Physical Sciences and Mathematics Commons^™

Guess Again (And Again And Again): Measuring Password Strength By Simulating Password-Cracking Algorithms (Cmu-Cylab-11-008), Patrick Kelley, Saranga Komanduri, Michelle L. Mazurek, Richard Shay, Tim Vidas, Lujo Bauer, Nicolas Christin, Lorrie Faith Cranor, Julio Lopez

Lorrie F Cranor

Text-based passwords remain the dominant authentication method in computer systems, despite significant advancement in attackers’ capabilities to perform password cracking. In response to this threat, password composition policies have grown increasingly complex. However, there is insufficient research defining metrics to characterize password strength and evaluating password-composition policies using these metrics. In this paper, we describe an analysis of 12,000 passwords collected under seven composition policies via an online study. We develop an efficient distributed method for calculating how effectively several heuristic password-guessing algorithms guess passwords. Leveraging this method, we investigate (a) the resistance of passwords created under different conditions to …

From Facebook Regrets To Facebook Privacy Nudges, Yang Wang, Pedro Giovanni Leon, Xiaoxuan Chen, Saranga Komanduri, Gregory Norcie, Alessandro Acquisti, Lorrie Faith Cranor, Norman Sadeh

Lorrie F Cranor

As social networking sites (SNSs) gain in popularity, instances of regrets following online (over)sharing continue to be reported. In June 2010, a pierogi mascot for the Pittsburgh Pirates was fired because he posted disparaging comments about the team on his Facebook page. More recently, a high school teacher was forced to resign because she posted a picture on Facebook in which she was holding a glass of wine and a mug of beer. These incidents illustrate how, in addition to fostering socialization and interaction between friends and strangers, the ease and immediacy of communication that SNSs make possible can sometimes …

A Survey Of The Use Of Adobe Flash Local Shared Objects To Respawn Http Cookies (Cmu-Cylab-11-001), Aleecia M. Mcdonald, Lorrie Faith Cranor

Lorrie F Cranor

Website developers can use Adobe’s Flash Player product to store information locally on users’ disks with Local Shared Objects (LSOs). LSOs can be used to store state information and user identifiers, and thus can be used for similar purposes as HTTP cookies. In a paper by Soltani et al, researchers documented at least four instances of “respawning,” where users deleted their HTTP cookies only to have the HTTP cookies recreated based on LSO data. In addition, the Soltani team found half of the 100 most popular websites used Flash technologies to store information about users. Both respawning and using LSOs …

An Empirical Analysis Of Phishing Blacklists, Steve Sheng, Brad Wardman, Gary Warner, Lorrie Faith Cranor, Jason Hong, Chengshan Zhang

Lorrie F Cranor

In this paper, we study the effectiveness of phishing blacklists. We used 191 fresh phish that were less than 30 minutes old to conduct two tests on eight anti-phishing toolbars. We found that 63% of the phishing campaigns in our dataset lasted less than two hours. Blacklists were ineffective when protecting users initially, as most of them caught less than 20% of phish at hour zero. We also found that blacklists were updated at different speeds, and varied in coverage, as 47% - 83% of phish appeared on blacklists 12 hours from the initial test. We found that two tools …

Your Location Has Been Shared 5,398 Times! A Field Study On Mobile App Privacy Nudging (Cmu-Isr-14-116), Hazim Almuhimedi, Florian Schaub, Norman Sadeh, Idris Adjerid, Alessandro Acquisti, Joshua Gluck, Lorrie Cranor, Yuvraj Agarwal

Lorrie F Cranor

Smartphone users are often unaware of the data collected by apps running on their devices. We report on a study that evaluates the benefits of giving users an app permission manager and of sending them nudges intended to raise their awareness of the data collected by their apps. Our study provides both qualitative and quantitative evidence that these approaches are complementary and can each play a significant role in empowering users to more effectively control their privacy. For instance, even after a week with access to the permission manager, participants benefited from nudges showing them how often some of their …

The Privacy And Security Behaviors Of Smartphone App Developers, Rebecca Balebako, Abigail Marsh, Jialiu Lin, Jason I. Hong, Lorrie Faith Cranor

Lorrie F Cranor

Smartphone app developers have to make many privacy-related decisions about what data to collect about endusers, and how that data is used. We explore how app developers make decisions about privacy and security. Additionally, we examine whether any privacy and security behaviors are related to characteristics of the app development companies. We conduct a series of interviews with 13 app developers to obtain rich qualitative information about privacy and security decision-making. We use an online survey of 228 app developers to quantify behaviors and test our hypotheses about the relationship between privacy and security behaviors and company characteristics. We find …

