title: Limitations and future of electrocardiography devices: A review and the perspective from the Internet of Things

abstract: Electrocardiography (ECG) devices are considered essential medical tools for detecting and preventing cardiovascular diseases. These devices are used to obtain information about the structure and function of the human heart. Given the various medical uses of the information from ECG devices, it is essential that these devices provide accurate information about the heart in a precise manner. Although many advances in ECG devices have been made over the years, the literature reveals not only limitations in conventional devices but also highlights how the Internet of Things (IoT) has the potential to improve medical applications in ECG devices. This paper reviews previous works on ECG devices to identify their limitations as well as provides some insights into the development of IoT-based ECG devices.


title: Using XQuery for problem

abstract: XQuery is a recommendation by the World Wide Web Consortium as a standard XML query language. In addition to being a special-purpose query language, XQuery has also features that support unexpected applications such as problem solving. We demonstrate and discuss these features by presenting several XQuery programs for solving recreational problems and puzzles. Copyright © 2011 John Wiley & Sons, Ltd.


title: Fingerprint authentication and security risks in smart devices

abstract: The smartphone market is growing rapidly and enriching our digital lives by instant of information sharing. As smartphone devices contain sensitive data of users, it is very essential to protect this information efficiently. To fulfill this, modern smartphones are adopting fingerprint scanners for biometric authentication. Fingerprint biometric authentication is simple and quick, however, it is vulnerable to attacks as hackers can steel fingerprint biometric data. Therefore, a consistent fingerprint biometric authentication mechanism is required to protect personal information of the user as well as biometrics data. In this paper, we will analyze different attacks and security risks associated with fingerprint authentication. Previously, there are eight levels of attacks studied to compromise biometric data. Here, we propose two additional levels of attacks to provide a more robust mechanism to deal with security vulnerabilities in future.


title: Electro-thermal analysis of flexible micro-heater

abstract: Micro heaters have particular applications in the gas sensor and the biotechnological field. The micro heaters are used to obtain the desired constant temperatures over the entire heating surface. Additionally the power consumption should be as low as possible. In this paper we present the electro-thermal characterization of a rectangular pattern mesh micro heater placed on a flexible substrate. Electrical measurements are used as an input for thermal simulation of the heater system. IR measurements show the temperature distribution across the surface of the micro-heater. Thermal analysis is performed using steady state FEM simulations.


title: A Human-Aware Manipulation Planner

abstract: With recent advances in safe and compliant hardware and control, robots are close to finding their places in our homes. As the safety barrier between humans and robots is beginning to fade, the necessity to design pertinent robot behavior in human environments is becoming a crucial step. In order to obtain a safe, comfortable, and socially acceptable interaction, the robot should be engineered from top to bottom by considering the presence of the human. In this paper, we present a manipulation planning framework and its implementation human-aware manipulation planner. This planner generates paths not only safe but comfortable and “socially acceptable” as well by reasoning explicitly on human's kinematics, vision field, posture, and preferences. The planner, which is applied into “robot handing over an object” scenarios, breaks the human centric interaction that depends mostly on human effort and allows the robot to take initiative by computing automatically where the interaction takes place, thus decreasing the cognitive weight of interaction on human side.


title: Neuromarketing and visual attention study using eye tracking techniques

abstract: This paper presents a study of human behavior related to different marketing stimuli based on pupillometry data. The consumer's gaze points on products of interest are acquired with a remote eye tracker and the obtained heatmaps and timestamps are used for further analysis. An application for recording user's visual attention on web pages and advertising slides is implemented. A sensor for electrodermal activity is also used in order to have an additional response regarding consumer arousal. The results reveal that the human emotions and visual attention are highly correlated with practical marketing applications.


title: Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation

abstract: Image segmentation is the process of partitioning the image into significant regions easier to analyze. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the Conjugate Gradient algorithm (CG) for image segmentation, based on the Hidden Markov Random Field. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.


University of Houston @ CL-SciSumm 2017: Positional language Models, Structural Correspondence Learning and Textual Entailment


title: Tutorial: introduction to the rational unified process®

abstract: The Rational Unified Process® (RUP®) is a software engineering process framework. It captures many of the best practices in modern software development in a form that is suitable for a wide range of projects and organizations. It embeds object-oriented techniques and uses the UML as the principal notation for the several models that are built during the development. The RUP is also an open process framework that allows software organizations to tailor the process to their specific need, and to capture their own specific process know-how in the form of process components. Many process components are now developed by various organizations to cover different domains, technologies, tools, or type of development, and these components can be assembled to rapidly compose a suitable process. This tutorial will introduce the basic concepts and principles, which lie under the RUP framework, and show concrete examples of its usage.


title: Preprint typeset using L ATEX style emulateapj v. 6/22/04 SYNTHETIC LISA: SIMULATING TIME DELAY INTERFEROMETRY IN A MODEL LISA

abstract: We report on three numerical experiments on the implementation of Time-Delay Interferometry (TDI) for LISA, performed with Synthetic LISA, a C++/Python package that we developed to simulate the LISA science process at the level of scientific and technical requirements. Specifically, we study the laser-noise residuals left by first-generation TDI when the LISA armlengths have a realistic time dependence; we characterize the armlength-measurement accuracies that are needed to have effective laser-noise cancellation in both first- and second-generation TDI; and we estimate the quantization and telemetry bitdepth needed for the phase measurements. Synthetic LISA generates synthetic time series of the LISA fundamental noises, as filtered through all the TDI observables; it also provides a streamlined module to compute the TDI responses to gravitational waves according to a full model of TDI, including the motion of the LISA array and the temporal and directional dependence of the armlengths. We discuss the theoretical model that underlies the simulation, its implementation, and its use in future investigations on system-characterization and data-analysis prototyping for LISA.


title: Visualisation of Temporal Interval Association Rules

abstract: Temporal intervals and the interaction of interval-based events are fundamental in many domains including medicine, commerce, computer security and various types of normalcy analysis. In order to learn from temporal interval data we have developed a temporal interval association rule algorithm. In this paper, we will provide a definition for temporal interval association rules and present our visualisation techniques for viewing them. Visualisation techniques are particularly important because the complexity and volume of knowledge that is discovered during data mining often makes it difficult to comprehend. We adopt a circular graph for visualising a set of associations that allows underlying patterns in the associations to be identified. To visualize temporal relationships, a parallel coordinate graph for displaying the temporal relationships has been developed.


title: Making Apps Useable on Multiple Different Mobile Platforms: On Interoperability for Business Application Development on Smartphones

abstract: The relevance of enabling mobile access to business enterprise information systems for experts working in the field has grown significantly in the last years due to the increasing availability of smartphones; the shipment of smartphones exceeded that of personal computers in 2011. However, the screen sizes and display resolutions of different devices vary to a large degree, along with different aspect ratios and the complexity of mobile tasks. These obstacles are a major challenge for software developers, especially when they try to reach the largest possible audience and develop for multiple mobile platforms or device types. On the other side, the end users’ expectations regarding the usability of the applications are increasing. Consequently, for a successful mobile application the user interface needs to be well-designed, thus justifying research to overcome these obstacles. In this paper, we report on experiences during an industrial project on building user interfaces for database access to a business enterprise information system for professionals in the field. We discuss our systematic analysis of standards and conventions for design of user interfaces for various mobile platforms, as well as scaling methods operational on different physical screen sizes. The interoperability of different systems, including HTML5, Java and .NET is also within the focus of this work.


