title: Internet banking diffusion: A country-level analysis

abstract: Graphical abstractDisplay Omitted This study examines country-level factors that influence Internet banking diffusion.Direct and mediating effects of economic and technological factors are modeled.National culture is used to explain diffusion levels across different country groups.PLS and cluster analysis provide support for mediating and moderating relationships.Findings yield implications on how to promote Internet banking for policy makers. In the last decade, Internet banking technology has made remarkable progress. However, there is a huge disparity across different nations all over the world in the diffusion of Internet banking services. This leads to the research question of this study: why different countries exhibit different levels of Internet banking adoption? Previous studies provide limited insight as they were mostly conducted at the individual user level with single-country samples. At the country level, this study proposes an Internet banking diffusion model that examines the impact of economical, technological and cultural factors on Internet banking diffusion. The hypothesized relationships in the research model were statistically tested with the secondary data collected from a sample of 33 European countries. The results indicate that the effects of socio-economic and technology-related factors on Internet banking diffusion are fully mediated by Internet access. Furthermore, the findings suggest that national culture is an important moderator as it make differences in Internet banking diffusion as well as Internet access across different country groups. The country-level analysis contributes to the advancement of Internet banking theory and practice, and provides some useful insights to researchers, practitioners and policy makers on how to enhance Internet banking diffusion.


title: An object detection method for describing soccer games from video

abstract: We propose a novel object detection and tracking method in order to detect and track objects necessary to describe contents of a soccer game. On the contrary to intensity oriented conventional object detection methods, the proposed method refers to color rarity and local edge property, and integrally evaluates them by a fuzzy function to achieve better detection quality. These image features were chosen considering the characteristics of soccer video images, that most non-object regions are roughly single colored (green) and most objects tend to have locally strong edges. We also propose a simple object tracking method, that could track objects with occlusion with other objects using a color based template matching. The result of an evaluation experiment applied to actual soccer video showed very high detection rate in detecting player regions without occlusion, and promising ability for regions with occlusion.


title: Fast smooth rank approximation for tensor completion

abstract: In this paper we consider the problem of recovering an N-dimensional data from a subset of its observed entries. We provide a generalization for the smooth Shcatten-p rank approximation function in [1] to the N-dimensional space. In addition, we derive an optimization algorithm using the Augmented Lagrangian Multiplier in the N-dimensional space to solve the tensor completion problem. We compare the performance of our algorithm to state-of-the-art tensor completion algorithms using different color images and video sequences. Our experimental results showed that the proposed algorithm converges faster (approximately half the execution time), and at the same time it achieves comparable performance to state-of-the-art tensor completion algorithms.


title: Affect recognition from face and body: early fusion vs. late fusion

abstract: This paper presents an approach to automatic visual emotion recognition from two modalities: face and body. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information first at a feature-level, in which the data from both modalities are combined before classification, and later at a decision-level, in which we integrate the outputs of the monomodal systems by the use of suitable criteria. We then evaluate these two fusion approaches, in terms of performance over monomodal emotion recognition based on facial expression modality only. In the experiments performed the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual facial modality. Moreover, fusion at the feature-level proved better recognition than fusion at the decision-level.


title: Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching

abstract: There are various types of applications for fingerprint recognition which is used for different purposes. Fingerprint is one of the challenging pattern Recognition problem. The Fingerprint Recognition system is divided into four stages. First is Acquisition stage to capture the fingerprint image, The second is Pre-processing stage to enhancement, binarization, thinning fingerprint image. The Third stage is Feature Extraction Stage to extract the feature from the thinning image by use minutiae extractor methods to extract ridge ending and ridge bifurcation from thinning. The fourth stage is matching(Identification, Verification) to match two minutiae points by using minutiae matcher method in which similarity and distance measure are used. The algorithm is tested accurately and reliably by using fingerprint images from different databases. In this paper the fingerprint databases used are FVC2000 and FVC2002 Databases, we see that, the FVC2002 database perform better results compare with FVC2000 database. The recognition system evaluate with two factor FAR and FRR, In this system the result of FAR is 0.0154 and FRR is 0.0137 with Accuracy equal to 98.55%.


title: The battle of the giants: a case study of GPU vs FPGA optimisation for real-time image processing

abstract: This paper focuses on a thorough comparison of the two main hardware targets for real-time optimization of a computer vision algorithm: GPU and FPGA. Based on a complex case study algorithm for threaded isle detection, implementation on both hardware targets is compared in terms of resulting time performance, code translation effort, hardware cost, power efficiency and integrateability. A real-life case study as described in this paper is a very useful addition to discussions on a more theoretical level, going beyond artificial experiments. In our experiments, we show the speed-up gained by porting our algorithm to FPGA using manually written VHDL and to a heterogeneous GPU/CPU architecture with the OpenCL language. Also, issues and problems occurring during the code porting are detailed.


title: Like What You Like: Knowledge Distill via Neuron Selectivity Transfer

abstract: Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expense of high storage and computational costs. Consequently, the acceleration and compression of neural networks have attracted much attention recently. Knowledge Transfer (KT), which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the popular solutions. In this paper, we propose a novel knowledge transfer method by treating it as a distribution matching problem. Particularly, we match the distributions of neuron selectivity patterns between teacher and student networks. To achieve this goal, we devise a new KT loss function by minimizing the Maximum Mean Discrepancy (MMD) metric between these distributions. Combined with the original loss function, our method can significantly improve the performance of student networks. We validate the effectiveness of our method across several datasets, and further combine it with other KT methods to explore the best possible results. Last but not least, we fine-tune the model to other tasks such as object detection. The results are also encouraging, which confirm the transferability of the learned features.


Using NLP for Enhancing Second Language Acquisition


title: Understanding Convolutional Neural Networks with A Mathematical Model

abstract: Abstract This work attempts to address two fundamental questions about the structure of the convolutional neural networks (CNN): (1) why a nonlinear activation function is essential at the filter output of all intermediate layers? (2) what is the advantage of the two-layer cascade system over the one-layer system? A mathematical model called the “REctified-COrrelations on a Sphere” (RECOS) is proposed to answer these two questions. After the CNN training process, the converged filter weights define a set of anchor vectors in the RECOS model. Anchor vectors represent the frequently occurring patterns (or the spectral components). The necessity of rectification is explained using the RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are used to illustrate discussion points. Finally, the RECOS model is generalized to a multilayer system with the AlexNet as an example.


title: AeroRP performance in highly-dynamic airborne networks using 3D Gauss-Markov mobility model

abstract: Emerging airborne networks require domainspecific routing protocols to cope with the challenges faced by the highly-dynamic aeronautical environment. We present an ns-3 based performance comparison of the AeroRP protocol with conventional MANET routing protocols. To simulate a highly-dynamic airborne network, accurate mobility models are needed for the physical movement of nodes. The fundamental problem with many synthetic mobility models is their random, memoryless behavior. Airborne ad hoc networks require a flexible memory-based 3-dimensional mobility model. Therefore, we have implemented a 3-dimensional Gauss-Markov mobility model in ns-3 that appears to be more realistic than memoryless models such as random waypoint and random walk. Using this model, we are able to simulate the airborne networking environment with greater realism than was previously possible and show that AeroRP has several advantages over other MANET routing protocols.


title: Salt and sugar: their effects on blood pressure

abstract: Both dietary salt and sugar are related to blood pressure (BP). The evidence for salt is much stronger, and various types of studies have consistently shown that salt is a major cause of raised BP, and a reduction from the current intake of ≈ 9-12 g/day in most countries of the world to the recommended level of 5-6 g/day lowers BP in both hypertensive and normotensive individuals, in men and women, in all age groups and in all ethnic groups. Countries such as Finland and the UK that have successfully reduced salt intake have demonstrated a reduction in population BP and cardiovascular mortality, with major cost savings to the health service. The mechanisms whereby salt raises BP are not fully understood. The traditional concepts focus on the tendency for an increase in extracellular fluid volume. Increasing evidence suggests that small increases in plasma sodium may play an important role. There are several other factors that also increase BP, one of which is added sugars. The current high intake of added sugars increases obesity which, in turn, raises BP. Recent studies also suggest that added sugars, particularly those in soft drinks, may have a direct effect on BP. However, the relationship between soft drink consumption and BP could be, at least partially, mediated by the effect of salt intake on increasing soft drink consumption. Actions to reduce salt and sugar intake across the whole population will have major beneficial effects on health along with major cost savings.


title: Nonperiodic solid phase in a two-dimensional hard-dimer system.

abstract: We report Monte Carlo simulations of a system of two-dimensional, hard, homonuclear dimers. The equation of state and the Gibbs free energy were computed for the fluid phase and several crystalline and noncrystalline (aperiodic) solid structures. We observe that the differences in Gibbs free energy between the various solid structures are much less than the contribution to the entropy due to degeneracy of the ``ground state'' of the aperiodic solid. Hence, the thermodynamically stable solid structure of the system corresponds to an aperiodic arrangement of the molecular centers of mass and orientations. The melting point determined for this aperiodic solid is located within the observed narrow hystersis region.