Design And Implementation Of A Practical Security-Conscious Electronic Polling System, Lorrie Cranor, Ron Cytron

Lorrie F Cranor

We present the design and implementation of Sensus, a practical, secure and private system for conducting surveys and elections over computer networks. Expanding on the work of Fujioka, Okamoto, and Ohta, Sensus uses blind signatures to ensure that only registered voters can vote and that each registered voter only votes once, while at the same time maintaining voters' privacy. Sensus allows voters to verify independently that their votes were counted correctly, and anonymously challenge the results should their votes be miscounted. We outline seven desirable properties of voting systems and show that Sensus satisfied these properties well, in some cases …

“I Read My Twitter The Next Morning And Was Astonished” A Conversational Perspective On Twitter Regrets, Manya Sleeper, Justin Cranshaw, Patrick Kelley, Blase Ur, Alessandro Acquisti, Lorrie Cranor, Norman Sadeh

Lorrie F Cranor

We present the results of an online survey of 1,221 Twitter users, comparing messages individuals regretted either saying during in-person conversations or posting on Twitter. Participants generally reported similar types of regrets in person and on Twitter. In particular, they often regretted messages that were critical of others. However, regretted messages that were cathartic/expressive or revealed too much information were reported at a higher rate for Twitter. Regretted messages on Twitter also reached broader audiences. In addition, we found that participants who posted on Twitter became aware of, and tried to repair, regret more slowly than those reporting in-person regrets. …

Disjunctive Answer Set Solvers Via Templates, Remi Brochenin, Yuliya Lierler, Marco Maratea

Yuliya Lierler

Effect Of Retiring Custom Web Applications On Business And Information Technology Alignment, Shubhashree Thekahally

Shubhashree Thekahally 7340504

Web applications provide the information technology (IT) implementation of business and align IT with business. Retirement of IT applications should ensure stability of business and IT alignment. The current study investigated the alignment gaps created between business and IT resulting from retiring IT software applications. The purpose of this study was to identify IT integration points with business and provide a process-based solution that sustained IT alignment with business after retiring IT applications. The theoretical framework strategic alignment model aided in identifying 3 IT domains as the IT integration points with business: enterprise architecture, configuration management database, and service-level agreement. …

Can You Summarize This? Identifying Correlates Of Input Difficulty For Generic Multi-Document Summarization, Ani Nenkova, Annie Louis

Ani Nenkova

Different summarization requirements could make the writing of a good summarymore difficult, or easier. Summary length and the characteristics of the input are such constraints influencing the quality of a potential summary. In this paper we report the results of a quantitative analysis on data from large-scale evaluations of multi-document summarization, empirically confirming this hypothesis. We further show that features measuring the cohesiveness of the input are highly correlated with eventual summary quality and that it is possible to use these as features to predict the difficulty of new, unseen, summarization inputs.

Discourse Indicators For Content Selection In Summaization, Annie Louis, Aravind K. Joshi, Ani Nenkova

Ani Nenkova

We present analyses aimed at eliciting which specific aspects of discourse provide the strongest indication for text importance. In the context of content selection for single document summarization of news, we examine the benefits of both the graph structure of text provided by discourse relations and the semantic sense of these relations. We find that structure information is the most robust indicator of importance. Semantic sense only provides constraints on content selection but is not indicative of important content by itself. However, sense features complement structure information and lead to improved performance. Further, both types of discourse information prove complementary …

Automatic Sense Prediction For Implicit Discourse Relations In Text, Emily Pitler, Annie Louis, Ani Nenkova

Ani Nenkova

We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as “but” or “because”. We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. In addition, we revisit past approaches using lexical pairs from unannotated text as features, explain some of their shortcomings and …

Automatic Evaluation Of Linguistic Quality In Multi-Document Summarization, Emily Pitler, Annie Louis, Ani Nenkova

Ani Nenkova

To date, few attempts have been made to develop and validate methods for automatic evaluation of linguistic quality in text summarization. We present the first systematic assessment of several diverse classes of metrics designed to capture various aspects of well-written text. We train and test linguistic quality models on consecutive years of NIST evaluation data in order to show the generality of results. For grammaticality, the best results come from a set of syntactic features. Focus, coherence and referential clarity are best evaluated by a class of features measuring local coherence on the basis of cosine similarity between sentences, coreference …

Structural Features For Predicting The Linguistic Quality Of Text: Applications To Machine Translation, Automatic Summarization And Human-Authored Text, Ani Nenkova, Jieun Chae, Annie Louis, Emily Pitler

