title: Smartphone eye tracking toolbox: accurate gaze recovery on mobile displays

abstract: Human Interaction with mobile devices has recently been estab-lished as application field in eye tracking research. Current technologies for gaze recovery on mobile displays cannot enable fully natural interaction with the mobile device: users are condi-tioned to interact with tightly mounted displays or distracted by markers in their view. We propose a novel approach that cap-tures point-of-regards (PORs) with eye tracking glasses (ETG) and then uses computer vision methodology for the robust local-ization of the smartphone in the head camera video. We present an integrated software package, i.e., the Smartphone Eye Track-ing Toolbox (SMET) that enables accurate gaze recovery on mobile displays with heat mapping of recent attention. We re-port the performance of the computer vision approach and demonstrate it with various natural interaction scenarios using the SMET Toolbox, enable ROI settings on the mobile display and show results from eye movement analysis, such as, ROI dwell time and statistics on eye gaze event (saccades, fixations).


title: Named Person Coreference in English News

abstract: People are often entities of interest in tasks such as search and information extraction. In these tasks, the goal is to find as much information as possible about people specified by their name. However in text, some of the references to people are by pronouns (she, his) or generic descriptions (the professor, the German chancellor). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct person name. Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she--the student---no name). We introduce new metrics for evaluating named person coreference that address these discrepancies. We present a simple rule-based named entity recognition driven system, which outperforms the current state-of-the-art systems on these task-specific metrics and performs on par with them on traditional coreference evaluations. Finally, we present similar evaluation for coreference resolution of other named entities and show that the rule-based approach is effective only for person named coreference, not other named entity types.


title: Autonomous Positioning of Eye Surgical Robot Using the Tool Shadow and Kalman Filtering

abstract: Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon's coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions-namely, a micropipette, its shadow, and the eye ground-using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.


Learning to Actively Learn Neural Machine Translation


title: GraphIE: A Graph-Based Framework for Information Extraction

abstract: Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks -- namely social media, textual and visual information extraction -- shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.


title: Smart Collaborative Caching for Information-Centric IoT in Fog Computing

abstract: The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.


title: Structural Influences on Global E-Commerce Activity

abstract: An important line of research on global information management examines the effects of national culture on IT development, operations, management and use. This paper argues that global information management researchers should not lose sight of structural conditions related to business-to-business and business-to-consumer e-commerce activity. Structural conditions are physical, social and economic arrangements that shape e-commerce business models and influence individual and organizational use of the Internet. Examples include geography (which affects the physical distribution of goods purchased online), space (which influences the choice of access technology for e-commerce) and financial infrastructure (which is related to credit card use). Structural conditions differ from country to countryâ€”and even from location to location within country, but they are not necessarily related to dimensions of natural culture. Therefore, valid explanations of global differences in e-commerce activity require a careful assessment of relevant structural factors.


title: Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

abstract: Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.


m: Ciudad: An Infrastructure for Creation and Sharing of End User Generated Microservices


title: Cryptographic Cloud Storage

abstract: We consider the problem of building a secure cloud storage service on top of a public cloud infrastructure where the service provider is not completely trusted by the customer. We describe, at a high level, several architectures that combine recent and non-standard cryptographic primitives in order to achieve our goal. We survey the benefits such an architecture would provide to both customers and service providers and give an overview of recent advances in cryptography motivated specifically by cloud storage.


title: Video pulse rate variability analysis in stationary and motion conditions

abstract: BackgroundIn the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. To date, most of these works have obtained HRV parameters in stationary conditions, and there are practically no studies that obtain these parameters in motion scenarios and by conducting an in-depth statistical analysis.MethodsIn this study, a video pulse rate variability (PRV) analysis is conducted by measuring the pulse-to-pulse (PP) intervals in stationary and motion conditions. Firstly, given the importance of the sampling rate in a PRV analysis and the low frame rate of commercial cameras, we carried out an analysis of two models to evaluate their performance in the measurements. We propose a selective tracking method using the Viola–Jones and KLT algorithms, with the aim of carrying out a robust video PRV analysis in stationary and motion conditions. Data and results of the proposed method are contrasted with those reported in the state of the art.ResultsThe webcam achieved better results in the performance analysis of video cameras. In stationary conditions, high correlation values were obtained in PRV parameters with results above 0.9. The PP time series achieved an RMSE (mean ± standard deviation) of 19.45 ± 5.52 ms (1.70 ± 0.75 bpm). In the motion analysis, most of the PRV parameters also achieved good correlation results, but with lower values as regards stationary conditions. The PP time series presented an RMSE of 21.56 ± 6.41 ms (1.79 ± 0.63 bpm).ConclusionsThe statistical analysis showed good agreement between the reference system and the proposed method. In stationary conditions, the results of PRV parameters were improved by our method in comparison with data reported in related works. An overall comparative analysis of PRV parameters in motion conditions was more limited due to the lack of studies or studies containing insufficient data analysis. Based on the results, the proposed method could provide a low-cost, contactless and reliable alternative for measuring HR or PRV parameters in non-clinical environments.


title: Dependent Gaussian Processes

abstract: Gaussian processes are usually parameterised in terms of their covariance functions. However, this makes it difficult to deal with multiple outputs, because ensuring that the covariance matrix is positive definite is problematic. An alternative formulation is to treat Gaussian processes as white noise sources convolved with smoothing kernels, and to parameterise the kernel instead. Using this, we extend Gaussian processes to handle multiple, coupled outputs.


DUAL-BAND EQUAL / UNEQUAL WILKINSON POWER DIVIDERS BASED ON COUPLED-LINE SECTION WITH SHORT-CIRCUITED


Working knowledge: how organizations manage what they know [Book Review]


title: Alpha-power law MOSFET model and its applications to CMOS inverter delay and other formulas

abstract: An alpha -power-law MOS model that includes the carrier velocity saturation effect, which becomes prominent in short-channel MOSFETs, is introduced. The model is an extension of Shockley's square-law MOS model in the saturation region. Since the model is simple, it can be used to handle MOSFET circuits analytically and can predict the circuit behavior in the submicrometer region. Using the model, closed-form expressions for the delay, short-circuit power, and transition voltage of CMOS inverters are derived. The delay expression includes input waveform slope effects and parasitic drain/source resistance effects and can be used in simulation and/or optimization CAD tools. It is found that the CMOS inverter delay becomes less sensitive to the input waveform slope and that short-circuit dissipation increases as the carrier velocity saturation effect in short-channel MOSFETs gets more severe. >


title: Approaches to Online Learning and Concept Drift for User Identification in Computer Security

abstract: The task in the computer security domain of anomaly detection is to characterize the behaviors of a computer user (the 'valid' , or 'normal' user) so that unusual occurrences can be detected by comparison of the current input stream to the valid user's profile. This task requires an online learning system that can respond to concept drift and handle discrete non-metric time sequence data. We present an architecture for online learning in the anomaly detection domain and address the issues of incremental updating of system parameters and instance selection. We demonstrate a method for measuring direction and magnitude of concept drift in the classification space and present and evaluate approaches to the above stated issues which make use of the drift measurement.


title: Factored conditional restricted Boltzmann Machines for modeling motion style

abstract: The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplicative three-way interactions that allow the effective interaction weight between two units to be modulated by the dynamic state of a third unit. We factor the three-way weight tensor implied by the multiplicative model, reducing the number of parameters from O(N3) to O(N2). The result is an efficient, compact model whose effectiveness we demonstrate by modeling human motion. Like the CRBM, our model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve the model's ability to blend motion styles or to transition smoothly among them.


