Query: Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields

Relevant passages:
Unsupervised Models for Named Entity Classification <=SEP=> This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabeled data can reduce the requirements for supervision to just 7 simple “seed” rules. The approach gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context in which it appears are sufficient to determine its type. We present two algorithms. The first method uses a similar algorithm to that of (Yarowsky 95), with modifications motivated by (Blum and Mitchell 98). The second algorithm extends ideas from boosting algorithms, designed for supervised learning tasks, to the framework suggested by (Blum and Mitchell 98).

Semi-Supervised Learning for Natural Language <=SEP=> Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. Unlabeled data has shown promise in improving the performance of a number of tasks, e.g. word sense disambiguation, information extraction, and natural language parsing. In this thesis, we focus on two segmentation tasks, named-entity recognition and Chinese word segmentation. The goal of named-entity recognition is to detect and classify names of people, organizations, and locations in a sentence. The goal of Chinese word segmentation is to find the word boundaries in a sentence that has been written as a string of characters without spaces. Our approach is as follows: In a preprocessing step, we use raw text to cluster words and calculate mutual information statistics. The output of this step is then used as features in a supervised model, specifically a global linear model trained using the Perceptron algorithm. We also compare Markov and semi-Markov models on the two segmentation tasks. Our results show that features derived from unlabeled data substantially improves performance, both in terms of reducing the amount of labeled data needed to achieve a certain performance level and in terms of reducing the error using a fixed amount of labeled data. We find that sometimes semi-Markov models can also improve performance over Markov models. Thesis Supervisor: Michael Collins Title: Assistant Professor, CSAIL

A framework and graphical development environment for robust NLP tools and applications. <=SEP=> 

Max-Margin Markov Networks <=SEP=> In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M ) networks incorporate both kernels, which efficiently deal with highdimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition, demonstrate very significant gains over previous approaches.

Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction <=SEP=> Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by ha nd, making them a good application for machine learning. We present an algorithm, R APIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that dire ctly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techni ques from several inductive logic programming systems. We have implemented t h algorithm in a system that allows patterns to have constraints on the words, part-of-speech t ags, and semantic classes present in the filler and the surrounding text. We present encouraging expe rimental results on two domains.

Smart clothing: Perceived benefits vs. perceived fears <=SEP=> Smart textile technologies integrate computer functionality into textiles. Since a few years, smart clothing has been coming up in the sport and health sector and is increasingly implemented in everyday objects within private spaces. Especially the use of textiles for medical reasons and their potential use within Ambient Assisted Living-Concepts (AAL) make it necessary to understand users' perspectives on these technologies and the willingness to use them. Today, the understanding in which way individual attitudes and emotional and cognitive abilities, may impact the acceptance of pervasive health care technologies, is restricted. This research is focused on the users' hopes and fears towards smart clothing and examines perceived benefits and barriers. As women have a higher life expectancy and will dominate the group of old people in the future - gender was chosen as one central factor of interest. As the second factor we examined technical experience in order to learn if the acceptance for smart clothing is connected to the degree of users previous experience with technology. Outcomes revealed both factors - gender and technical experience - to be decisive factors for the acceptance of smart clothing. Generally, women and persons with low technical experience show considerable caveats towards the usage of smart clothing technologies what becomes most evident in the perceived barriers and fears connected to the usage of this new technology.

Generalized Zero-Shot Learning with Deep Calibration Network <=SEP=> A technical challenge of deep learning is recognizing target classes without seen data. Zero-shot learning leverages semantic representations such as attributes or class prototypes to bridge source and target classes. Existing standard zero-shot learning methods may be prone to overfitting the seen data of source classes as they are blind to the semantic representations of target classes. In this paper, we study generalized zero-shot learning that assumes accessible to target classes for unseen data during training, and prediction on unseen data is made by searching on both source and target classes. We propose a novel Deep Calibration Network (DCN) approach towards this generalized zero-shot learning paradigm, which enables simultaneous calibration of deep networks on the confidence of source classes and uncertainty of target classes. Our approach maps visual features of images and semantic representations of class prototypes to a common embedding space such that the compatibility of seen data to both source and target classes are maximized. We show superior accuracy of our approach over the state of the art on benchmark datasets for generalized zero-shot learning, including AwA, CUB, SUN, and aPY.