A Recent Survey on Unstructured Data to Structured Data in Distributed Data Mining


title: Recursive Neural Networks Can Learn Logical Semantics

abstract: Supervised recursive neural network models (RNNs) for sentence meaning have been successful in an array of sophisticated language tasks, but it remains an open question whether they can learn compositional semantic grammars that support logical deduction. We address this question directly by for the first time evaluating whether each of two classes of neural model — plain RNNs and recursive neural tensor networks (RNTNs) — can correctly learn relationships such as entailment and contradiction between pairs of sentences, where we have generated controlled data sets of sentences from a logical grammar. Our first experiment evaluates whether these models can learn the basic algebra of logical relations involved. Our second and third experiments extend this evaluation to complex recursive structures and sentences involving quantification. We find that the plain RNN achieves only mixed results on all three experiments, whereas the stronger RNTN model generalizes well in every setting and appears capable of learning suitable representations for natural language logical inference.


Better Together: Combining Language and Social Interactions into a Shared Representation


title: On Incident Handling and Response : A state-ofthe-art approach

abstract: Incident Response has always been an important aspect of Information Security but it is often overlooked by security administrators. Responding to an incident is not solely a technical issue but has many management, legal, technical and social aspects that are presented in this paper. We propose a detailed management framework along with a complete structured methodology that contains best practices and recommendations for appropriately handling a security incident. We also present the state-of-the art technology in computer, network and software forensics as well as automated trace-back artifacts, schemas and protocols. Finally, we propose a generic Incident Response process within a corporate environment.


title: Second Generation 60-GHz Transceiver Chipset Supporting Multiple Modulations at Gb/s data rates (Invited)

abstract: A feature-rich second-generation 60-GHz transceiver chipset is introduced. It integrates dual-conversion superheterodyne receiver and transmitter chains, a sub-integer frequency synthesizer, full programmability from a digital interface, modulator and demodulator circuits to support analog modulations (e.g. MSK, BPSK), as well as a universal I&Q interface for digital modulation formats (e.g. OFDM). Achieved performance includes 6-dB receiver noise figure and 12 dBm transmitter output ldB compression point. Wireless link experiments with different modulation formats for 2-Gb/s real-time uncompressed HDTV transmission are discussed. Additionally, recent millimeter-wave package and antenna developments are summarized and a 60GHz silicon micromachined antenna is presented.


title: Bayesian Inference in Model-Based Machine Vision

abstract: We present a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects and their relations in a system for reasoning with geometry in machine vision. Bayesian inference provides a framework for accruing probabilities to rank order hypotheses. This is a preliminary version of visual interpretation in SUCCESSOR, an intelligent, model-based vision system integrating multiple sensors.


title: Integration of graph clustering with ant colony optimization for feature selection

abstract: A novel supervised filter-based feature selection method using ACO is proposed.Our method integrates graph clustering with a modified ant colony search process.Each feature set is evaluated using a novel measure without using any learning model.The sizes of the final feature set is determined automatically.The method is compared to the state-of-the-art filter and wrapper based methods. Feature selection is an important preprocessing step in machine learning and pattern recognition. The ultimate goal of feature selection is to select a feature subset from the original feature set to increase the performance of learning algorithms. In this paper a novel feature selection method based on the graph clustering approach and ant colony optimization is proposed for classification problems. The proposed method's algorithm works in three steps. In the first step, the entire feature set is represented as a graph. In the second step, the features are divided into several clusters using a community detection algorithm and finally in the third step, a novel search strategy based on the ant colony optimization is developed to select the final subset of features. Moreover the selected subset of each ant is evaluated using a supervised filter based method called novel separability index. Thus the proposed method does not need any learning model and can be classified as a filter based feature selection method. The proposed method integrates the community detection algorithm with a modified ant colony based search process for the feature selection problem. Furthermore, the sizes of the constructed subsets of each ant and also size of the final feature subset are determined automatically. The performance of the proposed method has been compared to those of the state-of-the-art filter and wrapper based feature selection methods on ten benchmark classification problems. The results show that our method has produced consistently better classification accuracies.


title: IoT Based Compressive Sensing for ECG Monitoring

abstract: The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied. Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed. Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme. The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms. The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms. The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently. Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 3.3 % in the worst case scenario.


title: GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

abstract: Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks when compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques. GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter $\alpha$. Thus, what was once a tedious search process that incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks. Ultimately, we will demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.


title: Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

abstract: Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrat...


title: Mechanical traction for neck pain with or without radiculopathy

abstract: Background ::: Neck pain is a frequently reported complaint of the musculoskeletal system which can be disabling and costly to society. Mechanical traction is often used as an adjunct therapy in outpatient rehabilitation. ::: ::: Objectives ::: To assess the effects of mechanical traction for neck disorders. ::: ::: Search methods ::: A research librarian searched computerized bibliographic databases without language restrictions up to March 2008 for randomized controlled trials (RCTs) from the medical, chiropractic, and allied health literature. ::: ::: Selection criteria ::: The RCTs we selected examined adults with neck disorders who received mechanical traction alone or in combination with other treatments compared to a placebo or another treatment. Our outcomes of interest were pain, function, disability, global perceived effect, patient satisfaction, and quality of life measures. ::: ::: Data collection and analysis ::: Two review authors with different backgrounds in medicine, physiotherapy, massage therapy and chiropractics independently conducted study selection, risk of bias assessment and data abstraction using pre-piloted forms. We resolved disagreement through consensus. ::: ::: Main results ::: Of the seven selected RCTs (total participants = 958), only one (N = 100) had a low risk of bias. It found no statistically significant difference (SMD -0.16: 95%CI: -0.59 to 0.27) between continuous traction and placebo traction in reducing pain or improving function for chronic neck disorders with radicular symptoms. Our review found no evidence from RCTs with a low potential for bias that clearly supports or refutes the use of either continuous or intermittent traction for neck disorders. ::: ::: Authors' conclusions ::: The current literature does not support or refute the efficacy or effectiveness of continuous or intermittent traction for pain reduction, improved function or global perceived effect when compared to placebo traction, tablet or heat or other conservative treatments in patients with chronic neck disorders. Large, well conducted RCTs are needed to first determine the efficacy of traction, then the effectiveness, for individuals with neck disorders with radicular symptoms.


title: A New Metric for Quality of Network Community Structure

abstract: Modularity is widely used to effectively measure the strength of the community structure found by community detection algorithms. However, modularity maximization suffers from two opposite yet coexisting problems: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones. The latter tendency is known in the literature as the resolution limit problem. To address them, we propose to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity. We refer to the modified metric as Modularity Density and we demonstrate that it indeed resolves both problems mentioned above. We describe the motivation for introducing this metric by using intuitively clear and simple examples. We also prove that this new metric solves the resolution limit problem. Finally, we discuss the results of applying this metric, modularity, and several other popular community quality metrics to two real dynamic networks. The results imply that Modularity Density is consistent with all the community quality measurements but not modularity, which suggests that Modularity Density is an improved measurement of the community quality compared to modularity.


title: Interactive skeleton techniques for enhancing motion dynamics in key frame animation

abstract: A significant increase in the capability for controlling motion dynamics in key frame animation is achieved through skeleton control. This technique allows an animator to develop a complex motion sequence by animating a stick figure representation of an image. This control sequence is then used to drive an image sequence through the same movement. The simplicity of the stick figure image encourages a high level of interaction during the design stage. Its compatibility with the basic key frame animation technique permits skeleton control to be applied selectively to only those components of a composite image sequence that require enhancement.


title: ZenCrowd: Leveraging Probabilistic Reasoning and Crowdsourcing Techniques for Large-Scale Entity Linking

abstract: We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.


