Query: DramaBank: Annotating Agency in Narrative Discourse

Relevant passages:
Automatically Producing Plot Unit Representations for Narrative Text <=SEP=> In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies “projection rules” to map the affect states onto the characters in a story. We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e.g., being eaten is an undesirable state, but being fed is a desirable state). We harvest these “patient polarity verbs” from a Web corpus using two techniques: co-occurrence with Evil/Kind Agent patterns, and bootstrapping over conjunctions of verbs. We evaluate the plot unit representations produced by our system on a small collection of Aesop’s fables.

Plot Units and Narrative Summarization <=SEP=> In order to summarize a story, i t is necessary to access a high level analysis of the story that highlights its central concepts. A technique of memory representation based on plot units appears to provide a rich foundation for such an analysis. Plot units are conceptual structures that overlap with each other when a narrative is cohesive. When overlapping intersections between plot units are interpreted as arcs in a graph of plot units, the resulting graph encodes the plot of the story. Structural features of the graph then reveal which concepts are central to the story, and which concepts are peripheral. Plot unit analysis is currently being investigated as a processing strategy for narrative summarization by both computer simulotion ond psychological experiments.

Building a Bank of Semantically Encoded Narratives <=SEP=> We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).

Modeling narrative discourse <=SEP=> Modeling Narrative Discourse

Constructing inferences during narrative text comprehension. <=SEP=> The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.

Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions <=SEP=> In this paper, we present a survey of recent works in developing neuromorphic or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms, such as backpropagation, as well as novel algorithms, such as structural plasticity, developed specially for low-resolution synapses. We cover works related to both spike-based and more traditional non-spike-based algorithms. This is followed by developments in novel devices, such as floating-gate MOS, memristors, and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices, such as memristors, are presented. Common architectures, such as crossbar or island style arrays, are discussed, along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware, such as brain–machine interfaces, robotics, etc., and identify future research trends in the field.

Health risks in areas close to urban solid waste landfill sites. <=SEP=> OBJECTIVE
To evaluate the association between living close to solid waste landfill sites and occurrences of cancer and congenital malformations among populations in their vicinity.


METHODS
Deaths among people living in the municipality of São Paulo, Southeastern Brazil, between 1998 and 2002 were selected and geocoded, according to selected causes. Over the period evaluated, there were 351 deaths due to liver cancer, 160 due to bladder cancer and 224 due to leukemia, among adults, 25 due to childhood leukemia and 299 due to congenital malformation, in areas close to landfill sites. Buffer zones of radius 2 km around the 15 sites delimited the areas exposed. Standardized mortality ratios for each outcome were analyzed in Bayesian spatial models.


RESULTS
In a general manner, the highest values for the standardized mortality ratios were found in more central areas of the municipality, while the landfill sites were located in more peripheral areas. The standardized mortality ratios did not indicate any excess risk for people living in areas close to solid waste landfill sites in the municipality of São Paulo. For landfill sites in operation, there was a greater risk of bladder and liver cancer, and death due to congenital malformation, but without statistical significance.


CONCLUSIONS
No increase in the risk of cancer or congenital malformations was found in areas in the vicinity of urban waste dumps in the municipality of São Paulo. The weak associations and the imprecision of the estimates obtained did not allow any causal relationship to be established.

PEMOGEN: Automatic adaptive performance modeling during program runtime <=SEP=> Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the application is running. This allows us to greatly reduce the storage overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling framework that automatically instruments applications to generate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce storage cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation R2 of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with regards to any input parameter and the number of processes of a parallel application.

FAST CALCULATION OF HARALICK TEXTURE FEATURES <=SEP=> It is our aim in this research to optimize the numerical computation of the Haralick texture features [1] that consists of two steps. Haralick texture features are used as a primary component to discern between different protein structures in microscopic bio-images. The first of these two parts is the construction of the co-occurrence matrix. Upon completion of this implementation, we will attempt to optimize the code using a novel recursive blocking algorithm. We will also use standard practices of optimizing code such as scalar replacement and unrolling. The second part, feature calculation, will be optimized by putting to use information about the data that we will be working with and eliminating any redundancies in the code. We will also apply the practices of unrolling and scalar replacement. With these optimizations in place, we will show that we reduce the runtime by a20% in the co-occurrence construction phase and by a factor of20 in the feature calculation phase of computation.