title: HPI Question Answering System in BioASQ 2016

abstract: Question answering (QA) systems are crucial when searching for exact answers for natural language questions in the biomedical domain. Answers to many of such questions can be extracted from the 26 millions biomedical publications currently included in MEDLINE when relying on appropriate natural language processing (NLP) tools. In this work we describe our participation in the task 4b of the BioASQ challenge using two QA systems that we developed for biomedicine. Preliminary results show that our systems achieved first and second positions in the snippet retrieval sub-task and for the generation of ideal answers.


title: Probabilistic Graphlet Transfer for Photo Cropping

abstract: As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.


title: A vector light detector for proximity sensing applications

abstract: In this paper, we propose a novel design for a three dimensional proximity sensor. The novel design takes advantage of rapid prototyping capabilities offered by 3D printing to produce mesa pyramids with a 55 ° base angle that comprise a two-pixel system. The two pixel system combines photoconductivity and plane wave propagation properties of a radiative source to approximate the source distance as well as direction with respect to the two-pixel system. We have used commercially available silicon photodiodes as active sensing material on the facets of the mesa pyramid bases, whereby each facet would measure the incoming radiation separately. The design offers a simple geometry suitable for integration with other electronic designs and allows for cost effective directional photoconductive sensors that can be implemented in high precision applications such as autonomous vehicles, robotics and industrial automation processes.


title: Software tools for processing of sampled values transmitted via IEC 61850-9-2 protocol in LabView

abstract: Sampled Values transferred via process bus according to IEC 61850 are used for protection functions in substation. Nowadays a lot of researchers are dealing with their processing and using them for protection algorithms. There are some software tools that could be used for their processing. This paper is focused on package of software tools developed at Brno University of Technology and they allow also to use Sampled Values in LabView development environment. The paper describes possibilities for complex testing of developed algorithms in LabView.


title: A free energy principle for the brain

abstract: By formulating Helmholtz’s ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses. In this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure. 2006 Published by Elsevier Ltd.


title: A dynamic longitudinal examination of social media use, needs, and gratifications among college students

abstract: This study extends the U&G theoretical perspective to account for the situated, adaptive, and dynamic nature of mediated cognition and behavior. It specifies dynamic uses and gratifications of social media (compared to other media) in the everyday lives of college students using experience sampling data across 4weeks. The study tests and quantifies reciprocal causal relationships between needs, social media use, and gratifications, as well as their self-sustaining endogenous (i.e., feedback) effects. Social media use is significantly driven by all four categories of needs examined (emotional, cognitive, social, and habitual), but only gratifies some of them. Ungratified needs accumulate over time and drive subsequent social media use. Interpersonal social environments also affect social media use. In particular, solitude and interpersonal support increase social media use, and moderate the effects of needs on social media use.


title: A signal-processing framework for inverse rendering

abstract: Realism in computer-generated images requires accurate input models for lighting, textures and BRDFs. One of the best ways of obtaining high-quality data is through measurements of scene attributes from real photographs by inverse rendering. However, inverse rendering methods have been largely limited to settings with highly controlled lighting. One of the reasons for this is the lack of a coherent mathematical framework for inverse rendering under general illumination conditions. Our main contribution is the introduction of a signal-processing framework which describes the reflected light field as a convolution of the lighting and BRDF, and expresses it mathematically as a product of spherical harmonic coefficients of the BRDF and the lighting. Inverse rendering can then be viewed as deconvolution. We apply this theory to a variety of problems in inverse rendering, explaining a number of previous empirical results. We will show why certain problems are ill-posed or numerically ill-conditioned, and why other problems are more amenable to solution. The theory developed here also leads to new practical representations and algorithms. For instance, we present a method to factor the lighting and BRDF from a small number of views, i.e. to estimate both simultaneously when neither is known.


title: Cyber attack modeling and simulation for network security analysis

abstract: Cyber security methods are continually being developed. To test these methods many organizations utilize both virtual and physical networks which can be costly and time consuming. As an alternative, in this paper, we present a simulation modeling approach to represent computer networks and intrusion detection systems (IDS) to efficiently simulate cyber attack scenarios. The outcome of the simulation model is a set of IDS alerts that can be used to test and evaluate cyber security systems. In particular, the simulation methodology is designed to test information fusion systems for cyber security that are under development.


Learning about Teaching Initial Findings from the Measures of Effective Teaching Project


title: Visualized Insights into the Optimization Landscape of Fully Convolutional Networks

abstract: Many image processing tasks involve image-to-image mapping, which can be addressed well by fully convolutional networks (FCN) without any heavy preprocessing. Although empirically designing and training FCNs can achieve satisfactory results, reasons for the improvement in performance are slightly ambiguous. Our study is to make progress in understanding their generalization abilities through visualizing the optimization landscapes. The visualization of objective functions is obtained by choosing a solution and projecting its vicinity onto a 3D space. We compare three FCN-based networks (two existing models and a new proposed in this paper for comparison) on multiple datasets. It has been observed in practice that the connections from the pre-pooled feature maps to the post-upsampled can achieve better results. We investigate the cause and provide experiments to shows that the skip-layer connections in FCN can promote flat optimization landscape, which is well known to generalize better. Additionally, we explore the relationship between the models generalization ability and loss surface under different batch sizes. Results show that large-batch training makes the model converge to sharp minimizers with chaotic vicinities while small-batch method leads the model to flat minimizers with smooth and nearly convex regions. Our work may contribute to insights and analysis for designing and training FCNs.


title: Semantic Method for Query Expansion in an Intelligent Search System

abstract: Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search effectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion methods have been extensively studied in information retrieval despite the recent advances in search quality; the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results.One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. Query expansion is one such method for enhancing user query to improve search engine performance and satisfy the user need.Adaptive query expansion (QE) allows users to better define their search domain by supplementing the original query with additional terms related to their preferences and information needs. In this work wepropose a novel semantic method based query expansion technique using WordNet, which allows disambiguating queries submitted to search engines. This technique can be seen to significantly improve search engine performance, particularly recall.


title: OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media

abstract: It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.


title: Detecting and Preventing IP-spoofed Distributed DoS Attacks

abstract: In this paper, we explore mechanisms for defending against Distributed Denial of Service (DDoS) attacks, have become one of the major threats to the operation of the Internet today. We propose a novel scheme for detecting and preventing the most harmful and di‐cult to detect DDoS Attacks|those that use IP address spoofing to disguise the attack ∞ow. Our scheme is based on a flrewall that can distinguish the attack packets (containing spoofed source addresses) from the packets sent by legitimate users, and thus fllters out most of the attack packets before they reach the victim. Unlike the other packet-marking based solutions, our scheme has a very low deployment cost; We estimate that an implementation of this scheme would require the cooperation of only about 20% of the Internet routers in the marking process. The scheme allows the flrewall system to conflgure itself based on the normal tra‐c of a Web server, so that the occurrence of an attack can be quickly and precisely detected. We have extensively tested our scheme by simulating DDoS attacks with up to several thousand attackers and the experimental results show that more than 90% of attack packets can be efiectively flltered-out without much afiecting the ∞ow of legitimate packets to the victim Web-server.


title: Unsupervised feature learning by augmenting single images

abstract: When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main component of an unsupervised feature learning architecture. To that end we sample a set of random image patches and declare each of them to be a separate single-image surrogate class. We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches. Finally we train a convolutional neural network to discriminate between these surrogate classes. The feature representation learned by the network can then be used in various vision tasks. We find that this simple feature learning algorithm is surprisingly successful, achieving competitive classification results on several popular vision datasets (STL-10, CIFAR-10, Caltech-101).