An Unsupervised Method for Detection of XSS Attack


title: Eager decision tree

abstract: Decision Tree induction is commonly used classification algorithm. One of the important problems is how to use records with unknown values from training as well as testing data. Many approaches have been proposed to address the impact of unknown values at training on accuracy of prediction. However, very few techniques are there to address the problem in testing data. In our earlier work, we discussed and summarized these strategies in details. In Lazy Decision Tree, the problem of unknown attribute values in test instance is completely eliminated by delaying the construction of tree till the classification time and using only known attributes for classification. In this paper we present novel algorithm ‘Eager Decision Tree’ which constructs a single prediction model at the time of training which considers all possibilities of unknown attribute values from testing data. It naturally removes the problem of handing unknown values in testing data in Decision Tree induction like Lazy Decision Tree.


title: Personalized Citation Recommendation via Convolutional Neural Networks

abstract: Automatic citation recommendation based on citation context, together with consideration of users’ preference and writing patterns is an emerging research topic. In this paper, we propose a novel personalized convolutional neural networks (p-CNN) discriminatively trained by maximizing the conditional likelihood of the cited documents given a citation context. The proposed model not only nicely represents the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also includes authorship information. It includes each paper’s author into our neural network’s input layer and thus can generate semantic content features and representative author features simultaneously. The results show that the proposed model can effectively captures salient representations and hence significantly outperforms several baseline methods in citation recommendation task in terms of recall and Mean Average Precision rates.


title: Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

abstract: This paper presents the results of an annotation study focused on the fine-grained analysis of argumentation structures in scientific publications. Our new annotation scheme specifies four types of binary argumentative relations between sentences, resulting in the representation of arguments as small graph structures. We developed an annotation tool that supports the annotation of such graphs and carried out an annotation study with four annotators on 24 scientific articles from the domain of educational research. For calculating the inter-annotator agreement, we adapted existing measures and developed a novel graphbased agreement measure which reflects the semantic similarity of different annotation graphs.


title: Where do emotions come from? Predicting the Emotion Stimuli Map

abstract: Which parts of an image evoke emotions in an observer? To answer this question, we introduce a novel problem in computer vision — predicting an Emotion Stimuli Map (ESM), which describes pixel-wise contribution to evoked emotions. Building a new image database, EmotionROI, as a benchmark for predicting the ESM, we find that the regions selected by saliency and objectness detection do not correctly predict the image regions which evoke emotion. Although objects represent important regions for evoking emotion, parts of the background are also important. Based on this fact, we propose using fully convolutional networks for predicting the ESM. Both qualitative and quantitative experimental results confirm that our method can predict the regions which evoke emotion better than both saliency and objectness detection.


title: Secure and Deduplicated Spatial Crowdsourcing: A Fog-Based Approach

abstract: With the proliferation of mobile devices, spatial crowdsourcing is rising as a new paradigm that enables individuals to participate in tasks related to some locations in the physical world. Nevertheless, how to allocate these tasks to proper mobile users and improve communication efficiency are critical in spatial crowdsourcing. In this paper, we propose Fo-DSC, a fog-based deduplicated spatial crowdsourcing framework to achieve precise task allocation and secure data deduplication. Specifically, by integrating fog computing, we design a two-step task allocation mechanism to improve the accuracy of tasks allocation in spatial crowdsourcing. The fog nodes can detect and erase the repeated data in crowdsensing reports without learning any information about the reports. Furthermore, Fo-DSC efficiently records the contributions of mobile users whose data are reduplicated and deleted. As a result, these users do not become discouraged. Finally, we demonstrate that Fo-DSC satisfies the properties of fog-based task allocation and secure data deduplication with low computational and communication overheads.


Towards Environmental Awareness in Nigeria


title: High-throughput purity estimation and characterisation of synthetic peptides by electrospray mass spectrometry

abstract: High-throughput analysis for purity and molecular weight determination of synthetic peptides including characterisation of any peptidic byproducts arising from synthesis is described. The data from electrospray mass spectrometry are processed with an algorithm that calculates the contribution of the target peptide and each of the identifiable contaminants to the total ionizable material in a sample of synthetic peptide. All essential data are obtained by one instrumental technique in <3 min per sample. The technique has distinct advantages in the rapid analysis of the many hundreds of peptides/peptidomimetics required in systematic quantitative structure-activity relationship and other investigations. ©Munksgaard 1996.


title: Locally Planar Patch Features for Real-Time Structure from Motion

abstract: The performance of sequential structure from motion systems, where scene mapping is sparse to permit real-time operation, depends greatly on the ability to repeatedly measure the same visual features from a wide range of viewpoints. While previous systems have tracked features as 2D templates in image space, we show that long-term tracking is improved by treating salient feature patches as observations of locally planar regions on 3D world surfaces. Within a SLAM framework for motion and structure estimation, a gradient-based image alignment method is used to deduce estimates feature surface normal estimates, enabling pre-warping of templates for matching. As an added benet these normals provide a richer description of the scene.


title: DOREMUS: A Graph of Linked Musical Works

abstract: Three major French cultural institutions—the French National Library (BnF), Radio France and the Philharmonie de Paris—have come together in order to develop shared methods to describe semantically their catalogs of music works and events. This process comprises the construction of knowledge graphs representing the data contained in these catalogs following a novel agreed upon ontology that extends CIDOC-CRM and FRBRoo, the linking of these graphs and their open publication on the web. A number of specialized tools that allow for the reproduction of this process are developed, as well as web applications for easy access and navigation through the data. The paper presents one of the main outcomes of this project—the DOREMUS knowledge graph, consisting of three linked datasets describing classical music works and their associated events (e.g., performances in concerts). This resource fills an important gap between library content description and music metadata. We present the DOREMUS pipeline for lifting and linking the data, the tools developed for these purposes, as well as a search application allowing to explore the data.


Integration of GaAs LEDs on Si by Epi-lift-off


title: AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

abstract: Background ::: Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling.


title: Sequential Monte Carlo Methods to Train Neural Network Models

abstract: We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.


Identifying Temporality of Word Senses Based on Minimum Cuts


title: Overt and Relational Aggression in Adolescents: Social-Psychological Adjustment of Aggressors and Victims

abstract: Examined the relative and combined associations among relational and overt forms of aggression and victimization and adolescents' concurrent depression symptoms, loneliness, self-esteem, and externalizing behavior. An ethnically diverse sample of 566 adolescents (55% girls) in Grades 9 to 12 participated. Results replicated prior work on relational aggression and victimization as distinct forms of peer behavior that are uniquely associated with concurrent social-psychological adjustment. Victimization was associated most closely with internalizing symptoms, and peer aggression was related to symptoms of disruptive behavior disorder. Findings also supported the hypothesis that victims of multiple forms of aggression are at greater risk for adjustment difficulties than victims of one or no form of aggression. Social support from close friends appeared to buffer the effects of victimization on adjustment.


title: Transfer learning for activity recognition: a survey

abstract: Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.


title: Access control for home data sharing: Attitudes, needs and practices

abstract: As digital content becomes more prevalent in the home, non-technical users are increasingly interested in sharing that content with others and accessing it from multiple devices. Not much is known about how these users think about controlling access to this data. To better understand this, we conducted semi-structured, in-situ interviews with 33 users in 15 households. We found that users create ad-hoc access-control mechanisms that do not always work; that their ideal policies are complex and multi-dimensional; that a priori policy specification is often insufficient; and that people's mental models of access control and security are often misaligned with current systems. We detail these findings and present a set of associated guidelines for designing usable access-control systems for the home environment.