title: A Simulink-based robotic toolkit for simulation and control of the PUMA 560 robot manipulator

abstract: A Simulink robotic toolkit (SRTK) for the Puma 560 robot manipulator is developed on the MATLAB/Simulink-based platform. Through the use of the real-time Linux target and the real-time Windows target, the SRTK can be executed on the Linux or Win32-based operating systems in real-time. Moreover, the graphical user-friendly nature of Simulink allows the SRTK to be a flexible tool that can easily be customized to fit the specific needs of the user, that is, based on the layered approach of the SRTK, the user can perform operations such as calibration, joint control, Cartesian control, Cartesian PD control, impedance control, some trajectory generation tasks, and real-time simulation of the Puma 560 through a user-friendly MATLAB-based graphical user interface without writing any code. The SRTK allows a researcher to use the Puma 560 without the burden of the external issues related to the control, interface, and software issues, while providing for the flexibility for easily modifying components for increased functionality.


title: Integrated feature selection and higher-order spatial feature extraction for object categorization

abstract: In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature selection and feature extraction. Higher-order spatial features are progressively extracted based on selected lower order ones, thereby avoiding exhaustive computation. The method can be based on any additive feature selection algorithm such as boosting. Experimental results show that the method is computationally much more efficient than previous approaches, without sacrificing accuracy.


title: Egocentric and Allocentric Spatial Memory in Korsakoff’s Amnesia

abstract: The goal of the present study was to investigate spatial memory in a group of patients with amnesia due to Korsakoff’s syndrome (KS). We used a virtual spatial memory task that allowed us to separate the use of egocentric and allocentric spatial reference frames to determine object locations. Research investigating the ability of patients with Korsakoff’s amnesia to use different reference frames is scarce and it remains unclear whether these patients are impaired in using ego- and allocentric reference frames to the same extent. Twenty Korsakoff patients and 24 matched controls watched an animation of a bird flying in one of three trees standing in a virtual environment. After the bird disappeared, the camera turned around, by which the trees were briefly out of sight and then turned back to the center of the environment. Participants were asked in which tree the bird was hiding. In half of the trials, a landmark was shown. Half of the trials required an immediate response whereas in the other half a delay of 10 s was present. Patients performed significantly worse than controls. For all participants trials with a landmark were easier than without a landmark and trials without a delay were easier than with a delay. While controls were above chance on all trials patients were at chance in allocentric trials without a landmark present and with a memory delay. Patients showed no difference in the ego- and the allocentric condition. Together the findings suggest that despite the amnesia, spatial memory and especially the use of ego- and allocentric reference frames in Korsakoff patients are spared.


title: Towards a unified behavior trees framework for robot control

abstract: This paper presents a unified framework for Behavior Trees (BTs), a plan representation and execution tool. The available literature lacks the consistency and mathematical rigor required for roboti ...


title: Sinkhorn Distances: Lightspeed Computation of Optimal Transport

abstract: Optimal transport distances are a fundamental family of distances for probability measures and histograms of features. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost can quickly become prohibitive whenever the size of the support of these measures or the histograms' dimension exceeds a few hundred. We propose in this work a new family of optimal transport distances that look at transport problems from a maximum-entropy perspective. We smooth the classic optimal transport problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transport solvers. We also show that this regularized distance improves upon classic optimal transport distances on the MNIST classification problem.


title: SMArTIC: towards building an accurate, robust and scalable specification miner

abstract: Improper management of software evolution, compounded by imprecise, and changing requirements, along with the "short time to market" requirement, commonly leads to a lack of up-to-date specifications. This can result in software that is characterized by bugs, anomalies and even security threats. Software specification mining is a new technique to address this concern by inferring specifications automatically. In this paper, we propose a novel API specification mining architecture called SMArTIC Specification Mining Architecture with Trace fIltering and Clustering) to improve the accuracy, robustness and scalability of specification miners. This architecture is constructed based on two hypotheses: (1) Erroneous traces should be pruned from the input traces to a miner, and (2) Clustering related traces will localize inaccuracies and reduce over-generalizationin learning. Correspondingly, SMArTIC comprises four components: an erroneous-trace filtering block, a related-trace clustering block, a learner, and a merger. We show through experiments that the quality of specification mining can be significantly improved using SMArTIC.


title: Development of camber and steering control system in hot strip mill

abstract: Camber and off-center in hot strip rolling process cause various problems including clogging, defects on the strip, and telescope phenomenon in a coil. To reduce camber and prevent off-center, we propose the integrated camber and steering control system based on adjusting roll gap level by previously designed mathematical model. By considering difficulties in the measurement and knowhow in the operation of plants, we improve measuring position and control methods in the system. We apply it to real plants and show the effectiveness of the system.


title: Color Constancy Using 3D Scene Geometry Derived From a Single Image

abstract: The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g., gray-world and white patch assumption). In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models, images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods are selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics, and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared with the best-performing single color constancy algorithm.


title: Geometric motion planning: The local connection, Stokes’ theorem, and the importance of coordinate choice

abstract: The locomotion of articulated mechanical systems is often complex and unintuitive, even when considered with the aid of reduction principles from geometric mechanics. In this paper, we present two tools for gaining insights into the underlying principles of locomotion: connection vector fields and connection height functions. Connection vector fields illustrate the geometric structure of the relationship between internal shape changes and the system body velocities they produce. Connection height functions measure the curvature of their respective vector fields and capture the net displacement over any cyclic shape change, or gait , allowing for the intuitive selection of gaits to produce desired displacements. Height function approaches have been previously attempted, but such techniques have been severely limited by their basis in a rotating body frame, and have only been useful for calculating planar rotations and infinitesimal translations. We circumvent this limitation by introducing a notion of optimal coordinates defining a body frame that rotates very little in response to shape changes, while still meeting the requirements of the geometric mechanics theory on which the vector fields and height functions are based. In these optimal coordinates, the height functions provide close approximations of the net displacement resulting from a broad selection of possible gaits.


title: A novel broad-band Chebyshev-response rat-race ring coupler

abstract: A novel broad-band rat-race ring coupler with a unit element at each port and an ideal phase inverter at one of the ring arms is proposed and analyzed. Design equations based on Chebyshev equiripple functions are derived. The design curves of equal or unequal power division are also presented. Theoretical data show that the bandwidth of unequal power division with a specific return-loss value increases as the power division ratio increases or decreases from unity. For a 180/spl deg/ hybrid with equal power division (3 dB) and 15-dB return loss, the proposed rat-race ring may have a bandwidth of 4:1. Three circuits of this novel rat-race ring are realized using finite-ground-plane coplanar waveguide. The measured results match very well with the theory.


title: NVWAL: Exploiting NVRAM in Write-Ahead Logging

abstract: Emerging byte-addressable non-volatile memory is considered an alternative storage device for database logs that require persistency and high performance. In this work, we develop NVWAL (NVRAM Write-Ahead Logging) for SQLite. The contribution of NVWAL consists of three elements: (i) byte-granularity differential logging that effectively eliminates the excessive I/O overhead of filesystem-based logging or journaling, (ii) transaction-aware lazy synchronization that reduces cache synchronization overhead by two-thirds, and (iii) user-level heap management of the NVRAM persistent WAL structure, which reduces the overhead of managing persistent objects. We implemented NVWAL in SQLite and measured the performance on a Nexus 5 smartphone and an NVRAM emulation board - Tuna. Our performance study shows the following: (i) the overhead of enforcing strict ordering of NVRAM writes can be reduced via NVRAM-aware transaction management. (ii) From the application performance point of view, the overhead of guaranteeing failure atomicity is negligible; the cache line flush overhead accounts for only 0.8~4.6% of transaction execution time. Therefore, application performance is much less sensitive to the NVRAM performance than we expected. Decreasing the NVRAM latency by one-fifth (from 1942 nsec to 437 nsec), SQLite achieves a mere 4% performance gain (from 2517 ins/sec to 2621 ins/sec). (iii) Overall, when the write latency of NVRAM is 2 usec, NVWAL increases SQLite performance by at least 10x compared to that of WAL on flash memory (from 541 ins/sec to 5812 ins/sec).


