Query: TriggerSync: A time synchronisation tool

Relevant passages:
A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation <=SEP=> Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.

Unified temporal and spatial calibration for multi-sensor systems <=SEP=> In order to increase accuracy and robustness in state estimation for robotics, a growing number of applications rely on data from multiple complementary sensors. For the best performance in sensor fusion, these different sensors must be spatially and temporally registered with respect to each other. To this end, a number of approaches have been developed to estimate these system parameters in a two stage process, first estimating the time offset and subsequently solving for the spatial transformation between sensors. In this work, we present on a novel framework for jointly estimating the temporal offset between measurements of different sensors and their spatial displacements with respect to each other. The approach is enabled by continuous-time batch estimation and extends previous work by seamlessly incorporating time offsets within the rigorous theoretical framework of maximum likelihood estimation. Experimental results for a camera to inertial measurement unit (IMU) calibration prove the ability of this framework to accurately estimate time offsets up to a fraction of the smallest measurement period.

Estimation and Removal of Clock Skew from Network Delay Measurements <=SEP=> Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze netw ork performance. The clocks on the end-systems used to measure the dela ys, however, are not always synchronized, and this lack of synchron ization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. In this paper we introduce a linear programming-based algorithm to estimate the clock skew in network delay measur ements and compare it with three other algorithms. We show that our algorithm has time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew est imate is independent of the magnitude of the skew. We use traces of real Int ernet delay measurements to assess the algorithm, and compare its perfo rmance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms. Keywords— clock skew, clock ratio, end-to-end delay, delay measurement.

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM <=SEP=> Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier. Up to four cameras are interfaced through a modern ARM-FPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms. Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial motion estimation framework demonstrate the capabilities of the presented system.

TICSync: Knowing when things happened <=SEP=> Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.

A 3.3-V single-poly CMOS audio ADC delta-sigma modulator with 98-dB peak SINAD and 105-dB peak SFDR <=SEP=> This paper presents a second-order /spl Delta//spl Sigma/ modulator for audio-band analog-to-digital conversion implemented in a 3.3-V, 0.5-/spl mu/m, single-poly CMOS process using metal-metal capacitors that achieves 98-dB peak signal-to-noise-and-distortion ratio and 105-dB peak spurious-free dynamic range. The design uses a low-complexity, first-order mismatch shaping 33-level digital-to-analog converter and a 33-level flash analog-to-digital converter with digital common-mode rejection and dynamic element matching of comparator offsets. These signal-processing innovations, combined with established circuit techniques, enable state-of-the art performance in CMOS technology optimized for digital circuits.

The influence of chronotype and intelligence on academic achievement in primary school is mediated by conscientiousness, midpoint of sleep and motivation. <=SEP=> Individuals differ in their timing of sleep (bed times, rise times) and in their preference for morning or evening hours. Previous work focused on the relationship between academic achievement and these variables in secondary school students. The main aim of the study is to investigate the relationship between chronotype and academic achievement in 10-year-old children (n = 1125) attending 4th grade of primary school. They filled a cognitive test (Culture Fair Intelligence Test, CFT 20-R) and questions about rise times and bed times, academic achievement, conscientiousness and motivation. We used the "scales for the assessment of learning and performance motivation" (SELLMO; Skalen zur Erfassung der Lern- und Leistungsmotivation for motivation), the short version of the Five-Factor Personality Inventory Children (FFPI-C) to measure conscientiousness, and the Composite Scale of Morningness (CSM) to assess morningness-eveningness. Mean CSM score was 37.84 ± 6.66, midpoint of sleep was 1:36 ± 00:25 and average sleep duration (time in bed) was 10:15 ± 0:48. Morningness orientation was positively related to intelligence, conscientiousness and learning objectives. Eveningness orientation was related to avoidance performance objectives and work avoidance. Early midpoint of sleep, conscientiousness and intelligence were associated with better grades. The multivariate model showed that intelligence was the strongest predictor of good grades. Conscientiousness, motivation, younger age and an earlier midpoint of sleep were positively related to good grades. This is the first study in primary school pupils, and it shows that the relationship between evening orientation and academic achievement is already prevalent at this age even when controlling for important predictors of achievement.

Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning <=SEP=> The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which makes them challenging but often predictable opponents. In this paper, we examine the problem of teaching a bot to play like a human in first-person shooter game combat scenarios. Our bot learns attack, exploration and targeting policies from data collected from expert human player demonstrations in Unreal Tournament. We hypothesize that one key difference between human players and autonomous bots lies in the relative valuation of game states. To capture the internal model used by expert human players to evaluate the benefits of different actions, we use inverse reinforcement learning to learn rewards for different game states. We report the results of a human subjects’ study evaluating the performance of bot policies learned from human demonstration against a set of standard bot policies. Our study reveals that human players found our bots to be significantly more human-like than the standard bots during play. Our technique represents a promising stepping-stone toward addressing challenges such as the Bot Turing Test (the CIG Bot 2K Competition).

Service Level Agreement in Cloud Computing <=SEP=> Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement in a Service Oriented Architecture (SOA). We use the third party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.

Social-driven Internet of Connected Objects <=SEP=> Internet evolution has been recently related with some aspect of user empowerment, mostly in terms of content distribution, and this has been ultimately accelerated by the fast-paced introduction and expansion of wireless technologies. Hence, the Internet should start to be seen as a communications infrastructure able to support the integration of a myriad of embedded and personal wireless objects. This way a future Internet will support the interaction between users’ social, physical and virtual sphere. This position paper aims to raise some discussion about the technology required to ensure an efficient interaction between the physical, social and virtual worlds by extending the Internet by means of interconnected objects. Namely, it is argued that an efficient interaction between the physical, social and virtual worlds requires the development of a data-centric architecture based on IP-driven opportunisitc networking able to make useful data available to people when and where they really need it, augmenting their social and environmental awareness.

MC-HOG Correlation Tracking with Saliency Proposal <=SEP=> Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.

Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval. <=SEP=> Early neuroimaging studies often failed to obtain evidence of medial temporal lobe (MTL) activation during episodic encoding or retrieval, but a growing number of studies using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have provided such evidence. We review data from fMRI studies that converge on the conclusion that posterior MTL is associated with episodic encoding; too few fMRI studies of retrieval have reported MTL activations to allow firm conclusions about their exact locations. We then turn to a recent meta-analysis of PET studies (Lepage et al., Hippocampus 1998;8:313-322) that appears to contradict the fMRI encoding data. Based on their analysis of the rostrocaudal distribution of activations reported during episodic encoding or retrieval, Lepage et al. (1998) concluded that anterior MTL is strongly associated with episodic encoding, whereas posterior MTL is strongly associated with episodic retrieval. After considering the evidence reviewed by Lepage et al. (1998) along with additional studies, we conclude that PET studies of encoding reveal both anterior and posterior MTL activations. These observations indicate that the contradiction between fMRI and PET studies of encoding was more apparent than real. However, PET studies have reported anterior MTL encoding activations more frequently than have fMRI studies. We consider possible sources of these differences.

Active learning: theory and applications to automatic speech recognition <=SEP=> We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.

Ensemble Named Entity Recognition (NER): Evaluating NER Tools in the Identification of Place Names in Historical Corpora <=SEP=> The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with geographic information systems. For instance, automated place name identification is possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. In addition, the results showed that these NER systems are not strongly dependent on preprocessing and translation to Modern English.

Flickr Circles: Aesthetic Tendency Discovery by Multi-View Regularized Topic Modeling <=SEP=> Aesthetic tendency discovery is a useful and interesting application in social media. In this paper we propose to categorize large-scale Flickr users into multiple circles, each containing users with similar aesthetic interests (e.g., landscapes). We notice that: (1) an aesthetic model should be flexible as different visual features may be used to describe different image sets; (2) the numbers of photos from different Flickr users vary significantly and some users may have very few photos; and (3) visual features from each Flickr photo should be seamlessly integrated at both low-level and high-level. To meet these challenges, we propose to fuze color, textural, and semantic channel features using a multi-view learning framework, where the feature weights are adjusted automatically. Then, a regularized topic model is developed to quantify each user's aesthetic interest as a distribution in the latent space. Afterward, a graph is constructed to describe the discrepancy of aesthetic interests among users. Apparently, densely connected users are with similar aesthetic interests. Thus, an efficient dense subgraph mining algorithm is adopted to group Flickr users into multiple circles. Experiments have shown that our approach performs competitively on a million-scale image set crawled from Flickr. Besides, our method can enhance the transferal-based photo cropping [40] as reported by the user study.