Semantic Wordification of Document Collections <=SEP=> Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process.

Holonomic Control of a robot with an omni-directional drive . <=SEP=> This paper shows how to control a robot with omnidirectional wheels, using as example robots with four motors, and generalizing to n motors. More than three wheels provide redundancy: many combinations of motors speeds can provide the same Euclidean movement. Since the system is over-determined, we show how to compute a set of consistent and optimal motor forces and speeds using the pseudoinverse of coupling matrices. This approach allows us also to perform a consistency check to determine whether a wheel is slipping on the floor or not. We show that it is possible to avoid wheel slippage by driving the robot with a motor torque under a certain threshold or handle it and make high accelerations possible.

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition <=SEP=> RECENTLY , several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches. Firstly, this dissertation will present a new framework for multi-target tracking in a single camera. We proposed a novel data association technique using Dominant set clustering (DCS) framework. We formulate the tracking task as finding dominant sets on the constructed undirected edge weighted graph. Unlike most techniques which are limited in temporal locality (i.e. few frames are considered), we utilized a pairwise relationships (in appearance and position) between different detections across the whole temporal span of the video for data association in a global manner. Meanwhile, temporal sliding window technique is utilized to find tracklets and perform further merging on them. Our robust tracklet merging step renders our tracker to long term occlusions with more robustness. DSC leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories(clusters) one-by-one (peel off strategy), causing change in the scale of the problem. Secondly, the algorithm used to enumerate dominant sets, i.e., replicator dynamics, have quadratic computational complexity, which makes it impractical on larger graphs. To address these problems and extend tracking problem to multiple non-overlapping cameras, we proposed a novel and unified three-layer hierarchical approach. Given a video and a set of detections (obtained by any person detector), we first solve withincamera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant set clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as

An Empirical Study on Using the National Vulnerability Database to Predict Software Vulnerabilities <=SEP=> Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.

Introducing Entity-Based Concepts to Business Process Modeling <=SEP=> The so-called Internet of Things (IoT) that comprises interconnected physical devices such as sensor networks and its technologies like Radio Frequency Identification (RFID) is increasingly adopted in many industries and thus becomes highly relevant for process modeling and execution. As BPMN 2.0 does not yet consider the idiosyncrasies of real-world entities we suggest new modeling concepts for a physical entity as well as a sensing task and an actuation task to make BPMN IoT-aware.

Vocabulary learning by mobile-assisted authentic content creation and social meaning-making: two case studies <=SEP=> In recent years, we have witnessed the concomitant rise of communicative and contextualised approaches as well as the paradigmatic development of the Mobile Assisted Language Learning (MALL) framework in analyzing language learning. The focus of MALL research has gradually shifted from content-based (delivery of learning content through mobile devices) to design-oriented (authentic and/or social mobile learning activities) study. In this paper, we present two novel case studies of MALL that emphasize learner-created content. In learning English prepositions and Chinese idioms respectively, the primary school students used the mobile devices assigned to them on a one-to-one basis to take photos in real-life contexts so as to construct sentences with the newly acquired prepositions or idioms. Subsequently, the learners were voraciously engaged in classroom or online discussion of their semantic constructions, thereby enhancing their understanding of the proper usage of the prepositions or idioms. This work shows the potential of transforming language learning into an authentic seamless learning experience.

Testing using Log File Analysis: Tools, Methods, and Issues <=SEP=> Large software systems often keep log files of events. Such log files can be analyzed to check whether a run of a program reveals faults in the system. We discuss how such log files can be used in software testing. We present a framework for automatically analyzing log files, and describe a language for specifying analyzer programs and an implementation of that language. The language permits compositional, compact specifications of software, which act as test oracles; we discuss the use and efficacy of these oracles for unitand system-level testing in various settings. We explore methodological issues such as efficiency and logging policies, and the scope and limitations of the framework. We conclude that testing using log file analysis constitutes a useful methodology for software verification, somewhere between current testing practice and formal verification methodologies.