title: A practical multi-viewer tabletop autostereoscopic display

abstract: This paper introduces a multi-user autostereoscopic tabletop display and its associated real-time rendering methods. Tabletop displays that support both multiple viewers and autostereoscopy have been extremely difficult to construct. Our new system is inspired by the “Random Hole Display” design [11] that modified the pattern of openings in a barrier mounted in front of a flat panel display from thin slits to a dense pattern of tiny, pseudo-randomly placed holes. This allows viewers anywhere in front of the display to see a different subset of the display's native pixels through the random-hole screen. However, a fraction of the visible pixels will be observable by more than a single viewer. Thus the main challenge is handling these “conflicting” pixels, which ideally must show different colors to each viewer. We introduce several solutions to this problem and describe in detail the current method of choice, a combination of color blending and approximate error diffusion, performing in real time in our GPU-based implementation. The easily reproducible design uses a pattern film barrier affixed to the display by means of a transparent polycarbonate layer spacer. We use a commercial optical tracker for viewers' locations and synthesize the appropriate image (or a stereoscopic image pair) for each viewer. The system supports graceful degradation with increasing number of simultaneous views, and graceful improvement as the number of views decreases.


title: Willow: a user-programmable SSD

abstract: We explore the potential of making programmability a central feature of the SSD interface. Our prototype system, called Willow, allows programmers to augment and extend the semantics of an SSD with application-specific features without compromising file system protections. The SSD Apps running on Willow give applications low-latency, high-bandwidth access to the SSD's contents while reducing the load that IO processing places on the host processor. The programming model for SSD Apps provides great flexibility, supports the concurrent execution of multiple SSD Apps in Willow, and supports the execution of trusted code in Willow. ::: ::: We demonstrate the effectiveness and flexibility of Willow by implementing six SSD Apps and measuring their performance. We find that defining SSD semantics in software is easy and beneficial, and that Willow makes it feasible for a wide range of IO-intensive applications to benefit from a customized SSD interface.


title: RF Characterization and Modeling of 10 µm Fine-Pitch Cu-Pillar on a High Density Silicon Interposer

abstract: This work aims at providing a RLCG modeling ofthe 10 µm fine-pitch microbump type interconnects in the 100 MHz-40 GHz frequency band based on characterization approach. RF measurements are performed on two-port test structures within a short-loop with chip to wafer assembly using the fine pitch 10 µm Cu-pillar on a 10 Ohm.cm substrate resistivity silicon interposer. Accuracy is obtained thanks to a coplanar transmission line using 44 Cu-pillar transitions. To the author knowledge, it is the first time that RLCG modeling of fine-pitch Cu-pillar is extracted from experimental results. Another goal of this work is to get a better understanding of the main physical effects over a wide frequency range, especially concerning the key parameter of fine pitch Cu-pillar, i.e. the resistance. Finally, analysis based on the proposed RLCG modeling are performed to optimize over frequency the resistive interposer-to-chip link thanks to process modifications mitigating high frequency parasitic effects.


title: Sentiment Analysis Using Deep Learning Techniques: A Review

abstract: The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their ::: automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.


title: SSVM: a simple SVM algorithm

abstract: We present a fast iterative algorithm for identifying the support vectors of a given set of points. Our algorithm works by maintaining a candidate support vector set. It uses a greedy approach to pick points for inclusion in the candidate set. When the addition of a point to the candidate set is blocked because of other points already present in the set, we use a backtracking approach to prune away such points. To speed up convergence we initialize our algorithm with the nearest pair of points from opposite classes. We then use an optimization based approach to increase or prune the candidate support vector set. The algorithm makes repeated passes over the data to satisfy the KKT constraints. The memory requirements of our algorithm scale as O(|SI|/sup 2/) in the average case, where |S| is the size of the support vector set. We show that the algorithm is extremely competitive as compared to other conventional iterative algorithms like SMO and the NPA. We present results on a variety of real life datasets to validate our claims.


title: Getting the Most Out of Wikidata: Semantic Technology Usage in Wikipedia’s Knowledge Graph

abstract: Wikidata is the collaboratively curated knowledge graph of the Wikimedia Foundation (WMF), and the core project of Wikimedia’s data management strategy. A major challenge for bringing Wikidata to its full potential was to provide reliable and powerful services for data sharing and query, and the WMF has chosen to rely on semantic technologies for this purpose. A live SPARQL endpoint, regular RDF dumps, and linked data APIs are now forming the backbone of many uses of Wikidata. We describe this influential use case and its underlying infrastructure, analyse current usage, and share our lessons learned and future plans.


title: Integrated magnetics for current-doubler rectifiers

abstract: This paper presents a new integrated magnetic structure for current-doubler rectifiers (CDR). Compared to previously published structures, the proposed design allows the equivalent filtering inductance to be increased with significantly less increase in total winding conduction loss. The increased inductance leads to reduced current and voltage ripple, thereby reducing the size of capacitors required for output filtering. The lower current and flux ripple also helps to reduce the conduction loss and the magnetic core loss. As the result, the overall converter efficiency can be improved, especially under light load when the losses due to ripple current and ripple flux are more significant. Analysis of the proposed structure is presented in the paper, along with an equivalent circuit model suitable for steady-state and dynamic response analyses. Detailed design procedures are also provided by using a half-bridge converter as example. Experimental results from two 100-W, 3.3-V output half-bridge converters are presented to validate the concept and the analyses.


title: Singularity avoidance for nonholonomic, omnidirectional wheeled mobile platforms with variable footprint

abstract: One characteristic attribute of mobile platforms equipped with a set of independent steering wheels is their omnidirectionality and the ability to realize complex translational and rotational trajectories. An accurate coordination of steering angle and spinning rate of each wheel is necessary for a consistent motion. Since the orientations of the wheels must align to the Instantaneous Center of Rotation (ICR), the current location and velocity of this specific point is essential for describing the state of the platform. However, singular configurations of the controlled system exist depending on the ICR, leading to unfeasible control inputs, i.e., infinite steering rates. Within this work we address and analyze this problem in general. Furthermore, we propose a solution for mobile platforms with variable footprint. An existing controller based on dynamic feedback linearization is augmented by a new potential field-based algorithm for singularity avoidance which uses the tunable leg lengths as an additional control input to minimize deviations from the nominal motion trajectory. Simulations and experimental results on the mobile platform of DLR's humanoid manipulator Justin support our approach.