title: Comparative analysis of underwater image enhancement methods in different color spaces

abstract: A difficult challenge in obtaining clear images in underwater environment is because of poor visibility of objects due to light attenuation and color distortion. A solution to this is to do some form of enhancement of the image that eventually leads to better visualization. This paper presents a comparative analysis of three different enhancements techniques: contrast stretching (CS), histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in the RGB and HSV color spaces for underwater images. Besides visual inspection, two different quantitative performance evaluation metrics, namely the Edge Contrast (EC) and BRISQUE, are used to assess the quality of the enhanced underwater images. Experimental results show that the CLAHE method performs better than CS and HE methods in both color spaces from the quality scores obtained in the EC as well as with the subjective evaluations.


title: Computer Vision Onboard UAVs for Civilian Tasks

abstract: Computer vision is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs’ functionality because of the big amount of information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that vision is our main interface to world understanding. Our current research’s focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e.g. when signal dropouts occur (which usually happens in urban areas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets—including moving ones—without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer vision for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this study will encompass power line inspection, mobile target tracking, stereo distance estimation, mapping and positioning.


title: Self-Organizing Graphs - A Neural Network Perspective of Graph Layout

abstract: The paper presents self-organizing graphs, a novel approach to graph layout based on a competitive learning algorithm. This method is an extension of self-organization strategies known from unsupervised neural networks, namely from Kohonen's self-organizing map. Its main advantage is that it is very flexibly adaptable to arbitrary types of visualization spaces, for it is explicitly parameterized by a metric model of the layout space. Yet the method consumes comparatively little computational resources and does not need any heavy-duty preprocessing. Unlike with other stochastic layout algorithms, not even the costly repeated evaluation of an objective function is required. To our knowledge this is the first connectionist approach to graph layout. The paper presents applications to 2D-layout as well as to 3D-layout and to layout in arbitrary metric spaces, such as networks on spherical surfaces.


title: Smart traffic optimization using image processing

abstract: Currently the traffic control system in place in our country is non-flexible and non-adaptive to the ever growing number of vehicles on the road. It does not take into account the changing density of traffic during the different hours of the day. Consequently the roads get congested frequently and intersections get blocked. Time and fuel, two highly important resources get wasted in this inefficient working of the present-day system. In this article, we propose a dynamic system that overcomes all these drawbacks. Our system uses cameras installed at the red lights and intersections to monitor the traffic dynamically. It then processes this information using image processing, computes the volume of the real time traffic, sets the timer of the signal accordingly. Simultaneously, it monitors if there is any scope of congestion at the intersection and adjusts the timer to prevent it. The entire system works autonomously and has a quick turnaround time, saving critical resources at every junction. The system also has the potential to adopt machine learning techniques in order to recognize the different emerging patterns of the future traffic and reach an optimal solution.


Anomaly Detection in Industrial Software Systems - Using Variational Autoencoders


title: Iso 24617-2: A semanticallybased standard for dialogue annotation

abstract: This paper summarizes the latest, final version of ISO standard 24617-2 “Semantic annotation framework, Part 2: Dialogue acts”. Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of ‘ideal concrete syntax’. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.


title: Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning

abstract: Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing result using a word analogy prediction formulation and hand-selected relations, but the generality of the finding over a broader range of lexical relation types and different learning settings has not been evaluated. In this paper, we carry out such an evaluation in two learning settings: (1) spectral clustering to induce word relations, and (2) supervised learning to classify vector differences into relation types. We find that word embeddings capture a surprising amount of information, and that, under suitable supervised training, vector subtraction generalises well to a broad range of relations, including over unseen lexical items.


INPP 5 E mutations cause primary cilium signaling defects , ciliary instability and ciliopathies in human and mouse


title: IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery

abstract: IBM's Physical Analytics Integrated Data Repository and Services (PAIRS) is a geospatial Big Data service. PAIRS contains a massive amount of curated geospatial (or more precisely spatio-temporal) data from a large number of public and private data resources, and also supports user contributed data layers. PAIRS offers an easy-to-use platform for both rapid assembly and retrieval of geospatial datasets or performing complex analytics, lowering time-to-discovery significantly by reducing the data curation and management burden. In this paper, we review recent progress with PAIRS and showcase a few exemplary analytical applications which the authors are able to build with relative ease leveraging this technology.


title: Preliminary comparison of techniques for dealing with imbalance in software defect prediction

abstract: Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step. Further Results and replication package: http://www.cc.uah.es/drg/ease14


title: Affective computing: a review

abstract: Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.


title: Rotation-Discriminating Template Matching Based on Fourier Coefficients of Radial Projections with Robustness to Scaling and Partial Occlusion

abstract: We consider brightness/contrast-invariant and rotation-discriminating template matching that searches an image to analyze A for a query image Q. We propose to use the complex coefficients of the discrete Fourier transform of the radial projections to compute new rotation-invariant local features. These coefficients can be efficiently obtained via FFT. We classify templates in ''stable'' and ''unstable'' ones and argue that any local feature-based template matching may fail to find unstable templates. We extract several stable sub-templates of Q and find them in A by comparing the features. The matchings of the sub-templates are combined using the Hough transform. As the features of A are computed only once, the algorithm can find quickly many different sub-templates in A, and it is suitable for finding many query images in A, multi-scale searching and partial occlusion-robust template matching.


title: A New Anchor-Labeling Method For Oriented Text Detection Using Dense Detection Framework

abstract: This letter proposes a new method for dense scene text detection anchor box labeling using single-shot multibox detection (SSD) as the base framework and VGG16 as the backbone, enhanced for scene text detection. This method can be further generalized to other detection tasks with various aspect ratios. We argue that the IoU criterion used by the dense object detection framework may have low recall ratios in extreme aspect ratio cases and oriented objects, and we propose a new criterion of the anchor-labeling method for these kinds of objects. The result shows that this method has better performance on public datasets compared with the previous labeling methods.


title: Deep Learning Based Attribute Classification Insider Threat Detection for Data Security

abstract: With the evolution of network threat, identifying threat from internal is getting more and more difficult. To detect malicious insiders, we move forward a step and propose a novel attribute classification insider threat detection method based on long short term memory recurrent neural networks (LSTM-RNNs). To achieve high detection rate, event aggregator, feature extractor, several attribute classifiers and anomaly calculator are seamlessly integrated into an end-to-end detection framework. Using the CERT insider threat dataset v6.2 and threat detection recall as our performance metric, experimental results validate that the proposed threat detection method greatly outperforms k-Nearest Neighbor, Isolation Forest, Support Vector Machine and Principal Component Analysis based threat detection methods.


title: An Event-Related Potential (ERP) Analysis of Semantic Congruity and Repetition Effects in Sentences

abstract: In two experiments, event-related brain potentials (ERPs) and cued-recall performance measures were used to examine the consequences of semantic congruity and repetition on the processing of words in sentences. A set of sentences, half of which ended with words that rendered them semantically incongruous, was repeated either once (eg, Experiment 1) or twice (e.g., Experiment 2). After each block of sentences, subjects were given all of the sentences and asked to recall the missing final words. ::: ::: Repetition benefited the recall of both congruous and incongruous endings and reduced the amplitude and shortened the duration of the N400 component of the ERP more for (1) incongruous than congruous words, (2) open class than closed class words, and (3) low-frequency than high-frequency open class words. For incongruous sentence terminations, repetition increased the amplitude of a broad positive component subsequent to the N400. ::: ::: Assuming additive factors logic and a traditional view of the lexicon, our N400 results indicate that in addition to their singular effects, semantic congruiry, repetition, and word frequency converge to influence a common stage of lexical processing. Within a parallel distributed processing framework, our results argue for substantial temporal and spatial overlap in the activation of codes subserving word recognition so as to yield the observed interactions of repetition with semantic congruity, lexical class, and word frequency effects.