Crowdsourcing and the crisis-affected community <=SEP=> This article reports on Mission 4636, a real-time humanitarian crowdsourcing initiative that processed 80,000 text messages (SMS) sent from within Haiti following the 2010 earthquake. It was the first time that crowdsourcing (microtasking) had been used for international relief efforts, and is the largest deployment of its kind to date. This article presents the first full report and analysis of the initiative looking at the accuracy and timeliness in creating structured data from the messages and the collaborative nature of the process. Contrary to all previous papers, studies and media reports about Mission 4636, which have typically chosen to exclude empirical analyses and the involvement of the Haitian population, it is found that the greatest volume, speed and accuracy in information processing was by Haitian nationals, the Haitian diaspora, and those working closest with them, and that no new technologies played a significant role. It is concluded that international humanitarian organizations have been wrongly credited for large-scale information processing initiatives (here and elsewhere) and that for the most part they were largely just witnesses to crisis-affected communities bootstrapping their own recovery through communications technologies. The particular focus is on the role of the diaspora, an important population that are increasingly able to contribute to response efforts thanks to their increased communication potential. It is recommended that future humanitarian deployments of crowdsourcing focus on information processing within the populations they serve, engaging those with crucial local knowledge wherever they happen to be in the world.

Cross-lingual Semantic Generalization for the Detection of Metaphor <=SEP=> In this work, we describe a supervised cross-lingual methodology for detecting novel and conventionalized metaphors that derives generalized semantic patterns from a collection of metaphor annotations. For this purpose, we model each metaphor annotation as an abstract tuple – (source, target, relation, metaphoricity) – that packages a metaphoricity judgement with a relational grounding of the source and target lexical units in text. From these annotations, we derive a set of semantic patterns using a three-step process. First, we employ several generalized representations of the target using a variety of WordNet information and representative domain terms. Then, we generalize relations using a rule-based, pseudo-semantic role labeling. Finally, we generalize the source by partitioning a semantic hierarchy (defined by the target and the relation) into metaphoric and non-metaphoric regions so as to optimally account for the evidence in the annotated data. Experiments show that by varying the generality of the source, target, and relation representations in our derived patterns, we are able to significantly extend the impact of our annotations, detecting metaphors in a variety of domains at an F-measure of between 0.88 and 0.92 for English, Spanish, Russian, and Farsi. This generalization process both enhances our ability to jointly detect novel and conventionalized metaphors and enables us to transfer the knowledge encoded in metaphoricity annotations to novel languages.

Reconfiguring the Imaging Pipeline for Computer Vision <=SEP=> Advancements in deep learning have ignited an explosion of research on efficient hardware for embedded computer vision. Hardware vision acceleration, however, does not address the cost of capturing and processing the image data that feeds these algorithms. We examine the role of the image signal processing (ISP) pipeline in computer vision to identify opportunities to reduce computation and save energy. The key insight is that imaging pipelines should be be configurable: to switch between a traditional photography mode and a low-power vision mode that produces lower-quality image data suitable only for computer vision. We use eight computer vision algorithms and a reversible pipeline simulation tool to study the imaging system's impact on vision performance. For both CNN-based and classical vision algorithms, we observe that only two ISP stages, demosaicing and gamma compression, are critical for task performance. We propose a new image sensor design that can compensate for these stages. The sensor design features an adjustable resolution and tunable analog-to-digital converters (ADCs). Our proposed imaging system's vision mode disables the ISP entirely and configures the sensor to produce subsampled, lower-precision image data. This vision mode can save ~75% of the average energy of a baseline photography mode with only a small impact on vision task accuracy.

DISCO: A Multilingual Database of Distributionally Similar Words <=SEP=> This paper1 presents DISCO, a tool for retrieving the distributional similarity between two given words, and for retrieving the distributionally most similar words for a given word. Pre-computed word spaces are freely available for a number of languages including English, German, French and Italian, so DISCO can be used off the shelf. The tool is implemented in Java, provides a Java API, and can also be called from the command line. The performance of DISCO is evaluated by measuring the correlation with WordNet-based semantic similarities and with human relatedness judgements. The evaluations show that DISCO has a higher correlation with semantic similarities derived from WordNet than latent semantic analysis (LSA) and the web-based PMI-IR.

Semi-global matching based disparity estimate using fast Census transform <=SEP=> The Census transform is very useful in stereo matching algorithms. But the traditional Census transform cost too much computation, and its accuracy and robustness are not satisfying. In this paper, a modified semi-global matching (SGM) algorithm based on the fast Census transform is proposed to improve the quality of the range map and decrease the computation. In addition, we modify the disparity refinement to enhance the quality of the estimated range map. Our experiments show that the proposed method performs better than the original SGM algorithm in terms of the accuracy and the anti-noise capability.