title: A Taxonomy of Internet Communication Tools

abstract: When looking at the social phenomenon that are arise from the use of Internet communications tools, one must consider the properties of the tool that influence human-to-human interaction. This paper presents a number of such properties and discusses their importance. In addition, existing Internet communications tools are described both in general and with respect to the properties.


title: Deductive database languages: problems and solutions

abstract: Deductive databases result from the integration of relational database and logic programming techniques. However, significant problems remain inherent in this simple synthesis from the language point of view. In this paper, we discuss these problems from four different aspects: complex values, object orientation, higher-orderness, and updates. In each case, we examine four typical languages that address the corresponding issues.


title: Low complexity, low power, 10 GHz super-regenerative transceiver

abstract: In this paper a 10 GHz quasi-Hybrid/MMIC super-regenerative transceiver/antenna chip is presented. The circuit is the highest frequency super-regenerative transceiver presented in the literature and is amongst the lowest power - certainly the lowest power at X-band frequency. The chip is fabricated on GaAs substrate and uses a MMIC process for the passive components and an RFMD PHEMT chip device bonded into the circuit for the active components. The transceiver chip measures 10 × 10 mm and consumes 0.75 mW Tx and 0.9 mW Rx. When mounted into a pcb carrier substrate containing antenna, bias circuitry and low pass filtering the board measures 26 × 42 mm and operates over a range of 1 m.


title: An integrated approach to achieving campus sustainability: assessment of the current campus environmental management practices

abstract: Abstract Universities can nowadays be regarded as ‘small cities’ due to their large size, population, and the various complex activities taking place in campuses, which have some serious direct and indirect impacts on the environment. The environmental pollution and degradation caused by universities in form of energy and material consumption via activities and operations in teaching and research, provision of support services and in residential areas could be considerably reduced by an effective choice of organizational and technical measures. Although many environmental protection measures can be seen at some universities, but a more systematic and sustainable approach to reducing the negative impacts of those activities and making the campuses more sustainable, is generally lacking. Therefore, this paper proposes a framework of a more suitable approach to achieving campus sustainability that could remedy the limitations of the current environmental management practices in universities and ensures more sustainability through the integration of three strategies, namely: university Environmental Management System (EMS); public participation and social responsibility; and promoting sustainability in teaching and research.


title: Class Imbalance Problem in Data Mining Review

abstract: In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, data-preprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.


title: Wireless energy harvesting massive MIMO relays

abstract: The trade-off between the achievable sum rate and harvested energy is investigated for multi-user/multi-cell massive multiple-input multiple-output (MIMO) relay networks. To this end, the antenna array at the relay is switched periodically between the energy harvesting and information processing modes, and the harvested energy is utilized for the relay's transmissions. Closed-form expressions for the asymptotic signal-to-interference-plus-noise ratio, achievable sum rate and harvested energy at the relay are derived whenever the numbers of antennas at the relay and destination grow without bound. This analysis reveals that the presence of co-channel interference (CCI) can be exploited to significantly boost the harvested energy at the massive MIMO relay. However, the detrimental effects of CCI for signal decoding can effectively be mitigated by using a zero-forcing linear detector at the destination in the limit of infinitely many relay and destination antennas.


title: Waveguide-Fed Cavity Backed Slot Antenna Array with high efficiency in the Ku-band

abstract: This paper reports the design of a Waveguide-Fed Cavity Backed Slot Antenna Array in Ku-band. The antenna is made entire via simple milling process. The overall antenna structure consists of 3 layers. The bottom layer is a waveguide feed network to provide corporate power division. In turn, the waveguide feed network is fed by a conventional SMA connector from the back. The waveguide network couples energy to an array of cavity via an aperture. This constitutes the middle layer. Each cavity then excites an array of 2×2 radiating slots in the top layer. The radiating slot elements and the feed network are designed to achieve wide bandwidth and gain performance. Finally, an 8×8 array antenna is designed with about 25dBi gain and bandwidth of 1.6GHz in the Ku-band.


title: Realtime high-speed network traffic monitoring using ntopng

abstract: Monitoring network traffic has become increasingly challenging in terms of number of hosts, protocol proliferation and probe placement topologies. Virtualised environments and cloud services shifted the focus from dedicated hardware monitoring devices to virtual machine based, software traffic monitoring applications. This paper covers the design and implementation of ntopng, an open-source traffic monitoring application designed for high-speed networks. ntopng's key features are large networks real-time analytics and the ability to characterise application protocols and user traffic behaviour. ntopng was extensively validated in various monitoring environments ranging from small networks to .it ccTLD traffic analysis.


title: A Corpus for Reasoning About Natural Language Grounded in Photographs

abstract: We introduce a new dataset for joint reasoning about language and vision. The data contains 107,296 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a photograph. We present an approach for finding visually complex images and crowdsourcing linguistically diverse captions. Qualitative analysis shows the data requires complex reasoning about quantities, comparisons, and relationships between objects. Evaluation of state-of-the-art visual reasoning methods shows the data is a challenge for current methods.


title: Parameter design of voltage balancing circuit for series connected HV-IGBTs

abstract: Applying power semiconductor devices in series connection is a direct and effective scheme to improve the voltage rating and power rating of power electronic converter. The key issue of device series connection is voltage balancing in static switching state and dynamic switching state. Active clamping circuit samples overvoltage on insulated gate bipolar transistor (IGBT), injects current into the gate and thus could increase the gate voltage and suppress the overvoltage effectively. In this paper, a voltage balancing circuit for high voltage IGBT (HV-IGBT) series connection is presented. The voltage balancing circuit is composed of static state voltage balancing sub-circuit across collector and emitter, dynamic state voltage balancing sub-circuit across collector and emitter and active clamping sub-circuit across collector and gate. The functions and principles of the three sub-circuits are then described. Besides the topology, the parameters of the voltage balancing circuit influence the voltage balance of series connected HV-IGBTs seriously. Based on quantitatively analyzing the relationships among the parameters of HV-IGBTs, the parameters of voltage balancing circuit and the voltage balancing indexes of the series connected devices, a parameter design method for the presented voltage balancing circuit is proposed. This parameter design method comprehensively considers the switching loss of HV-IGBT, the loss of voltage balancing circuit, the electrical stress on HV-IGBT and the switching frequency in order to guarantee the efficiency, reliability and performance of the system. The proposed parameter design method is applied to the development process of a series connection circuit with Infineon 6500V/600A HV-IGBTs. Experimental results verify the validity and feasibility of the proposed method.


title: DVB-S2 full cross-layer design for QoS provision

abstract: The second generation of the Digital Video Broadcasting standard for Satellite transmission, DVB-S2, is the evolution of the highly successful DVB-S satellite distribution technology. DVB-S2 has benefited from the latest progress in channel coding and modulation such as Low Density Parity Check Codes and higher order constellations to achieve performance that approaches Shannon?s theoretical limit. We present a cross-layer design for Quality-of-Service (QoS) provision of interactive services, which is not specified in the standard. Our cross-layer approach exploits the satellite channel characteristics of space-time correlation via a cross-layer queueing architecture and an adaptive cross-layer scheduling policy. We show that our approach not only allows system load control but also rate adaptation to channel conditions and traffic demands on the coverage area. We also present the extension of our cross-layer design for mobile gateways focusing on the railway scenario. We illustrate the trade-off between system-wide and individual throughput by means of simulation, and that this trade-off could be a key metric in measuring the service level of DVB-S2 Broadband Service.