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming <=SEP=> Support vector machine (SVM) soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifiers are especially efficient for very large size samples. But little is known about their convergence, compared with the well-understood quadratic programming SVM classifier. In this article, we point out the difficulty and provide an error analysis. Our analysis shows that the convergence behavior of the linear programming SVM is almost the same as that of the quadratic programming SVM. This is implemented by setting a stepping-stone between the linear programming SVM and the classical 1-norm soft margin classifier. An upper bound for the misclassification error is presented for general probability distributions. Explicit learning rates are derived for deterministic and weakly separable distributions, and for distributions satisfying some Tsybakov noise condition.

Python robotics: an environment for exploring robotics beyond LEGOs <=SEP=> This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.

Heat, vibration, dust, salt spray, weather - taking wireless positioning to the extreme <=SEP=> There are lots of local positioning systems available for indoor applications, partly even for outdoor use. Many of them rely on a rather clean environment which does not interfere in their measurements too much. Industrial environments, in- or outdoors, often do not meet these requirements. The positioning applications here are surrounded by dirt, dust and vibrations. The number of local positioning systems being able to perform properly here at all times is limited to such not dependent on optical or sonic measurements. Local secondary radar systems proved to be a reliable source of distance and position measurements, even in harsh environments. This paper examines a few use cases and depicts the performance of local positioning radar systems there.

Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety <=SEP=> Mathematics anxiety (MA), a state of discomfort associated with performing mathematical tasks, is thought to affect a notable proportion of the school age population. Some research has indicated that MA negatively affects mathematics performance and that girls may report higher levels of MA than boys. On the other hand some research has indicated that boys’ mathematics performance is more negatively affected by MA than girls’ performance is. The aim of the current study was to measure girls’ and boys’ mathematics performance as well as their levels of MA while controlling for test anxiety (TA) a construct related to MA but which is typically not controlled for in MA studies. Four-hundred and thirty three British secondary school children in school years 7, 8 and 10 completed customised mental mathematics tests and MA and TA questionnaires. No gender differences emerged for mathematics performance but levels of MA and TA were higher for girls than for boys. Girls and boys showed a positive correlation between MA and TA and a negative correlation between MA and mathematics performance. TA was also negatively correlated with mathematics performance, but this relationship was stronger for girls than for boys. When controlling for TA, the negative correlation between MA and performance remained for girls only. Regression analyses revealed that MA was a significant predictor of performance for girls but not for boys. Our study has revealed that secondary school children experience MA. Importantly, we controlled for TA which is typically not controlled for in MA studies. Girls showed higher levels of MA than boys and high levels of MA were related to poorer levels of mathematics performance. As well as potentially having a detrimental effect on ‘online’ mathematics performance, past research has shown that high levels of MA can have negative consequences for later mathematics education. Therefore MA warrants attention in the mathematics classroom, particularly because there is evidence that MA develops during the primary school years. Furthermore, our study showed no gender difference in mathematics performance, despite girls reporting higher levels of MA. These results might suggest that girls may have had the potential to perform better than boys in mathematics however their performance may have been attenuated by their higher levels of MA. Longitudinal research is needed to investigate the development of MA and its effect on mathematics performance.

The Virtual Human Interface: A Photorealistic Digital Human <=SEP=> o mimic the quality of everyday human communication, future computer interfaces must combine the benefits of high visual fidelity with conversational intelligence and—above all—the ability to modulate the emotions of their users. From a computer graphics or visual perspective, achieving this goal requires creating synthetic digital humans that have photorealistic faces capable of expressing the finest shades of emotion. From a perceptual point of view, the same system must also read users' emotional reactions and adapt the behavior of the digital human accordingly. Our research focuses on implementing this concept to create a face-to-face system called the Virtual Human Interface (VHI). Photorealistic virtual humans— in contrast to nonrealistic virtual characters—make a more effective communication interface between computers and humans because they can stimulate declarative and procedural memory in the human brain. Declarative memory is our memory of information and events. Procedural memory, on the other hand, is the memory of how to do something, such as riding a bike. Most computer interfaces primarily access users' declarative memory; the person learns information about a particular topic. The VHI system, however, engages the user through entertaining emotional responses that help engrave knowledge in the user's mind and even modify the user's behavior through procedural memory. During the past several decades, researchers have conducted countless studies on agents and human animation. Early work mainly addressed low-polygon virtual humans that users could animate in real time. 1 Many of these animation systems initially addressed purposes other than the needs of human–computer interaction. However, the requirements of facial ani-mation—and especially speech synthesis—demand a different underlying architecture that can effectively model how real faces move and change. 2-4 As a result, research began to focus on creating autonomous agents that could exhibit rich personalities and interact in virtual worlds inhabited by other characters. To provide the illusion of a lifelike character, researchers have developed detailed emotional and personality models that can control the animation channels as a function of the virtual human's personality, mood, and emotions. 7-9 The real-time interaction with these virtual characters poses an extra set of technical challenges in terms of the speed, computational power, and visual quality required to make the user believe that he or she is interacting with a living creature. To achieve this goal, researchers eventually replaced precrafted animated actions with intelligent behavior modules that could control speech, locomotion, gaze, blinks, gestures (including various postures), and interaction with the …

SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data <=SEP=> We present SWELLSHARK, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SWELLSHARK achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SWELLSHARK achieved within 5.1% of a crowdsourced annotation approach – which originally utilized 20 teams over the course of several weeks – in 24 hours.

European option pricing with transaction costs and trading restrictions <=SEP=> In this paper, using the utility based option pricing approach, pioneered by Hodges and Neuberger, we propose a new model in a continuous-time market with proportional transaction costs and several trading restrictions. According to this model, we study the problem of option pricing and prove that the value function of our model is a unique constrained viscosity solution of a HJB equation.

Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines <=SEP=> In this paper, we consider the problem of federated (or decentralized) learning using ADMM with multiple agents. We consider a scenario where a certain fraction of agents (referred to as Byzantines) provide falsified data to the system. In this context, we study the convergence behavior of the decentralized ADMM algorithm. We show that ADMM converges linearly to a neighborhood of the solution to the problem under certain conditions. We next provide guidelines for network structure design to achieve faster convergence. Next, we provide necessary conditions on the falsified updates for exact convergence to the true solution. To tackle the data falsification problem, we propose a robust variant of ADMM. We also provide simulation results to validate the analysis and show the resilience of the proposed algorithm to Byzantines.

PIP Loss : A Unitary-invariant Metric for Understanding Functionality and Dimensionality of Vector Embeddings <=SEP=> In this paper, we present a theoretical framework for understanding vector embedding, a fundamental building block of many deep learning models, especially in NLP. We discover a natural unitary-invariance in vector embeddings, which is required by the distributional hypothesis. This unitary-invariance states the fact that two embeddings are essentially equivalent if one can be obtained from the other by performing a relative-geometry preserving transformation, for example a rotation. This idea leads to the Pairwise Inner Product (PIP) loss, a natural unitary-invariant metric for the distance between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that it is tightly connect to two fundamental properties, namely similarity and compositionality. By formulating the embedding training process as matrix factorization under noise, we reveal a fundamental bias-variance tradeoff in dimensionality selection. With tools from perturbation and stability theory, we provide an upper bound on the PIP loss using the signal spectrum and noise variance, both of which can be readily inferred from data. Our framework sheds light on many empirical phenomena, including the existence of an optimal dimension, and the robustness of embeddings against over-parametrization. The bias-variance tradeoff of PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings. 1 ar X iv :1 80 3. 00 50 2v 3 [ st at .M L ] 1 2 A pr 2 01 8

A Big Data system supporting Bosch Braga Industry 4.0 strategy <=SEP=> People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).

UAVSim: A simulation testbed for unmanned aerial vehicle network cyber security analysis <=SEP=> Increased use of unmanned systems in various tasks enables users to complete important missions without risking human lives. Nonetheless, these systems pose a huge threat if the operational cyber security is not handled properly, especially for the unmanned aerial vehicle systems (UAVS), which can cause catastrophic damages. Therefore, it is important to check the impact of various attack attempts on the UAV system. The most economical and insightful way to do this is to simulate operational scenarios of UAVs in advance. In this paper, we introduce UAVSim, a simulation testbed for Unmanned Aerial Vehicle Networks cyber security analysis. The testbed allows users to easily experiment by adjusting different parameters for the networks, hosts and attacks. In addition, each UAV host works on well-defined mobility framework and radio propagation models, which resembles real-world scenarios. Based on the experiments performed in UAVSim, we evaluate the impact of Jamming attacks against UAV networks and report the results to demonstrate the necessity and usefulness of the testbed.

Evaluation of the Patient with Nasal Obstruction. <=SEP=> Nasal obstruction is often multifactorial and knowledge of the contributing factors is critical to appropriate evaluation, diagnosis, and execution of a treatment plan. Recognizing and appropriately managing all components of nasal obstruction will increase the likelihood of symptomatic improvement and patient satisfaction.