Ani Nenkova

Sentence structure is considered to be an important component of the overall linguistic quality of text. Yet few empirical studies have sought to characterize how and to what extent structural features determine fluency and linguistic quality. We report the results of experiments on the predictive power of syntactic phrasing statistics and other structural features for these aspects of text. Manual assessments of sentence fluency for machine translation evaluation and text quality for summarization evaluation are used as gold-standard. We find that many structural features related to phrase length are weakly but significantly correlated with fluency and classifiers based on the …

Measuring Importance And Query Relevance In Toopic-Focused Multi-Document Summarization, Surabhi Gupta, Ani Nenkova, Dan Jurafsky

Ani Nenkova

The increasing complexity of summarization systems makes it difficult to analyze exactly which modules make a difference in performance. We carried out a principled comparison between the two most commonly used schemes for assigning importance to words in the context of query focused multi-document summarization: raw frequency (word probability) and log-likelihood ratio. We demonstrate that the advantages of log-likelihood ratio come from its known distributional properties which allow for the identification of a set of words that in its entirety defines the aboutness of the input. We also find that LLR is more suitable for query-focused summarization since, unlike raw …

Using Entity Features To Classify Implicit Discourse Relations, Annie Louis, Aravind K. Joshi, Rashmi Prasad, Ani Nenkova

Ani Nenkova

We report results on predicting the sense of implicit discourse relations between adjacent sentences in text. Our investigation concentrates on the association between discourse relations and properties of the referring expressions that appear in the related sentences. The properties of interest include coreference information, grammatical role, information status and syntactic form of referring expressions. Predicting the sense of implicit discourse relations based on these features is considerably better than a random baseline and several of the most discriminative features conform with linguistic intuitions. However, these features do not perform as well as lexical features traditionally used for sense prediction.

Creating Local Coherence: An Empirical Assessment, Annie Louis, Ani Nenkova

Ani Nenkova

Two of the mechanisms for creating natural transitions between adjacent sentences in a text, resulting in local coherence, involve discourse relations and switches of focus of attention between discourse entities. These two aspects of local coherence have been traditionally discussed and studied separately. But some empirical studies have given strong evidence for the necessity of understanding how the two types of coherence-creating devices interact. Here we present a joint corpus study of discourse relations and entity coherence exhibited in news texts from the Wall Street Journal and test several hypotheses expressed in earlier work about their interaction.

Automatically Evaluating Content Selection In Summarization Without Human Models, Annie Louis, Ani Nenkova

Ani Nenkova

We present a fully automatic method for content selection evaluation in summarization that does not require the creation of human model summaries. Our work capitalizes on the assumption that the distribution of words in the input and an informative summary of that input should be similar to each other. Results on a large scale evaluation from the Text Analysis Conference show that input-summary comparisons are very effective for the evaluation of content selection. Our automatic methods rank participating systems similarly to manual model-based pyramid evaluation and to manual human judgments of responsiveness. The best feature, Jensen- Shannon divergence, leads to …

Entity-Driven Rewrite For Multi-Document Summarization, Ani Nenkova

Ani Nenkova

In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting for multi-document summarization of news. The approach leads to 20% to 50% different content in the summary in comparison to an extractive summary produced using the same underlying approach, showing the promise the technique has to offer. In addition, summaries produced using entity-driven rewrite have higher linguistic quality than a comparison non-extractive system. Some improvement is also seen in content selection over extractive summarization as measured by pyramid method evaluation.

Detecting Prominence In Conversational Speech: Pitch Accent, Givenness And Focus, Vivek Kumar Rangarajan Sridhar, Ani Nenkova, Shrikanth Narayanan, Dan Jurafsky

Ani Nenkova

The variability and reduction that are characteristic of talking in natural interaction make it very difficult to detect prominence in conversational speech. In this paper, we present analytic studies and automatic detection results for pitch accent, as well as on the realization of information structure phenomena like givenness and focus. For pitch accent, our conditional random field model combining acoustic and textual features has an accuracy of 78%, substantially better than chance performance of 58%. For givenness and focus, our analysis demonstrates that even in conversational speech there are measurable differences in acoustic properties and that an automatic detector for …

Modelling Prominence And Emphasis Improves Unit-Selection Synthesis, Volker Strom, Ani Nenkova, Robert Clark, Yolanda Vazquez-Alvarez, Jason Brenier, Simon King, Dan Jurafsky

