title: PIEFA: Personalized Incremental and Ensemble Face Alignment

abstract: Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications. Both generic and joint alignment approaches have been proposed with varying degrees of success. However, many generic methods are heavily sensitive to initializations and usually rely on offline-trained static models, which limit their performance on sequential images with extensive variations. On the other hand, joint methods are restricted to offline applications, since they require all frames to conduct batch alignment. To address these limitations, we propose to exploit incremental learning for personalized ensemble alignment. We sample multiple initial shapes to achieve image congealing within one frame, which enables us to incrementally conduct ensemble alignment by group-sparse regularized rank minimization. At the same time, personalized modeling is obtained by subspace adaptation under the same incremental framework, while correction strategy is used to alleviate model drifting. Experimental results on multiple controlled and in-the-wild databases demonstrate the superior performance of our approach compared with state-of-the-arts in terms of fitting accuracy and efficiency.


title: Ontology Modeling with Domain Experts: The GeoVocamp Experience

abstract: A series of GeoVoCamps, run at least twice a year in locations in the U.S., have focused on ontology design patterns as an approach to inform metadata and data models, and on applications in the GeoSciences. In this note, we will redraw the brief history of the series as well as rationales for the particular approach which was chosen, and report on the ongoing uptake of the approach.


title: Material classification and semantic segmentation of railway track images with deep convolutional neural networks

abstract: The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN). We show that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classification accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.


title: A SPN-Based Delay Analysis of LEO Satellite Networks

abstract: In this paper, we focus on the average time delay analysis of Low Earth Orbit (LEO) satellite networks. Firstly, a geometrical analysis of LEO satellite networks is given according to the characteristics of communication process. By taking the communication process of LEO satellite networks into account, average time delay is formulated by four parameters, i.e. average altitude of LEO satellite networks, uplink bandwidth, Inter-satellite links (ISLs) bandwidth and downlink bandwidth. Then, a Stochastic Petri Net (SPN) model is constructed. Accordingly, in order to represent the characteristics of average time delay, the arrival rate in the SPN model is mapped from the four above-mentioned parameters. Furthermore, the effects of these parameters on average time delay are also analyzed with Stochastic Petri Net Package (SPNP) 6.0. Because of its easiness and accuracy, the proposed approach has great benefit to the design and performance analysis of satellite networks.


title: Shaping CIO's competencies and activities to improve company performance: an empirical study

abstract: The importance of and the fast changes in the CIO's role in organizations are widely recognized. So is the need for CIOs to acquire the appropriate set of knowledge and managerial skills to lead the organization's effort in effectively using Information Technology. This study is based on an extensive survey of the literature addressing the desirable features of modern CIOs and the Information system area main activities. This study aims at evaluate (1) the impact of such features on the management performance of the IS department main activities and (2) their consequent impact on overall company performance. To empirically test these two relationships, 111 CEOs of companies located in Northern Italy were interviewed. The analysis of collected data provides some insights on the desirable features for modern CIOs, which in turn can be useful for CIO recruiting and professional development, and highlight the importance to properly manage the IS subfunctions in order to improve overall company performance.


title: Dynamic Watermarking: Active Defense of Networked Cyber–Physical Systems

abstract: The coming decades may see the large scale deployment of networked cyber-physical systems to address global needs in areas such as energy, water, healthcare, and transportation. However, as recent events have shown, such systems are vulnerable to cyber attacks. Being safety critical, their disruption or misbehavior can cause economic losses or injuries and loss of life. It is therefore important to secure such networked cyber-physical systems against attacks. In the absence of credible security guarantees, there will be resistance to the proliferation of cyber-physical systems, which are much needed to meet global needs in critical infrastructures and services. ::: This paper addresses the problem of secure control of networked cyber-physical systems. This problem is different from the problem of securing the communication network, since cyber-physical systems at their very essence need sensors and actuators that interface with the physical plant, and malicious agents may tamper with sensors or actuators, as recent attacks have shown. ::: We consider physical plants that are being controlled by multiple actuators and sensors communicating over a network, where some sensors could be "malicious," meaning that they may not report the measurements that they observe. We address a general technique by which the actuators can detect the actions of malicious sensors in the system, and disable closed-loop control based on their information. This technique, called "watermarking," employs the technique of actuators injecting private excitation into the system which will reveal malicious tampering with signals. We show how such an active defense can be used to secure networked systems of sensors and actuators.


title: A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series

abstract: Similarity search on time series is an essential operation in many applications. In the state-of-the-art methods, such as the R-tree based methods, SAX and iSAX, time series are by default divided into equi-length segments globally, that is, all time series are segmented in the same way. Those methods then focus on how to approximate or symbolize the segments and construct indexes. In this paper, we make an important observation: global segmentation of all time series may incur unnecessary cost in space and time for indexing time series. We develop DSTree, a data adaptive and dynamic segmentation index on time series. In addition to savings in space and time, our new index can provide tight upper and lower bounds on distances between time series. An extensive empirical study shows that our new index DSTree supports time series similarity search effectively and efficiently.


title: Chronic polyhydramnios is a syndrome with a lactogen receptor defect in the chorion laeve

abstract: Summary. Human chorion laeve contains a lactogenic hormone [prolactin (PRL), growth hormone (GH) and placental lactogen (hPL)] receptor. Here, we studied binding of the potent lactogen, human GH. to this receptor in 18 normal pregnant women, in 12 patients whose pregnancies were complicated by chronic polyhydramnios and in 13 with chronic oligohydramnios. Polyhydramnios was classified clinically as idiopathic in seven patients. and secondary and associated with various disorders in the remaining five patients. Lactogenic hormone binding was lower in association with polyhydramnios (mean 1·60. SEM 0·15%)) than with normal amniotic fluid volume (mean 3·05, SEM 0·40%; P<0·05): Scatchard analysis indicated that a reduced number of lactogen receptors within the chorion laeve was the reason. The lactogenic hormone receptor defect in the chorion laeve was present in pregnancies complicated by either idiopathic or secondary polyhydramnios. In contrast to chronic polyhydramnios, tissue from patients with chronic oligohydramnios bound lactogenic ligands in a normal fashion. Insulin binding sites were also identified in the chorion laeve, and, in contrast to the lactogens, binding parameters were equivalent in the three patient groups. PRL resistance developing subsequent to this chorion laeve receptor defect might produce the excessive amniotic fluid volume characteristic of polyhydramnios. We propose that a chorionic PRL receptor deficiency underlies the various clinical forms of chronic polyhydramnios.


title: Symbolic Music Genre Transfer with CycleGAN

abstract: Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.


title: On social networks and collaborative recommendation

abstract: Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.


title: Ideal carrier profile control for high-speed switching of 1200 V IGBTs

abstract: A novel 1200 V Insulated Gate Bipolar Transistor (IGBT) for high-speed switching that combines Shorted Dummy-cell (SD) to control carrier extraction at the emitter side and P/P - collector to reduce hole injection from the backside is proposed. The SD-IGBT with P/P - collector has achieved 37 % reduction of turn-off power dissipation compared with a conventional Floating Dummy-cell (FD) IGBT. The SD-IGBT with P/P - collector also has high turn-off current capability because it extracts carriers uniformly from the dummy-cell. These results show the proposed device has a ideal carrier profile for high-speed switching.