A Strategic Empty Container Logistics Optimization in a Major Shipping Company <=SEP=> In this paper, we present a system that Compañía Sud Americana de Vapores (CSAV), one of the world’s largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV’s main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO’s multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.

Consumer Search Behavior in Online Shopping Environments <=SEP=> This paper explores search behavior of online shoppers. Information economics literature suggests that search cost in electronic markets has essentially been reduced to zero as consumers are able to use powerful search tools free of charge to easily find and compare product and shopping information on the Internet. In the present research, however, we present a research model proposing that users need to spend time and effort when completing search tasks resulting in significant search cost and a trade-off between search cost and search performance. Preliminary findings from an Internet experiment indicate that search task complexity, search engine capability, search strategy and user experience are important factors determining search cost and performance.

Colony of NPUs : Scaling the Efficiency of Neural Accelerators <=SEP=> Trading small amounts of output accuracy to obtain significant performance improvement or reduce power consumption is a promising research direction. Many modern applications from different domains such as image and signal processing, computer vision, data mining, machine learning and speech recognition are amenable to approximate computing. In many cases, their output is interpreted by humans, so infrequent variation in outputs are not noticeable by users. Moreover, the input of these applications is noisy which prevents strict constraints on the output of the system. Hence, these domains have significant potential for the aforementioned trade-offs, and these applications are ideal targets for approximate computing. Existing approaches for approximate computing include modifying the ISA [6], compiler [1], programming language [2, 4], underlying hardware [14] or the entire framework [3, 8, 10]. Approximation accelerators [5, 7, 13] use some of these methods to trade off accuracy for higher performance or energy savings. These accelerators require the programmer to annotate code sections which are amenable for approximation. Approximate parts are offloaded to the accelerator and other parts are executed by the CPU at run time. Esmaeilzadeh et. al. [7] proposed a Neural Processing Unit (NPU) as a programmable approximate accelerator. The key idea is to train a neural network (nn) to mimic an approximable region of original code and replace that with an efficient computation of the learned model. Although the proposed approximate accelerators such as NPU demonstrate acceptable results on the benchmarks from different domains, they have some shortcomings. Firstly, different invocations of a program might produce varying output qualities because the output quality is dependent on the input values. Consequently, using an NPU with fixed configuration for a wide range of inputs may produce outputs with poor accuracies. On the other hand, if the output quality drops below a determined threshold, one way to improve the quality is re-executing the whole program on the exact hardware. However, the overhead of this recovery process may offset the gains of approximation. Secondly, existing techniques usually measure the output quality by averaging errors in individual output elements, e.g., pixels in an image. Previous works in approximate computing [9, 11, 12] show that although most of the output elements have small errors, there exist a few output elements that have considerably large errors which may degrade the whole user experience. Therefore, some inputs might need more specialized methods to deal with the issue of output quality. Lastly, it is challenging to tune the output quality of an approximate hardware dynamically. If different NPUs with different configurations are available in the system, defining the number of active NPUs per invocation will be a reasonable knob for changing output quality based on users preferences. NPU Output Input Monolithic NPU

Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints <=SEP=> In this study we show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This ’test of realness’ can be applied to synthetic fingerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz versus real fingerprint images. We conclude with suggestions for the improvement of synthetic fingerprint generation.

Artificial Neural Networks Modeling in Water Resources Engineering : Infrastructure and Applications <=SEP=> The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water r esources engineering are being increasing rapidly. Infrastru ctural applications of ANN in terms of selection of inputs, architectur e of networks, training algorithms, and selection of training para meters in different types of neural networks used in water resources en gin ering have been reported. ANN modeling conducted for water res ources engineering variables (river sediment and discharge ) published in high impact journals since 2002 to 2011 have been e xamined and presented in this review. ANN is a vigorous techniq ue to develop immense relationship between the input and output v ariables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of exam ples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data b efore network modeling, which can facilitate to design an efficie nt network. An appropriate training based ANN model is able to ado pt the physical understanding between the variables and may generat more effective results than conventional prediction techniques. Keywords—ANN, discharge, modeling, prediction, sediment,

WIRELESS BIO-RADAR SENSOR FOR HEARTBEAT AND RESPIRATION DETECTION <=SEP=> In this study, a wireless bio-radar sensor was designed to detect a human heartbeat and respiration signals without direct skin contact. In order to design a wireless bio-radar sensor quantitatively, the signal-to-noise ratio (SNR) in the baseband output of a sensor should be calculated. Therefore, we analyzed the SNR of the wireless bio-radar sensor, considering the signal power attenuation in a human body and all kinds of noise sources. Especially, we measured a residual phase noise of a typical free-running oscillator and used its value for the SNR analysis. Based on these analysis and the measurement results, a compact, low-cost 2.4 GHz direct conversion bio-radar sensor was designed and implemented in a printed circuit board. The demonstrated sensor consists of two printed antennas, a