Mesh Generation And Optimal Triangulation


title: What you look at is what you get: eye movement-based interaction techniques

abstract: In seeking hitherto-unused methods by which users and computers can communicate, we investigate the usefulness of eye movements as a fast and convenient auxiliary user-to-computer communication mode. The barrier to exploiting this medium has not been eye-tracking technology but the study of interaction techniques that incorporate eye movements into the user-computer dialogue in a natural and unobtrusive way. This paper discusses some of the human factors and technical considerations that arise in trying to use eye movements as an input medium, describes our approach and the first eye movement-based interaction techniques that we have devised and implemented in our laboratory, and reports our experiences and observations on them.


title: Inferring international and internal migration patterns from Twitter data

abstract: Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.


title: Domain-specific languages and program generation with meta-AspectJ

abstract: Meta-AspectJ (MAJ) is a language for generating AspectJ programs using code templates. MAJ itself is an extension of Java, so users can interleave arbitrary Java code with AspectJ code templates. MAJ is a structured metaprogramming tool: a well-typed generator implies a syntactically correct generated program. MAJ promotes a methodology that combines aspect-oriented and generative programming. A valuable application is in implementing small domain-specific language extensions as generators using unobtrusive annotations for syntax extension and AspectJ as a back-end. The advantages of this approach are twofold. First, the generator integrates into an existing software application much as a regular API or library, instead of as a language extension. Second, a mature language implementation is easy to achieve with little effort since AspectJ takes care of the low-level issues of interfacing with the base Java language. In addition to its practical value, MAJ offers valuable insights to metaprogramming tool designers. It is a mature metaprogramming tool for AspectJ (and, by extension, Java): a lot of emphasis has been placed on context-sensitive parsing and error reporting. As a result, MAJ minimizes the number of metaprogramming (quote/unquote) operators and uses type inference to reduce the need to remember type names for syntactic entities.


title: How will Your Workload Look Like in 6 Years? Analyzing Wikimedia's Workload

abstract: Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.


title: A 7.3 THz Cut-Off Frequency, Inline, Chalcogenide Phase-Change RF Switch Using an Independent Resistive Heater for Thermal Actuation

abstract: An inline chalcogenide phase change RF switch utilizing germanium telluride (GeTe) and driven by an integrated, electrically isolated thin film heater for thermal actuation has been fabricated. A voltage or current pulse applied to the heater terminals was used to transition the phase change material between the crystalline and amorphous states. An on-state resistance of 1.2 Ω (0.036 Ω-mm), with an off-state capacitance and resistance of 18.1 fF and 112 kΩ respectively were measured. This results in an RF switch cut-off frequency (Fco) of 7.3 THz, and an off/on DC resistance ratio of 9 × 104. The heater pulse power required to switch the GeTe between the two states was as low as 0.5W, with zero power consumption during steady state operation, making it a non-volatile RF switch. To the authors' knowledge, this is the first reported implementation of an RF phase change switch in a 4-terminal, inline configuration.


title: A Fast Algorithm for 3-Dimensional Imaging with UWB Pulse Radar Systems

abstract: SUMMARY Ultra-wideband pulse radars are promising candidates for 3-dimensional environment measurements by autonomous robots. Estimating 3-dimensional target shapes by scanning with an omni-directional antenna is an ill-posed inverse problem. Conventional algorithms such as the synthetic aperture method or parametric algorithms have a problem in terms of their calculation times. We have clarified the existence of a reversible transform between received data and target shapes for 3-dimensional systems. Calculation times are remarkably reduced by applying this transform because it directly estimates target shapes without iterations. We propose a new algorithm based on the transform and present an application example using numerical simulations. We confirm that the proposed algorithm has sufficient accuracy and a short calculation time.


title: Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation

abstract: A recent review on the application of business intelligence to the banking domain.Coverage of the last twelve years of scientific literature on those subjects.Usage of text mining and the latent Dirichlet allocation to analyze articles.Provide new insights and future research trends which may benefit banking business. This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.


title: A Modified RSA Encryption Technique Basedon Multiple public keys

abstract: In the today‟s world, security is required to transmit confidential information over the network. Security is also demanding in wide range of applications. Cryptographic algorithms play a vital role in providing the data security against malicious attacks.RSA algorithm is extensively used in the popular implementations of Public Key Infrastructures. In asymmetric key cryptography, also called Public Key cryptography, two different keys (which form a key pair) are used. One key is used for encryption & only the other corresponding key must be used for decryption. No other key can decrypt the message – not even the original (i.e. the first) key used for encryption. The beauty of this scheme is that every communicating party needs just a key pair for communicating with any number of other communicating parties. Once someone obtains a key pair, he /she can communicate with anyone else. In this paper, we have done an efficient implementation of RSA algorithm using two public key pairs and using some mathematical logic rather than sending the e value directly as a public key.Because if an attacker has opportunity of getting the e value they can directly find d value and decrypt the message.


Cooperating Robots for Search and Rescue


title: Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

abstract: Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation for characterizing the statistical properties of natural language. In this study, we propose to use BLSTM-RNN with word embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is achieved. Without using morphological features, this approach can also achieve a good performance comparable with the Stanford POS tagger.


title: Bayesian Boolean Matrix Factorisation

abstract: Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion. This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.


title: Interactive images: cuboid proxies for smart image manipulation

abstract: Images are static and lack important depth information about the underlying 3D scenes. We introduce interactive images in the context of man-made environments wherein objects are simple and regular, share various non-local relations (e.g., coplanarity, parallelism, etc.), and are often repeated. Our interactive framework creates partial scene reconstructions based on cuboid-proxies with minimal user interaction. It subsequently allows a range of intuitive image edits mimicking real-world behavior, which are otherwise difficult to achieve. Effectively, the user simply provides high-level semantic hints, while our system ensures plausible operations by conforming to the extracted non-local relations. We demonstrate our system on a range of real-world images and validate the plausibility of the results using a user study.


title: When does start-up innovation spur the gale of creative destruction? The RAND

abstract: This paper is motivated by the substantial differences in start-up commercialization strategies observed across different high-technology sectors. Specifically, we evaluate the conditions under which start-up innovators earn their returns on innovation through product market competition with more established firms (such as in many areas of the electronics industry) as opposed to cooperation with these incumbents (either through licensing, strategic alliances or outright acquisition as observed in the pharmaceutical industry). While the former strategy challenges incumbent market power, the latter strategy tends to reinforce current market structure. Though the benefits of cooperation include forestalling the costs of competition in the product market and avoiding duplicative investment in sunk assets, imperfections in the market for ideas' may lead to competitive behavior in the product market. Specifically, if the transaction costs of bargaining are high or incumbents are likely to expropriate ideas from start-up innovators, then product market competition is more likely. We test these ideas using a novel dataset of the commercialization strategies of over 100 start-up innovators. Our principal robust findings are that the probability of cooperation is increasing in the innovator's control over intellectual property rights, association with venture capitalists (which reduce their transactional bargaining costs), and in the relative cost of control of specialized complementary assets. Our conclusion is that the propensity for pro-competitive benefits from start-up innovators reflects an earlier market failure, in the market for ideas.'


title: Teachers' Education, Classroom Quality, and Young Children's Academic Skills: Results From Seven Studies of Preschool Programs

abstract: In an effort to provide high-quality preschool education, policymakers are increasingly requiring public preschool teachers to have at least a Bachelor's degree, preferably in early childhood education. Seven major studies of early care and education were used to predict classroom quality and children's academic outcomes from the educational attainment and major of teachers of 4-year-olds. The findings indicate largely null or contradictory associations, indicating that policies focused solely on increasing teachers' education will not suffice for improving classroom quality or maximizing children's academic gains. Instead, raising the effectiveness of early childhood education likely will require a broad range of professional development activities and supports targeted toward teachers' interactions with children.


title: Computer aided clothing pattern design with 3D editing and pattern alteration

abstract: The traditional apparel product development process is a typical iterative 'optimization' process that involves trial-and-error. In order to confirm the design and achieve a satisfactory fit, a number of repeated cycles of sample preparation, trial fitting and pattern alteration must be conducted. The process itself is time-consuming, costly, and dependent on the designer's skills and experience. In this paper, a novel computer aided design (CAD) solution for virtual try-on, fitting evaluation and style editing is proposed to speed up the clothing design process. A series of new techniques from cross parameterization, geometrical and physical integrated deformation, to novel editing methods are proposed. First, a cross parameterization technique is employed to map clothing pattern pieces on a model surface. The pattern can be precisely positioned to form the initial shape with low distortion. Next, a new deformation method called hybrid pop-up is proposed to approximate the virtual try-on shape. This method is an integration of geometrical reconstruction and physical based simulation. In addition, user interactive operations are introduced for style editing and pattern alteration in both 2D and 3D manners. The standard rules regulating pattern editing in the fashion industry can be incorporated in the system, so that the resulting clothing patterns are suitable for everyday production.