title: Pulsatile blood flow in human bone assessed by laser-Doppler flowmetry and the interpretation of photoplethysmographic signals

abstract: Human bone blood flow, mean blood speed and the number of moving red blood cells were assessed (in arbitrary units), as a function of time, during one cardiac cycle. The measurements were obtained non-invasively on five volunteers by laser-Doppler flowmetry at large interoptode spacing. The investigated bones included: patella, clavicle, tibial diaphysis and tibial malleolus. As hypothesized, we found that in all bones the number of moving cells remains constant during cardiac cycles. Therefore, we concluded that the pulsatile nature of blood flow must be completely determined by the mean blood speed and not by changes in blood volume (vessels dilation). Based on these results, it is finally demonstrated using a mathematical model (derived from the radiative transport theory) that photoplethysmographic (PPG) pulsations observed by others in the literature, cannot be generated by oscillations in blood oxygen saturation, which is physiologically linked to blood speed. In fact, possible oxygen saturation changes during pulsations decrease the amplitude of PPG pulsations due to specific features of the PPG light source. It is shown that a variation in blood oxygen saturation of 3% may induce a negative change of ∼1% in the PPG signal. It is concluded that PPG pulsations are determined by periodic ‘positive’ changes of the reduced scattering coefficient of the tissue and/or the absorption coefficient at constant blood volume. No explicit experimental PPG measurements have been performed. As a by-product of this study, an estimation of the arterial pulse wave velocity obtained from the analysis of the blood flow pulsations give a value of 7.8 m s −1 (95% confidence interval of the sample mean distribution: [6.7, 9.5] m s −1 ), which is perfectly compatible with data in the literature. We hope that this note will contribute to


title: Performance Enhancement following a Strength and Injury Prevention Program: A 26-Week Individualized and Supervised Intervention in Adolescent Female Volleyball Players:

abstract: The aim of this study was to evaluate the effects of a 26-weekindividualized and supervised strength and injury prevention program onperformance enhancement in young female volleyball players whocompleted resistance training with either a supervised and individualizedtraining program (experimental group, n=10) or an unsupervised nonindividualizedtraining program (control group, n=17). Exposure and injurydata were collected during the 2006-2007 season (baseline season) andthe 26-week program was conducted during the 2007-2008 season(intervention season). All players were tested for physical performance. Atpost-test, the players in the experimental group had improved significantlymore than the players in the control group for squat (p<0.0001), benchpress (p=0.048), push-ups (p=0.02) and sit-ups (p<0.0001) but not for thevertical jump test. Thirty-five percent (6/17) of the players from the controlgroup and 80% (8/10) of the players in the experimental group completedthe resistance training with compliance of no less than 50%. The presentstudy shows the importance of individualization and supervision forresistance training in young female athletes when it comes to compliance,strength gains and performance.


title: NORAX: Enabling Execute-Only Memory for COTS Binaries on AArch64

abstract: Code reuse attacks exploiting memory disclosure vulnerabilities can bypass all deployed mitigations. One promising defense against this class of attacks is to enable execute-only memory (XOM) protection on top of fine-grained address space layout randomization (ASLR). However, recent works implementing XOM, despite their efficacy, only protect programs that have been (re)built with new compiler support, leaving commercial-off-the-shelf (COTS) binaries and source-unavailable programs unprotected. We present the design and implementation of NORAX, a practical system that retrofits XOM into stripped COTS binaries on AArch64 platforms. Unlike previous techniques, NORAX requires neither source code nor debugging symbols. NORAX statically transforms existing binaries so that during runtime their code sections can be loaded into XOM memory pages with embedded data relocated and data references properly updated. NORAX allows transformed binaries to leverage the new hardware-based XOM support—a feature widely available on AArch64 platforms (e.g., recent mobile devices) yet virtually unused due to the incompatibility of existing binaries. Furthermore, NORAX is designed to co-exist with other COTS binary hardening techniques, such as in-place randomization (IPR). We apply NORAX to the commonly used Android system binaries running on SAMSUNG Galaxy S6 and LG Nexus 5X devices. The results show that NORAX on average slows down the execution of transformed binaries by 1.18% and increases their memory footprint by 2.21%, suggesting NORAX is practical for real-world adoption.


title: Semi-Supervised Deep Learning for Monocular Depth Map Prediction

abstract: Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the measurements are typically much sparser than the camera images. In this paper, we propose a novel approach to depth map prediction from monocular images that learns in a semi-supervised way. While we use sparse ground-truth depth for supervised learning, we also enforce our deep network to produce photoconsistent dense depth maps in a stereo setup using a direct image alignment loss. In experiments we demonstrate superior performance in depth map prediction from single images compared to the state-of-the-art methods.


title: The English Resultative as a Family of Constructions

abstract: English resultative expressions have been a major focus of research on the syntax-semantics ::: interface. We argue in this article that a family of related constructions is required to account for ::: their distribution. We demonstrate that a number of generalizations follow from the semantics of ::: the constructions we posit: the syntactic argument structure of the sentence is predicted by general ::: principles of argument linking; and the aspectual structure of the sentence is determined by the ::: aspectual structure of the constructional subevent, which is in turn predictable from general principles ::: correlating event structure with change, extension, motion, and paths. Finally, the semantics ::: and syntax of resultatives explain the possibilities for temporal relations between the two subevents. ::: While these generalizations clearly exist, there is also a great deal of idiosyncrasy involved ::: in resultatives. Many idiosyncratic instances and small subclasses of the construction must be ::: learned and stored individually. This account serves to justify aspects of what we share in our ::: overall vision of grammar, whatwe might call the CONSTRUCTIONAL view. To the extent that our ::: treatment of the resultative can be stated only within the constructional view, it serves as evidence ::: for this view as a whole.


title: Assessing Problematic Video Gaming Using the Theory of Planned Behavior: A Longitudinal Study of Dutch Young People

abstract: Although excessive video gaming has been linked to a range of psychological problems in young people, there have been few systematic attempts to conceptualize problem gaming using established psychological theory. The aim of this study was to examine problematic game use (PGU) using the Theory of Planned Behavior (TPB). A two-wave, six-month longitudinal study examined relationships between core components of the TPB model, video gaming activity and problematic video-game play. Respondents were recruited from nine pre-vocational and senior vocational schools in the western region of the Netherlands. The sample consisted of 810 video game-playing adolescents and young adults (72.8 % boys) aged 12 to 22 years. The results showed that TPB predictors, including subjective norm, perceived behavioral control (PBC) and descriptive norm, explained 13 % of the variance in video gaming intention. Although TBP variables accounted for a significant amount of variance in PGU scores at baseline, the TPB model was less useful in predicting future gaming behavior and PGU. Perceived behavioral control was found to be the most important factor in predicting problem video-gaming behavior, this has some practical implications with regard to the treatment of problem video-gaming among young people. For example, assessing a client’s perceived lack of control over gaming may be a simple but useful screening measure to evaluate risk of future problem play. Furthermore, treatment strategies may be aimed at helping the client to rebuild self-control.


title: Enhanced sensing properties of SnO2 nanofibers with a novel structure by carbonization

abstract: Abstract Carbonization followed by calcination in air has been developed to synthesize porous SnO2 based nanofibers. The precursor nanofibers were synthesized through electrospinning. The optimal synthesized condition was investigated according to the gas sensing properties of pure SnO2 nanofibers through adjusting the heating rate in air. Gas sensors based on 1% Pd doped SnO2 nanofibers under this optimal synthesized condition were fabricated, and the gas sensing properties was systematically investigated. The experimental results indicate that all the carbonized nanofibers have a porous microstructure. For the pure SnO2 nanofibers, carbonized nanofibers also have a hollow structure. At the optimal synthesized condition, the sensor based on the carbonized SnO2 nanofibers Exhibits 2.6 times higher response (20.4) than pristine pure SnO2 nanofibers (7.7) toward 100 ppm ethanol. However, the selectivity is almost unchanged. For the Pd doped SnO2 nanofibers, the gas response is improved from 10 to 24.6 to 100 ppm toluene at optimum operation temperature under the carbonization process. The sensors also exhibit a low detecting limit (1.6–500 ppb toluene) and a short response time (∼3 s). The evolution process and the formation mechanism were also discussed.