Research in Education ( CPRE ) 1-2001 Teacher Turnover , Teacher Shortages , and the Organization of Schools <=SEP=> Contemporary educational theory holds that one of the pivotal causes of inadequate school performance is the inability of schools to adequately staff classrooms with qualified teachers. Contemporary theory also holds that these staffing problems are primarily due to shortages of teachers, which, in turn, are primarily due to recent increases in teacher retirements and student enrollments. This analysis investigates the possibility that there are other factors that might have an impact on teacher turnover levels, and, in turn, the staffing problems of schools, factors rooted in the organizational characteristics and conditions of schools. The data utilized in this investigation are from the Schools and Staffing Survey and its supplement, the Teacher Followup Survey, a large, comprehensive, nationally representative survey of teachers and schools conducted by the National Center for Education Statistics. The results of this analysis show that, net of teacher effects, there are significant effects of school characteristics and organizational conditions on teacher turnover which have largely been overlooked by previous research. For example, the data show that while high-poverty public schools have moderately higher rates, contrary to conventional wisdom, neither larger schools, nor public schools in large school districts, nor urban public schools have especially high rates of teacher turnover. In contrast, small private schools stand out for their high rates of turnover. Moreover, the data show, again contrary to popular wisdom, that the amount of turnover accounted for by retirement is relatively minor, especially when compared to that resulting from two related causes – teacher job dissatisfaction and teachers pursuing other jobs. The data show that, in particular, low salaries, inadequate support from the school administration, student discipline problems, and limited faculty input into school decision-making all contribute to higher rates of turnover, after controlling for the characteristics of both teachers and schools. The results of this investigation suggest that school staffing problems are neither synonymous with, nor primarily due to, teacher shortages in the conventional sense of a deficit in the supply of teachers. Rather, this study suggests that school staffing problems are primarily due to excess demand resulting from a "revolving door" – where large numbers of teachers depart their jobs for reasons other than retirement. This study also suggests that popular education initiatives, such as teacher recruitment programs, will not solve the staffing problems of such schools if they do not also address the organizational sources of low teacher retention. Disciplines Educational Administration and Supervision | Education Economics | Social and Philosophical Foundations of Education | Teacher Education and Professional Development Comments View on the CPRE website. This report is available at ScholarlyCommons: https://repository.upenn.edu/cpre_researchreports/12 ctp Center for the Study of Teaching and Policy U N I V E R S I T Y O F W A S H I N G T O N Teacher Turnover, Teacher Shortages, and the Organization of Schools by Richard M. Ingersoll University of Pennsylvania

The impact of agile practices on communication in software development <=SEP=> Agile software development practices such as eXtreme Programming (XP) and SCRUM have increasingly been adopted to respond to the challenges of volatile business environments, where the markets and technologies evolve rapidly and present the unexpected. In spite of the encouraging results so far, little is known about how agile practices affect communication. This article presents the results from a study which examined the impact of XP and SCRUM practices on communication within software development teams and within the focal organization. The research was carried out as a case study in F-Secure where two agile software development projects were compared from the communication perspective. The goal of the study is to increase the understanding of communication in the context of agile software development: internally among the developers and project leaders and in the interface between the development team and stakeholders (i.e. customers, testers, other development teams). The study shows that agile practices improve both informal and formal communication. However, it further indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication. The study highlights the fact that hurdles and improvements in the communication process can both affect the feature requirements and task subtask dependencies as described in coordination theory. While the use of SCRUM and some XP practices facilitate team and organizational communication of the dependencies between product features and working tasks, the use of agile practices requires that the team and organization use also additional plan-driven practices to ensure the efficiency of external communication between all the actors of software development.

Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework <=SEP=> In recent years, there has been a considerable interest in the area of mobile robotics and educational technologies [1-7]. For control engineers and researchers, there is a wealth of literature dealing with wheeled mobile robots (WMR) control and their applications. However, while the subject of kinematic modeling of WMR is well documented and easily understood by students, the subject of dynamic modeling of WMR has not been addressed adequately in the literature. The dynamics of WMR are highly nonlinear and involve non-holonomic constraints which makes difficult their modeling and analysis especially for new engineering students starting their research in this field. Therefore, a detailed and accurate dynamic model describing the WMR motion need to be developed to offer students a general framework for simulation analysis and model based control system design.