title: Distinction between handwritten and machine-printed text based on the bag of visual words model

abstract: In a variety of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may coexist in the same document image, raising significant issues within the recognition pipeline. It is, therefore, necessary to separate the two types of text so that it becomes feasible to apply different recognition methodologies to each modality. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words model (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a decision scheme which relies upon the combination of binary SVM classifiers. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with new datasets dedicated to the problem upon consideration. HighlightsSeparating handwritten from machine printed text using the BoVW model.Exploring various local features and weighting types.A decision scheme which relies upon the combination of binary SVM classifiers.Three distinct databases are provided for future evaluations.Each database contains different machine/handwritten separation scenarios.


title: Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

abstract: The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach is to perform automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the limitations of the gala library and how we intend to address them.


title: Let me in: Control of HIV nuclear entry at the nuclear envelope.

abstract: The nuclear envelope is a physical barrier that isolates the cellular DNA from the rest of the cell, thereby limiting pathogen invasion. The Human Immunodeficiency Virus (HIV) has a remarkable ability to enter the nucleus of non-dividing target cells such as lymphocytes, macrophages and dendritic cells. While this step is critical for replication of the virus, it remains one of the less understood aspects of HIV infection. Here, we review the viral and host factors that favor or inhibit HIV entry into the nucleus, including the viral capsid, integrase, the central viral DNA flap, and the host proteins CPSF6, TNPO3, Nucleoporins, SUN1, SUN2, Cyclophilin A and MX2. We review recent perspectives on the mechanism of action of these factors, and formulate fundamental questions that remain. Overall, these findings deepen our understanding of HIV nuclear import and strengthen the favorable position of nuclear HIV entry for antiviral targeting.


title: Learning From Agile Software Development -- Part One

abstract: An improved water faucet having a valve body with control elements formed by two discs wherein the first disc is stationary and has water inlet openings and an outlet opening, connected on one side to a valve manifold. The other side is directed toward the second disc, which is movably arranged on the first disc. The second disc has a surface cavity overlapping the inlet openings and outlet opening of the first disc. A noise reducing assembly including a plurality of overlapping screens is disposed within the surface cavity of the second disc. The assembly includes a plurality of overlapping screens with at least one protrusion extending from the screens into the flow passageway between the inlet and outlet openings while maintaining a free minimum passageway to thereby reduce the noise level during operation of the water faucet.


title: Simulation and Implementation of Grid-connected Inverters

abstract: Solar, wind and hydro are renewable energy sources that are seen as reliable alternatives to conventional energy sources such as oil or natural gas. However, the efficiency and the performance of renewable energy systems are still under development. Consequently, the control structures of the gridconnected inverter as an important section for energy conversion and transmission should be improved to meet the requirements for grid interconnection. In this paper, a comprehensive simulation and implementation of a threephase grid-connected inverter is presented. The control structure of the grid-side inverter is firstly discussed. Secondly, the space vector modulation SVM is presented. Thirdly, the synchronization for grid-connected inverters is discussed. Finally, the simulation of the grid-connected inverter system using PSIM simulation package and the system implementation are presented to illustrate concepts and compare their results.


title: Learning to rank for information retrieval

abstract: This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.


title: LIMTopic: A Framework of Incorporating Link Based Importance into Topic Modeling

abstract: distinguishing the importance of documents on different topics. In this paper, we propose a framework LIMTopic to incorporate link based importance into topic modeling. To instantiate the framework, RankTopic and HITSTopic are proposed by incorporating topical pagerank and topical HITS into topic modeling respectively. Specifically, ranking methods are first used to compute the topical importance of documents. Then, a generalized relation is built between link importance and topic modeling. We empirically show that LIMTopic converges after a small number of iterations in most experimental settings. The necessity of incorporating link importance into topic modeling is justified based on KL-Divergences between topic distributions converted from topical link importance and those computed by basic topic models. To investigate the document network summarization performance of topic models, we propose a novel measure called log-likelihood of ranking-integrated document-word matrix. Extensive experimental results show that LIMTopic performs better than baseline models in generalization performance, document clustering and classification, topic interpretability and document network summarization performance. Moreover, RankTopic has comparable performance with relational topic model (RTM) and HITSTopic performs much better than baseline models in document clustering and classification.


title: Collective Behavior of Networks with Linear (VLSI) Integrate-and-Fire Neurons

abstract: We analyze in detail the statistical properties of the spike emission process of a canonical integrate-and-fire neuron, with a linear integrator and a lower bound for the depolarization, as often used in VLSI implementations (Mead, 1989). The spike statistics of such neurons appear to be qualitatively similar to conventional (exponential) integrate-and-fire neurons, which exhibit a wide variety of characteristics observed in cortical recordings. We also show that, contrary to current opinion, the dynamics of a network composed of such neurons has two stable fixed points, even in the purely excitatory network, corresponding to two different states of reverberating activity. The analytical results are compared with numerical simulations and are found to be in good agreement.


title: Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

abstract: Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.


title: Sequence-based Structured Prediction for Semantic Parsing

abstract: We propose an approach for semantic parsing that uses a recurrent neural network to map a natural language question into a logical form representation of a KB query. Building on recent work by (Wang et al., 2015), the interpretable logical forms, which are structured objects obeying certain constraints, are enumerated by an underlying grammar and are paired with their canonical realizations. In order to use sequence prediction, we need to sequentialize these logical forms. We compare three sequentializations: a direct linearization of the logical form, a linearization of the associated canonical realization, and a sequence consisting of derivation steps relative to the underlying grammar. We also show how grammatical constraints on the derivation sequence can easily be integrated inside the RNN-based sequential predictor. Our experiments show important improvements over previous results for the same dataset, and also demonstrate the advantage of incorporating the grammatical constraints.


title: Random Walks for Knowledge-Based Word Sense Disambiguation

abstract: Word Sense Disambiguation WSD systems automatically choose the intended meaning of a word in context. In this article we present a WSD algorithm based on random walks over large Lexical Knowledge Bases LKB. We show that our algorithm performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet. Our algorithm and LKB combination compares favorably to other knowledge-based approaches in the literature that use similar knowledge on a variety of English data sets and a data set on Spanish. We include a detailed analysis of the factors that affect the algorithm. The algorithm and the LKBs used are publicly available, and the results easily reproducible.


title: A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

abstract: In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.