Sexually transmitted diseases treatment guidelines, 2010. <=SEP=> These guidelines for the treatment of persons who have or are at risk for sexually transmitted diseases (STDs) were updated by CDC after consultation with a group of professionals knowledgeable in the field of STDs who met in Atlanta on April 18-30, 2009. The information in this report updates the 2006 Guidelines for Treatment of Sexually Transmitted Diseases (MMWR 2006;55[No. RR-11]). Included in these updated guidelines is new information regarding 1) the expanded diagnostic evaluation for cervicitis and trichomoniasis; 2) new treatment recommendations for bacterial vaginosis and genital warts; 3) the clinical efficacy of azithromycin for chlamydial infections in pregnancy; 4) the role of Mycoplasma genitalium and trichomoniasis in urethritis/cervicitis and treatment-related implications; 5) lymphogranuloma venereum proctocolitis among men who have sex with men; 6) the criteria for spinal fluid examination to evaluate for neurosyphilis; 7) the emergence of azithromycin-resistant Treponema pallidum; 8) the increasing prevalence of antimicrobial-resistant Neisseria gonorrhoeae; 9) the sexual transmission of hepatitis C; 10) diagnostic evaluation after sexual assault; and 11) STD prevention approaches.

Continental Airlines Flies High with Real-Time Business Intelligence <=SEP=> Real-time business intelligence (BI) is taking Continental Airlines to new heights. Powered by a real-time data warehouse, the company has dramatically changed all aspects of its business. Continental’s president and COO, Larry Kellner, describes the impact of real-time BI in the following way: “Real-time BI is critical to the accomplishment of our business strategy and has created significant business benefits." In fact, Continental has realized more than $500 million in cost savings and revenue generation over the past six years from its BI initiatives, producing an ROI of more than 1,000 percent.

Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review <=SEP=> Recent demand and interest in wireless, mobile-based healthcare has driven significant interest towards developing alternative biopotential electrodes for patient physiological monitoring. The conventional wet adhesive Ag/AgCl electrodes used almost universally in clinical applications today provide an excellent signal but are cumbersome and irritating for mobile use. While electrodes that operate without gels, adhesives and even skin contact have been known for many decades, they have yet to achieve any acceptance for medical use. In addition, detailed knowledge and comparisons between different electrodes are not well known in the literature. In this paper, we explore the use of dry/noncontact electrodes for clinical use by first explaining the electrical models for dry, insulated and noncontact electrodes and show the performance limits, along with measured data. The theory and data show that the common practice of minimizing electrode resistance may not always be necessary and actually lead to increased noise depending on coupling capacitance. Theoretical analysis is followed by an extensive review of the latest dry electrode developments in the literature. The paper concludes with highlighting some of the novel systems that dry electrode technology has enabled for cardiac and neural monitoring followed by a discussion of the current challenges and a roadmap going forward.

Intermanual apparent tactile motion on handheld tablets <=SEP=> Handheld and wearable devices frequently engage users with simple haptic feedback, such as alerting, shaking, and pulsating. Here we explored intermanual apparent tactile motion-illusory movement between two hands-as a means to enrich such feedback. A series of psychophysical experiments determined the control space for generating smooth and consistent motion across the hands while users held the device. Experiment 1 calibrated the system and showed that vibrotactile detection thresholds decreased with increasing frequency, with similar trends for both hands. Experiment 2 measured effects of vibrotactile parameters on perceived motion. Both duration and temporal separation of stimuli, but not frequency and amplitude, affected subjective motion ratings. In Experiment 3, subjective ratings showed that stimuli with gradual onsets produced a stronger percept of motion than those with abrupt onsets. Finally, Experiment 4 determined a multimodal factor to match moving visual cues across the screen to moving tactile motion across hands. Our results showed compression of visual duration by the tactile system by a factor of approximately 1/3 at two test frequencies. The results of this research are useful for media designers and developers to generate reliable motion across the hands and integrate haptic motion with visual media.

Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition <=SEP=> Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set.