title: Unsupervised joke generation from big data

abstract: Humor generation is a very hard problem. It is difficult to say exactly what makes a joke funny, and solving this problem algorithmically is assumed to require deep semantic understanding, as well as cultural and other contextual cues. We depart from previous work that tries to model this knowledge using ad-hoc manually created databases and labeled training examples. Instead we present a model that uses large amounts of unannotated data to generate I like my X like I like my Y, Z jokes, where X, Y, and Z are variables to be filled in. This is, to the best of our knowledge, the first fully unsupervised humor generation system. Our model significantly outperforms a competitive baseline and generates funny jokes 16% of the time, compared to 33% for human-generated jokes.


title: Deflectometry setup definition for automatic chrome surface inspection

abstract: A recurrent problem in the industrial sector is the quality control and surface inspection of reflecting pieces with non-planar surfaces. This is an extended and non-solved problem because it is related not only to the material itself but also to the coating. This problem appears in a wide spectrum of industrial sectors such as automation, aeronautics or orthopaedics. In recent years, a new imaging technology called deflectometry has been introduced in the field of surface inspection for industrial applications. This technology features a high resolution camera and a dedicated illumination system-based on displaying fringe patterns in a monitor-allowing the detection of irregularities in surfaces. However, the introduction of this technology into automated quality control systems remains a challenging task, due to the wide range of defects and shapes that can appear. It becomes thus necessary to characterize different types of errors and their associated detection setups. In this paper we propose a novel methodology to define and analyse the best setup for each pattern. We also explore an efficient technique to maximize the number of different pieces inspected without modifying the setup of the acquisition system. Experimental results show that the presented methodology defines an inspection method that can be installed in an automatic quality control device for non-planar surfaces analysis of manufactured products.


title: Common errors in the execution of preoperative templating for primary total hip arthroplasty.

abstract: We reviewed 75 primary total hip arthroplasty preoperative and postoperative radiographs and recorded limb length discrepancy, change in femoral offset, acetabular position, neck cut, and femoral component positioning. Interobturator line, as a technique to measure preoperative limb length discrepancy, had the least amount of variance when compared with interteardrop and intertuberosity lines (Levene test, P = .0527). The most common error in execution of preoperative templating was excessive limb lengthening (mean, 3.52 mm), primarily due to inferior acetabular cup positioning (Pearson correlation coefficient, P = .036). Incomplete medialization of the acetabular component contributed the most to offset discrepancy. The most common errors in the execution of preoperative templating resulted in excessive limb lengthening and increased offset. Identifying these errors can lead to more accurate templating techniques and improved intraoperative execution.


title: Advanced control strategies for balancing LED usage of AC LED driver

abstract: This paper presents two enhanced methods to balance LEDs energy usage in the AC LED Driver. While conventional AC LED driver has energy consumption imbalance, the proposed control methods provide equally distributed energy usage. First method is to apply the appropriate firing voltage that is the threshold voltage for equalizing energy consumption of LEDs. This method improves balancing energy consumption within a rectified line cycle. The other method is to change the order of LEDs turning on sequence by every two rectified line cycle. These two approaches are compared with mathematical analysis in terms of the energy consumption of each LED. A prototype driver with three 12 W-LED strings is built. The proposed method 1 and method 2 achieve 0.0030 J and 0.0033 J standard deviation of energy consumption, respectively, compared to the 0.0070 J deviation for the conventional approach.


title: Discovering Internal Representations from Object-CNNs Using Population Encoding

abstract: In this paper, we provide a method for understanding the internal representations of Convolutional Neural Networks (CNNs) trained on objects. We hypothesize that the information is distributed across multiple neuronal responses and propose a simple clustering technique to extract this information, which we call \emph{population encoding}. The population encoding technique looks into the entrails of an object-CNN at multiple layers of the network and shows the implicit presence of mid-level object part semantics distributed in the neuronal responses. Our qualitative visualizations show that population encoding can extract mid-level image patches that are visually tighter than the patches that produce high single-filter activations. Moreover, our comprehensive quantitative experiments using the object key point annotations from the PASCAL3D+ dataset corroborate the visualizations by demonstrating the superiority of population encoding over single-filter detectors, in the task of object-part detection. We also perform some preliminary experiments where we uncover the compositional relations between the adjacent layers using the parts detected by population encoding clusters. Finally, based on the insights gained from this work, we point to various new directions which will enable us to have a better understanding of the CNN's internal representations.


title: Regularizing Neural Machine Translation by Target-bidirectional Agreement

abstract: Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.


title: Clinically applicable deep learning for diagnosis and referral in retinal disease

abstract: The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.


title: xLED: Covert Data Exfiltration from Air-Gapped Networks via Switch and Router LEDs

abstract: An air-gapped network is a type of IT network that is separated from the Internet - physically – due to the sensitive information it stores. Even if such a network is compromised with a malware, the hermetic isolation from the Internet prevents an attacker from leaking out any data - thanks to the lack of connectivity. In this paper we show how attackers can covertly leak sensitive data from air-gapped networks via the row of status LEDs on networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device (‘side-channel’), malware controlling the status LEDs to carry any type of data (‘covert-channel’) has never studied before. Sensitive data can be covertly encoded over the blinking of the LEDs and received by remote cameras and optical sensors. A malicious code is executed in a compromised LAN switch or router allowing the attacker direct, low-level control of the LEDs. We provide the technical background on the internal architecture of switches and routers at both the hardware and software level which enables these attacks. We present different modulation and encoding schemas, along with a transmission protocol. We implement prototypes of the malware and discuss its design and implementation. We tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and discuss detection and prevention countermeasures. Our experiments show that sensitive data can be covertly leaked via the status LEDs of switches and routers at bit rates of 1 bit/sec to more than 2000 bit/sec per LED.


title: Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach

abstract: Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modified version of the Landsat satellite dataset, the modified version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show significant improvement over the baseline methods.


title: Efficient hardware acceleration of CNNs using logarithmic data representation with arbitrary log-base

abstract: Efficient acceleration of Deep Neural Networks is a manifold task. In order to save memory requirements and reduce energy consumption we propose the use of dedicated accelerators with novel arithmetic processing elements which use bit shifts instead of multipliers. While a regular power-of-2 quantization scheme allows for multiplierless computation of multiply-accumulate-operations, it suffers from high accuracy losses in neural networks. Therefore, we evaluate the use of powers-of-arbitrary-log-bases and confirmed their suitability for quantization of pre-trained neural networks. The presented method works without retraining of the neural network and therefore is suitable for applications in which no labeled training data is available. In order to verify our proposed method, we implement the log-based processing elements into a neural network accelerator on an FPGA. The hardware efficiency is evaluated in terms of FPGA utilization and energy requirements in comparison to regular 8-bit-fixed-point multiplier based acceleration. Using this approach hardware resources are minimized and power consumption is reduced by 22.3%.


