Second, English Wikipedia has been the primary test-bed for research. Yet, Wikipedia has 200+ language editions and use of localized features impairs portability. This work implements an extensive set of language-independent indicators and evaluates them using three corpora (German, English, Spanish). The work then extends to include language-specific signals. Quantifying their performance benefit, we find that such features can moderately increase classifier accuracy, but significant effort and language fluency are required to capture this utility.

Aside from these novel aspects, this effort also broadly addresses the task, implementing 65 total features. Evaluation produces 0.840 PR-AUC on the zero-delay task and 0.906 PR-AUC with ex post facto evidence (averaging languages). Performance matches the state-of-the-art (English), sets novel baselines (German, Spanish), and is validated by a first-place finish over the 2011 PAN-CLEF test set.

Herein, we analyze one year of Wikipedia's public deletion log and use brute-force strategies to learn about privately handled redactions. This permits insight about the prevalence of deletion, the reasons that induce it, and the extent of end-user exposure to dangerous content. While Wikipedia's approach is generally quite reactive, we find that copyright issues prove most problematic of those behaviors studied.

Recent research has exposed vulnerabilities in Wikipedia's link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriers-to-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination).

In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using "wiki" metadata, landing site analysis, and external data sources. The resulting classifier attains 64% recall at 0.5% false-positives (ROC-AUC=0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed.

In this paper, we focused on the challenges involved in securing networked medical devices. We provide an overview of a generic networked medical device system model, a comprehensive attack and adversary model, and describe some of the challenges present in building security solutions to manage the attacks. Finally, we provide an overview of two areas of research that we believe will be crucial for making medical device system security solutions more viable in the long run: forensic data logging, and building security assurance cases.

We study the impact of video frame drops in buffer constrained multiprocessor system-on-chip (MPSoC) platforms. Since on-chip buffer memory occupies a significant amount of silicon area, accurate buffer sizing has attracted a lot of research interest lately. However, all previous work studied this problem with the underlying assumption that no video frame drops can be tolerated. In reality, multimedia applications can often tolerate some frame drops without significantly deteriorating their output quality. Although system simulations can be used to perform video quality driven buffer sizing, they are time consuming. In this paper, we first demonstrate a dual-buffer management scheme to drop only the less significant frames. Based on this scheme, we then propose a formal framework to evaluate the buffer size vs. video quality trade-offs, which in turn will help a system designer to perform quality driven buffer sizing. In particular, we mathematically characterize the maximum numbers of frame drops for various buffer sizes and evaluate how they affect the worst-case PSNR value of the decoded video. We evaluate our proposed framework with an MPEG-2 decoder and compare the obtained results with that of a cycle-accurate simulator. Our evaluations show that for an acceptable quality of 30 dB, it is possible to reduce the buffer size by upto 28.6% which amounts to 25.88 megabits.

Among the key characteristics of cyber-physical systems are the ability to adapt to changes during operation, the multidimensional complexity of multi-functionality and the underlying heterogeneous distributed architecture, as well as resource use efficiency. In this paper, we propose a compositional multi-modal approach to modeling, analyzing, and designing such systems. We introduce a general framework for modeling and compositional analysis of multi-mode systems on a distributed architecture that facilitates adaptivity, efficient use of resources, and incremental integration. We present some preliminary results, and we describe some of the remaining challenges and future directions.

Our analysis focuses on the "wiki" model and the collaborative encyclopedia, Wikipedia, in particular. A principal goal of spammers is to maximize *exposure*, the quantity of people who view a link. Creating and analyzing the first Wikipedia link spam corpus, we find that existing spam strategies perform quite poorly in this regard. The status quo spamming model relies on link persistence to accumulate exposures, a strategy that fails given the diligence of the Wikipedia community. Instead, we propose a model that exploits the latency inherent in human anti-spam enforcement.

Statistical estimation suggests our novel model would produce significantly more link exposures than status quo techniques. More critically, the strategy could prove economically viable for perpetrators, incentivizing its exploitation. To this end, we address mitigation strategies.

First, we conduct a measurement study on the dynamics of blacklists and email spam at-large. The magnitude/duration of the data enables scrutiny of long-term trends, at scale. Further, these statistics help parameterize our second task: the mining of blacklist history for temporal association rules. That is, we search for IP addresses with correlated histories. Strong correlations would suggest group members are not independent entities and likely share botnet membership.

Unfortunately, we find that statistically significant groupings are rare. This result is reinforced when rules are evaluated in terms of their ability to: (1) identify shared botnet members, using ground-truth from botnet infiltrations and sinkholes, and (2) predict future blacklisting events. In both cases, performance improvements over a control classifier are nominal. This outcome forces us to re-examine the appropriateness of blacklist data for this task, and suggest refinements to our mining model that may allow it to better capture the dynamics by which botnets operate.

We believe that all of these scenarios are similar, and share a common framework of design. In attempting to solve a particular instance of the problem, that of device alarm fatigue due to numerous false alarms, we devised a modular system based around this framework. This modular design allows us to easily customize the framework to address the specific needs of the various applications, and at the same time enables us to perform checking of consistency of the system.

In the paper we discuss potential specific clinical applications of a generic smart alarm framework, present the proposed architecture of such a framework, and motivate the benefits of a generic framework for the development of new smart alarm or clinical decision support systems.

This paper discusses our experiences of building and expanding the Columbia Emerging Scholars program, and addresses such topics as recruiting, training, scheduling, student behavior, and evaluation. We expect that this paper will provide a valuable set of lessons learned to other educators who seek to launch or grow a PLTL program at their institution as well.