Ani Nenkova

We describe the results of large scale perception experiments showing improvements in synthesising two distinct kinds of prominence: standard pitch-accent and strong emphatic accents. Previously prominence assignment has been mainly evaluated by computing accuracy on a prominence-labelled test set. By contrast we integrated an automatic pitch-accent classifier into the unit selection target cost and showed that listeners preferred these synthesised sentences. We also describe an improved recording script for collecting emphatic accents, and show that generating emphatic accents leads to further improvements in the fiction genre over incorporating pitch accent only. Finally, we show differences in the effects of prominence …

Predicting The Fluency Of Text With Shallow Structural Features: Case Studies Of Machine Tanslation And Human-Written Text, Jieun Chae, Ani Nenkova

Ani Nenkova

Sentence fluency is an important component of overall text readability but few studies in natural language processing have sought to understand the factors that define it. We report the results of an initial study into the predictive power of surface syntactic statistics for the task; we use fluency assessments done for the purpose of evaluating machine translation. We find that these features are weakly but significantly correlated with fluency. Machine and human translations can be distinguished with accuracy over 80%. The performance of pairwise comparison of fluency is also very high—over 90% for a multi-layer perceptron classifier. We also test …

Class-Level Spectral Features For Emotion Recognition, Dmitri Bitouk, Ragini Verma, Ani Nenkova

Ani Nenkova

The most common approaches to automatic emotion recognition rely on utterance-level prosodic features. Recent studies have shown that utterance-level statistics of segmental spectral features also contain rich information about expressivity and emotion. In our work we introduce a more fine-grained yet robust set of spectral features: statistics of Mel-Frequency Cepstral Coefficients computed over three phoneme type classes of interest – stressed vowels, unstressed vowels and consonants in the utterance. We investigate performance of our features in the task of speaker-independent emotion recognition using two publicly available datasets. Our experimental results clearly indicate that indeed both the richer set of spectral …

Performance Confidence Estimation For Automatic Summarization, Annie Louis, Ani Nenkova

Ani Nenkova

We address the task of automatically predicting if summarization system performance will be good or bad based on features derived directly from either single- or multi-document inputs. Our labelled corpus for the task is composed of data from large scale evaluations completed over the span of several years. The variation of data between years allows for a comprehensive analysis of the robustness of features, but poses a challenge for building a combined corpus which can be used for training and testing. Still, we find that the problem can be mitigated by appropriately normalizing for differences within each year. We examine …

Implementing And Testing A Novel Chaotic Cryptosystem, Samuel Jackson, Scott Kerlin, Jeremy Straub

Jeremy Straub

Cryptography in the domain of small satellites is a relatively new area of research. Compared to typical desktop computers, small satellites have limited bandwidth, processing power, and battery power. Many of the current encryption schemes were developed for desktop computers and servers, and as such may be unsuitable for small satellites. In addition, most cryptographic research in the domain of small satellites focuses on hardware solutions, which can be problematic given the limited space requirements of small satellites.

This paper investigates potential software solutions that could be used to encrypt and decrypt data on small satellites and other devices with …

Simulation Experiment Of Disaster Response Organizational Structures With Alternative Optimization Techniques, Geun Lee, Jang Bae, Namkyung Oh, Jeong Hong, Il-Chul Mood

Namkyung Oh

Disaster response operations are critical for decreasing the devastating impacts that result in casualties and property damages. Since these operations require cooperation in dynamic and complex situations, the responding organizations require a solid organizational structure collectively. This article introduces computational designs and evaluations of alternative organizational structures for disaster responses to resolve the disconnections between resource demands and supplies. In particular, this research consists of (1) organizational structure designs with two optimization techniques, (2) agent-based simulations that virtually replicate disaster response contexts, and (3) social network analysis to interpret the relations between the structures and the performances from the network …

Information Technology And Computer Science Programs: How Do We Relate?, Bonnie K. Mackellar, Gregory Hislop, Mihaela C. Sabin, Amber Settle

Amber Settle

Performance Tuning In Answer Set Programming, Matt Buddenhagen, Yuliya Lierler

Yuliya Lierler

Enhancing Manufacturing Planning And Control Systems Through Artificial Intelligence Techniques, Ronald S. Dattero, John J. Kanet, Edna M. White

John J. Kanet

Manufacturing planning and control systems are currently dominated by systems based upon Material Requirements Planning (MRP). MRP systems have a number of fundamental flaws. A potential alternative to MRP systems is suggested after research into the economic batch scheduling problem. Based on the ideas of economic batch scheduling, and enhanced through artificial intelligence techniques, an alternative approach to manufacturing planning and control is developed. A framework for future research on this alternative to MRP is presented.