title: Application of Edge Detection for Brain Tumor Detection

abstract: Brain tumors are created by abnormal and uncontrolled cell division in brain itself. If the growth becomes more than 50%, then the patient is not able to recover. So the detection of brain tumor needs to be fast and accurate. The objective of this paper is to provide an efficient algorithm for detecting the edges of brain tumor. The first step starts with the acquisition of MRI scan of brain and then digital imaging techniques are applied for getting the exact location and size of tumor. MRI images consist of gray and white matter and the region containing tumor has more intensity. So, first noise filters are used for noise removal and then enhancement techniques are applied to the given MRI scan of brain. After that the basic morphological operations are applied for extracting the region suffering from tumor. And then verification of region detected is done by using watershed segmentation.


title: SybilInfer: Detecting Sybil Nodes using Social Networks

abstract: SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.


EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks


title: Weakly Supervised Learning of Single-Cell Feature Embeddings

abstract: We study the problem of learning representations for single cells in microscopy images to discover biological relationships between their experimental conditions. Many new applications in drug discovery and functional genomics require capturing the morphology of individual cells as comprehensively as possible. Deep convolutional neural networks (CNNs) can learn powerful visual representations, but require ground truth for training; this is rarely available in biomedical profiling experiments. While we do not know which experimental treatments produce cells that look alike, we do know that cells exposed to the same experimental treatment should generally look similar. Thus, we explore training CNNs using a weakly supervised approach that uses this information for feature learning. In addition, the training stage is regularized to control for unwanted variations using mixup or RNNs. We conduct experiments on two different datasets; the proposed approach yields single-cell embeddings that are more accurate than the widely adopted classical features, and are competitive with previously proposed transfer learning approaches.


title: Evaluation of the vulnerability of phasor measurement units to GPS spoofing attacks

abstract: Abstract Results of Global Positioning System (GPS) spoofing tests against phasor measurement units (PMUs) are presented, which demonstrate that PMUs are vulnerable to spoofing attacks. A GPS spoofer can manipulate PMU time stamps by injecting a counterfeit ensemble of GPS signals into the antenna of the PMU's time reference receiver. A spoofer-induced timing error of only a few tens of microseconds causes a PMU to violate the maximum phase error allowed by the applicable standard. These and other larger errors can give automated or human power grid controllers a false perception of the state of the grid, leading to unnecessary, and possibly destabilizing, remedial control actions. To emphasize this threat, this paper shows that a particular PMU-based automatic control scheme currently implemented in Mexico whose control architecture and setpoints have been published in the open literature could be induced by a GPS spoofing attack to trip a primary generator.


title: Distributed Quality-of-Service Routing in Ad Hoc Networks

abstract: In an ad hoc network, all communication is done over wireless media, typically by radio through the air, without the help of wired base stations. Since direct communication is allowed only between adjacent nodes, distant nodes communicate over multiple hops. The quality-of-service (QoS) routing in an ad hoc network is difficult because the network topology may change constantly, and the available state information for routing is inherently imprecise. In this paper, we propose a distributed QoS routing scheme that selects a network path with sufficient resources to satisfy a certain delay (or bandwidth) requirement in a dynamic multihop mobile environment. The proposed algorithms work with imprecise state information. Multiple paths are searched in parallel to find the most qualified one. Fault-tolerance techniques are brought in for the maintenance of the routing paths when the nodes move, join, or leave the network. Our algorithms consider not only the QoS requirement, but also the cost optimality of the routing path to improve the overall network performance. Extensive simulations show that high call admission ratio and low-cost paths are achieved with modest routing overhead. The algorithms can tolerate a high degree of information imprecision.


title: Multi-Objective Non-parametric Sequential Prediction

abstract: Online-learning research has mainly been focusing on minimizing one objective function. In many real-world applications, however, several objective functions have to be considered simultaneously. Recently, an algorithm for dealing with several objective functions in the i.i.d. case has been presented. In this paper, we extend the multi-objective framework to the case of stationary and ergodic processes, thus allowing dependencies among observations. We first identify an asymptomatic lower bound for any prediction strategy and then present an algorithm whose predictions achieve the optimal solution while fulfilling any continuous and convex constraining criterion.


title: An Exploratory Study on Drivers and Deterrents of Collaborative Consumption in Travel

abstract: Due to the rise of businesses utilizing the sharing economy concept, it is important to better understand the motivational factors that drive and hinder collaborative consumption in the travel and tourism marketplace. Based on responses from 754 adult travellers residing in the US, drivers and deterrents of the use of peer-to-peer accommodation rental services were identified. Factors that deter the use of peer-to-peer accommodation rental services include lack of trust, lack of efficacy with regards to technology, and lack of economic benefits. The motivations that drive the use of peer-to-peer accommodation include the societal aspects of sustainability and community, as well as economic benefits. Based on the empirical evidence, this study suggests several propositions for future studies and implications for tourism destinations and hospitality businesses on how to manage collaborative consumption.


title: Gender progress and government expenditure as determinants of femicide

abstract: Purpose We sought to explore the effect of economic/political factors and gender progress on femicide. Methods An ecological and retrospective study was undertaken that focused on 61 countries and analyzed the relationships of femicide with the following statistics from the period 1990 to 1999: economic indicators (domestic consumption, gross capital formation, imports and exports per capita, unemployment rate and percentage of urban population), political indicators (government final consumption expenditure, GINI coefficient—a summary measure of the extent to which the actual distribution of income or consumption expenditure or a related variable differs from a hypothetical distribution in which each person receives an identical share—civil liberties and political rights index), and gender progress indicators (female and male unemployment rates, percentage of girls in primary education, gender ratio for primary and secondary education, and percentage of parliamentary seats occupied by women). Bivariate and multivariate logistic regression analyses (likelihood ratio) were performed to explore the relationships between these variables. Results The bivariate analysis revealed strong links between reductions in government final consumption expenditure per capita (odds ratio [OR] 20.83;95% confidence interval [95% CI] 5.622–77.205), domestic consumption and gross capital formation (both with OR 16.67, 95% CI 4.715–58.911), and the civil liberties and political rights index (OR 7.91, 95% CI 2.526–24.747). In the multivariate stage, statistically significant associations were only observed between government expenditure per capita (OR 61.75;95% CI 7.064–539.81) and occupation of parliamentary seats by women (OR 10.95;95% CI 1.26–95.06). Conclusion The reduction in government final consumption expenditure and democratic backwardness in terms of gender equality appear to be relevant factors in deaths caused by gender-based violence. To fight femicide effectively, gender-related structural, political, and economic responses should be considered.


title: Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks

abstract: This paper introduces new analytical techniques for performing vulnerability analysis of state estimation when it is subject to a hidden false data injection cyber-attack on a power grid's SCADA system. Specifically, we consider ac state estimation and describe how the physical properties of the system can be used as an advantage in protecting the power system from such an attack. We present an algorithm based on graph theory which allows determining how many and which measurement signals an attacker will attack in order to minimize his efforts in keeping the attack hidden from bad data detection. This provides guidance on which measurements are vulnerable and need increased protection. Hence, this paper provides insights into the vulnerabilities but also the inherent strengths provided by ac state estimation and network topology features such as buses without power injections.


