539 research outputs found
Extended fast search clustering algorithm: widely density clusters, no density peaks
CFSFDP (clustering by fast search and find of density peaks) is recently
developed density-based clustering algorithm. Compared to DBSCAN, it needs less
parameters and is computationally cheap for its non-iteration. Alex. at al have
demonstrated its power by many applications. However, CFSFDP performs not well
when there are more than one density peak for one cluster, what we name as "no
density peaks". In this paper, inspired by the idea of a hierarchical
clustering algorithm CHAMELEON, we propose an extension of CFSFDP,E_CFSFDP, to
adapt more applications. In particular, we take use of original CFSFDP to
generating initial clusters first, then merge the sub clusters in the second
phase. We have conducted the algorithm to several data sets, of which, there
are "no density peaks". Experiment results show that our approach outperforms
the original one due to it breaks through the strict claim of data sets.Comment: 18 pages, 10 figures, DBDM 201
Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review
In the realm of data protection, a striking disconnect prevails between
traditional domains of doctrinal, legal, theoretical, and policy-based
inquiries and a burgeoning body of empirical evidence. Much of the scholarly
and regulatory discourse remains entrenched in abstract legal principles or
normative frameworks, leaving the empirical landscape uncharted or minimally
engaged. Since the birth of EU data protection law, a modest body of empirical
evidence has been generated but remains widely scattered and unexamined. Such
evidence offers vital insights into the perception, impact, clarity, and
effects of data protection measures but languishes on the periphery,
inadequately integrated into the broader conversation. To make a meaningful
connection, we conduct a comprehensive review and synthesis of empirical
research spanning nearly three decades (1995- March 2022), advocating for a
more robust integration of empirical evidence into the evaluation and review of
the GDPR, while laying a methodological foundation for future empirical
research
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Fully exploring correlation among points in point clouds is essential for
their feature modeling. This paper presents a novel end-to-end graph model,
named Point2Node, to represent a given point cloud. Point2Node can dynamically
explore correlation among all graph nodes from different levels, and adaptively
aggregate the learned features. Specifically, first, to fully explore the
spatial correlation among points for enhanced feature description, in a
high-dimensional node graph, we dynamically integrate the node's correlation
with self, local, and non-local nodes. Second, to more effectively integrate
learned features, we design a data-aware gate mechanism to self-adaptively
aggregate features at the channel level. Extensive experiments on various point
cloud benchmarks demonstrate that our method outperforms the state-of-the-art.Comment: AAAI2020(oral
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Time series anomaly detection (TSAD) is an important data mining task with
numerous applications in the IoT era. In recent years, a large number of deep
neural network-based methods have been proposed, demonstrating significantly
better performance than conventional methods on addressing challenging TSAD
problems in a variety of areas. Nevertheless, these deep TSAD methods typically
rely on a clean training dataset that is not polluted by anomalies to learn the
"normal profile" of the underlying dynamics. This requirement is nontrivial
since a clean dataset can hardly be provided in practice. Moreover, without the
awareness of their robustness, blindly applying deep TSAD methods with
potentially contaminated training data can possibly incur significant
performance degradation in the detection phase. In this work, to tackle this
important challenge, we firstly investigate the robustness of commonly used
deep TSAD methods with contaminated training data which provides a guideline
for applying these methods when the provided training data are not guaranteed
to be anomaly-free. Furthermore, we propose a model-agnostic method which can
effectively improve the robustness of learning mainstream deep TSAD models with
potentially contaminated data. Experiment results show that our method can
consistently prevent or mitigate performance degradation of mainstream deep
TSAD models on widely used benchmark datasets
A Survey of DeFi Security: Challenges and Opportunities
DeFi, or Decentralized Finance, is based on a distributed ledger called
blockchain technology. Using blockchain, DeFi may customize the execution of
predetermined operations between parties. The DeFi system use blockchain
technology to execute user transactions, such as lending and exchanging. The
total value locked in DeFi decreased from \$200 billion in April 2022 to \$80
billion in July 2022, indicating that security in this area remained
problematic. In this paper, we address the deficiency in DeFi security studies.
To our best knowledge, our paper is the first to make a systematic analysis of
DeFi security. First, we summarize the DeFi-related vulnerabilities in each
blockchain layer. Additionally, application-level vulnerabilities are also
analyzed. Then we classify and analyze real-world DeFi attacks based on the
principles that correlate to the vulnerabilities. In addition, we collect
optimization strategies from the data, network, consensus, smart contract, and
application layers. And then, we describe the weaknesses and technical
approaches they address. On the basis of this comprehensive analysis, we
summarize several challenges and possible future directions in DeFi to offer
ideas for further research
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