Query: ECDH Key-Extraction via Low-Bandwidth Electromagnetic Attacks on PCs

Relevant passages:
CACHE MISSING FOR FUN AND PROFIT <=SEP=> Simultaneous multithreading — put simply, the sharing of the execution resources of a superscalar processor between multiple execution threads — has recently become widespread via its introduction (under the name “Hyper-Threading”) into Intel Pentium 4 processors. In this implementation, for reasons of efficiency and economy of processor area, the sharing of processor resources between threads extends beyond the execution units; of particular concern is that the threads share access to the memory caches. We demonstrate that this shared access to memory caches provides not only an easily used high bandwidth covert channel between threads, but also permits a malicious thread (operating, in theory, with limited privileges) to monitor the execution of another thread, allowing in many cases for theft of cryptographic keys. Finally, we provide some suggestions to processor designers, operating system vendors, and the authors of cryptographic software, of how this attack could be mitigated or eliminated entirely.

Remote Timing Attacks Are Practical <=SEP=> Timing attacks are usually used to attack weak computing devices such as smartcards. We show that timing attacks apply to general software systems. Specifically, we devise a timing attack against OpenSSL. Our experiments show that we can extract private keys from an OpenSSL-based web server running on a machine in the local network. Our results demonstrate that timing attacks against network servers are practical and therefore security systems should defend against them.

Introduction to differential power analysis <=SEP=> The power consumed by a circuit varies according to the activity of its individual transistors and other components. As a result, measurements of the power used by actual computers or microchips contain information about the operations being performed and the data being processed. Cryptographic designs have traditionally assumed that secrets are manipulated in environments that expose no information beyond the specified inputs and outputs. This paper examines how information leaked through power consumption and other side channels can be analyzed to extract secret keys from a wide range of devices. The attacks are practical, non-invasive, and highly effective—even against complex and noisy systems where cryptographic computations account for only a small fraction of the overall power consumption. We also introduce approaches for preventing DPA attacks and for building cryptosystems that remain secure even when implemented in hardware that leaks.

The EM Side-Channel(s) <=SEP=> We present results of a systematic investigation of leakage of compromising information via electromagnetic (EM) emanations from CMOS devices. These emanations are shown to consist of a multiplicity of signals, each leaking somewhat different information about the underlying computation. We show that not only can EM emanations be used to attack cryptographic devices where the power side–channel is unavailable, they can even be used to break power analysis countermeasures.

Cache Games -- Bringing Access-Based Cache Attacks on AES to Practice <=SEP=> Side channel attacks on cryptographic systems exploit information gained from physical implementations rather than theoretical weaknesses of a scheme. In recent years, major achievements were made for the class of so called access-driven cache attacks. Such attacks exploit the leakage of the memory locations accessed by a victim process. In this paper we consider the AES block cipher and present an attack which is capable of recovering the full secret key in almost real time for AES-128, requiring only a very limited number of observed encryptions. Unlike previous attacks, we do not require any information about the plaintext (such as its distribution, etc.). Moreover, for the first time, we also show how the plaintext can be recovered without having access to the cipher text at all. It is the first working attack on AES implementations using compressed tables. There, no efficient techniques to identify the beginning of AES rounds is known, which is the fundamental assumption underlying previous attacks. We have a fully working implementation of our attack which is able to recover AES keys after observing as little as 100 encryptions. It works against the OpenS SL 0.9.8n implementation of AES on Linux systems. Our spy process does not require any special privileges beyond those of a standard Linux user. A contribution of probably independent interest is a denial of service attack on the task scheduler of current Linux systems (CFS), which allows one to observe (on average) every single memory access of a victim process.

Recursive circle packing problems <=SEP=> This paper presents a class of packing problems where circles may be placed either inside or outside other circles, the whole set being packed in a rectangle. This corresponds to a practical problem of packing tubes in a container; before being inserted in the container, tubes may be put inside other tubes in a recursive fashion. A variant of the greedy randomized adaptive search procedure is proposed for tackling this problem, and its performance assessed in a set of benchmark instances.