title: Rotational population and alignment distributions for inelastic scattering and trapping/desorption of NO on Pt(111)

abstract: Rotationally resolved experiments on the NO/Pt(111) system explore the mechanisms of inelastic scattering and trapping/desorption. The rotational dynamics associated with these two regimes are markedly different. A neat supersonic NO beam is scattered at normal incidence from a Pt(111) crystal at 375–475 K. The non‐Boltzmann rotational population distribution of the scattered species exhibits considerable rotational excitation beyond the energy available from the incident beam. Thus, a surface vibration to rotational energy transfer mechanism must be operative. The accompanying rotational alignment data reveal that highly excited rotational states exhibit predominantly ‘‘cartwheel’’ motion. In contrast, rotationally excited molecules that desorb from a 553 K Pt(111) surface show a preference for ‘‘helicopter’’ motion. The opposite preferences for rotational alignment in the two dynamical regimes provide insight into the anisotropy of molecule–surface interactions.


title: Power flow and transient stability models of FACTS controllers for voltage and angle stability studies

abstract: This paper presents transient stability and power flow models of thyristor controlled reactor (TCR) and voltage sourced inverter (VSI) based flexible AC transmission system (FACTS) controllers. Models of the static VAr compensator (SVC), the thyristor controlled series compensator (TCSC), the static VAr compensator (STATCOM), the static synchronous source series compensator (SSSC), and the unified power flow controller (UPFC) appropriate for voltage and angle stability studies are discussed in detail. Validation procedures obtained for a test system with a detailed as well as a simplified UPFC model are also presented and briefly discussed.


title: Automated monitoring and control system for shrimp farms based on embedded system and wireless sensor network

abstract: This paper presents a versatile solution in an effort of improving the accuracy in monitoring the environmental conditions and reducing manpower for industrial households shrimp farming. A ZigBee-based wireless sensor network (WSN) was used to monitor the critical environmental conditions and all the control processes are done with the help of a series of low-power embedded MSP430 microcontrollers from Texas Instruments. This system is capable of collecting, analyzing and presenting data on a Graphical User Interface (GUI), programmed with LabVIEW. It also allows the user to get the updated sensor information online based on Google Spreadsheets application, via Internet connectivity, or at any time through the SMS gateway service and sends alert message promptly enabling user interventions when needed. Thereby the system minimizes the effects of environmental fluctuations caused by sudden changes and reduces the expended labor power of farms. Because of that, the proposed system saves the cost of hiring labor as well as the electricity usage. The design promotes a versatile, low-cost, and commercial version which will function best for small to medium sized farming operations as it does not require any refitting or reconstruction of the pond.


title: Nicotinic acid, nicotinamide, and nicotinamide riboside: a molecular evaluation of NAD+ precursor vitamins in human nutrition.

abstract: Although baseline requirements for nicotinamide adenine dinucleotide (NAD+) synthesis can be met either with dietary tryptophan or with less than 20 mg of daily niacin, which consists of nicotinic acid and/or nicotinamide, there is growing evidence that substantially greater rates of NAD+ synthesis may be beneficial to protect against neurological degeneration, Candida glabrata infection, and possibly to enhance reverse cholesterol transport. The distinct and tissue-specific biosynthetic and/or ligand activities of tryptophan, nicotinic acid, nicotinamide, and the newly identified NAD+ precursor, nicotinamide riboside, reviewed herein, are responsible for vitamin-specific effects and side effects. Because current data suggest that nicotinamide riboside may be the only vitamin precursor that supports neuronal NAD+ synthesis, we present prospects for human nicotinamide riboside supplementation and propose areas for future research.


title: CSF-1 Receptor Signaling in Myeloid Cells

abstract: The CSF-1 receptor (CSF-1R) is activated by the homodimeric growth factors colony-stimulating factor-1 (CSF-1) and interleukin-34 (IL-34). It plays important roles in development and in innate immunity by regulating the development of most tissue macrophages and osteoclasts, of Langerhans cells of the skin, of Paneth cells of the small intestine, and of brain microglia. It also regulates the differentiation of neural progenitor cells and controls functions of oocytes and trophoblastic cells in the female reproductive tract. Owing to this broad tissue expression pattern, it plays a central role in neoplastic, inflammatory, and neurological diseases. In this review we summarize the evolution, structure, and regulation of expression of the CSF-1R gene. We discuss the structures of CSF-1, IL-34, and the CSF-1R and the mechanism of ligand binding to and activation of the receptor. We further describe the pathways regulating macrophage survival, proliferation, differentiation, and chemotaxis downstream from the CSF-1R.


title: Autonomic Dysfunction after Mild Traumatic Brain Injury

abstract: A mild traumatic brain injury (mTBI) is a complex pathophysiologic process that has a systemic effect on the body aside from solely an impairment in cognitive function. Dysfunction of the autonomic nervous system (ANS) has been found to induce abnormalities in organ systems throughout the body, and may contribute to cardiovascular dysregulation and increased mortality. Autonomic dysfunction, also known as dysautonomia, has been studied in moderate and severe TBI, and has emerged as a major contributing factor in the symptomatology in mTBI as well. Analysis of the ANS has been studied through changes in heart rate variability (HRV), pupillary dynamics, eye pressure, and arterial pulse wave in those with mild TBI. Graded exercise testing has been studied as both a method of diagnosis and as a means of recovery in those with mild TBI, especially in those with persistent symptoms. Given the studies showing persistence of autonomic dysfunction after symptomatic resolution of concussions, further research is needed to establish return to play protocols.


title: StreamRec: a real-time recommender system

abstract: Research and development of recommender systems has been a vibrant field for over a decade, having produced proven methods for “preference-aware” computing. Recommenders use community opinion histories to help users identify interesting items from a considerably large search space (e.g., inventory from Amazon [7], movies from Netflix [9]). Personalization, recommendation, and the “human side" of data-centric applications are even becoming important topics in the data management community [3]. A popular recommendation method used heavily in practice is collaborative filtering, consisting of two phases: (1) An offline model-building phase that uses community opinions of items (e.g., movie ratings, “Diggs” [6]) to build a model storing meaningful correlations between users and items. (2) An on-demand recommendation phase that uses the model to produce a set of recommended items when requested from a user or application. To be effective, recommender systems must evolve with their content. In current update-intensive systems (e.g., social networks, online news sites), the restriction that a model be generated offline is a significant drawback, as it hinders the system’s ability to evolve quickly. For instance, new users enter the system changing the collective opinions over items, or the system adds new items quickly (e.g., news posts, Facebook postings), which widens the recommendation pool. These updates affect the recommender model, that in turn affect the system’s recommendation quality in terms of providing accurate answers to recommender queries. In such systems, a completely real-time recommendation process is paramount. Unfortunately, most traditional state-of-the-art recommenders are “hand-built", implemented as custom software not built for a real-time recommendation process [1]. Further, for some


title: The Effects of Chronic Multitasking on Analytical Writing

abstract: Chronic multitaskers perform worse on core multitasking skills: memory management, cognitive filtering and task switching, likely due to their inability to filter irrelevant stimuli [17]. Our experiment examines effects of chronic multitasking with task-relevant and irrelevant distractors on analytical writing quality. We found a general switch cost and, when controlling for that cost, effects of chronic multitasking habits: heavy multitaskers write worse essays in the irrelevant condition and better essays in the relevant condition. Our study changes multitasking research paradigms in two fundamental ways: it studied a realistic writing scenario including access to both irrelevant and relevant distractors. We found that the effect of chronic multitasking is complex; heavy multitaskers are seduced by unrelated distractors but able to integrate multiple sources of relevant information.


title: Laplacian based structure-aware error diffusion

abstract: In this paper, we propose a new halftoning scheme that preserves the structure and tone similarities of images while maintaining the simplicity of Floyd-Steinberg error diffusion. Our algorithm is based on the Floyd-Steinberg error diffusion algorithm, but the threshold modulation part is modified to improve the over-blurring issue of the Floyd-Steinberg error diffusion algorithm. By adding some structural information on images obtained using the Laplacian operator to the quantizer thresholds, the structural details in the textured region can be preserved. The visual artifacts of the original error diffusion that is usually visible in the uniform region is greatly reduced by adding noise to the thresholds. This is especially true for the low contrast region because most existing error diffusion algorithms cannot preserve structural details but our algorithm preserves them clearly using threshold modulation. Our algorithm has been evaluated using various types of images including some with the low contrast region and assessed numerically using the MSSIM measure with other existing state-of-art halftoning algorithms. The results show that our method performs better than existing approaches both in the textured region and in the uniform region with the faster computation speed.