A Supervised Learning Approach for Imbalanced Text Classification of Biomedical Literature Triage <=SEP=> A Supervised Learning Approach for Imbalanced Text Classification of Biomedical Literature Triage

Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges <=SEP=> In past few years, advancement in mobile applications and their integration with Cloud computing services has introduced a new computing paradigm known as Mobile Cloud Computing. Although Cloud services support a wide range of mobile applications, access to these services suffers from several performance issues such as WAN latency, jitter, and packet losses. Cloudlet frameworks are proposed to overcome these performance issues. More specifically, Cloudlets aim to bring the Cloud or a specific part of the Cloud closer to the mobile device by utilizing proximate computing resources to perform compute-intensive tasks. This paper presents a comprehensive survey on the state-of-the-art mobile Cloudlet architectures. We also classify the state-of-the-art Cloudlet solutions by presenting a hierarchical taxonomy. Moreover, the areas of Cloudlet applications are also identified and presented. Cloudlet discovery, resource management, data security, mobility, application offloading, and most importantly incentives to deploy a Cloudlet are areas that still needs to be investigated by the research community. The critical aspects of the current Cloudlet frameworks in Mobile Cloud Computing are analyzed to determine the strengths and weaknesses of the frameworks. The similarities and differences of the frameworks based on the important parameters, such as scalability, mobility support, Internet dependency, dynamic configuration, energy savings, and execution cost, are also investigated. The requirements for deploying the Cloudlet in a Local Wireless Network are also highlighted and presented. We also discuss open research challenges that Cloudlet deployments face in Local Wireless Networks. & 2015 Elsevier Ltd. All rights reserved.

Occurrence of partenites in mollusks and the influence that metacercaria of Apophallus muehlingi (Jagerskiold, 1898) and Posthodiplostomum cuticola (Nordmann, 1832) has on some biochemical parameters in fish <=SEP=> A heavy parasitic load from trematode partenites of Apophallus muehlingi (Jagerskiold, 1898) was observed in the mollusk population of Lithoglyphus naticoides (Pfeiffer) after its natural invasion into the Rybinsk Reservoir. An elevated concentration of glycogen was detected in the muscles of infected fish, both in the natural populations and under experimental conditions. Interspecific differences were found for the protein concentrations when elevated concentrations were observed in the muscles of the infected roach, and no differences were discovered for the goldfish (both infected and parasite-free). The ties between the biochemical parameters and motor activity are discussed for the infected fish.

Personality and Psychological Aspects of Cosmetic Surgery <=SEP=> In recent years, cosmetic surgery in Iran, which is provided almost entirely by the private sector, has gained popularity despite evidence of its potential risks. In most cases, cosmetic surgeries are done to increase self-satisfaction and self-esteem, thus seeking cosmetic surgery potentially shows an individual’s psychological profile. Current evidence needs studies on the psychological profile of Asian cosmetic surgery patients. The present study investigates psychological profile and personality traits of people seeking cosmetic surgery in Iran. The present prospective observational study was conducted with a sample of 274 randomly selected persons seeking cosmetic surgery (rhinoplasty, blepharoplasty, face/jaw implant, mammoplasty, and liposuction). All participants completed the validated and reliable the Global Severity Index (GSI)—Symptom Checklist-90-Revised (SCL-90-R)—and the short Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI). The prevalence rate of psychiatric problems based on the GSI cut-off point (>63) of SCL-90-R was about 51 %, and interpersonal sensitivity and psychosis were the highest and lowest endorsed syndromes among the subjects, respectively. Openness had the lowest mean score; agreeableness and extroversion had the highest mean. The current study shows that understanding and psychological evaluation prior to surgery is necessary and screening can reduce the number of unnecessary surgeries and may enhance satisfaction with surgical results. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Mining Social Entrepreneurship Strategies Using Topic Modeling <=SEP=> Despite the burgeoning research on social entrepreneurship (SE), SE strategies remain poorly understood. Drawing on extant research on the social activism and social change, empowerment and SE models, we explore, classify and validate the strategies used by 2,334 social entrepreneurs affiliated with the world's largest SE support organization, Ashoka. The results of the topic modeling of the social entrepreneurs' strategy profiles reveal that they employed a total of 39 change-making strategies that vary across resources (material versus symbolic strategies), specificity (general versus specific strategies), and mode of participation (mass versus elite participation strategies); they also vary across fields of practice and time. Finally, we identify six meta-SE strategies-a reduction from the 39 strategies-and identify four new meta-SE strategies (i.e., system reform, physical capital development, evidence-based practices, and prototyping) that have been overlooked in prior SE research. Our findings extend and deepen the research into SE strategies and offer a comprehensive model of SE strategies that advances theory, practice and policy making.