title: Exploring regular expression usage and context in Python

abstract: Due to the popularity and pervasive use of regular expressions, researchers have created tools to support their creation, validation, and use. However, little is known about the context in which regular expressions are used, the features that are most common, and how behaviorally similar regular expressions are to one another. In this paper, we explore the context in which regular expressions are used through a combination of developer surveys and repository analysis. We survey 18 professional developers about their regular expression usage and pain points. Then, we analyze nearly 4,000 open source Python projects from GitHub and extract nearly 14,000 unique regular expression patterns. We map the most common features used in regular expressions to those features supported by four major regex research efforts from industry and academia: brics, Hampi, RE2, and Rex. Using similarity analysis of regular expressions across projects, we identify six common behavioral clusters that describe how regular expressions are often used in practice. This is the first rigorous examination of regex usage and it provides empirical evidence to support design decisions by regex tool builders. It also points to areas of needed future work, such as refactoring regular expressions to increase regex understandability and context-specific tool support for common regex usages.


title: Sentiment Analysis on Twitter

abstract: With the rise of social networking epoch, there has been a surge of user generated content. Microblogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time microblogging service, Twitter, where users post real time reactions to and opinions� about� “everything”. In this paper, we expound a hybrid approach using both corpus based and dictionary based methods to determine the semantic orientation of the opinion words in tweets. A case study is presented to illustrate the use and effectiveness of the proposed system.


title: Mechanisms of emotional arousal and lasting declarative memory

abstract: Abstract Neuroscience is witnessing growing interest in understanding brain mechanisms of memory formation for emotionally arousing events, a development closely related to renewed interest in the concept of memory consolidation. Extensive research in animals implicates stress hormones and the amygdaloid complex as key, interacting modulators of memory consolidation for emotional events. Considerable evidence suggests that the amygdala is not a site of long-term explicit or declarative memory storage, but serves to influence memory-storage processes in other brain regions, such as the hippocampus, striatum and neocortex. Human-subject studies confirm the prediction of animal work that the amygdala is involved with the formation of enhanced declarative memory for emotionally arousing events.


title: The impact of financial and economic crisis on SME’s in Greece and Ireland

abstract: LACINA LUBOR, VAVŘINA JAN: The impact of fi nancial and economic crisis on SME’s in Greece and Ireland. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013, LXI, No. 4, pp. 1005– 1016 The impact of fi nancial and economic crisis infl uencing economic development in EU countries is analysed predominately on macroeconomic level. Major part of economic studies analyse the eff ect of crisis on both real and potential economic growth, unemployment, infl ation and debt dynamic. However the eff ects of the crisis are visible also at microeconomic level. The economic results of businesses are signifi cantly infl uenced by the negative macroeconomic development at both national and international level. Both decrease in potential growth and purchasing power due to economic recession and more restrictive fi scal policy have a direct impact on aggregate demand and thus the microeconomic sector as whole. Additional source of problems is connected with banking sector crisis and the access to fi nancing mainly for small and medium-sized enterprises (SME’s). The aim of the paper is to fi ll the gap in economic research and to analyse the impact of the ongoing crisis on business entities in selected eurozone member countries. Authors selected two eurozone member countries from EU periphery (Greece and Ireland). Both countries are severely hit by economic crisis and authors suppose that also their business sector will be signifi cantly infl uenced. However author believes that the structural diff erences will lead over the time in faster recovery of Irish businesses in contrast to the Greek one. We create dataset using Amadeus database which contains the harmonized data about statistically signifi cant set of business in selected countries. Authors then sorting the data according selected variables such as size of the company, NACE categorization and comparative indicators of individual business economic performance, namely representatives of indicators groups profi tability, solvency, liquidity and indebtedness/fi nancial structure of business entities. For the purpose of analysis authors analyse the data series three years before the crisis (2005, 2006 and 2007) and the three years of crisis (2008, 2009 and 2010). The dataset consists of 3,567 business entities in both observed countries. In the study we use the cluster analysis to detect some basic patterns and trends in business sector in terms of homogeneity within dataset. Based on the results of microeconomic environment analysis (impact of crisis and national economic policies) authors assess the optimal reaction of economic policy (both national and EU) to improve the condition of businesses which authors believe are the source of future economic recovery. economic crisis, fi nancial analysis, eurozone, quantitative research, SME’s, economic policy


title: Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector

abstract: In light of the powerful learning capability of deep neural networks (DNNs), deep (convolutional) models have been built in recent years to address the task of salient object detection. Although training such deep saliency models can significantly improve the detection performance, it requires large-scale manual supervision in the form of pixel-level human annotation, which is highly labor-intensive and time-consuming. To address this problem, this paper makes the earliest effort to train a deep salient object detector without using any human annotation. The key insight is “supervision by fusion”, i.e., generating useful supervisory signals from the fusion process of weak but fast unsupervised saliency models. Based on this insight, we combine an intra-image fusion stream and a inter-image fusion stream in the proposed framework to generate the learning curriculum and pseudo ground-truth for supervising the training of the deep salient object detector. Comprehensive experiments on four benchmark datasets demonstrate that our method can approach the same network trained with full supervision (within 2-5% performance gap) and, more encouragingly, even outperform a number of fully supervised state-of-the-art approaches.


title: Bitcoin: under the hood

abstract: The myths, the hype, and the true worth of bitcoins.


title: Design and implementation of SDN-based lightweight IPv6 mobility manager in ONOS

abstract: Since Software-Defined Networking (SDN) gains popularity, mobile/wireless support is mentioned with importance to be handled as one of the crucial aspects in SDN. SDN introduces a centralized entity called SDN controller with the holistic view of the topology on the separated control/data plane architecture. Leveraging the features provided in the SDN controller, mobility management can be simply designed and lightweight, thus there is no need to define and rely on new mobility entities such as given in the traditional IP mobility management architectures. In this paper, we design and implement lightweight IPv6 mobility management in Open Network Operating System (ONOS) that is an open-source SDN control platform for service providers. For the lightweight mobility management, we implement the Neighbor Discovery Proxy (ND Proxy) function into the OpenFlow-enabled AP and switches, and ONOS controller module to handle the receiving ICMPv6 message and to send the unique home network prefix address to an IPv6 host. Thus this approach enables mobility management without bringing or integrating on traditional IP mobility protocols. The proposed idea was experimentally evaluated in the ONOS controller and Raspberry Pi based testbed, identifying the obtained handoff signaling latency is in the acceptable performance range.


title: Outcomes of Posterior Component Separation With Transversus Abdominis Muscle Release and Synthetic Mesh Sublay Reinforcement.

abstract: OBJECTIVE ::: To evaluate the safety and efficacy of transversus abdominis muscle release (TAR) with retrorectus synthetic mesh reinforcement in a large series of complex hernia patients. ::: ::: ::: BACKGROUND ::: Posterior component separation via TAR during abdominal wall reconstruction (AWR) continues to gain popularity. Although our early experience with TAR has been promising, long-term outcomes have not been reported. ::: ::: ::: METHODS ::: From December 2006 to December 2014, consecutive patients undergoing open AWR utilizing TAR were identified in our prospectively maintained database and reviewed retrospectively. Main outcome measures included demographics, perioperative details, wound complications, and recurrences. ::: ::: ::: RESULTS ::: During the study period, 428 consecutive TAR procedures were analyzed. Mean age was 58, with mean body mass index 34.4 kg/m (range 20-65). Major comorbidities included diabetes (21%), chronic obstructive pulmonary disease (12%), and immunosuppression (3%). Mean hernia defect area was 606 cm (range 180-1280) and average mesh size was 1220 cm (range 600-4500). The majority of cases (66%) were clean, 26% were clean-contaminated, and 8% were contaminated. Eighty (18.7%) surgical-site events occurred, of which 39 (9.1%) were surgical-site infections. Three patients required mesh debridement; however, no instances of mesh explantation occurred. Of the 347 (81%) patients with at least 1-year follow-up (mean 31.5 mo), there were 13 (3.7%) recurrences. ::: ::: ::: CONCLUSIONS ::: Complex AWR represents a formidable surgical challenge. In this large series, we demonstrated that posterior component separation via TAR with wide synthetic mesh sublay provides a very durable repair with low morbidity, even in comorbid patients with large defects. We strongly advocate TAR as a robust addition to the armamentarium of reconstructive surgeons.