A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data <=SEP=> Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

Power Converter EMI Analysis Including IGBT Nonlinear Switching Transient Model <=SEP=> It is well known that very high dv/dt and di/dt during the switching instant is the major high-frequency electromagnetic interference (EMI) source. This paper proposes an improved and simplified EMI-modeling method considering the insulated gate bipolar transistor switching-behavior model. The device turn-on and turn-off dynamics are investigated by dividing the nonlinear transition by several stages. The real device switching voltage and current are approximated by piecewise linear lines and expressed using multiple dv/dt and di/dt superposition. The derived EMI spectra suggest that the high-frequency noise is modeled with an acceptable accuracy. The proposed methodology is verified by experimental results using a dc-dc buck converter

Large scale predictive process mining and analytics of university degree course data <=SEP=> For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.

Bethe-ADMM for Tree Decomposition based Parallel MAP Inference <=SEP=> We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.

Using a Programmable Toy at Preschool Age: Why and How? <=SEP=> Robotic toys bring new dimension to role-play activities in kindergarten. Some preschool curricula clearly identify reasons for their inclusion. However, preschool teacher needs to revise usual teaching methods in order to use them. Offering a programmable toy or robotic-related activity doesn't mean immediate success in work with children. We document our research with concrete programmable device in a preschool classroom. Details of robotic-related sessions can help reader to design the quality game for preschool-age based on using a programmable toy.

Design Space Exploration of Network Processor Architectures <=SEP=> We describe an approach to explore the design space for architectures of packet processing devices on the system level. Our method is specific to the application domain of packet processors and is based on (1) models for packet processing tasks, a specification of the workload generated by traffic streams, and a description of the feasible space of architectures including computation and communication resources, (2) a measure to characterize the performance of network processors under different usage scenarios, (3) a new method to estimate end-to-end packet delays and queuing memory, taking task scheduling policies and bus arbitration schemes into account, and (4) a evolutionary algorithm for multi-objective design space exploration. Our method is analytical and based on a high level of abstraction, where the goal is to quickly identify interesting architectures, which may be subjected to a more detailed evaluation, e.g. using simulation. The feasibility of our approach is shown by a detailed case study, where the final output is three candidate architectures, representing different cost versus performance tradeoffs.

Membrane-distillation desalination : status and potential <=SEP=> This paper presents an assessment of membrane distillation (MD) based on the available state of the art and on our preliminary analysis. The process has many desirable properties such as low energy consumption, ability to use low temperature beat, compactness, and perceivably more immunity to fouling than other membrane processes. Within the tested range, the operating parameters of conventional MD configurations have the following effects:(1) the permeate fluxes can significantly be improved by increasing the hot feed temperature (increasing the temperature from 50 to 70°C increases the flux by more than three-fold), and by reducing the vapor/air gap (reducing the vapor air gap thickness from 5 to 1 mm increase the flux 2.3-fold); (2) the mass flow rate of the feed solution has a smaller effect: increasing it three-fold increases the flux by about 1.3-fold; (3) the concentration of the solute has slight effect: increasing the concentration by more than five-fold decreases the flux by just 1.I 5-fold; (4) the cold side conditions have a lower effect (about half) on the flux than the hot side; (5) the coolant mass flow rate has a negligible effect; (6) the coolant temperature has a lower effect than the mass flow rate of the hot solution. Fouling effects, membranes used, energy consumption, system applications and configurations, and very approximate cost estimates are presented. The permeate fluxes obtained by the different researchers seem to disagree by an order of magnitude, and better experimental work is needed.

AutoModeling: Integrated Approach for Automated Model Generation by Ensemble Selection of Feature Subset and Classifier <=SEP=> Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a ‘model’ for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.