title: A measurement-based study of MultiPath TCP performance over wireless networks

abstract: With the popularity of mobile devices and the pervasive use of cellular technology, there is widespread interest in hybrid networks and on how to achieve robustness and good performance from them. As most smart phones and mobile devices are equipped with dual interfaces (WiFi and 3G/4G), a promising approach is through the use of multi-path TCP, which leverages path diversity to improve performance and provide robust data transfers. In this paper we explore the performance of multi-path TCP in the wild, focusing on simple 2-path multi-path TCP scenarios. We seek to answer the following questions: How much can a user benefit from using multi-path TCP over cellular and WiFi relative to using the either interface alone? What is the impact of flow size on average latency? What is the effect of the rate/route control algorithm on performance? We are especially interested in understanding how application level performance is affected when path characteristics (e.g., round trip times and loss rates) are diverse. We address these questions by conducting measurements using one commercial Internet service provider and three major cellular carriers in the US.


title: The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects

abstract: Abstract Brain–Computer Interface (BCI) systems establish a direct communication channel from the brain to an output device. These systems use brain signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications. BCI systems that bypass conventional motor output pathways of nerves and muscles can provide novel control options for paralyzed patients. One classical approach to establish EEG-based control is to set up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature. In contrast, the Berlin Brain–Computer Interface (BBCI) uses well established motor competencies of its users and a machine learning approach to extract subject-specific patterns from high-dimensional features optimized for detecting the user's intent. Thus the long subject training is replaced by a short calibration measurement (20 min) and machine learning (1 min). We report results from a study in which 10 subjects, who had no or little experience with BCI feedback, controlled computer applications by voluntary imagination of limb movements: these intentions led to modulations of spontaneous brain activity specifically, somatotopically matched sensorimotor 7–30 Hz rhythms were diminished over pericentral cortices. The peak information transfer rate was above 35 bits per minute (bpm) for 3 subjects, above 23 bpm for two, and above 12 bpm for 3 subjects, while one subject could achieve no BCI control. Compared to other BCI systems which need longer subject training to achieve comparable results, we propose that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.


title: Long-term electroencephalogram measurement using polymer-based dry microneedle electrode

abstract: In this paper, we report a successful electroencephalogram (EEG) measurement using polymer-based dry microneedle electrodes. The electrodes consist of needle-shaped substrates of SU-8, a silver film, and a nanoporous parylene protective film. Differently from conventional wet electrodes, microneedle electrodes do not require skin preparation and a conductive gel. SU-8 is superior as a structural material to poly(dimethylsiloxane) (PDMS; Dow Corning Toray Sylgard 184) in terms of hardness, which was used in our previous work, and facilitates the penetration of needles through the stratum corneum. SU-8 microneedles can be successfully inserted into the skin without breaking and could maintain a sufficiently low skin-electrode contact impedance for EEG measurement. The electrodes successfully measured EEG from the frontal pole, and the quality of acquired signals was verified to be as high as those obtained using commercially available wet electrodes without any skin preparation or a conductive gel. The electrodes are readily applicable to record brain activities for a long period with little stress involved in skin preparation to the users.


title: Measure of Uncertainty in Process Models Using Stochastic Petri Nets and Shannon Entropy

abstract: When modelling and analysing business processes, the main emphasis is usually put on model validity and accuracy, i.e., the model meets the formal specification and also models the relevant system. In recent years, a series of metrics has begun to develop, which allows the quantification of the specific properties of process models. These characteristics are, for instance, complexity, comprehensibility, cohesion, and uncertainty. This work is focused on defining a method that allows us to measure the uncertainty of a process model, which was modelled by using stochastic Petri nets (SPN). The principle of this method consists of mapping of all reachable marking of SPN into the continuous-time Markov chain and then calculating its stationary probabilities. The uncertainty is then measured as the entropy of the Markov chain (it is possible to calculate the uncertainty of the specific subset of places as well as of whole net). Alternatively, the uncertainty index is quantified as a percentage of the calculated entropy against maximum entropy (the resulting value is normalized to the interval ). The calculated entropy can also be used as a measure of the model complexity.


title: Modeling and simulation analysis of PV fed Cuk, Sepic, Zeta and Luo DC-DC converter

abstract: This paper presents a modeling and simulation analysis of Photovoltaic (PV) fed Cuk, Sepic, Zeta and Luo DC-DC converters. The performance of all the MATLAB/Simulink based DC-DC converters is compared for the PV application. The results show the performance of all the designed converters and all the parameters used in designing the advanced DC-DC converters are calculated by using the respective converter mathematical analysis and summarized in tabulation form. The transient and steady state analysis of all the designed advanced DC-DC converters has been carried out in terms of voltage, current and power at fixed resistive load.


Patterns of lung cancer risk in ex-smokers


title: Cartoon rendering of smoke animations

abstract: We describe a technique for generating cartoon style animations of smoke. Our method takes the output of a physically-based simulator and uses it to drive particles that are rendered using a variant of the depth differences technique (originally used for rendering trees). Specific issues we address include the placement and evolution of primitives in the flow and the maintenance of temporal coherence. The results are visually simple, flicker-free animations that convey the turbulent, dynamic nature of the gas with simple outlines.


title: Why is Mobility in India so Low? Social Insurance, Inequality, and Growth

abstract: This paper examines the hypothesis that the persistence of low spatial and marital mobility in rural India, despite increased growth rates and rising inequality in recent years, is due to the existence of sub-caste networks that provide mutual insurance to their members. Unique panel data providing information on income, assets, gifts, loans, consumption, marriage, and migration are used to link caste networks to household and aggregate mobility. Our key finding, consistent with the hypothesis that local risk-sharing networks restrict mobility, is that among households with the same (permanent) income, those in higher-income caste networks are more likely to participate in caste-based insurance arrangements and are less likely to both out-marry and out-migrate. At the aggregate level, the networks appear to have coped successfully with the rising inequality within sub-castes that accompanied the Green Revolution. The results suggest that caste networks will continue to smooth consumption in rural India for the foreseeable future, as they have for centuries, unless alternative consumption-smoothing mechanisms of comparable quality become available.


title: A cortical neural prosthesis for restoring and enhancing memory

abstract: A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.


title: Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision

abstract: We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data. Using only the existing 3D pose data and 2D pose data, we show state-of-the-art performance on established benchmarks through transfer of learned features, while also generalizing to in-the-wild scenes. We further introduce a new training set for human body pose estimation from monocular images of real humans that has the ground truth captured with a multi-camera marker-less motion capture system. It complements existing corpora with greater diversity in pose, human appearance, clothing, occlusion, and viewpoints, and enables an increased scope of augmentation. We also contribute a new benchmark that covers outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows better in-the-wild performance than existing annotated data, which is further improved in conjunction with transfer learning from 2D pose data. All in all, we argue that the use of transfer learning of representations in tandem with algorithmic and data contributions is crucial for general 3D body pose estimation.


title: Hierarchical Recurrent Neural Network for Document Modeling

abstract: This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling. After establishing a RNN to capture the coherence between sentences in a document, HRNNLM integrates it as the sentence history information into the word level RNN to predict the word sequence with cross-sentence contextual information. A two-step training approach is designed, in which sentence-level and word-level language models are approximated for the convergence in a pipeline style. Examined by the standard sentence reordering scenario, HRNNLM is proved for its better accuracy in modeling the sentence coherence. And at the word level, experimental results also indicate a significant lower model perplexity, followed by a practical better translation result when applied to a Chinese-English document translation reranking task.