title: Edge Boxes: Locating Object Proposals from Edges

abstract: The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box’s boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96% object recall at overlap threshold of 0.5 and over 75% recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.


title: Power losses and efficiency analysis of multilevel dc-dc converters

abstract: This paper presents a detailed analysis about the power losses and efficiency of multilevel dc-dc converters. The analysis considers different loss mechanisms and gives out quantitative descriptions of the power losses and useful design criteria. The analysis is based on a three-level multilevel dc-dc converter and can be extended to other switched-capacitor converters. The comparison between the theoretical analysis and the experimental results are shown to substantiate the theory


title: An efficient clustering-based retrieval framework for real crime scene footwear marks

abstract: As the most common type of evidence at crime scenes, footwear marks are found more often than fingerprints, and yet left largely unused due to lack of efficient and reliable tools. While the central task is stated simply – retrieve the closest matches among a database of known outsole prints – the difficulty is the poor quality of the marks and a very large and increasing number of outsole patterns. Since grouping the database into clusters can dramatically speed-up retrieval, we propose clustering based on recurring outsole patterns. The clustered database is used to retrieve similar prints for a given crime scene mark. Geometric shapes like line segments, circles and ellipses are proposed as features for crime scene marks. Then these features are structurally represented in the form of an attributed relational graph (ARG). Robust ARG matching is achieved with the introduced footwear print distance (FPD), a similarity measure for footwear prints. Sensitivity analysis of FPD is performed to show its robustness. The proposed system is invariant to scale, translation, rotation and insensitive to noise and degradations of the prints. Experiments show that the approach outperforms other state-of-the-art footwear print retrieval systems.


Regularized Weighted Linear Regression for High-dimensional Censored Data


title: Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras.

abstract: This paper describes a general continuous-time framework for visual-inertial simultaneous localization and mapping and calibration. We show how to use a spline parameterization that closely matches the torque-minimal motion of the sensor. Compared to traditional discrete-time solutions, the continuous-time formulation is particularly useful for solving problems with high-frame rate sensors and multiple unsynchronized devices. We demonstrate the applicability of the method for multi-sensor visual-inertial SLAM and calibration by accurately establishing the relative pose and internal parameters of multiple unsynchronized devices. We also show the advantages of the approach through evaluation and uniform treatment of both global and rolling shutter cameras within visual and visual-inertial SLAM systems.


Steering Behaviors For Autonomous Characters


title: Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images

abstract: Immunohistochemistry (IHC) staining is a widely used technique in the diagnosis of abnormal cells such as cancer. For instance, it can be used to determine the distribution and localization of the differentially expressed biomarkers of immune cells (such as T-cells or B-cells) in cancerous tissue for an immune response study. Typically, the immunological data of interest includes the type, density and location of the immune cells within the tumor samples; this data is of particular interest to pathologists for accurate patient survival prediction. However, to manually count each subset of immune cells under a bright-field microscope for each piece of IHC stained tissue is usually extremely tedious and time consuming. This makes automatic detection very attractive, but it can be very challenging due to the wide variety of cell appearances resulting from different tissue types, block cuttings, and staining processes. This paper presents a novel method for automatic immune cell counting on digitally scanned images of IHC stained slides. The method first uses a sparse color unmixing technique to separate the IHC image into multiple color channels that correspond to different cell structures. Since the immune cell biomarkers that we are interested in are membrane markers, the detection problem is formulated into a deep learning framework using the membrane image channel. The algorithm is evaluated on a clinical data set containing a large number of IHC slides and demonstrates more effective detection than the existing technique and the result is also in accordance with the human observer’s output.


title: Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification

abstract: In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic label propagation (DLP), which performs transductive learning through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation, our algorithm instead emphasizes dynamic metric fusion with label information. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multi-class and multi-label tasks.


title: Pharmacological Foundations of Cannabis Chemovars.

abstract: An advanced Mendelian Cannabis breeding program has been developed utilizing chemical markers to maximize the yield of phytocannabinoids and terpenoids with the aim to improve therapeutic efficacy and safety. Cannabis is often divided into several categories based on cannabinoid content. Type I, Δ9-tetrahydrocannabinol-predominant, is the prevalent offering in both medical and recreational marketplaces. In recent years, the therapeutic benefits of cannabidiol have been better recognized, leading to the promotion of additional chemovars: Type II, Cannabis that contains both Δ9-tetrahydrocannabinol and cannabidiol, and cannabidiol-predominant Type III Cannabis. While high-Δ9-tetrahydrocannabinol and high-myrcene chemovars dominate markets, these may not be optimal for patients who require distinct chemical profiles to achieve symptomatic relief. Type II Cannabis chemovars that display cannabidiol- and terpenoid-rich profiles have the potential to improve both efficacy and minimize adverse events associated with Δ9-tetrahydrocannabinol exposure. Cannabis samples were analyzed for cannabinoid and terpenoid content, and analytical results are presented via PhytoFacts, a patent-pending method of graphically displaying phytocannabinoid and terpenoid content, as well as scent, taste, and subjective therapeutic effect data. Examples from the breeding program are highlighted and include Type I, II, and III Cannabis chemovars, those highly potent in terpenoids in general, or single components, for example, limonene, pinene, terpinolene, and linalool. Additionally, it is demonstrated how Type I - III chemovars have been developed with conserved terpenoid proportions. Specific chemovars may produce enhanced analgesia, anti-inflammatory, anticonvulsant, antidepressant, and anti-anxiety effects, while simultaneously reducing sequelae of Δ9-tetrahydrocannabinol such as panic, toxic psychosis, and short-term memory impairment.


title: Hybrid-Switching Full-Bridge DC–DC Converter With Minimal Voltage Stress of Bridge Rectifier, Reduced Circulating Losses, and Filter Requirement for Electric Vehicle Battery Chargers

abstract: This paper first presents a hybrid-switching step-down dc-dc converter, and then, by introducing transformer isolation, a novel hybrid-switching phase-shift full-bridge dc-dc converter is derived for electric vehicle battery chargers. The proposed converter provides wide zero-voltage-switching range in the leading-leg switches, achieves zero-current-switching for lagging-leg switches, and uses a hybrid-switching method to avoid freewheeling circulating losses in the primary side. Because the resonant capacitor voltage of the hybrid-switching circuit is applied between the bridge rectifier and the output inductor for the duration of the freewheeling intervals, a smaller sized output inductor can be utilized. With the current rectifier diode of the hybrid-switching circuit providing a clamping path, the voltage overshoots that arise during the turn-off of the rectifier diodes are eliminated and the voltage stress of bridge rectifier is clamped to the minimal achievable value, which is equal to secondary-reflected input voltage of the transformer. The inductive energy stored in the output inductor and the capacitive energy stored in the resonant capacitor of the hybrid-switching circuit are transferred to the output simultaneously during the freewheeling intervals with only one diode in series in the current path, achieving more effective and efficient energy transfer. The effectiveness of the proposed converter was experimentally verified using a 3.6-kW prototype circuit designed for electric vehicle onboard chargers. Experimental results of the hardware prototype show that the converter achieves a peak efficiency of 98.1% and high system efficiencies over wide output voltage and power ranges.


title: Deriving a recipe similarity measure for recommending healthful meals

abstract: A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.


title: A One Round Protocol for Tripartite Diffie–Hellman

abstract: In this paper we propose a three participants variation of the Diffie--Hellman protocol. This variation is based on the Weil and Tate pairings on elliptic curves, which were first used in cryptography as cryptanalytic tools for reducing the discrete logarithm problem on some elliptic curves to the discrete logarithm problem in a finite field.


title: Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification

abstract: We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion. Furthermore, our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors. We validate our approach with a user study and compare against the state of the art, where we show significant improvements. Furthermore, we demonstrate our method extensively on many different types of images, including black-and-white photography from over a hundred years ago, and show realistic colorizations.


title: Processing performance on Apache Pig, Apache Hive and MySQL cluster

abstract: MySQL Cluster is a famous clustered database that is used to store and manipulate data. The problem with MySQL Cluster is that as the data grows larger, the time required to process the data increases and additional resources may be needed. With Hadoop and Hive and Pig, processing time can be faster than MySQL Cluster. In this paper, three data testers with the same data model will run simple queries and to find out at how many rows Hive or Pig is faster than MySQL Cluster. The data model taken from GroupLens Research Project [12] showed a result that Hive is the most appropriate for this data model in a low-cost hardware environment.