Shanghai Modern: Reflections on Urban Culture in China in the 1930s


title: Combining contour and shape primitives for object detection and pose estimation of prefabricated parts

abstract: Man-made objects such as mechanical construction parts can typically be described as a composition of shape primitives like cylinders, planes, cones and spheres. We propose a robust method for the detection and pose estimation of such objects in 3D point clouds. Our main contribution is to enhance a probabilistic graph-matching approach that detects objects using 3D shape primitives with distinct 2D primitives such as circular contours. With this extension, our method copes with difficult occlusion situations and can be applied for object manipulation in complex scenarios such as grasping from a pile or bin-picking. We demonstrate the performance of our approach in a comparison with a state-of-the-art feature-based method for objects of generic shape and a primitive-based approach using only 3D shapes and no contours.


title: Augmenting data center networks with multi-gigabit wireless links

abstract: The 60 GHz wireless technology that is now emerging has the potential to provide dense and extremely fast connectivity at low cost. In this paper, we explore its use to relieve hotspots in oversubscribed data center (DC) networks. By experimenting with prototype equipment, we show that the DC environment is well suited to a deployment of 60GHz links contrary to concerns about interference and link reliability. Using directional antennas, many wireless links can run concurrently at multi-Gbps rates on top-of-rack (ToR) switches. The wired DC network can be used to sidestep several common wireless problems. By analyzing production traces of DC traffic for four real applications, we show that adding a small amount of network capacity in the form of wireless flyways to the wired DC network can improve performance. However, to be of significant value, we find that one hop indirect routing is needed. Informed by our 60GHz experiments and DC traffic analysis, we present a design that uses DC traffic levels to select and adds flyways to the wired DC network. Trace-driven evaluations show that network-limited DC applications with predictable traffic workloads running on a 1:2 oversubscribed network can be sped up by 45% in 95% of the cases, with just one wireless device per ToR switch. With two devices, in 40% of the cases, the performance is identical to that of a non-oversubscribed network.


title: Determination of 10 amide pesticides in fruits and vegetables by liquid chromatography-tandem mass spectrometry

abstract: Objective To establish a method for the determination of alachlor, acetochlor, metolachlor, napropamid, boscalid, fenhexamid, metalaxyl, tebufenpyrad, tolfenpyrad, and propyzamide in fruits and vegetables by liquid chromatography-tandem mass spectrometry. Methods Ten amide pesticides were extracted from samples with acetonitrile, cleaned up by solid-phase extraction, separated on a C18 column using 0.1% formic acid-5 mmol/L ammonium acetate solution-methanol as mobile phase, and then determined by tandem mass spectrometry. An external standard method was used for quantitative analysis. Results The calibration curves for all amide pesticides were linear in the range of 2.0~50 μg/L, the correlation coefficient was more than 0.99, and the limit of quantitation for all amide pesticides was 0.01 mg/kg. The recoveries were between 71% and 98% with relative standard deviations no more than 9.5%. Conclusion This method is suitable for the determination of 10 amide pesticides in fruits and vegetables at the same time.


title: Deep learning for single-molecule science

abstract: Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data.


A COMPARISON BETWEEN HYBRID APPROACHES OF ANN AND ARIMA FOR INDIAN STOCK TREND FORECASTING


title: Distributed and decentralized multi-camera tracking: a survey

abstract: In recent years the decreasing cost of cameras and advances in miniaturization have favoured the deployment of largescale camera networks. This growing number of cameras enables new signal processing applications that use cooperatively multiple sensors over wide areas. In particular, object tracking is an important step in many applications related to security, traffic monitoring and event recognition. Such applications require the optimal trade-off between accuracy, communication and computing across the network. The costs associated to communication and computing depend on the type and amount of cooperation performed among cameras for information gathering, sharing and processing to validate decisions as well as to rectify (or to reduce) estimation errors and uncertainties. In this survey we discuss data fusion and tracking methods for camera networks and compare their performance. In particular we cover decentralized and distributed trackers and the challenges to be addressed for the design of accurate and energy-efficient algorithms.


title: The case for smartwatch-based diet monitoring

abstract: We explore the use of gesture recognition on a wrist-worn smartwatch as an enabler of an automated eating activity (and diet monitoring) system. We show, using small-scale user studies, how it is possible to use the accelerometer and gyroscope data from a smartwatch to accurately separate eating episodes from similar non-eating activities, and to additionally identify the mode of eating (i.e., using a spoon, bare hands or chopsticks). Additionally, we investigate the likelihood of automatically triggering the smartwatch's camera to capture clear images of the food being consumed, for possible offline analysis to identify what (and how much) the user is eating. Our results show both the promise and challenges of this vision: while opportune moments for capturing such useful images almost always exist in an eating episode, significant further work is needed to both (a) correctly identify the appropriate instant when the camera should be triggered and (b) reliably identify the type of food via automated analyses of such images.


title: Global and Local Information Based Deep Network for Skin Lesion Segmentation

abstract: With a large influx of dermoscopy images and a growing shortage of dermatologists, automatic dermoscopic image analysis plays an essential role in skin cancer diagnosis. In this paper, a new deep fully convolutional neural network (FCNN) is proposed to automatically segment melanoma out of skin images by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN is capable of using both local and global information to segment melanoma by adopting skipping layers. The public benchmark database consisting of 150 validation images, 600 test images and 2000 training images in the melanoma detection challenge 2017 at International Symposium Biomedical Imaging 2017 is used to test the performance of our algorithm. All large size images (for example, $4000\times 6000$ pixels) are reduced to much smaller images with $384\times 384$ pixels (more than 10 times smaller). We got and submitted preliminary results to the challenge without any pre or post processing. The performance of our proposed method could be further improved by data augmentation and by avoiding image size reduction.


title: A Little Is Enough: Circumventing Defenses For Distributed Learning

abstract: Distributed learning is central for large-scale training of deep-learning models. However, it is exposed to a security threat in which Byzantine participants can interrupt or control the learning process. Previous attack models assume that the rogue participants (a) are omniscient (know the data of all other participants), and (b) introduce large changes to the parameters. Accordingly, most defense mechanisms make a similar assumption and attempt to use statistically robust methods to identify and discard values whose reported gradients are far from the population mean. We observe that if the empirical variance between the gradients of workers is high enough, an attacker could take advantage of this and launch a non-omniscient attack that operates within the population variance. We show that the variance is indeed high enough even for simple datasets such as MNIST, allowing an attack that is not only undetected by existing defenses, but also uses their power against them, causing those defense mechanisms to consistently select the byzantine workers while discarding legitimate ones. We demonstrate our attack method works not only for preventing convergence but also for repurposing of the model behavior (``backdooring''). We show that less than 25\% of colluding workers are sufficient to degrade the accuracy of models trained on MNIST, CIFAR10 and CIFAR100 by 50\%, as well as to introduce backdoors without hurting the accuracy for MNIST and CIFAR10 datasets, but with a degradation for CIFAR100.