The questionable contribution of medical measures to the decline of mortality in the United States in the twentieth century. <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

A new approach for robot motion planning using rapidly-exploring Randomized Trees <=SEP=> In the last few years, car-like robots became increasingly important. Thus, motion planning algorithms for this kind of problem are needed more than ever. Unfortunately, this problem is computational difficult and so probabilistic approaches like Probabilistic Roadmaps or Rapidly-exploring Randomized Trees are often used in this context. This paper introduces a new concept for robot motion planning especially for car-like robots based on Rapidly-exploring Randomized Trees. In contrast to the conventional method, the presented approach uses a pre-computed auxiliary path to improve the distribution of random states. The main contribution of this approach is the significantly increased quality of the computed path. A proof-of-concept implementation evaluates the quality and performance of the proposed concept.

A Robust Bayesian Truth Serum for Small Populations <=SEP=> Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n ≥ 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n ≥ 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n ≥ 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational. Introduction Web services that are built around user-generated content are ubiquitous. Examples include reputation systems, where users leave feedback about the quality of products or services, and crowdsourcing platforms, where users (workers) are paid small rewards to do human computation tasks, such as annotating an image. Whereas statistical estimation techniques (Raykar et al. 2010) can be used to resolve noisy inputs, for example in order to determine the image tags most likely to be correct, they are appropriate only when user inputs are informative in the first place. But what if providing accurate information is costly for users, or if users otherwise have an external incentive for submitting false inputs? The peer prediction method (Miller, Resnick, and Zeckhauser 2005) addresses the quality control problem by providing payments (in cash, points or otherwise) that align an agent’s own interest with providing inputs that are predictive of the inputs that will be provided by other agents. Formally, the peer prediction method provides strict incentives for providing truthful inputs (e.g., in regard to a user’s information about the quality of a product, or a user’s view on Copyright c © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. the correct label for a training example) for a system of two or more agents, and when there is a common prior amongst agents and, critically, known to the mechanism. The Bayesian Truth Serum (BTS) by Prelec (2004) still assumes that agents share a common prior, but does not require this to be known by the mechanism. In addition to an information report from an agent, BTS asks each agent for a prediction report, that reflects the agent’s belief about the distribution of information reports in the population. An agent’s payment depends on both reports, with an information component that rewards reports that are “surprisingly common,” i.e., more common than collectively predicted, and a prediction component that rewards accurate predictions of the reports made by others. A significant drawback of BTS is that it only aligns incentives for a large enough number of agents, where this number depends on the prior and is thus unknown to the mechanism. In addition, BTS may leave a participant with a negative payment, and is not numerically robust for all inputs. In this paper, we present the robust Bayesian Truth Serum (RBTS) mechanism, which, to the best of our knowledge, is the first peer prediction mechanism that does not rely on knowledge of the common prior to provide strict incentive compatibility for every number of agents n ≥ 3. RBTS is also ex post individually rational (so that no agent makes a negative payment in any outcome) and numerically robust, being well defined for all possible agent reports. Moreover, the mechanism seems conceptually simpler than BTS, and the incentive analysis is more straightforward. The main limitation of RBTS relative to earlier mechanisms, is that it applies only to the elicitation of binary information; e.g., good or bad experiences, or true or false classification labels.1 Extending RBTS to incorporate more than two signals is the most important direction for future research. RBTS takes the same reports as BTS, and an agent’s payment continues to consist of one component that depends on an agent’s information report and a second component that Many interesting applications involve binary information reports. This is supported by the fact that Prelec’s own experimental papers have adopted the binary signal case (Prelec and Seung 2006; John, Loewenstein, and Prelec 2011). Indeed, as the number of possible information reports increases, so does the difficulty imposed on users in providing the prediction report, which must include estimates for the additional possible information reports. depends on an agent’s prediction report. The main innovation is to induce a “shadow” posterior belief report for an agent i from her information report and the prediction report of another agent j, adjusting this prediction report in the direction suggested by agent i’s information report. We couple this with a particularity of the quadratic scoring rule, by which an agent prefers a shadow posterior belief that is as close as possible to her true posterior. In order to determine the agent’s payment, we then apply both the shadow posterior belief and the agent’s prediction report to the quadratic scoring rule, adopting the information report of a third agent k as the event to be predicted.