title: The Influence of the Distance from the Contact Point to the Crest of Bone on the Presence of the Interproximal Dental Papilla

abstract: Background: Loss of the interproximal dental papilla may cause functional and, especially in the maxillary anterior region, phonetic and severe esthetic problems. The purpose of this study was to investigate whether the distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth could be correlated with the presence of the interproximal papilla in Taiwanese patients. Methods: In total, 200 interproximal sites of maxillary anterior teeth in 45 randomly selected patients were examined. Selected subjects were adult Taiwanese with fully erupted permanent dentition. The presence of the interproximal papilla was determined visually. If there was no visible space apical to the contact area, the papilla was recorded as being present. The distance from the contact point to the crest of bone was measured on standardized periapical radiographs using a paralleling technique with a RinnXCP ® holder. Results: Data revealed that when the distance from the contact point to the bone crest on standardized periapical radiographs was 5 mm or less, the papillae were almost 100% present. When the distance was 6 mm, 51% of the papillae were present, and when the distance was 7 mm or greater, only 23% of the papillae were present. Conclusion: The distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth is highly associated with the presence or absence of the interproximal papilla in Taiwanese patients, and is a useful guide for clinical evaluation. (Chang Gung Med J 2003;26:822-8)


title: Output perturbation with query relaxation

abstract: Given a dataset containing sensitive personal information, a statistical database answers aggregate queries in a manner that preserves individual privacy. We consider the problem of constructing a statistical database using output perturbation, which protects privacy by injecting a small noise into each query result. We show that the state-of-the-art approach, e-differential privacy, suffers from two severe deficiencies: it (i) incurs prohibitive computation overhead, and (ii) can answer only a limited number of queries, after which the statistical database has to be shut down. To remedy the problem, we develop a new technique that enforces e-different privacy with economical cost. Our technique also incorporates a query relaxation mechanism, which removes the restriction on the number of permissible queries. The effectiveness and efficiency of our solution are verified through experiments with real data.


title: A Test Architecture for Machine Learning Product

abstract: As machine learning (ML) technology continues to spread by rapid evolution, the system or service using Machine Learning technology, called ML product, makes big impact on our life, society and economy. Meanwhile, Quality Assurance (QA) for ML product is quite more difficult than hardware, non-ML software and service because performance of ML technology is much better than non-ML technology in exchange for the characteristics of ML product, e.g. low explainability. We must keep rapid evolution and reduce quality risk of ML product simultaneously. In this paper, we show a Quality Assurance Framework for Machine Learning product. Scope of QA in this paper is limited to product evaluation. First, a policy of QA for ML Product is proposed. General principles of product evaluation is introduced and applied to ML product evaluation as a part of the policy. They are composed of A-ARAI: Allowability, Achievability, Robustness, Avoidability and Improvability. A strategy of ML Product Evaluation is constructed as another part of the policy. Quality Integrity Level for ML product is also modelled. Second, we propose a test architecture of ML product testing. It consists of test levels and fundamental test types of ML product testing, including snapshot testing, learning testing and confrontation testing. Finally, we defines QA activity levels for ML product.


title: Hormonal changes when falling in love

abstract: Summary To fall in love is the first step in pair formation in humans and is a complex process which only recently has become the object of neuroscientific investigation. The little information available in this field prompted us to measure the levels of some pituitary, adrenal and gonadal hormones in a group of 24 subjects of both sexes who had recently (within the previous six months) fallen in love, and to compare them with those of 24 subjects who were single or were part of a long-lasting relationship. The following hormones were evaluated by means of standard techniques: FSH, LH, estradiol, progesterone, dehydroepiandrosterone sulphate (DHEAS), cortisol, testosterone and androstenedione. The results showed that estradiol, progesterone, DHEAS and androstenedione levels did not differ between the groups and were within the normal ranges. Cortisol levels were significantly higher amongst those subjects who had recently fallen in love, as compared with those who had not. FSH and testosterone levels were lower in men in love, while women of the same group presented higher testosterone levels. All hormonal differences were eliminated when the subjects were re-tested from 12 to 24 months later. The increased cortisol and low FSH levels are suggestive of the ‘‘stressful’’ and arousing conditions associated with the initiation of a social contact. The changes of testosterone concentrations, which varied in opposite directions in the two sexes, may reflect changes in behavioural and/or temperamental traits which have yet to be clarified. In conclusion, the findings of the present study would indicate that to fall in love provokes transient hormonal changes some of which seem to be specific to each sex. # 2003 Elsevier Ltd. All rights reserved.


title: A Lightweight 3-D Printed $X$-Band Bandpass Filter Based on Spherical Dual-Mode Resonators

abstract: A 3-D printed fourth-order cavity bandpass filter (BPF) centered at 10 GHz and with a 3% fractional bandwidth is presented in this letter. The BPF was designed using two high- $Q$ spherical dual-mode cavity resonators, and was fabricated using a stereolithography-based 3-D printing technique. Compared to dual-mode filters constructed by square or cylindrical resonators, the use of spherical resonator gives a wider spurious-free region. In order to fully exhibit the light weight advantage of additive manufacturing, the redundant material outside of the filter was removed. In addition, rectangular apertures were added through the cavities and waveguide walls without interrupting the surface current distributions, which further yields reduced filter weight as well as easier electroplating. Measured results of the BPF exhibit an excellent agreement with simulations.


title: Development and Validation of a Single Wrist Mounted Inertial Sensor for Biomechanical Performance Analysis of an Elite Netball Shot

abstract: The primary objective of the game netball is to score more goals than the opposition. Subsequently, there is impetus on improving shooting accuracy to maximize scoring. Understanding the kinematic factors of shooting that lead to scoring success allows for iterative adjustment and optimization of technique. Three-dimensional retro-reflective motion capture has been used to assess kinematics; however, these systems are expensive, require substantial setup and postprocessing time, and cannot be used in game-such as environments. With modern advancements in wearable microelectromechanical systems based technology, the ability to monitor shooting kinematics in the performance environment has become possible. This article evaluates the efficacy of a single wireless inertial measurement unit (IMU) sensor to monitor shooting kinematics, in terms of forearm angle at ball release. Four elite female shooters shot a total of 30 shots each (totaling 120 shots) from three different distances wearing both reference retroreflective motion capture (Vicon) markers and an IMU. To assess whether wearing the IMU had adverse effects on the kinematic shooting chain, a further ten shots each were taken without wearing the IMU. When contrasted with the gold standard reference of retro-reflective motion capture, the IMU sensor overestimated the forearm angle at ball release established by a mean percentage error of 4.03 1.58. Shots with and without the IMU indicated that the IMU did not biomechanically alter the shooting action. Important advantages of using IMU's over motion-capture solutions include that they are ubiquitous, low cost, require minimal user intervention, and can be used in representative training environments under defensive pressure. This information can enhance the understanding of the athlete's distal segment coordination patterns, providing actionable insights to enable performance to athletes and coaching staff.


title: Temperature compensation of total power radiometers

abstract: This paper describes a new technique to compensate output variations of total power radiometers due to physical temperature changes of the instrument. This technique performs the correction without the addition of expensive microwave hardware required in Dicke switching or many other widely used methods. A characterization period, over which the input antenna temperature is known, indicates the appropriate output adjustment needed for a change in physical temperature of the radiometer. The method effectively corrects the output in an example radiometer system built with inexpensive commercially available parts. For a 30-K variation in physical temperature, the measured data shows an improvement from 60-K peak-to-peak error to 6.9 K with an average absolute error of 1.1 K.