title: Phylogenetic Relationships of Deep-sea Anglerfishes of the Suborder Ceratioidei (Teleostei: Lophiiformes) Based on Morphology

abstract: Abstract Phylogenetic relationships of the 11 families and 35 genera of deep-sea anglerfishes of the lophiiform suborder Ceratioidei, characterized most strikingly by an extreme sexual dimorphism that extends to all taxa, were analyzed on the basis of 71 morphological characters applicable to metamorphosed females. All characters were treated as unordered and unweighted, and were polarized by outgroup comparison with taxa of the lophiiform suborders Lophioidei, Antennarioidei, Chaunacoidei, and Ogcocephaloidei. The analysis produced five equally parsimonious trees, with a total length of 153, a consistency index of 0.5560, and a retention index of 0.7952. Lack of resolution was totally attributed to a single family, the relatively poorly understood Oneirodidae, containing 16 genera and 65 species, nearly 40% of all recognized ceratioids. Monophyly for the Ceratioidei was confirmed and all ceratioid genera were placed in currently recognized monophyletic families. Sister families Centrophrynidae and Cerati...


title: Dispersion compensation-free fiber laser mode-locked and stabilized by a high-contrast saturable absorber mirror

abstract: We report here a compact diode-pumped fiber laser that represents a promising route to designing a portable picosecond light source that is rugged and compact. The laser design presented in this paper is based on a high-contrast semiconductor saturable absorber mirror (SESAM) and targets a reliable picosecond-range fiber laser. The cavity is simple, since no dispersion compensators are used, the SESAM-based mode locking mechanism is robust, and self-starting resulted in low-maintenance turn-key operation. We investigated pulse formation in a short-length fiber cavity and found that nonlinear effects in a near-resonant SESAM in combination with the large cavity dispersion are the predominant mechanism that causes self-starting and stabilization of mode-locking. The effect of the recovery time of the SESAM on the stretched-pulse width and spectrum for resonant-type absorber mirrors was also studied.


title: A Direct Path Interference Cancellation Approach to Passive Radar Based on FM Radio transmitter

abstract: Target detection by non-cooperative illuminator is a research hotspot in electronic warfare field, with `four countering' potential advantages. One of the major problems in bistatic radars with continuous waves is the direct path interference (DPI). The conventional solution to this problem is to use an adaptive antenna by steering null towards the interference. Unfortunately the null depth obtained by this technology is not enough for surveillance radar. First of all, DPI based on FM broadcast transmitter in passive radar is analyzed in this paper. Secondly, a DPI cancellation approach based on adaptive fractional delay estimation (AFDE) is in detail introduced how to deal with this problem. Finally, a passive radar experimental system based on FM broadcast transmitter is discussed. Simulation results by applying real collected data show the provided method is effective.


title: Pattern recognition as a quest for minimum entropy

abstract: Abstract We shall show that many known algorithms of clustering and pattern recognition can be characterized as efforts to minimize entropy, when suitably defined.


title: A Probabilistic Approach for Optimizing Spectral Clustering

abstract: Spectral clustering enjoys its success in both data clustering and semi-supervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely to be trapped by the local optimum. In this paper, we present a new spectral clustering algorithm, named "Soft Cut". It improves the normalized cut algorithm by introducing soft membership, and can be efficiently computed using a bound optimization algorithm. Our experiments with a variety of datasets have shown the promising performance of the proposed clustering algorithm.


title: Pulsewidth modulation for electronic power conversion

abstract: The efficient and fast control of electric power forms part of the key technologies of modern automated production. It is performed using electronic power converters. The converters transfer energy from a source to a controlled process in a quantized fashion, using semiconductor switches which are turned on and off at fast repetition rates. The algorithms which generate the switching functions-pulsewidth-modulation techniques-are manifold. They range from simple averaging schemes to involved methods of real-time optimization. This paper gives an overview. >


title: A Psychodynamic Approach to the Diagnosis and Treatment of Closet Narcissism

abstract: The purpose of this article was to increase awareness of an alternative presentation of narcissistic personality disorder, offer treatment strategies specifically geared toward such patients, and demonstrate how an understanding of object relations and defense mechanisms can guide interventions and improve diagnostic refinement. A case study was presented to illustrate this approach. This case study draws mainly from James Masterson’s developmental, self, and object relations approach to the treatment of closet narcissism. Additional psychodynamic theories were also incorporated to provide a more comprehensive conceptualization of the client. Treatment consisted of psychodynamic therapy in which therapeutic neutrality, and analysis of the transference were core features.


title: Deriving decision rules to locate export containers in container yards

abstract: Abstract A methodology is proposed to determine the storage location of an arriving export container considering its weight. We consider the configuration of the container stack and the weight distribution of containers in the yard-bay. A dynamic programming model is formulated to determine the storage location to minimize the number of relocation movements expected for the loading operation. We also develop a decision tree from the set of the optimal solutions to support real time decisions. The performance of the decision tree is evaluated by the number of decisions that are different from the optimal solutions of the slower dynamic programming solution method.


title: Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system

abstract: In this paper, we present a new method to detect pterygiums using cornea images. Due to the similarity of appearances and spatial locations between pterygiums and cortical cataracts, pterygiums are often falsely detected as cortical cataracts on retroillumination images by a computer-aided grading system. The proposed method can be used to filter out the pterygium which improves the accuracy of cortical cataract grading system. This work has three major contributions. First, we propose a new pupil segmentation method for visible wavelength images. Second, an automatic detection method of pterygiums is proposed. Third, we develop an enhanced compute-aided cortical cataract grading system that excludes pterygiums. The proposed method is tested using clinical data and the experimental results demonstrate that the proposed method can improve the existing automatic cortical cataract grading system.


title: Research on an anti-crawling mechanism and key algorithm based on sliding time window

abstract: Inadequate crawling behavior of the crawler will have a very serious impact on the site, so anti crawling mechanism is an important function for the website. Most of the existing anti crawling methods are non real time detection, and the recognition accuracy is low. By analyzing the characteristics of Crawler, a real-time crawler detection method based on sliding time window is proposed, which improves the accuracy and efficiency of detection of non compliance with the rules of Crawler.


title: Higher education as a crucial factor of staff development

abstract: Today's market reality requires competitiveness from enterprises in all areas of activity. All aware companies set much store by the proper development of staff. Caring for professional development in the process of constant increasing of qualifications, skills and powers are the basic elements for the proper management of human resources. Investing in human resources is a significant expense in the countries of Western Europe, while in Poland it is still far from enough. The experience of European countries constitute a proof of the relevance of investing in human resources, which directly influences on the success of both the companies and the individuals.