Explainable Artificial Intelligence via Bayesian Teaching


title: Sequence-aware Recommender Systems

abstract: The majority of research works in the field of collaborative filtering recommender systems is based on the assumption that the input to the recommendation algorithms is a matrix containing user-item interactions. In reality, however, the input often is a sequence of various types of user-item interactions that are recorded over time and where we can have multiple data points per user-item pair. These sequential logs contain a variety of useful information that can be leveraged in the recommendation process, e.g., to predict the immediate next action of a user or to detect short-term trends in the community. In this tutorial we review what we call sequence-aware recommenders, i.e., approaches that aim to exploit the information in sequential interaction logs for a variety of different purposes. We in particular focus on sequential and session-based recommendation techniques and discuss algorithmic proposals as well as evaluation challenges.


title: fMRI evidence for both generalized and specialized components of attentional control

abstract: A central question in the study of selective attention is whether top-down attentional control mechanisms are generalized or specialized for the type of information that is to be attended. The current study examined this question using a voluntary orienting task that cued observers to attend to either one of two locations or to one of two colors. Location (spatial) and color (nonspatial) conditions were presented either randomly intermixed within the same block of trials or in separate blocks. Functional magnetic resonance imaging revealed that directing attention to a location or to a color activated a network of overlapping dorsal frontal and parietal areas, previously implicated in attentional control. The pattern of observed overlap was not affected by the intermixed versus blocked presentation of location and color conditions. Although portions of the frontal-parietal network were more active in response to location cues than to color cues, a secondary analysis also revealed that medial dorsal frontal and parietal cortex were specifically engaged in shifting visual attention regardless of the cued dimension (location or color). Together, the present results support the conclusion that attentional control is the combination of a generalized network that works in concert with subregions of the frontoparietal network that are highly specialized for directing attention based on the content of the to-be-attended information.


title: Business-IT alignment maturity of companies in China

abstract: Business-IT alignment has been consistently ranked as the number one concern of IT and business executives. Maintaining the alignment has been found to lead to stronger business performance, higher perceived value of IT and improved IS strategic planning; therefore addressing the issue is both important and timely. The economic growth in China in the last decade has led to greater availability of IT and its widespread use as a critical resource. Despite the increasing interest in Chinese IS issues by both researchers and practitioners, empirical research focusing on human- and organization-related IS issues in China is scarce. Using data collected from 130 business and IT executives from 22 companies in China, 11 of which were multinationals operating in China, we explored several questions in the area of business-IT alignment. An instrument designed to measure the six dimensions of business-IT alignment maturity was cross-validated using a sample of companies in China. Then, the instrument was refined to develop a measurement method that offered better reliability and validity in the context of Chinese companies. The relationship between the alignment maturity dimensions and IS strategic alignment was then examined. Finally, the alignment maturity of companies in China was assessed to provide a snapshot of business-IT alignment in China. A comparison between the 11 Chinese domestic companies and 11 multinational operating in China provided insight into the different IT practices of the two types of companies. Implications for researchers and practitioners are discussed.


title: A randomized, single-blind, postmarketing study of multiple energy levels of high-intensity focused ultrasound for noninvasive body sculpting.

abstract: BACKGROUND ::: High-intensity focused ultrasound (HIFU) is a nonsurgical, noninvasive body sculpting method. ::: ::: ::: OBJECTIVE ::: To investigate preferences for treatment settings using a HIFU device. ::: ::: ::: MATERIALS AND METHODS ::: HIFU was applied to the anterior abdomen in three passes of decreasing depth (1.6, 1.3, and 1.1 cm) in patients randomized to HIFU energy levels (each of 3 passes [total]) of 47 (141), 52 (156), or 59 (177) J/cm(2). The primary assessment was week 12 post-treatment change from baseline waist circumference at the level of the iliac crest for all treatment groups combined. ::: ::: ::: RESULTS ::: The primary assessment achieved statistical significance (least squares mean 2.51 cm, 95% confidence interval [CI] = -3.14 to -1.88; p < .001), with no significant differences between groups. At week 12, 69% to 86% of patients and 73% to 79% of investigators rated appearance as improved or much improved. The average worst pain (100-mm visual analog scale) experienced during treatment was mild (47 J/cm(2): 17.1 mm, 95% CI = 4.33-29.81 mm; 52 J/cm(2): 24.6 mm, 95% CI = 12.24-36.95 mm; 59 J/cm(2): 30.9 mm, 95% CI = 18.71-43.17 mm). There were no serious adverse events. ::: ::: ::: CONCLUSION ::: HIFU treatment at different energy levels and multiple tissue depths was well tolerated and effective in reducing waist circumference.


title: On-line identification of PMSM electrical parameters based on decoupling control

abstract: In this paper, an online identification method of a PMSM's (permanent magnet synchronous motor) electrical parameters based on decoupling control is proposed. Its practical implementation is very simple, especially in the case of a nonsalient pole PMSM. A global stability study based on the second method of Lyapunov guarantees the convergence of the estimated parameters under certain conditions. The simulation results as well as the experimental ones illustrate the validity of the analytic approach and the efficiency of the proposed method.


title: A systematic mapping study on the open source software development process

abstract: Background: There is no globally accepted open source software development process to define how open source software is developed in practice. A process description is important for coordinating all the software development activities involving both people and technology. Aim: The research question that this study sets out to answer is: What activities do open source software process models contain? The activity groups on which it focuses are Concept Exploration, Software Requirements, Design, Maintenance and Evaluation. Method: We conduct a systematic mapping study (SMS). A SMS is a form of systematic literature review that aims to identify and classify available research papers concerning a particular issue. Results: We located a total of 29 primary studies, which we categorized by the open source software project that they examine and by activity types (Concept Exploration, Software Requirements, Design, Maintenance and Evaluation). The activities present in most of the open source software development processes were Execute Tests and Conduct Reviews, which belong to the Evaluation activities group. Maintenance is the only group that has primary studies addressing all the activities that it contains. Conclusions: The primary studies located by the SMS are the starting point for analyzing the open source software development process and proposing a process model for this community. The papers in our paper pool that describe a specific open source software project provide more regarding our research question than the papers that talk about open source software development without referring to a specific open source software project.


title: Publication Bias in Research Synthesis: Sensitivity Analysis Using A Priori Weight Functions.

abstract: Publication bias, sometimes known as the file-drawer problem or funnel-plot asymmetry, is common in empirical research. The authors review the implications of publication bias for quantitative research synthesis (meta-analysis) and describe existing techniques for detecting and correcting it. A new approach is proposed that is suitable for application to meta-analytic data sets that are too small for the application of existing methods. The model estimates parameters relevant to fixed-effects, mixed-effects or random-effects meta-analysis contingent on a hypothetical pattern of bias that is fixed independently of the data. The authors illustrate this approach for sensitivity analysis using 3 data sets adapted from a commonly cited reference work on research synthesis (H. M. Cooper & L. V. Hedges, 1994).


title: A Grounded Theory of Young Tennis Players’ Use of Music to Manipulate Emotional State

abstract: The main objectives of this study were (a) to elucidate young tennis players? use of music to manipulate emotional states, and (b) to present a model grounded in present data to illustrate this phenomenon and to stimulate further research. Anecdotal evidence suggests that music listening is used regularly by elite athletes as a preperformance strategy, but only limited empirical evidence corroborates such use. Young tennis players (N = 14) were selected purposively for interview and diary data collection. Results indicated that participants consciously selected music to elicit various emotional states; frequently reported consequences of music listening included improved mood, increased arousal, and visual and auditory imagery. The choice of music tracks and the impact of music listening were mediated by a number of factors, including extramusical associations, inspirational lyrics, music properties, and desired emotional state. Implications for the future investigation of preperformance music are discussed.


title: Wiener–Granger Causality in Network Physiology With Applications to Cardiovascular Control and Neuroscience

abstract: Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wiener–Granger causality (WGC) has been one of the most relevant concepts exploited by modern time series analysis. Indeed, in networks formed by multiple components, working according to the notion of segregation and interacting with each other according to the principle of integration, inferring causality has opened a window on the effective connectivity of the network and has linked experimental evidences to functions and mechanisms. This tutorial reviews predictability improvement, information-based and frequency domain methods for inferring WGC among physiological processes from multivariate realizations and quantifying the strength of the cause–effect relations in network physiology. Studies relevant to cardiovascular control and neuroscience are listed as examples of applications in prominent biomedical fields in which WGC analysis led to remarkable advancements in our knowledge. The review pays special attention to procedures for checking the reliability of the WGC approaches according to the statistical framework of hypothesis testing.