Detecting Predatory Behaviour in Game Chats <=SEP=> While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviours in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different datasets and features.

Using k-Poselets for Detecting People and Localizing Their Keypoints <=SEP=> A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.

Regularization for Unsupervised Deep Neural Nets <=SEP=> Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a “partial” approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.

IEEE 802 . 11 ad : Directional 60 GHz Communication for Multi-Gbps <=SEP=> With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave (mm-Wave) band. However, multi-Gbps throughput and small interference footprint come at the price of adverse signal propagation characteristics and require a fundamental rethinking of Wi-Fi communication principles. This paper describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular we study the transition from omni-directional to highly directional communication and its impact on the design of

Scholarly communication and bibliometrics <=SEP=> Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990–2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments. Given the amount of attention these topics have received, what is new and exciting enough to warrant a full chapter in 2001? What is new is that electronic scholarly communication is reaching critical mass, and CHAPTER 1

Improvements to BM25 and Language Models Examined <=SEP=> Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°Δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.

Age and Gender Estimation of Unfiltered Faces <=SEP=> This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of data-we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

Performance analysis of downlink NOMA networks over Rayleigh fading channels <=SEP=> This paper analyzes the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels for arbitrary number of network users. Specifically, closed-form expressions for the average SNR, average achievable rate, and outage probability are derived, where it has been shown that network users achieve diversity orders equivalent to their ordered channel gains. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results.

FPGA Implementations of the Round Two SHA-3 Candidates <=SEP=> The second round of the NIST-run public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). This paper presents the full implementations of all of the second round candidates in hardware with all of their variants. In order to determine their computational efficiency, an important aspect in NIST's round two evaluation criteria, this paper gives an area/speed comparison of each design both with and without a hardware interface, thereby giving an overall impression of their performance in resource constrained and resource abundant environments. The implementation results are provided for a Virtex-5 FPGA device. The efficiency of the architectures for the hash functions are compared in terms of throughput per unit area.

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks <=SEP=> Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github. com/hexiangnan/attentional factorization machine

Review on Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning <=SEP=> Big Data Analytics and Deep Learning are two high-center of information science. Big Data has become important because of companies i.e. both public and private have been gathering huge measures of domain-specific data, which can contain helpful data about issues, for example, national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning algorithms extract high-level, complex reflections as information representations through a progressive learning process. A advantage of Deep Learning is the study and learning of huge size of unsupervised information, making it a significant instrument for Big Data Analytics where crude information is to a great extent unlabeled and un-sorted. The present survey gives an idea of the previous work done by several researchers on the Big Data Analytics and Deep Learning applications.

Increasing ATM Efficiency withAssistant Based Speech Recognition <=SEP=> Initiatives to integrate Automatic Speech Recognition into Air Traffic Management (ATM) exists at least since the late 90s. Some success to replace pseudo pilots have been reported, but its integration into controller assistant tools is missing. German Aerospace Center (DLR) and Saarland University developed Assistant Based Speech Recognition (ABSR) enabling command recognition rates better than 95%. However, good recognition rates are no convincing argument for decision makers. Therefore, we conducted an ABSR validation study with eight air traffic controllers to quantify the benefits with respect to workload and efficiency. The study validates that ABSR does not just reduce controllers’ workload, which would already be a lot, but this paper presents that ABSR significantly increases ATM efficiency. Fuel reductions of 60 liters (16 gallons) per flight and a throughput increase by two arrivals per hour are possible. Keywords—AcListant®, Automatic Speech Recognition (ASR), Assistant Based Speech Recognition (ABSR), ATM Efficiency, Electronic Flight Strips, Aircraft Label

The CoNLL 2007 Shared Task on Dependency Parsing <=SEP=> The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results.