title: High speed network traffic analysis with commodity multi-core systems

abstract: Multi-core systems are the current dominant trend in computer processors. However, kernel network layers often do not fully exploit multi-core architectures. This is due to issues such as legacy code, resource competition of the RX-queues in network interfaces, as well as unnecessary memory copies between the OS layers. The result is that packet capture, the core operation in every network monitoring application, may even experience performance penalties when adapted to multi-core architectures. This work presents common pitfalls of network monitoring applications when used with multi-core systems, and presents solutions to these issues. We describe the design and implementation of a novel multi-core aware packet capture kernel module that enables monitoring applications to scale with the number of cores. We showcase that we can achieve high packet capture performance on modern commodity hardware.


title: SLAM and static driver verifier: Technology transfer of formal methods inside Microsoft

abstract: The SLAM project originated in Microsoft Research in early 2000. Its goal was to automatically check that a C program correctly uses the interface to an external library. The project used and extended ideas from symbolic model checking, program analysis and theorem proving in novel ways to address this problem. The SLAM analysis engine forms the core of a new tool called Static Driver Verifier (SDV) that systematically analyzes the source code of Windows device drivers against a set of rules that define what it means for a device driver to properly interact with the Windows operating system kernel.


title: Community dynamics and functional characteristics of naphthalene‐degrading populations in contaminated surface sediments and hypoxic/anoxic groundwater

abstract: Earlier research on the biogeochemical factors affecting natural attenuation in coal-tar contaminated groundwater, at South Glens Falls, NY, revealed the importance of anaerobic metabolism and trophic interactions between degrader and bacterivore populations. Field-based characterizations of both phenomena have proven challenging, but advances in stable isotope probing (SIP), single-cell imaging and shotgun metagenomics now provide cultivation-independent tools for their study. We tracked carbon from 13 C-labelled naphthalene through microbial populations in contaminated surface sediments over 6 days using respiration assays, secondary ion mass spectrometry imaging and shotgun metagenomics to disentangle the contaminant-based trophic web. Contaminant-exposed communities in hypoxic/anoxic groundwater were contrasted with those from oxic surface sediments to identify putative features of anaerobic catabolism of naphthalene. In total, six bacteria were responsible for naphthalene degradation. Cupriavidus, Ralstonia and Sphingomonas predominated at the earliest stages of SIP incubations and were succeeded in later stages by Stenotrophomonas and Rhodococcus. Metagenome-assembled genomes provided evidence for the ecological and functional characteristics underlying these temporal shifts. Identical species of Stenotrophomonas and Rhodococcus were abundant in the most contaminated, anoxic groundwater. Apparent increases in bacterivorous protozoa were observed following exposure to naphthalene, though insignificant amounts of carbon were transferred between bacterial degraders and populations of secondary feeders.


title: Integrated Communication and Control Systems: Part I—Analysis

abstract: Computer networking is a reliable and efficient means for communications between disparate and distributed components in complex dynamical processes like advanced aircraft, spacecraft, and autonomous manufacturing plants. The role of Integrated Communication and Control Systems (ICCS) is to coordinate and perform inter related functions, ranging from real-time multi-loop control to information display and routine maintenance support. In ICCS, a feedback control loop is closed via the common communication channel which multiplexes digital data from the sensor to the controller and from the controller to the actuator along with the data traffic from other loops and management functions. Due to the asynchronous timedivision multiplexing of the network protocol, time-varying and possibly stochastic delays are introduced in the control system, which degrade the system dynamic per formance and are a source of potential instability. The paper is divided into two parts. In the first part, the delayed control system is represented by a finitedimensional, time-varying, discrete-time model which is less complex than the ex isting continuous-time models for time-varying delays; this approach allows for simpler schemes for analysis and simulation of ICCS. The second part of the paper addresses ICCS design considerations and presents simulation results for certain operational scenarios of ICCS.


title: Improving the Robustness of Ring Oscillator TRNGs

abstract: A ring oscillator-based true-random number generator design (Rings design) was introduced in Sunar et al. [2007]. The design was rigorously analyzed under a simple mathematical model and its performance characteristics were established. In this article we focus on the practical aspects of the Rings design on a reconfigurable logic platform and determine their implications on the earlier analysis framework. We make recommendations for avoiding pitfalls in real-life implementations by considering ring interaction, transistor-level effects, narrow signal rejection, transmission line attenuation, and sampler bias. Furthermore, we present experimental results showing that changing operating conditions such as the power supply voltage or the operating temperature may affect the output quality when the signal is subsampled. Hence, an attacker may shift the operating point via a simple noninvasive influence and easily bias the TRNG output. Finally, we propose modifications to the design which significantly improve its robustness against attacks, alleviate implementation-related problems, and simultaneously improve its area, throughput, and power performance.


title: FORENSICS ANDMITOCHONDRIALDNA: Applications, Debates, and Foundations

abstract: ▪ Abstract Debate on the validity and reliability of scientific methods often arises in the courtroom. When the government (i.e., the prosecution) is the proponent of evidence, the defense is obliged to challenge its admissibility. Regardless, those who seek to use DNA typing methodologies to analyze forensic biological evidence have a responsibility to understand the technology and its applications so a proper foundation(s) for its use can be laid. Mitochondrial DNA (mtDNA), an extranuclear genome, has certain features that make it desirable for forensics, namely, high copy number, lack of recombination, and matrilineal inheritance. mtDNA typing has become routine in forensic biology and is used to analyze old bones, teeth, hair shafts, and other biological samples where nuclear DNA content is low. To evaluate results obtained by sequencing the two hypervariable regions of the control region of the human mtDNA genome, one must consider the genetically related issues of nomenclature, reference population ...


title: Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets

abstract: We conduct the first natural field experiment to explore the relationship between the “meaningfulness” of a task and worker effort. We employed about 2500 workers from Amazon's Mechanical Turk (MTurk), an online labor market, to label medical images. Although given an identical task, we experimentally manipulated how the task was framed. Subjects in the meaningful treatment were told that they were labeling tumor cells in order to assist medical researchers, subjects in the zero-context condition (the control group) were not told the purpose of the task, and, in stark contrast, subjects in the shredded treatment were not given context and were additionally told that their work would be discarded. We found that when a task was framed more meaningfully, workers were more likely to participate. We also found that the meaningful treatment increased the quantity of output (with an insignificant change in quality) while the shredded treatment decreased the quality of output (with no change in quantity). We believe these results will generalize to other short-term labor markets. Our study also discusses MTurk as an exciting platform for running natural field experiments in economics.


title: Medial Temporal Lobe Damage Causes Deficits in Episodic Memory and Episodic Future Thinking Not Attributable to Deficits in Narrative Construction

abstract: The medial temporal lobe (MTL) makes critical contributions to episodic memory, but its contributions to episodic future thinking remain a matter of debate. By one view, imagining future events relies on MTL mechanisms that also support memory for past events. Alternatively, it has recently been suggested that future thinking is independent of MTL-mediated processes and can be supported by regions outside the MTL. The current study investigated the nature and necessity of MTL involvement in imagining the future and tested the novel hypothesis that the MTL contributes to future thinking by supporting online binding processes related to narrative construction. Human amnesic patients with well characterized MTL damage and healthy controls constructed narratives about (1) future events, (2) past events, and (3) visually presented pictures. While all three tasks place similar demands on narrative construction, only the past and future conditions require memory/future thinking to mentally generate relevant narrative information. Patients produced impoverished descriptions of both past and future events but were unimpaired at producing detailed picture narratives. In addition, future-thinking performance positively correlated with episodic memory performance but did not correlate with picture narrative performance. Finally, future-thinking impairments were present when MTL lesions were restricted to the hippocampus and did not depend on the presence of neural damage outside the MTL. These results indicate that the ability to generate and maintain a detailed narrative is preserved in amnesia and suggest that a common MTL mechanism supports both episodic memory and episodic future thinking.


