formulate the graph reordering problem as an optimization of the compactness of BSR, and prove its strong NP-completeness. Thus we propose an approximate algorithm that can find a better ordering to enhance the intra-chunk parallelism. We conduct extensive experiments to confirm that our approach can improve the performance of set intersection in graph algorithms significantly.


title: BRITS: Bidirectional Recurrent Imputation for Time Series

abstract: Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing value imputation in time series data. Our proposed method directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during the backpropagation.BRITS has three advantages: (a) it can handle multiple correlated missing values in time series; (b) it generalizes to time series with nonlinear dynamics underlying; (c) it provides a data-driven imputation procedure and applies to general settings with missing data.We evaluate our model on three real-world datasets, including an air quality dataset, a health-care data, and a localization data for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression accuracies.


title: A Theoretical Framework for Serious Game Design: Exploring Pedagogy, Play and Fidelity and their Implications for the Design Process

abstract: It is widely acknowledged that digital games can provide an engaging, motivating and "fun" experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design Kiili, 2005; Seeney & Routledge, 2009. Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged Aldrich, 2005; Harteveld et al., 2007, 2010. This paper will explore a triadic theoretical framework for serious game design comprising play, pedagogy and fidelity. It will outline underpinning theories, review key literatures and identify challenges and issues involved in balancing these elements in the process of serious game design.


title: A traffic sign detection pipeline based on interest region extraction

abstract: In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions extraction rather than a sliding window detection scheme. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of Mandatory, Prohibitory and Danger traffic signs. Our proposal has been evaluated experimentally within the German Traffic Sign Detection Benchmark competition.


title: Deep neural network architectures for modulation classification

abstract: In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture of [1] and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR accuracies of approximately 83.5% and 86.6%, respectively. Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88.5% at high SNR.


title: FlexCRAN: A flexible functional split framework over ethernet fronthaul in Cloud-RAN

abstract: Thorough investigation of the Cloud-RAN (C-RAN) architecture has recently shown that C-RAN can bring advanced cooperated and coordinated processing capabilities as well as the multiplexing gains toward future radio access networks. The baseband processing of each base station instance can now be flexibly split into smaller functional components, that can be placed either at remote radio units (RRUs) or baseband units (BBUs), depending on the available fronthaul (FH) performance. Additionally, with the wide adoption of Ethernet in data centers and core networks, the Radio over Ethernet (RoE) approach is now considered as an off-the-shelf candidate for the FH link. To this end, we propose a unified RRU/BBU architectural framework for C-RAN that can support both a flexible functional split and a FH transport protocol over Ethernet. Furthermore, we experimentally evaluate the main key performance indicators (KPIs) of an operational C-RAN network built based on OpenAirInterface (OAI), a software implementation of LTE/LTE-A systems, under two functional splits and different deployment scenarios.


title: Dynamic Gesture Recognition Using a Smart Glove in Hand-Assisted Laparoscopic Surgery

abstract: This paper presents a methodology for movement recognition in hand-assisted laparoscopic surgery using a textile-based sensing glove. The aim is to recognize the commands given by the surgeon’s hand inside the patient’s abdominal cavity in order to guide a collaborative robot. The glove, which incorporates piezoresistive sensors, continuously captures the degree of flexion of the surgeon’s fingers. These data are analyzed throughout the surgical operation using an algorithm that detects and recognizes some defined movements as commands for the collaborative robot. However, hand movement recognition is not an easy task, because of the high variability in the motion patterns of different people and situations. The data detected by the sensing glove are analyzed using the following methodology. First, the patterns of the different selected movements are defined. Then, the parameters of the movements for each person are extracted. The parameters concerning bending speed and execution time of the movements are modeled in a prephase, in which all of the necessary information is extracted for subsequent detection during the execution of the motion. The results obtained with 10 different volunteers show a high degree of precision and recall.


title: Multifunctional Robotic Crawler for Inspection of Suspension Bridge Hanger Cables: Mechanism Design and Performance Validation

abstract: This paper presents a robotic system, called multifunctional robotic crawler for cable INspection II, which is developed for inspection of hanger cables of suspension bridges. The robot can move over hanger cables and perform inspections with instruments such as cameras, nondestructive testing tools, etc. The novel mechanisms for the robotic system are proposed under the consideration of the environmental conditions of suspension bridges. Its driving mechanism adopts a crawler type one driven by brushless direct current motors, and the detachable adhesion tool ensures simple and quick installation procedures. The safe-landing mechanism for emergency situations guarantees the retrieval of the robot while preserving the safety of operators. This paper presents the robot system and its field test results under a super long-span suspension bridge, called Yeong-Jong Grand Bridge in South Korea. For practical applications, the real environmental conditions of a suspension bridge and the other fields where the robot can be applied are discussed.


title: Digital Didactical Designs in iPad-Classrooms

abstract: Traditionally, Information and Communication Technology ICT "has been segregated from the normal teaching classroom" [12], e.g. in computer labs. This has been changed with the advent of smaller devices like iPads. There is a shift from separating ICT and education to co-located settings in which digital technology becomes part of the classroom. This paper presents the results from a study about exploring digital didactical designs using iPads applied by teachers in schools. Classroom observations and interviews in iPad-classrooms in Danish schools have been done with the aim to provide empirical evidence on the co-evolutionary design of both, didactical designs and iPads. The Danish community Odder has 7 schools where around 200 teachers and 2,000 students aged 6-16 use iPads in a 1:1 iPad-program. Three key aspects could be explored: The teachers' digital didactical designs embrace a new learning goals where more than one correct answer exists, b focus on producing knowledge in informal-in-formal learning spaces, c making learning visible in different products text, comics, podcasts etc.. The results show the necessity of rethinking traditional Didaktik towards Digital Didactics.