title: An end-to-end approach to host mobility

abstract: We present the design and implementation of an end-to-end architecture for Internet host mobility using dynamic updates to the Domain Name System (DNS) to track host location. Existing TCP connections are retained using secure and efficient connection migration, enabling established connections to seamlessly negotiate a change in endpoint IP addresses without the need for a third party. Our architecture is secure—name updates are effected via the secure DNS update protocol, while TCP connection migration uses a novel set of Migrate options—and provides a pure end-system alternative to routing-based approaches such as Mobile IP. Mobile IP was designed under the principle that fixed Internet hosts and applications were to remain unmodified and only the underlying IP substrate should change. Our architecture requires no changes to the unicast IP substrate, instead modifying transport protocols and applications at the end hosts. We argue that this is not a hindrance to deployment; rather, in a significant number of cases, it allows for an easier deployment path than Mobile IP, while simultaneously giving better performance. We compare and contrast the strengths of end-to-end and network-layer mobility schemes, and argue that end-to-end schemes are better suited to many common mobile applications. Our performance experiments show that hand-off times are governed by TCP migrate latencies, and are on the order of a round-trip time of the communicating peers.


title: Baseline CNN structure analysis for facial expression recognition

abstract: We present a baseline convolutional neural network (CNN) structure and image preprocessing methodology to improve facial expression recognition algorithm using CNN. To analyze the most efficient network structure, we investigated four network structures that are known to show good performance in facial expression recognition. Moreover, we also investigated the effect of input image preprocessing methods. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, difference of Gaussian) were tested, and the accuracy was compared. We trained 20 different CNN models (4 networks × 5 data input types) and verified the performance of each network with test images from five different databases. The experiment result showed that a three-layer structure consisting of a simple convolutional and a max pooling layer with histogram equalization image input was the most efficient. We describe the detailed training procedure and analyze the result of the test accuracy based on considerable observation.


title: Number plate recognition using template comparison for various fonts in MATLAB

abstract: In a diverse country like India, gated communities, corporate premises and university campuses witness a lot of unfamiliar vehicles with number plates in different formats, fonts, font sizes and sometimes even in various languages which enter and leave the estate every day, and it is difficult to register the vehicle numbers manually even for a multi-lingual person. This document aims to extract image frames from a streaming CCTV footage, recognize the vehicle number and convert it into its corresponding text format. Template matching has been used earlier in recognition of digits and letters, this paper also uses the concept of template matching methodology using a SIMULINK model in MATLAB which is developed to extract the vehicle number from the number plate. The alphanumeric values are extracted from the image containing the number plate, one after the other, and are matched with its corresponding template with the help of the template matching methodology. This template matching block compares each and every pixel value of the original image with the pixel values of the corresponding stored template and returns a template metrics value as an output. The templates stored in the model consists or various fonts in English as well as templates of some of the widely used languages (22 official languages) across India. The SIMULINK model uses the Digital Signal Processing Toolbox and the Computer System Vision Toolbox in MATLAB. Various factors affects the efficiency of this process such as luminance, blurred images, incomplete numbers or characters, damaged number plates, etc.


title: AN IMPROVED OF SPAM E-MAIL CLASSIFICATION MECHANISM USING K-MEANS CLUSTERING

abstract: Spam e-mails are considered as a serious violation of privacy. In addition, it has become costly and unwanted communication. Although, Support Vector Machine (SVM) has been widely used in e-mail spam detection, yet the problem of dealing with huge dat a is time and memory consuming and low accuracy. This study speeds up the computational time of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KSVM) algorithm proposed in this work. Furthermore, this paper proposes a mechanism for e-mail spam detection based on hybrid of SVM and K-means clustering and requires one more input parameter to be determined: the number of clusters. The experiment of the proposed mechanism was carried out using spambase standard dataset to evaluate the feasibility of the proposed method. The result of this hybrid method l ed to improved SVM classifier by reducing support vectors, increasing the accuracy and decreasing the time of e-mail spam detection. Experimental result s on spambase datasets showed that the improved SVM (KSVM) significantly outperforms SVM and many other recent spam detection methods in terms of cla ssification accuracy (effectiveness) and time consu ming (efficiency).


title: Remote Vehicle Diagnostics over the Internet using the DoIP Protocol

abstract: Next generation vehicles will provide powerful connectivity and telematics services, enabling many new applications of vehicle communication. We will in this paper study the opportunities of performing remote vehicle diagnostics, where the diagnostic tool (test equipment) and the vehicle are separated by an internetwork, e.g., the Internet. The development of a prototype system for remote vehicle diagnostics, based on the emerging Diagnostics over IP (DoIP) ISO standard, is presented and early usage experiments with synchronous remote diagnostic read-out and control are described. A number of safety related issues are identified that will need closer study before a broad deployment of remote diagnostics services is feasible. Furthermore, a classification of vehicle diagnostics applications is provided, which is intended to elucidate the differences between synchronous (online) and asynchronous (offline) operation in local and distributed settings.


title: Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery

abstract: Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps.


A Practical Client Application Based on Attribute Based Access Control for Untrusted Cloud Storage


title: Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua)

abstract: The daily average PM2.5 concentration forecast is a leading component nowadays in air quality research, which is necessary to perform in order to assess the impact of air on the health and welfare of every living being. The present work is aimed at analyzing and benchmarking a neural-network approach to the prediction of average PM2.5 concentrations. The model thus obtained will be indispensable, as a control tool, for the purpose of preventing dangerous situations that may arise. To this end we have obtained data and measurements based on samples taken during the early hours of the day. Results from three different topologies of neural networks were compared so as to identify their potential uses, or rather, their strengths and weaknesses: Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Square Multilayer Perceptron (SMLP). Moreover, two classical models were built (a persistence model and a linear regression), so as to compare their results with the ones provided by the neural network models. The results clearly demonstrated that the neural approach not only outperformed the classical models but also showed fairly similar values among different topologies. Moreover, a differential behavior in terms of stability and length of the training phase emerged during testing as well. The RBF shows up to be the network with the shortest training times, combined with a greater stability during the prediction stage, thus characterizing this topology as an ideal solution for its use in environmental applications instead of the widely used and less effective MLP.


title: A broadband Wilkinson balun using microstrip metamaterial lines

abstract: A metamaterial balun that converts a single-ended input to a differential output over a large bandwidth is presented. The device also exhibits excellent return loss, isolation, and through characteristics over the same frequency band. The balun comprises a Wilkinson divider, followed by a +90/spl deg/ negative-refractive-index (NRI) metamaterial (MM) phase-shifting line along the top branch, and a -90/spl deg/ MM phase-shifting line along the bottom branch. Utilizing MM lines for both the +90/spl deg/ and -90/spl deg/ branches allows the slopes of their phase responses to be matched, resulting in a broadband differential output signal. The theoretical performance of the balun is verified through circuit simulations and measurements of a fabricated prototype at 1.5 GHz. The MM balun exhibits a measured differential output phase bandwidth (180/spl deg//spl plusmn/10/spl deg/) of 1.16 GHz (77%), from 1.17 to 2.33 GHz. The measured isolation and return loss for all three ports remain below -10 dB over a bandwidth in excess of 2 GHz, while the output quantities |S/sub 21/| and |S/sub 31/| remain above -4 dB from 0.5 to 2.5 GHz.


Control of Pendulum on a Cart with State Dependent Riccati Equations