title: Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition

abstract: Hand gesture recognition is still a topic of great interest for the computer vision community. In particular, sign language and semaphoric hand gestures are two foremost areas of interest due to their importance in human–human communication and human–computer interaction, respectively. Any hand gesture can be represented by sets of feature vectors that change over time. Recurrent neural networks (RNNs) are suited to analyze this type of set thanks to their ability to model the long-term contextual information of temporal sequences. In this paper, an RNN is trained by using as features the angles formed by the finger bones of the human hands. The selected features, acquired by a leap motion controller sensor, are chosen because the majority of human hand gestures produce joint movements that generate truly characteristic corners. The proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language. On the latter, an accuracy of over 96% was achieved. Afterwards, by using the Shape Retrieval Contest (SHREC) dataset, a wide collection of semaphoric hand gestures, the method was also proven to outperform in accuracy competing approaches of the current literature.


MUSEEC: A Multilingual Text Summarization Tool


title: Full duplex MIMO radios

abstract: This paper presents the design and implementation of the first in-band full duplex WiFi-PHY based MIMO radios that practically achieve the theoretical doubling of throughput. Our design solves two fundamental challenges associated with MIMO full duplex: complexity and performance. Our design achieves full duplex with a cancellation design whose complexity scales almost linearly with the number of antennas, this complexity is close to the optimal possible. Further we also design novel digital estimation and cancellation algorithms that eliminate almost all interference and achieves the same performance as a single antenna full duplex SISO system, which is again the best possible performance. We prototype our design by building our own analog circuit boards and integrating them with a WiFi-PHY compatible standard WARP software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.


title: A Church-Rosser checker tool for conditional order-sorted equational Maude specifications

abstract: The Church-Rosser property, together with termination, is essential for an equational specification to have good executability conditions, and also for having a complete agreement between the specification's initial algebra, mathematical semantics, and its operational semantics by rewriting. Checking this property for expressive specifications that are order-sorted, conditional with possibly extra variables in their condition, and whose equations can be applied modulo different combinations of associativity, commutativity and identity axioms is challenging. In particular, the resulting conditional critical pairs that cannot be joined have often an intuitively unsatisfiable condition or seem intuitively joinable, so that sophisticated tool support is needed to eliminate them. Another challenge is the presence of different combinations of associativity, commutativity and identity axioms, including the very challenging case of associativity without commutativity for which no finitary unification algorithms exist. In this paper we present the foundations and illustrate the design and use of a completely new version of the Maude Church-Rosser Checker tool that addresses all the above-mentioned challenges and can deal effectively with complex conditional specifications modulo axioms.


CKY-based Convolutional Attention for Neural Machine Translation


title: Interactive robots for communication-care: a case-study in autism therapy

abstract: The paper proposes possible use of interactive robots in the remedial practice for children with autism, who have difficulties mainly in interpersonal communication. For this purpose, we built a small creature-like robot, Keepon, which was carefully designed to get autistic and non-autistic children involved in playful interaction. We observed how autistic children (2-4 years old) interacted with Keepon without any experimental setting or instructions in a playroom at a day-care center for children with special needs. From the longitudinal observation for a year and a half (totally, over 500 child-sessions), we found that Keepon's simple appearance and predictable responses gave the autistic children a playful and relaxed mood, in which they spontaneously engaged in dyadic play with Keepon, which would then expand into interpersonal communication where Keepon worked as the pivot of triadic play with adults or other children. Each child showed a different style and a different unfolding of interaction over time, which tell us a "story" of his or her personality and developmental profile, which would not be explained thoroughly by a diagnostic label like "autism".


title: A Review of Wideband Wide-Angle Scanning 2-D Phased Array and Its Applications in Satellite Communication

abstract: In this review, research progress on the wideband wide-angle scanning two-dimensional phased arrays is summarized. The importance of the wideband and the wide-angle scanning characteristics for satellite communication is discussed. Issues like grating lobe avoidance, active reflection coefficient suppression and gain fluctuation reduction are emphasized in this review. Besides, techniques to address these issues and methods to realize the wideband wide-angle scanning phased array are reviewed.


title: Interactive Multiobjective Optimization: A Review of the State-of-the-Art

abstract: Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker’s preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.


title: Commit guru: analytics and risk prediction of software commits

abstract: Software quality is one of the most important research sub-areas of software engineering. Hence, a plethora of research has focused on the prediction of software quality. Much of the software analytics and prediction work has proposed metrics, models and novel approaches that can predict quality with high levels of accuracy. However, adoption of such techniques remain low; one of the reasons for this low adoption of the current analytics and prediction technique is the lack of actionable and publicly available tools. We present Commit Guru, a language agnostic analytics and prediction tool that identifies and predicts risky software commits. Commit Guru is publicly available and is able to mine any GIT SCM repository. Analytics are generated at both, the project and commit levels. In addition, Commit Guru automatically identifies risky (i.e., bug-inducing) commits and builds a prediction model that assess the likelihood of a recent commit introducing a bug in the future. Finally, to facilitate future research in the area, users of Commit Guru can download the data for any project that is processed by Commit Guru with a single click. Several large open source projects have been successfully processed using Commit Guru. Commit Guru is available online at commit.guru. Our source code is also released freely under the MIT license.


title: A framework for evaluating Internet-of-Things platforms: Application provider viewpoint

abstract: Widespread adoption of Internet of Things (IoT) applications assumes a sustainable ecosystem of firms creating, distributing, and using these applications. Such an ecosystem often forms around a platform that implements commonly used functionality and offers it to the members of the ecosystem for creating applications and services. In IoT, such a platform may be materialized in a form of a cloud platform that supports the management of connected devices, and the development and use of the applications based on them. A number of cloud platforms targeting IoT applications are available in the market; to prosper, they need to assist the providers of the IoT applications in designing and developing the applications, as well as in deploying and operating the application software. In this paper, a framework for evaluating the IoT platforms from the perspective of how widely they cover the potential needs of the application providers is introduced. Based on the framework, the maturity of the available IoT platforms is evaluated, by using the publicly available information about the platforms' features and supporting services. As the results of this initial analysis suggest, none of the platforms today offers the comprehensive support. This can be seen as a factor contributing to a slow adaption of the IoT platforms, and therefore to the slower-than-expected takeoff of the IoT ecosystem in general.


title: Comparing and combining sentiment analysis methods

abstract: Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.


