Tor Bridge Discovery: Extensive Analysis and Large-scale Empirical Evaluation

Morality-based interventions designed to promote academic integrity are being used by educational institutions around the world. Although many such approaches have a strong theoretical foundation and are supported by laboratory-based evidence, they often have not been subjected to rigorous empirical evaluation in real-world contexts. In a naturalistic field study ( N = 296), we evaluated a recent research-inspired classroom innovation in which students are told, just prior to taking an unproctored exam, that they are trusted to act with integrity. Four university classes were assigned to a proctored exam or one of three types of unproctored exam. Students who took unproctored exams cheated significantly more, which suggests that it may be premature to implement this approach in college classrooms. These findings point to the importance of conducting ecologically valid and well-controlled field studies that translate psychological theory into practice when introducing large-scale educational reforms.

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How Do Children of Mixed Partnerships Fare in the United Kingdom? Understanding the Implications for Children of Parental Ethnic Homogamy and Heterogamy

The Annals of the American Academy of Political and Social Science ◽

10.1177/0002716212444853 ◽

2012 ◽

Vol 643 (1) ◽

pp. 239-266 ◽

Cited By ~ 17

Author(s):

Lucinda Platt

Keyword(s):

United Kingdom ◽

Ethnic Group ◽

Large Scale ◽

Growing Up ◽

Sociological Analysis ◽

Life Chances ◽

The United Kingdom ◽

Majority Group ◽

Extensive Analysis ◽

Mixed Ethnicity

Many claims are made about the significance of interethnic partnerships for individuals and for society. Such partnerships continue to be seen as a “barometer” of the openness of society and have spawned extensive analysis investigating their patterns, trends, and determinants. But we know little about the experience of children growing up in families of mixed parentage. In the United Kingdom, the increase in the self-defined “mixed” population is often celebrated. But there has been little quantitative sociological analysis that has investigated the circumstances of the children of mixed ethnicity partnerships. Using two large-scale UK datasets that cover a similar period, this article evaluates the extent to which mixed parentage families are associated with circumstances (both economic and in terms of family structure) that tend to be positive or negative for children’s future life chances and how these compare to those of children with parents from the same ethnic group. It shows that there is substantial variation according to the outcome considered but also according to ethnic group. Overall, children in mixed parentage families do not unequivocally experience the equality of outcomes with majority group children that the assimilation hypothesis implies.

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Analyst-Ready Large Scale Real Time Information Retrieval Tool for E-Governance

E-Government Diffusion, Policy, and Impact ◽

10.4018/978-1-60566-130-8.ch016 ◽

2009 ◽

pp. 268-294

Author(s):

Eugene Santos Jr. ◽

Eunice E. Santos ◽

Hien Nguyen ◽

Long Pan ◽

John Korah

Keyword(s):

Information Retrieval ◽

Real Time ◽

Homeland Security ◽

Large Scale ◽

Rapid Development ◽

Empirical Evaluation ◽

Heterogeneous Data ◽

Free Text ◽

Unified Framework ◽

Sophisticated User

With the proliferation of the Internet and rapid development of information and communication infrastructure, E-governance has become a viable option for effective deployment of government services and programs. Areas of E-governance such as Homeland security and disaster relief have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such databases/sources is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist analysts to find information quickly and effectively by incrementally collecting, processing and matching information nuggets. This framework has previously been used to develop a distributed, free text information retrieval application. In this chapter, we provide a comprehensive solution for the E-GOV analyst by extending the I-FGM framework to image collections and creating a “live” version of I-FGM deployable for real-world use. We present a Content Based Image Retrieval (CBIR) technique that incrementally processes the images, extracts low-level features and map them to higher level concepts. Our empirical evaluation of the algorithm shows that our approach performs competitively compared to some existing approaches in terms of retrieving relevant images while offering the speed advantages of a distributed and incremental process, and unified framework for both text and images. We describe our production level prototype that has a sophisticated user interface which can also deal with multiple queries from multiple users. The interface provides real-time updating of the search results and provides “under the hood” details of I-FGM processes as the queries are being processed.

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Understanding Offline Password-Cracking Methods: A Large-Scale Empirical Study

Security and Communication Networks ◽

10.1155/2021/5563884 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Ruixin Shi ◽

Yongbin Zhou ◽

Yong Li ◽

Weili Han

Keyword(s):

