instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But is this assumption true? And, in either case, what are the characteristics of such fraudulent behaviors? In this work, we set up honeypots, (“dummy” social network accounts), and buy fake followers (after careful IRB approval). We report the signs of such behaviors including oddities in local network connectivity, account attributes, and similarities and differences across fraud providers. Most valuably, we discover and characterize several types of fraud behaviors. We discuss how to leverage our insights in practice by engineering strongly performing entropy-based features and demonstrating high classification accuracy. Our contributions are (a) instrumentation: we detail our experimental setup and carefully engineered data collection process to scrape Twitter data while respecting API rate-limits, (b) observations on fraud multimodality: we analyze our honeypot fraudster ecosystem and give surprising insights into the multifaceted behaviors of these fraudster types, and (c) features: we propose novel features that give strong (>0.95 precision/recall) discriminative power on ground-truth Twitter data.
answer:
The Many Faces of Link Fraud


question:
A pseudo independent (PI) model is a proba­ bilistic domain model (PDM) where proper subsets of a set of collectively dependent variables display marginal independence. PI models cannot be learned correctly by many algorithms that rely on a single link search. Earlier work on learning PI models has sug­ gested a straightforward multi-link search al­ gorithm. However, when a domain contains recursively embedded PI submodels, it may escape the detection of such an algorithm. In this paper, we propose an improved al­ gorithm that ensures the learning of all em­ bedded PI submodels whose sizes are upper bounded by a predetermined parameter. We show that this improved learning capability only increases the complexity slightly beyond that of the previous algorithm. The perfor­ mance of the new algorithm is demonstrated through experiment.
answer:
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels


question:
This report presents a general model of the architecture of information systems for the children’s speech recognition. It presents a model of the speech data stream and how it works. The result of these studies and presented veins architectural model shows that research needs to be focused on acoustic-phonetic modeling in order to improve the quality of children's speech recognition and the sustainability of the systems to noise and changes in transmission environment. Another important aspect is the development of more accurate algorithms for modeling of spontaneous child speech.
answer:

On model architecture for a children’s speech recognition interactive dialog system