Video Super-Resolution using Temporal Fusion Generative Adversarial Network


title: Electromagnetic Property Characterization of Biological Tissues at D-Band

abstract: This paper presents, for the first time, measured complex dielectric and magnetic properties of liquid and solid biological tissues taken from human arteries in the frequency range 110–170 GHz. The complex permittivity and permeability are extracted from scattering parameters of a waveguide capsule loaded with biological tissue. The estimation of the dielectric properties was done by the Nicholson–Ross–Weir conversion process. A D-band waveguide setup for measuring with temperature stabilization at 37 °C was developed and is described herein. Results show large differences between dielectric properties of blood (10.7 + j2.9), adipose tissue (2.5 + j0.3), calcified tissue (3.2 + j0.84), and fibrous tissue (8.8 + j3.3). While calcified tissues and adipose tissues show relative permeability higher than 1 (1.8 − 1.4 + j0.2), fibrous tissues show μ r of 1 + j0.4 and blood proves to be diamagnetic with 0.7 + j0.5. The measured blood refractive index follows the frequency trend of the Cole–Cole model extrapolated data of previously measured blood at lower frequencies.


title: Trend Modeling for Traffic Time Series Analysis: An Integrated Study

abstract: This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.


title: Offline Handwriting Recognition on Devanagari Using a New Benchmark Dataset

abstract: Handwriting recognition (HWR) in Indic scripts, like Devanagari is very challenging due to the subtleties in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, we release a new handwritten word dataset for Devanagari, IIIT-HW-Dev to alleviate some of these issues. We benchmark the IIIT-HW-Dev dataset using a CNN-RNN hybrid architecture. Furthermore, using this architecture, we empirically show that usage of synthetic data and cross lingual transfer learning helps alleviate the issue of lack of training data. We use this proposed pipeline on a public dataset, RoyDB and achieve state of the art results.


title: Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms

abstract: Deep neural networks such as Convolutional Neural Networks (CNNs) have achieved several significant milestones in visual data analytics. Benefited from transfer learning, many researchers use pre-trained CNN models to accelerate the training process. However, there is still uncertainty about the deep learning models, structures, and applications. For instance, the diversity of the datasets may affect the performance of each pre-trained model. Therefore, in this paper, we proposed a new approach based on genetic algorithms to select or regenerate the best pre-trained CNN models for different visual datasets. A new genetic encoding model is presented which denotes different pre-trained models in our population. During the evolutionary process, the optimal genetic code that represents the best model is selected, or new competitive individuals are generated using the genetic operations. The experimental results illustrate the effectiveness of the proposed framework which outperforms several existing approaches in visual data classification.


title: ReFlex: Remote Flash ≈ Local Flash

abstract: Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity and IOPS within a datacenter. However, existing systems for remote Flash access either introduce significant performance overheads or fail to isolate the multiple remote clients sharing each Flash device. We present ReFlex, a software-based system for remote Flash access, that provides nearly identical performance to accessing local Flash. ReFlex uses a dataplane kernel to closely integrate networking and storage processing to achieve low latency and high throughput at low resource requirements. Specifically, ReFlex can serve up to 850K IOPS per core over TCP/IP networking, while adding 21us over direct access to local Flash. ReFlex uses a QoS scheduler that can enforce tail latency and throughput service-level objectives (SLOs) for thousands of remote clients. We show that ReFlex allows applications to use remote Flash while maintaining their original performance with local Flash.


title: Fast anomaly detection in traffic surveillance video based on robust sparse optical flow

abstract: Fast abnormal events detection in video is important for intelligent analysis of video. This paper proposes a fast anomaly detection algorithm based on sparse optical flow. We improve the efficiency of optical flow computation with foreground mask and spacial sampling and increase the robustness of optical flow with good feature (TK) points selecting and forward-backward filtering. A foreground channel is also added to the feature vector to help detect static or low speed objects. The algorithm is validated on real-life traffic surveillance to prove its effectiveness. It is also evaluated on a benchmark dataset and achieve detection results comparable to state-of-art methods and outperforms them at pixel-level when the false alarm rate is low. The strength of our algorithm is that it runs real-time on the benchmark dataset which is hundreds of times faster than comparative methods.


Collaborative BPM Based on Industry-specific Reference Models


title: Linguistic Knowledge as Memory for Recurrent Neural Networks

abstract: Training recurrent neural networks to model long term dependencies is difficult. Hence, we propose to use external linguistic knowledge as an explicit signal to inform the model which memories it should utilize. Specifically, external knowledge is used to augment a sequence with typed edges between arbitrarily distant elements, and the resulting graph is decomposed into directed acyclic subgraphs. We introduce a model that encodes such graphs as explicit memory in recurrent neural networks, and use it to model coreference relations in text. We apply our model to several text comprehension tasks and achieve new state-of-the-art results on all considered benchmarks, including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out of the 20 tasks with only 1000 training examples per task. Analysis of the learned representations further demonstrates the ability of our model to encode fine-grained entity information across a document.


Innovation and creativity in education and training in the EU member states: fostering creative learning and supporting innovative teaching


title: Robust Stereo Matching Using Adaptive Normalized Cross-Correlation

abstract: A majority of the existing stereo matching algorithms assume that the corresponding color values are similar to each other. However, it is not so in practice as image color values are often affected by various radiometric factors such as illumination direction, illuminant color, and imaging device changes. For this reason, the raw color recorded by a camera should not be relied on completely, and the assumption of color consistency does not hold good between stereo images in real scenes. Therefore, the performance of most conventional stereo matching algorithms can be severely degraded under the radiometric variations. In this paper, we present a new stereo matching measure that is insensitive to radiometric variations between left and right images. Unlike most stereo matching measures, we use the color formation model explicitly in our framework and propose a new measure, called the Adaptive Normalized Cross-Correlation (ANCC), for a robust and accurate correspondence measure. The advantage of our method is that it is robust to lighting geometry, illuminant color, and camera parameter changes between left and right images, and does not suffer from the fattening effect unlike conventional Normalized Cross-Correlation (NCC). Experimental results show that our method outperforms other state-of-the-art stereo methods under severely different radiometric conditions between stereo images.


title: Neural network approach to lumpy demand forecasting for spare parts in process industries

abstract: Accurate demand forecasting is one of the most crucial issues in inventory management of spare parts in process industries. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of these methods may perform poorly when demand for an item is lumpy. Furthermore, traditional time-series methods may not sometimes capture the nonlinear pattern in data. Artificial neural network modeling is a logical choice to overcome these limitations. In this study recurrent neural network has been used for lumpy demand forecasting of spare parts. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using two conventional methods, namely, Crostonpsilas method and Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network and generalized regression neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods.