title: CariGAN: Caricature Generation through Weakly Paired Adversarial Learning

abstract: Caricature generation is an interesting yet challenging task. The primary goal is to generate plausible caricatures with reasonable exaggerations given face images. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data, so that richer contents and styles can be generated. However, directly applying the GAN-based models to this task leads to unsatisfactory results because there is a large variance in the caricature distribution. Moreover, some models require strictly paired training data which largely limits their usage scenarios. In this paper, we propose CariGAN overcome these problems. Instead of training on paired data, CariGAN learns transformations only from weakly paired images. Specifically, to enforce reasonable exaggeration and facial deformation, facial landmarks are adopted as an additional condition to constrain the generated image. Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated. Finally, a Diversity Loss is proposed to encourage the model to produce diverse results to help alleviate the `mode collapse' problem of the conventional GAN-based models. Extensive experiments on a new large-scale `WebCaricature' dataset show that the proposed CariGAN can generate more plausible caricatures with larger diversity compared with the state-of-the-art models.


title: The irradiation of phage-infected bacteria: its bearing on the relationship between functional and genetic radiation damage.

abstract: Abstract The sensitivity to different kinds of irradiation of bacteria singly infected with the phages T2 or T4 has been measured during the first 5 minutes of the latent period. The radiations employed were ultraviolet light (with and without photoreactivation), soft X-rays, and P 32 decay. The only occasion in which any increase in radiation resistance was found during this period was when T2-infected bacteria were irradiated with ultraviolet light and their resistance measured before photoreactivation had been carried out. An explanation of this result is presented which is based on a distinction between functional radiation damage (which causes inactivation by inhibiting some essential biochemical step in phage synthesis) and genetic radiation damage (which causes inactivation by inhibiting replication). It is concluded that the photoreactivable damage is wholly functional and is not associated with genetic damage, while the damage by ionizing radiations and the nonphotoreactivable damage inflicted by ultraviolet are both functional and genetic.


title: New generation of high voltage integrated devices for ultrasound: 16 Channels pulser 5 levels with integrated transmit beamformer and 64 independent HV switches

abstract: More integration in the transmission chain for an ultrasound system moves the study of new possible solutions to reduce the area and simply the system. A sixteen channels 5 levels high-voltage transmitter device and a 64 high-voltage switches device for ultrasound medical imaging system are reported. The pulser device takes advantage of 16 analog channels able to provide +/-100V driven by 2 independent half bridges, providing up to ±2A peak output current. The power is conveniently managed by a digital control logic that implement beam forming functions, diagnostic checks with interrupt output signal and sixteen static RAM memory blocks, used to store one or more waveforms for each output channel, with a data compression methodology in case waveforms stored are repetitive. The new approach moves several functionalities and memory capability from the external system inside the pulser device (a simple microcontroller can be used instead FPGA as external controller). Since pulsing functions are not real time controlled, but configured before starting operations, number of control input signals is strongly reduced, compared to traditional ultrasound transmitter. The multiplexer device is composed by 64 independent high voltage switches that allows 32 deviators into a single chip solution.


title: Monitoring immune‐checkpoint blockade: response evaluation and biomarker development

abstract: Cancer immunotherapy using immune-checkpoint blockade (ICB) has created a paradigm shift in the treatment of advanced-stage cancers. The promising antitumour activity of monoclonal antibodies targeting the immune-checkpoint proteins CTLA-4, PD-1, and PD-L1 led to regulatory approvals of these agents for the treatment of a variety of malignancies. Patients might experience clinical benefits from treatment with these agents, despite unconventional patterns of tumour response that can be misinterpreted as disease progression, warranting a new, specific approach to evaluate responses to immunotherapy. In addition, biomarkers that can predict responsiveness to ICB are being extensively investigated to further advance precision immunotherapy. Herein, we review the biological mechanisms underlying the unconventional response patterns associated with ICB, describe strategies for the objective assessments of such responses, and also highlight the ongoing efforts to identify biomarkers, in order to guide treatment with ICB. We provide state-of-the-art knowledge of immune-related response evaluations, identify unmet needs requiring further investigations, and propose future directions to maximize the benefits of ICB therapy.


title: Unintended pregnancy and unsafe abortion in the Philippines: context and consequences.

abstract: Despite advances in reproductive health law many Filipino women experience unintended pregnancies and because abortion is highly stigmatized in the country many who seek abortion undergo unsafe procedures. This report provides a summary of reproductive health indicators in the Philippines-in particular levels of contraceptive use unplanned pregnancy and unsafe abortion-and describes the sociopolitical context in which services are provided the consequences of unintended pregnancy and unsafe abortionand recommendations for improving access to reproductive health services.


title: Distinguishing Optimism from Neuroticism (and Trait Anxiety, Self-Mastery, and Self-Esteem): A Reevaluation of the Life Orientation Test

abstract: Research on dispositional optimism as assessed by the Life Orientation Test (Scheier & Carver, 1985) has been challenged on the grounds that effects attributed to optimism are indistinguishable from those of unmeasured third variables, most notably, neuroticism. Data from 4,309 subjects show that associations between optimism and both depression and aspects of coping remain significant even when the effects of neuroticism, as well as the effects of trait anxiety, self-mastery, and self-esteem, are statistically controlled. Thus, the Life Orientation Test does appear to possess adequate predictive and discriminant validity. Examination of the scale on somewhat different grounds, however, does suggest that future applications can benefit from its revision


title: An accurate algebraic solution for moving source location using TDOA and FDOA measurements

abstract: This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.


title: A 0.92mW 10-bit 50-MS/s SAR ADC in 0.13μm CMOS process

abstract: This paper reports a 10-bit 50MS/s SAR ADC with a set-and-down capacitor switching method. Compared to the conventional method, the average switching energy is reduced about 81%. At 50MS/s and 1.2V supply, the ADC consumes 0.92mW and achieves an SNDR of 52.78dB, resulting in an FOM of 52fJ/Conversion-step. Fabricated in a 0.13μm 1P8M CMOS technology, the ADC only occupies 0.075mm2 active area.


Ecology of Weeds and Invasive Plants


title: Compact High Voltage IGBT Switch for Pulsed Power Applications

abstract: This paper describes the development of a semiconductor based switch for use in pulsed power technology. Because of the more stable operating conditions, given by the semiconductor devices, this switch could be used to replace the up to now in pulsed power technology common used spark gaps. We defined a technical concept for a semiconductor based switch with a maximum voltage of 20 kV and a current capability in the order of 1 kA. This concept also should enable to generate pulses widths of a few mus. We found an IGBT with a maximum blocking voltage of 1700 V, a signal rise time of 100 ns, and a short circuit current rate of 650 A to be suited to realize our concept for the switch. Because of the defined maximum voltage of 20 kV it was necessary to use a series connection of the IGBTs. To ensure a synchronous switching of the in series connected devices, a trigger unit was built up which was inductively connected to the driver circuits of the single IGBTs. Single devices where tested and separated for the series connection. With the handicap of a given 155 mm circuit board we designed a complete switch, basing on industrial standards, with 15 IGBTs and a finally maximum blocking voltage of 18 kV, and a current capability of 500 A. The design of this circuit also included the development of a protecting circuit (active clamp) to protect the single devices of an over voltage damage during the switching, and also to avoid an inhomogeneous split up of the voltage to the different devices.


title: Reduction of Speech Spectra by Analysis‐by‐Synthesis Techniques

abstract: Procedures are described for reducing the speech wave to a specification in terms of the time‐varying vocal‐tract resonances and source characteristics. The basic method, which has been called analysis by synthesis, involves the comparison of speech spectra with a series of spectra that are synthesized within the analyzer. Each comparison spectrum is generated according to a set of rules based on an acoustical theory of speech production. The result of the analysis of each input spectrum is a set of parameters that describes the synthesized spectrum providing the best match. In one version of the method convergence, towards the best match is controlled by the experimenter; in another version convergence to a match is accomplished automatically without the intervention of the experimenter. All the operations have been programmed on a general‐purpose digital computer and have been applied to the analysis of vowels and some consonants. The advantages of the analysis techniques are discussed.


