793 research outputs found
Four problems related to the Pseudo-Smarandache-Squarefree function
The main purpose of this paper is using the elementary methods to study several problems in [2], and four of them are solved
Why Does Flow Director Cause Packet Reordering?
Intel Ethernet Flow Director is an advanced network interface card (NIC)
technology. It provides the benefits of parallel receive processing in
multiprocessing environments and can automatically steer incoming network data
to the same core on which its application process resides. However, our
analysis and experiments show that Flow Director cannot guarantee in-order
packet delivery in multiprocessing environments. Packet reordering causes
various negative impacts. E.g., TCP performs poorly with severe packet
reordering. In this paper, we use a simplified model to analyze why Flow
Director can cause packet reordering. Our experiments verify our analysis
Distributed Data Streaming Algorithms for Network Anomaly Detection
Network attacks and anomalies such as DDoS attacks, service outages, email spamming are happening everyday, causing various problems for users such as financial loss, inconvenience due to service unavailability, personal information leakage and so on. Different methods have been studied and developed to tackle these network attacks, and among them data streaming algorithms are quite powerful, useful and flexible schemes that have many applications in network attack detection and identification. Data streaming algorithms usually use limited space to store aggregated information and report certain properties of the traffic in short and constant time.
There are several challenges for designing data streaming algorithms. Firstly, network traffic is usually distributed and monitored at different locations, and it is often desirable to aggregate the distributed monitoring information together to detect attacks which might be low-profile at a single location; thus data streaming algorithms have to support data merging without loss of information. Secondly, network traffic is usually in high-speed and large-volume; data streaming algorithms have to process data fast and smart to save space and time. Thirdly, sometimes only detection is not useful enough and identification of targets make more sense, in which case data streaming algorithms have to be concise and reversible.
In this dissertation, we study three different types of data streaming algorithms: hot item identification, distinct element counting and superspreader identification. We propose new algorithms to solve these problems and evaluate them with both theoretical analysis and experiments to show their effectiveness and improvements upon previous methods
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness
Adversarial vulnerability remains a major obstacle to constructing reliable
NLP systems. When imperceptible perturbations are added to raw input text, the
performance of a deep learning model may drop dramatically under attacks.
Recent work argues the adversarial vulnerability of the model is caused by the
non-robust features in supervised training. Thus in this paper, we tackle the
adversarial robustness challenge from the view of disentangled representation
learning, which is able to explicitly disentangle robust and non-robust
features in text. Specifically, inspired by the variation of information (VI)
in information theory, we derive a disentangled learning objective composed of
mutual information to represent both the semantic representativeness of latent
embeddings and differentiation of robust and non-robust features. On the basis
of this, we design a disentangled learning network to estimate these mutual
information. Experiments on text classification and entailment tasks show that
our method significantly outperforms the representative methods under
adversarial attacks, indicating that discarding non-robust features is critical
for improving adversarial robustness
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