C-ICAMA, a centralized intelligent channel assigned multiple access for multi-layer ad-hoc wireless networks with UAVs <=SEP=> Multi-layer ad hoc wireless networks with UAVs is an ideal infrastructure to establish a rapidly deployable wireless communication system any time any where in the world for military applications. In this tactical environment, information traflc is quite asynimetric. Ground jighting units are information consumers and receive jar more data than they transmit. The up-link is used for sending requests for information and some networking configuration overhead with a f a 0 kilobits, while the down-link is used to return the data requested with megabits size (e.g. multimedia file of images and charts). Centralized Intelligent Channel Assigned Multiple Access(C-ICAMA) is a MAC layer protocol proposed for ground backbone nodes to access UAV (Unmanned Aerie1 Vehicle) to solve the highly asymmetric data trafic in this tactical environment. With it’s intelligent scheduling algorithm, it can dynamically allocate bandwidth for up-link and downlink to jit the instantaneous status of symmetric trafJic. The results of C-ICAMA is very promising, due to the dynamic bandwidth allocation of asymmetric i.ip-link and down-link, the access delay is tremendously reduced,

Sentry-Based Power Management in Wireless Sensor Networks <=SEP=> This paper presents a sentry-based approach to power management in wireless sensor networks for applications such as intruder detection and tracking. To minimize average power consumption while maintaining sufficient node density for coarse sensing, nodes are partitioned dynamically into two sets: sentries and non-sentries. Sentry nodes provide sufficient coverage for continuous monitoring and basic communication services. Non-sentry nodes sleep for designated periods of time to conserve power, and switch to full power only when needed to provide more refined sensing for tracking. Non-sentry nodes check for beacons from sentry nodes to determine when they should remain on. Experimental results are presented demonstrating trade-offs between power savings and tracking performance for a network of seventeen nodes using the first implementation of a basic sentry-based power management scheme. The paper concludes with a brief description of a full set of power-management services being implemented as middle-ware for general wireless sensor applications.

Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module <=SEP=> Electrochemical analysis of sweat using soft bioelectronics on human skin provides a new route for noninvasive glucose monitoring without painful blood collection. However, sweat-based glucose sensing still faces many challenges, such as difficulty in sweat collection, activity variation of glucose oxidase due to lactic acid secretion and ambient temperature changes, and delamination of the enzyme when exposed to mechanical friction and skin deformation. Precise point-of-care therapy in response to the measured glucose levels is still very challenging. We present a wearable/disposable sweat-based glucose monitoring device integrated with a feedback transdermal drug delivery module. Careful multilayer patch design and miniaturization of sensors increase the efficiency of the sweat collection and sensing process. Multimodal glucose sensing, as well as its real-time correction based on pH, temperature, and humidity measurements, maximizes the accuracy of the sensing. The minimal layout design of the same sensors also enables a strip-type disposable device. Drugs for the feedback transdermal therapy are loaded on two different temperature-responsive phase change nanoparticles. These nanoparticles are embedded in hyaluronic acid hydrogel microneedles, which are additionally coated with phase change materials. This enables multistage, spatially patterned, and precisely controlled drug release in response to the patient’s glucose level. The system provides a novel closed-loop solution for the noninvasive sweat-based management of diabetes mellitus.

KNN Model-Based Approach in Classification <=SEP=> The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classification that is aimed at overcoming these shortcomings. Our method constructs a kNN model for the data, which replaces the data to serve as the basis of classification. The value of k is automatically determined, is varied for different data, and is optimal in terms of classification accuracy. The construction of the model reduces the dependency on k and makes classification faster. Experiments were carried out on some public datasets collected from the UCI machine learning repository in order to test our method. The experimental results show that the kNN based model compares well with C5.0 and kNN in terms of classification accuracy, but is more efficient than the standard kNN.