Effects of impulse drip irrigation on root response of aerobic rice.


title: Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness

abstract: We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with focus on Task 1: Check-Worthiness. The task asks to predict which claims in a political debate should be prioritized for fact-checking. In particular, given a debate or a political speech, the goal was to produce a ranked list of its sentences based on their worthiness for fact checking. We offered the task in both English and Arabic, based on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign. A total of 30 teams registered to participate in the Lab and seven teams actually submitted systems for Task~1. The most successful approaches used by the participants relied on recurrent and multi-layer neural networks, as well as on combinations of distributional representations, on matchings claims' vocabulary against lexicons, and on measures of syntactic dependency. The best systems achieved mean average precision of 0.18 and 0.15 on the English and on the Arabic test datasets, respectively. This leaves large room for further improvement, and thus we release all datasets and the scoring scripts, which should enable further research in check-worthiness estimation.


title: Smart City Ontologies: Improving the effectiveness of smart city applications

abstract: This paper addresses the problem of low impact of smart city applications observed in the fields of energy and transport, which constitute high-priority domains for the development of smart cities. However, these are not the only fields where the impact of smart cities has been limited. The paper provides an explanation for the low impact of various individual applications of smart cities and discusses ways of improving their effectiveness. We argue that the impact of applications depends primarily on their ontology, and secondarily on smart technology and programming features. Consequently, we start by creating an overall ontology for the smart city, defining the building blocks of this ontology with respect to the most cited definitions of smart cities, and structuring this ontology with the Protege 5.0 editor, defining entities, class hierarchy, object properties, and data type properties. We then analyze how the ontologies of a sample of smart city applications fit into the overall Smart City Ontology, the consistency between digital spaces, knowledge processes, city domains targeted by the applications, and the types of innovation that determine their impact. In conclusion, we underline the relationships between innovation and ontology, and discuss how we can improve the effectiveness of smart city applications, combining expert and user-driven ontology design with the integration and or-chestration of applications over platforms and larger city entities such as neighborhoods, districts, clusters, and sectors of city activities.


title: Logout in single sign-on systems: Problems and solutions

abstract: Web single sign-on (SSO) systems enable users to authenticate themselves to multiple online services with one authentication credential and mechanism offered by an identity provider. The topic is widely studied and many solutions exist. However, logging out of a service using SSO has received less attention. While previous studies note that users want single logout when using SSO, most of the existing services do not offer it, and the identity providers do not even keep track of the open sessions. This article describes challenges related to logout in federated identity management and analyzes unexpected behavior in logout situations. The examples are from the Shibboleth SSO system. Based on the analysis, we give guidelines for implementing reliable logout and describe a polling-based solution for creating a system-wide logout mechanisms that only requires minor changes to the existing code and does not burden the identity provider excessively. In addition to the system-wide logout, our solution gives users the option to log out of only one service. A usability test was conducted to evaluate the solution. The results show that the users liked the ability to choose between the two logout options, but they did not understand the words used to describe them. Another observation was that a majority of the users do not log out of the services at all; they just close the browser window, which should be taken into account in the design of web SSO systems.


title: A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu

abstract: This paper describes the simultaneous development of dependency structure and phrase structure treebanks for Hindi and Urdu, as well as a PropBank. The dependency structure and the PropBank are manually annotated, and then the phrase structure treebank is produced automatically. To ensure successful conversion the development of the guidelines for all three representations are carefully coordinated.


title: [Left lateral predisposition of endometrioma].

abstract: OBJECTIVE ::: To investigate whether asymmetry exists in the left- and right-handed distribution of ovarian cystic lesions in women with endometriosis. ::: ::: ::: MATERIALS AND METHODS ::: We evaluate operative reports of women who underwent first line surgical treatment of endometrioma (n=154) from January 2000 to December 2002. Data of all operative findings consisted of a written report, and a diagram, the revised American Fertility Society classification of endometriosis. ::: ::: ::: RESULTS ::: Endometrioma was found in the left ovary (n=81), in the right ovary (n=39), and bilaterally (n=34). Left ovarian endometrioma was found more frequently (67,5%) than the right endometrioma (P<.001, odds ratio 2.3, 95% confidence interval 1.5, 3.7). This trend was not related to the size of the endometrioma. ::: ::: ::: CONCLUSION ::: Our results confirm a left lateral predisposition of ovarian endometrioma. It is probably caused by the decreased fluid movement in the left side of the pelvis because of the presence of sigmoid colon. These findings may support the theory that the origin of endometriosis is from the regurgitated endometrial cells.


title: Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions

abstract: The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. Our approach captures characteristics of the lesion margin and of the lesion interior by creating two separate dictionaries for the margin and the interior regions of lesions (“dual dictionaries” of visual words). Based on these dictionaries, visual word histograms are generated for each region of interest within the lesion and its margin. For validation of our approach, we used two datasets from two different institutions, containing CT images of 194 liver lesions (61 cysts, 80 metastasis, and 53 hemangiomas). The final diagnosis of each lesion was established by radiologists. The classification accuracy for the images from the two institutions was 99% and 88%, respectively, and 93% for a combined dataset. Our new BoVW approach that uses dual dictionaries shows promising results. We believe the benefits of our approach may generalize to other application domains within radiology.


Bug Fixes, Improvements, ... and Privacy Leaks - A Longitudinal Study of PII Leaks Across Android App Versions


title: Water-Filling: A Geometric Approach and its Application to Solve Generalized Radio Resource Allocation Problems

abstract: In this paper, a simple and elegant geometric water-filling (GWF) approach is proposed to solve the unweighted and weighted radio resource allocation problems. Unlike the conventional water-filling (CWF) algorithm, we eliminate the step to find the water level through solving a non-linear system from the Karush-Kuhn-Tucker conditions of the target problem. The proposed GWF requires less computation than the CWF algorithm, under the same memory requirement and sorted parameters. Furthermore, the proposed GWF avoids complicated derivation, such as derivative or gradient operations in conventional optimization methods, while provides insights to the problems and the exact solutions to the target problems. Most importantly, the GWF can be extended to solve a generalized form of radio resource allocation problem with more stringent constraints: (weighted) optimization problem with individual peak power constraints (GWFPP), and to include (weighted) group bounded power constraints (GWFGBP). On the other side, the CWF cannot solve these two general forms of the RRA problems, due to the difficulty to solve the non-linear system with multiple non-linear equations and inequalities in multiple dual variables. Optimality of the proposed water-filling solution is strictly proved for each of the proposed algorithms. Furthermore, numerical results show that the proposed approach is effective, efficient, easy to follow and insight-seeing.