Empirical Study ◽

Large Scale ◽

Ad Hoc ◽

Empirical Evaluation ◽

Comparative Investigation ◽

Academic Community ◽

Data Sets ◽

The Past ◽

Cracking Performance ◽

Password Cracking

Researchers proposed several data-driven methods to efficiently guess user-chosen passwords for password strength metering or password recovery in the past decades. However, these methods are usually evaluated under ad hoc scenarios with limited data sets. Thus, this motivates us to conduct a systematic and comparative investigation with a very large-scale data corpus for such state-of-the-art cracking methods. In this paper, we present the large-scale empirical study on password-cracking methods proposed by the academic community since 2005, leveraging about 220 million plaintext passwords leaked from 12 popular websites during the past decade. Specifically, we conduct our empirical evaluation in two cracking scenarios, i.e., cracking under extensive-knowledge and limited-knowledge. The evaluation concludes that no cracking method may outperform others from all aspects in these offline scenarios. The actual cracking performance is determined by multiple factors, including the underlying model principle along with dataset attributes such as length and structure characteristics. Then, we perform further evaluation by analyzing the set of cracked passwords in each targeting dataset. We get some interesting observations that make sense of many cracking behaviors and come up with some suggestions on how to choose a more effective password-cracking method under these two offline cracking scenarios.

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The Psychophysiology of Political Ideology: Replications, Reanalysis and Recommendations

10.31234/osf.io/49hfg ◽

2019 ◽

Cited By ~ 3

Author(s):

Mathias Osmundsen ◽

David Hendry ◽

Lasse Laustsen ◽

Kevin Smith ◽

Michael Bang Petersen

Keyword(s):

Political Ideology ◽

Large Scale ◽

Electrodermal Activity ◽

Empirical Evaluation ◽

Empirical Support ◽

Reliability And Validity ◽

Emotional Reactions ◽

Psychophysiological Correlates ◽

Representative Samples ◽

Threatening Stimuli

This article presents a large-scale, empirical evaluation of the psychophysiological correlates of political ideology and, in particular, the claim that conservatives react with higher levels of electrodermal activity to threatening stimuli than liberals. We (1) conduct two large replications of this claim, using locally representative samples of Danes and Americans; (2) re-analyze all published studies and evaluate their reliability and validity; and (3) test several features to enhance the validity of psychophysiological measures and offer a number of recommendations. Overall, we find little empirical support for the claim. This is caused by significant reliability and validity problems related to measuring threat-sensitivity using electrodermal activity. When assessed reliably, electrodermal activity in the replications and published studies captures individual differences in the physiological changes associated with attention shifts, which are unrelated to ideology. In contrast to psychophysiological reactions, self-reported emotional reactions to threatening stimuli are reliably associated with ideology.

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Offline and Online Algorithms for SSD Management

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3491045 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-28

Author(s):

Tomer Lange ◽

Joseph (Seffi) Naor ◽

Gala Yadgar

Keyword(s):

Greedy Algorithm ◽

Prediction Error ◽

Large Scale ◽

Optimal Algorithm ◽

Empirical Evaluation ◽

Deterministic Algorithm ◽

Management Problem ◽

Solid State Drives ◽

Wide Range ◽

The Greedy Algorithm

Flash-based solid state drives (SSDs) have gained a central role in the infrastructure of large-scale datacenters, as well as in commodity servers and personal devices. The main limitation of flash media is its inability to support update-in-place: after data has been written to a physical location, it has to be erased before new data can be written to it. Moreover, SSDs support read and write operations in granularity of pages, while erasures are performed on entire blocks, which often contain hundreds of pages. When erasing a block, any valid data it stores must be rewritten to a clean location. As an SSD eventually wears out with progressing number of erasures, the efficiency of the management algorithm has a significant impact on its endurance. In this paper we first formally define the SSD management problem. We then explore this problem from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm that, given any input, performs a negligible number of rewrites (relative to the input length). We also discuss the hardness of the offline problem. In the online setting, we first consider algorithms that have no prior knowledge about the input. We prove that no deterministic algorithm outperforms the greedy algorithm in this setting, and discuss the possible benefit of randomization. We then augment our model, assuming that each request for a page arrives with a prediction of the next time the page is updated. We design an online algorithm that uses such predictions, and show that its performance improves as the prediction error decreases. We also show that the performance of our algorithm is never worse than that guaranteed by the greedy algorithm, even when the prediction error is large. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.

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An Empirical Evaluation of Topologies for Large Scale NoC

TELKOMNIKA Indonesian Journal of Electrical Engineering ◽

10.11591/telkomnika.v12i12.6840 ◽

2014 ◽

Vol 12 (12) ◽

Author(s):

Mehdi Baboli ◽

Nasir Shaikh Husin

Keyword(s):

Large Scale ◽

Empirical Evaluation

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Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Applied Sciences ◽

10.3390/app9132634 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2634 ◽

Cited By ~ 1

Author(s):

Ok ◽

Lee ◽

Kim

Keyword(s):

Random Walk ◽

Collaborative Filtering ◽

Real World ◽

Large Scale ◽

Online Shopping ◽

Experimental Studies ◽

Empirical Evaluation ◽

Personalized Recommendation ◽

User Interests ◽

Sparsity Problem

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

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