title: Visualization of the Static Aspects of Software: A Survey

abstract: Software is usually complex and always intangible. In practice, the development and maintenance processes are time-consuming activities mainly because software complexity is difficult to manage. Graphical visualization of software has the potential to result in a better and faster understanding of its design and functionality, thus saving time and providing valuable information to improve its quality. However, visualizing software is not an easy task because of the huge amount of information comprised in the software. Furthermore, the information content increases significantly once the time dimension to visualize the evolution of the software is taken into account. Human perception of information and cognitive factors must thus be taken into account to improve the understandability of the visualization. In this paper, we survey visualization techniques, both 2D- and 3D-based, representing the static aspects of the software and its evolution. We categorize these techniques according to the issues they focus on, in order to help compare them and identify the most relevant techniques and tools for a given problem.


title: A comparative evaluation of personality estimation algorithms for the twin recommender system

abstract: The appearance of the so-called recommender systems has led to the possibility of reducing the information overload experienced by individuals searching among online resources. One of the areas of application of recommender systems is the online tourism domain where sites like TripAdvisor allow people to post reviews of various hotels to help others make a good choice when planning their trip. As the number of such reviews grows in size every day, clearly it is impractical for the individual to go through all of them. We propose the TWIN ("Tell me What I Need") Personality-based Recommender System that analyzes the textual content of the reviews and estimates the personality of the user according to the Big Five model to suggest the reviews written by "twin-minded" people. In this paper we compare a number of algorithms to select the better option for personality estimation in the task of user profile construction.


title: States (2013)" Behind the Scenes of Online Attacks: an Analysis of Exploitation Behaviors on the Web

abstract: Web attacks are nowadays one of the major threats on the Internet, and several studies have analyzed them, providing details on how they are performed and how they spread. However, no study seems to have sufficiently analyzed the typical behavior of an attacker after a website has been compromised. This paper presents the design, implementation, and deployment of a network of 500 fully functional honeypot websites, hosting a range of different services, whose aim is to attract attackers and collect information on what they do during and after their attacks. In 100 days of experiments, our system automatically collected, normalized, and clustered over 85,000 files that were created during approximately 6,000 attacks. Labeling the clusters allowed us to draw a general picture of the attack landscape, identifying the behavior behind each action performed both during and after the exploitation of a web application.


title: CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

abstract: This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network -- CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.


title: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

abstract: This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.


title: A review of forensic approaches to digital image Steganalysis

abstract: Traditional or Binary Steganalysis brands a digital object such as an image as stego or innocent only but modern day information security requires deeper insight about the embedded message. Forensic Steganalysis is the systemic application of different research techniques to gather further in-depth knowledge about the hidden secret information. Once an image is categorised as being stego, additional investigations are carried out to find the steganographic algorithm used to insert the covert message, estimate the length of such message and finally the stego key used to embed the message in various pixels of the stego image. A lot more literature is available on the review of the traditional steganalysis techniques as compared to forensic steganalysis. The present paper gives details about important forensic techniques as available in the steganalysis literature. The techniques presented and discussed relate to digital image domain for determination of the embedding algorithm, estimation of the secret message payload and stego key determination. The paper describes and compares different features of these forensic techniques. Discussions about significant performance metrics and evaluation parameters used in all phases further elaborate the comparative perspective. We identify potential challenges and explore areas of future work to boost the capabilities of present forensic steganalyzers.


title: Bandgap Engineering of Coal-Derived Graphene Quantum Dots

abstract: Bandgaps of photoluminescent graphene quantum dots (GQDs) synthesized from anthracite have been engineered by controlling the size of GQDs in two ways: either chemical oxidative treatment and separation by cross-flow ultrafiltration, or by a facile one-step chemical synthesis using successively higher temperatures to render smaller GQDs. Using these methods, GQDs were synthesized with tailored sizes and bandgaps. The GQDs emit light from blue-green (2.9 eV) to orange-red (2.05 eV), depending on size, functionalities and defects. These findings provide a deeper insight into the nature of coal-derived GQDs and demonstrate a scalable method for production of GQDs with the desired bandgaps.


title: Reproductive parameters of female orangutans (Pongo pygmaeus wurmbii) 1971–2011, a 40-year study at Tanjung Puting National Park, Central Kalimantan, Indonesia

abstract: This study presents reproductive parameter data gathered by direct observation over a 40-year period (1971-2011) of the provisioned free-ranging population of orangutans at Camp Leakey in Tanjung Puting National Park, Central Kalimantan, Indonesia. Age at first reproduction, interbirth interval (IBI), sex ratio at birth, and infant mortality for 19 female orangutans (11 first-generation wild-born ex-captive mothers and 8 second-generation mothers) are included in this analysis. Age at first reproduction among the first-generation mothers was similar to that among wild orangutans, while second-generation mothers had a significantly earlier age at first reproduction. IBIs were similar among first- and second-generation mothers and were significantly shorter than those recorded in studies of wild orangutan populations. There was an expected male-biased sex ratio at birth and a slightly higher than expected rate of infant mortality when compared to wild populations. Infant mortality was primarily seen among second-generation mothers who gave birth before the age of 12, and among first births of some first-generation mothers. These results lend support to the ecological energetics hypothesis, which predicts that increased diet quality leads to a faster rate of reproduction.