What factors lead to software project failure? <=SEP=> It has been suggested that there is more than one reason for a software development project to fail. However, most of the literature that discusses project failure tends to be rather general, supplying us with lists of risk and failure factors, and focusing on the negative business effects of the failure. Very little research has attempted an in-depth investigation of a number of failed projects to identify exactly what are the factors behind the failure. In this research we analyze data from 70 failed projects. This data provides us with practitionerspsila perspectives on 57 development and management factors for projects they considered were failures. Our results show that all projects we investigated suffered from numerous failure factors. For a single project the number of such factors ranges from 5 to 47. While there does not appear to be any overarching set of failure factors we discovered that all of the projects suffered from poor project management. Most projects additionally suffered from organizational factors outside the project managerpsilas control. We conclude with suggestions for minimizing the four most common failure factors.

Attribute based object identification <=SEP=> Over the last years, the robotics community has made substantial progress in detection and 3D pose estimation of known and unknown objects. However, the question of how to identify objects based on language descriptions has not been investigated in detail. While the computer vision community recently started to investigate the use of attributes for object recognition, these approaches do not consider the task settings typically observed in robotics, where a combination of appearance attributes and object names might be used in referral language to identify specific objects in a scene. In this paper, we introduce an approach for identifying objects based on natural language containing appearance and name attributes. To learn rich RGB-D features needed for attribute classification, we extend recently introduced sparse coding techniques so as to automatically learn attribute-dependent features. We introduce a large data set of attribute descriptions of objects in the RGB-D object dataset. Experiments on this data set demonstrate the strong performance of our approach to language based object identification. We also show that our attribute-dependent features provide significantly better generalization to previously unseen attribute values, thereby enabling more rapid learning of new attribute values.

Automated Generation of Attack Trees <=SEP=> Attack trees are widely used to represent threat scenarios in a succinct and intuitive manner, suitable for conveying security information to non-experts. The manual construction of such objects relies on the creativity and experience of specialists, and therefore it is error-prone and impracticable for large systems. Nonetheless, the automated generation of attack trees has only been explored in connection to computer networks and levering rich models, whose analysis typically leads to an exponential blow-up of the state space. We propose a static analysis approach where attack trees are automatically inferred from a process algebraic specification in a syntax-directed fashion, encompassing a great many application domains and avoiding incurring systematically an exponential explosion. Moreover, we show how the standard propositional denotation of an attack tree can be used to phrase interesting quantitative problems, that can be solved through an encoding into Satisfiability Modulo Theories. The flexibility and effectiveness of the approach is demonstrated on the study of a national-scale authentication system, whose attack tree is computed thanks to a Java implementation of the framework.

Hybrid analog-digital backscatter: A new approach for battery-free sensing <=SEP=> After comparing the properties of analog backscatter and digital backscatter, we propose that a combination of the two can provide a solution for high data rate battery free wireless sensing that is superior to either approach on its own. We present a hybrid analog-digital backscatter platform that uses digital backscatter for addressability and control but switches into analog backscatter mode for high data rate transmission of sensor data. Using hybrid backscatter, we report the first digitally addressable real-time battery free wireless microphone. We develop the hybrid backscatter platform by integrating an electret microphone and RF switch with a digital RFID platform (WISP). The hybrid WISP operates by default in digital mode, transmitting and receiving digital data using the EPC Gen 2 RFID protocol but switching into analog mode to backscatter audio sensor data when activated by Gen 2 READ command. The data is recovered using a USRP-based Software Defined RFID reader. We report an operating range of 7.4 meters for the analog backscatter microphone and 2.7 meters for hybrid microphone with 26.7 dBm output power USRP-based RFID reader.