CSDSM: Cognitive switch-based DDoS sensing and mitigation in SDN-driven CDNi word <=SEP=> Content Delivery Networks (CDNs) are increasingly deployed for their efficient content delivery and are often integrated with Software Defined Networks (SDNs) to achieve centrality and programmability of the network. However, these networks are also an attractive target for network attackers whose main goal is to exhaust network resources. One attack approach is to over-flood the OpenFlow switch tables containing routing information. Due to the increasing number of different flooding attacks such as DDoS, it becomes difficult to distinguish these attacks from normal traffic when evaluated with traditional attack detection methods. This paper proposes an architectural method that classifies and defends all possible forms of DDoS attack and legitimate Flash Crowd traffic using a segregated dimension functioning cognitive process based in a controller module. Our results illustrate that the proposed model yields significantly enhanced performance with minimal false positives and false negatives when classified with optimal Support Vector Machine and Logistic Regression algorithms. The traffic classifications initiate deployment of security rules to the OpenFlow switches, preventing new forms of flooding attacks. To the best of our knowledge, this is the first work conducted on SDN-driven CDNi used to detect and defend against all possible DDoS attacks through traffic segregated dimension functioning coupled with cognitive classification.

Empirical formula for propagation loss in land mobile radio services <=SEP=> An empirical formula for propagation loss is derived from Okumura's report in order to put his propagation prediction method to computational use. The propagation loss in an urban area is presented in a simple form: A + B log10R, where A and B are frequency and antenna height functions and R is the distance. The introduced formula is applicable to system designs for UHF and VHF land mobile radio services, with a small formulation error, under the following conditions: frequency range 100-1500 MHz, distance 1-20 km, base station antenna height 30-200 m, and vehicular antenna height 1-10 m.

Comprehensive Dataset of Broadcast Soccer Videos <=SEP=> In the absence of a rich dataset, sports video analysis remains a challenging task. Shot analysis, event detection, and player tracking are important aspects of soccer game research, however currently available datasets focus mainly on single-type annotations, and have several limitations when applied to full video analysis; these include a lack of training samples, temporal annotations, and rich labels of different types. In this paper, we focus on broadcast soccer videos and present a comprehensive dataset for analysis. In its current version, the dataset contains 222 broadcast soccer videos, totaling 170 video hours. The dataset covers three annotation types: (1) A shot boundary with two shot transition types, and shot type annotations with five shot types; (2) Event annotations with 11 event labels, and a story annotation with 15 story labels at coarser granularity; and (3) bounding boxes of the players under analysis in a subset of 19908 video frames. We hope that the introduction of this dataset will enable the research community to develop soccer video analysis techniques.

SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling <=SEP=> Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies.

Modular Forms of Weight One <=SEP=> The first three sections are preliminary. The fourth section contains the statement of the main theorem and some corollaries. The proof occupies sections 5 through 9. The idea is as follows: one begins by constructing, for each prime number `, a representation of Gal(Q/Q) in characteristic ` (see §6); we then show that the image of Gal(Q/Q) is “small” under each of these representations, which permits us to lift this to characteristic 0 and obtain the complex representation we seek. The “smallness” in question itself results from an increase of the average of the eigenvalues of the Hecke operators (Rankin, §5). Section 9 contains an estimate of the coefficients of the modular forms of weight one. We note that we have used an essential point (§6, Thm. 6.1) of some results demonstrated by one of us (P. Deligne), but of which no complete proof has been published; this proof should appear in SGA 5, so we ask that the reader accept this for now.

Benefits from Using Continuous Rating Scales in Online Survey Research <=SEP=> The usage of Likert-type scales has become widespread practice in current IS research. Those scales require individuals to choose between a limited number of choices, and have been criticized in the literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but were rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form, i.e. to place a mark at an appropriate position on a continuous line. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. In this 1 Augasse 2-6, A-1090 Vienna, Austria Tel.: +43/1/31336/4480 Fax: +43/1/31336/746 E-Mail: Horst.Treiblmaier@wu.ac.at

Abstract Meaning Representation for Multi-Document Summarization <=SEP=> Generating an abstract from a collection of documents is a desirable capability for many realworld applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.

Analysis of clickjacking attacks and an effective defense scheme for Android devices <=SEP=> Smartphones bring users lots of convenience by integrating all useful functions people may need. While users are spending more time on their phones, have they ever questioned of being spoofed by the phone they are interacting with? This paper conducts a thorough study of the mobile clickjacking attacks. We first present how the clickjacking attack works and the key points to remain undiscovered. Then, we evaluate its potential threats by exploring the feasibility of launching clickjacking attacks on various UIs, including system app windows, 3rd-party app windows, and other system UIs. Finally, we propose a system-level defense scheme against clickjacking attacks on Android platform, which requires no user or developer effort and is compatible with existing apps. The performance of the countermeasure is evaluated with extensive experiments. The results show that our scheme can effectively prevent clickjacking attacks with only a minor impact to the system.