title: A high fidelity simulator for a quadrotor UAV using ROS and Gazebo

abstract: Flight tests of prototype UAV systems can be restricted by spatial constraints and they may bring risks of damage due to failures. Motivated by these, we presented a simulation approach based on Robot Operating System (ROS) and Gazebo. Unlike other state-of-the-art quadrotor simulators, we implemented the dynamics model of the UAV in ROS to achieve high fidelity behavior of the UAV. A hierarchical navigation system is also presented in our paper. The system layers include simultaneous localization and mapping (SLAM), mapping framework in Cartesian and polar coordinates, A∗ global path planner, revised vector field histogram plus (VFH+) for optimal local path selection and online trajectory algorithm (OTA) with collision checking for obstacle avoidance. In order to cater for vision-based applications, quadrotor is equipped with a monocular camera in the simulation model. The implementation of circle and landing pad detection and tracking algorithm demonstrates the functionality of vision guidance. In our simulation, various aspects including complex indoor and outdoor environments and on-board sensors are capable of simultaneously interacting with our navigation system to achieve certain surveillance missions. In the end, we demonstrated the applicability of our complex quadrotor systems by performing an autonomous navigation task in simulated complex environments. In comparison with the experimental data, simulation results align with the ones in flight tests in terms of real flight behaviors during navigation tasks in general.


title: Software Reliability Models: Assumptions, Limitations, and Applicability

abstract: A number of analytical models have been proposed during the past 15 years for assessing the reliability of a software system. In this paper we present an overview of the key modeling approaches, provide a critical analysis of the underlying assumptions, and assess the limitations and applicability of these models during the software development cycle. We also propose a step-by-step procedure for fitting a model and illustrate it via an analysis of failure data from a medium-sized real-time command and control software system.


title: Intelligent heart disease prediction system using data mining techniques

abstract: The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.


title: An adaptive approach for continuous multi-factor authentication in an identity eco-system

abstract: Multi-factor Authentication (MFA) is the current trend to genuinely identify the legitimate users in cyber eco-system through an active authentication process. This process includes passwords, security token, biometrics, human cognitive behavior metrics, etc. New sensors and better authentication modalities are evolving, which provide the opportunity for the security researchers to come up with new solutions facilitating MFA to different online resource access and identity management systems. This paper focuses on the design and development of a framework for continuous MFA where authentication modalities are selected adaptively through sensing many characteristics of the user's operating environment. The degree of adaptiveness in the selection of authentication modalities exhibits dynamism in the authentication process and lessens the burden of the users to use the same set of authentication process in less trustworthy environments. A trustworthy framework to quantify the available authentication modalities is proposed and the applicability of the framework for identity eco-system is illustrated in this paper.


title: Text normalization in mandarin text-to-speech system

abstract: Text normalization is an important component in text-to-speech system and the difficulty in text normalization is to disambiguate the non-standard words (NSWs). This paper develops a taxonomy of NSWs on the basis of a large scale Chinese corpus, and proposes a two-stage NSWs disambiguation strategy, finite state automata (FSA) for initial classification and maximum entropy (ME) classifiers for subclass disambiguation. Based on the above NSWs taxonomy, the two-stage approach achieves an F-score of 98.53% in open test, 5.23% higher than that of FSA based approach. Experiments show that the NSWs taxonomy ensures FSA a high baseline performance and ME classifiers make considerable improvement, and the two-stage approach adapts well to new domains.


title: Architecture of Internet of Things and Its Key Technology Integration Based-On RFID

abstract: In recent years, the IoT(Internet of Things)has been wide spreadly concerned as a new technology architecture integrated by many information technologies. However, IoT is a complex system. If there is no intensive study from the system point of view, it will be very hard to understand the key technologies of IoT. in order to better study IoT and its technologies this paper proposes an extent six-layer architecture of IoT based on network hierarchical structure firstly. then it discusses the key technologies involved in every layer of IoT, such as RFID, WSN, Internet, SOA, cloud computing and Web Service etc. According to this, an automatic recognition system is designed via integrating both RFID and WSN and a strategy is also brought forward to via integrating both RFID and Web Sevice. This result offers the feasibility of technology integration of IoT.


title: Topic Modeling in Semantic Space with Keywords

abstract: A common and convenient approach for user to describe his information need is to provide a set of keywords. Therefore, the technique to understand the need becomes crucial. In this paper, for the information need about a topic or category, we propose a novel method called TDCS(Topic Distilling with Compressive Sensing) for explicit and accurate modeling the topic implied by several keywords. The task is transformed as a topic reconstruction problem in the semantic space with a reasonable intuition that the topic is sparse in the semantic space. The latent semantic space could be mined from documents via unsupervised methods, e.g. LSI. Compressive sensing is leveraged to obtain a sparse representation from only a few keywords. In order to make the distilled topic more robust, an iterative learning approach is adopted. The experiment results show the effectiveness of our method. Moreover, with only a few semantic concepts remained for the topic, our method is efficient for subsequent text mining tasks.


title: A new fuzzy support vector machine to evaluate credit risk

abstract: Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.


title: An overview of clustering methods

abstract: Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an overview of the different representative clustering methods. In addition, several clustering validations indices are shown. Furthermore, approaches to automatically determine the number of clusters are presented. Finally, application of different heuristic approaches to the clustering problem is also investigated.


title: Gaussian random number generators

abstract: Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.


title: Wechsler adult intelligence scale and wechsler adult intelligence scale-revised in a neurologically impaired population

abstract: Compared Verbal, Performance, and Full Scale IQ scores from two groups of neurologically impaired patients (N = 114) similar in age, years of education, occupation, race, sex, and etiology and location of cerebral dysfunction. One group had been given the WAIS and the other the WAIS-R. All three IQ scores were higher for the WAIS group, with Full Scale and Verbal scores significantly (p < 0.05) higher. Changes in item content and standardization sample cohort effects are offered as partial possible explanation for the results. The IQ scores from the two tests cannot be considered as interchangeable for neurological patients.


title: The First Takeoff of a Biologically Inspired At-Scale Robotic Insect

abstract: Biology is a useful tool when applied to engineering challenges that have been solved in nature. Here, the emulous goal of creating an insect-sized, truly micro air vehicle is addressed by first exploring biological principles. These principles give insights on how to generate sufficient thrust to sustain flight for centimeter-scale vehicles. Here, it is shown how novel manufacturing paradigms enable the creation of the mechanical and aeromechanical subsystems of a microrobotic device that is capable of Diptera-like wing trajectories. The results are a unique microrobot: a 60 mg robotic insect that can produce sufficient thrust to accelerate vertically. Although still externally powered, this micromechanical device represents significant progress toward the creation of autonomous insect-sized micro air vehicles.


title: Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

abstract: The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.


title: Zero-Example Event Search using MultiModal Pseudo Relevance Feedback

abstract: We propose a novel method MultiModal Pseudo Relevance Feedback (MMPRF) for event search in video, which requires no search examples from the user. Pseudo Relevance Feedback has shown great potential in retrieval tasks, but previous works are limited to unimodal tasks with only a single ranked list. To tackle the event search task which is inherently multimodal, our proposed MMPRF takes advantage of multiple modalities and multiple ranked lists to enhance event search performance in a principled way. The approach is unique in that it leverages not only semantic features, but also non-semantic low-level features for event search in the absence of training data. Evaluated on the TRECVID MEDTest dataset, the approach improves the baseline by up to 158% in terms of the mean average precision. It also significantly contributes to CMU Team's final submission in TRECVID-13 Multimedia Event Detection.


title: Centralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids

abstract: This paper presents a centralized control system that coordinates parallel operations of different distributed generation (DG) inverters within a microgrid. The control design for the DG inverters employs a new Model Predictive Control algorithm that allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. An overall energy management system is also implemented for the microgrid to coordinate load sharing among different DG units during both grid-connected and islanded operations. The design concept of the proposed control system is evaluated through simulation studies under different test scenarios. The impact of the increased penetration of DG units on the distribution grid is also investigated using the proposed microgrid. The simulation results show that the operations of the DG units within the microgrid can be coordinated effectively under the proposed control system to ensure stable operation of the overall microgrid.