title: Not All Moods are Created Equal! Exploring Human Emotional States in Social Media

abstract: Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. As social media tools are increasingly used by individuals to broadcast their day-to-day happenings, or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better interpret and make sense of the behavior of millions of individuals. Motivated by literature in psychology, we study a popular representation of human mood landscape, known as the ‘circumplex model’ that characterizes affective experience through two dimensions: valence and activation. We identify more than 200 moods frequent on Twitter, through mechanical turk studies and psychology literature sources, and report on four aspects of mood expression: the relationship between (1) moods and usage levels, including linguistic diversity of shared content (2) moods and the social ties individuals form, (3) moods and amount of network activity of individuals, and (4) moods and participatory patterns of individuals such as link sharing and conversational engagement. Our results provide at-scale naturalistic assessments and extensions of existing conceptualizations of human mood in social media contexts.


title: License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

abstract: This work details Sighthounds fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs) interlaced with accurate and efficient algorithms. The CNNs are trained and fine-tuned so that they are robust under different conditions (e.g. variations in pose, lighting, occlusion, etc.) and can work across a variety of license plate templates (e.g. sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our system outperforms the leading license plate detection and recognition technology i.e. ALPR on several benchmarks. Our system is available to developers through the Sighthound Cloud API at this https URL


title: A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards

abstract: We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.


title: Simulation of pick and place robotics system using Solidworks Softmotion

abstract: This paper presents the design of pick and place robotics system using Solidworks Softmotion software. The software is used to design a Cartesian robot and an articulated industrial robotic arm with different grippers. The robot was designed using the Solidworks 3D CAD software to shorten the robot development time, and improve the speed and quality of the robot design. The Solidworks 3D CAD software consist of four sections which is manual drawing, part module, assembly module and drawing module. Solidworks software was chosen as it enables analysis and simulation of the pick and place industrial robotic arm design. The results of simulation Xpress study and motion study of the modelled articulated robot arm part and assembly are presented to demonstrate the pick and place robotics system. This project indicated that the Solidworks Software is a suitable tool that enabled the design of a robotic system to be carried out in a short duration.


title: l-DBSCAN : A Fast Hybrid Density Based Clustering Method

abstract: Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.


title: A dynamic compression technique for streaming kinect-based Point Cloud data

abstract: With the relative inexpensiveness of 3D sensors today, it has become easier to collect 3D spatial data. This opens up the usability for these data, called point clouds, in areas such as robotics, telemedicine, and entertainment. However, a primary issue is the data rate of point clouds being produced by sensors such as the Microsoft Kinect. When transmitted, the size of a single point cloud coupled with the high frame rate results in an impractical bandwidth requirement. Therefore, compression is required. However, current compression techniques offered by the Point Cloud Library (PCL) are static in terms of available network bandwidth, meaning the techniques do not adjust as the network changes. In this paper, we propose using a dynamic compression technique that adjusts the compression ratio in response to the network throughput. Experimental results under both static and dynamic network traffic conditions show that dynamic compression is promising in achieving higher and smoother frame rates compared with the built-in compression algorithm in PCL.


title: Feature Cross-Substitution in Adversarial Classification

abstract: The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.


title: Understanding Relationship Marketing Outcomes: An Integration of Relational Benefits and Relationship Quality

abstract: The importance of developing and maintaining enduring relationships with customers of service businesses is generally accepted in the marketing literature. A key challenge for researchers is to identify and understand how managerially controlled antecedent variables influence important relationship marketing outcomes (e.g., customer loyalty and word-of-mouth communication). Relational benefits, which have a focus on the benefits consumers receive apart from the core service, and relationship quality, which focuses on the overall nature of the relationship, represent two approaches to understanding customer loyalty and word of mouth. This article integrates these two concepts by positioning customer satisfaction and commitment as relationship quality dimensions that partially mediate the relationship between three relational benefits (confidence benefits, social benefits, and special treatment benefits) and the two outcome variables. The results provide support for the model and indicate that the concepts ...


title: A velocity-preserving trajectory simplification approach

abstract: By the rise of mobile devices, trajectory data could be easily collected and used in several applications, like destination prediction, public transportation optimization, and travel route recommendation. However, due to the spatio-temporal nature, raw trajectory data usually contain redundant movement information. This observation motivates the trajectory simplication approaches which discard some points with preserving some specific features, such as position features, direction features, and so on. Most of existing simplifications ignore the importance of velocity features. This paper proposes an adaptive trajectory approaches while taking the velocity feature into account. Specifically, the Adaptive Trajectory Simplification (ATS) algorithm is proposed, which not only preserves the position feature, but the velocity feature from the given trajectories. ATS algorithm groups the velocity values into several intervals, which are used to partition trajectories into velocity-preserving segments. The simplified trajectory could be derived by applying the position-preserving simplification approach on each segment, where the threshold in a position-preserving approach could be determined without manual setting. Extensive experiments are conducted by using a real trajectory dataset in Porto. The experimental results show ATS algorithm could simplify trajectories effectively while preserving the velocity feature and the position feature at the same time.


title: Bilateral filtering for gray and color images

abstract: Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.


title: The qualitative content analysis process.

abstract: AIM ::: This paper is a description of inductive and deductive content analysis. ::: ::: ::: BACKGROUND ::: Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. ::: ::: ::: DISCUSSION ::: When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. ::: ::: ::: CONCLUSION ::: Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.


Synthetic Image Generation with a Lens and Aperture Camera Model


title: “Put-that-there”: Voice and gesture at the graphics interface

abstract: Recent technological advances in connected-speech recognition and position sensing in space have encouraged the notion that voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality. The work described herein involves the user commanding simple shapes about a large-screen graphics display surface. Because voice can be augmented with simultaneous pointing, the free usage of pronouns becomes possible, with a corresponding gain in naturalness and economy of expression. Conversely, gesture aided by voice gains precision in its power to reference.


Contagion of Fire Sale on Security-Trader Network


title: Liquid biopsy: a step forward towards precision medicine in urologic malignancies

abstract: There is a growing trend towards exploring the use of a minimally invasive "liquid biopsy" to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.


title: A crowdfunding model for green energy investment

abstract: This paper studies a new renewable energy investment model through crowdfunding, which is motivated by emerging community solar farms. In this paper we develop a sequential game theory model to capture the interactions among crowdfunders, the solar farm owner, and an electricity company who purchases renewable energy generated by the solar farm in a multi-period framework. By characterizing a unique subgame-perfect equilibrium, and comparing it with a benchmark model without crowdfunding, we find that under crowdfunding although the farm owner reduces its investment level, the overall green energy investment level is increased due to the contribution of crowdfunders. We also find that crowdfunding can increase the penetration of green energy in consumption and thus reduce the energy procurement cost of the electricity company. Finally, the numerical results based on real data indicates crowdfunding is a simple but effective way to boost green generation.


title: CATiB: The Columbia Arabic Treebank

abstract: The Columbia Arabic Treebank (CATiB) is a database of syntactic analyses of Arabic sentences. CATiB contrasts with previous approaches to Arabic treebanking in its emphasis on speed with some constraints on linguistic richness. Two basic ideas inspire the CATiB approach: no annotation of redundant information and using representations and terminology inspired by traditional Arabic syntax. We describe CATiB's representation and annotation procedure, and report on inter-annotator agreement and speed.