353 THE ASSOCIATION OF ANTHROPOMETRICMEASUREMENTS AND KNEE OSTEOARTHRITIS IN NON-OBESE SUBJECTS

Editat anteriorment amb el títol: Guia de recurso

Scale Inside-Out: Rapid Mitigation of Cloud DDoS Attacks

The distributed denial of service (DDoS) attacks in cloud computing requires quick absorption of attack data. DDoS attack mitigation is usually achieved by dynamically scaling the cloud resources so as to quickly identify the onslaught features to combat the attack. The resource scaling comes with an additional cost which may prove to be a huge disruptive cost in the cases of longer, sophisticated, and repetitive attacks. In this work, we address an important problem, whether the resource scaling during attack, always result in rapid DDoS mitigation? For this purpose, we conduct real-time DDoS attack experiments to study the attack absorption and attack mitigation for various target services in the presence of dynamic cloud resource scaling. We found that the activities such as attack absorption which provide timely attack data input to attack analytics, are adversely compromised by the heavy resource usage generated by the attack. We show that the operating system level local resource contention, if reduced during attacks, can expedite the overall attack mitigation. The attack mitigation would otherwise not be completed by the dynamic scaling of resources alone. We conceived a novel relation which terms “Resource Utilization Factor” for each incoming request as the major component in forming the resource contention. To overcome these issues, we propose a new “Scale Inside-out” approach which during attacks, reduces the “Resource Utilization Factor” to a minimal value for quick absorption of the attack. The proposed approach sacrifices victim service resources and provides those resources to mitigation service in addition to other co-located services to ensure resource availability during the attack. Experimental evaluation shows up to 95 percent reduction in total attack downtime of the victim service in addition to considerable improvement in attack detection time, service reporting time, and downtime of co-located services

DDoS victim service containment to minimize the internal collateral damages in cloud computing

Recent Distributed Denial of Service (DDoS) attacks on cloud services demonstrate new attack effects, including collateral and economic losses. In this work, we show that DDoS mitigation methods may not provide the expected timely mitigation due to the heavy resource outage created by the attacks. We observe an important Operating System (OS) level internal collateral damage, in which the other critical services are also affected. We formulate the DDoS mitigation problem as an OS level resource management problem. We argue that providing extra resources to the victim's server is only helpful if we can ensure the availability of other services. To achieve these goals, we propose a novel resource containment approach to enforce the victim's resource limits. Our real-time experimental evaluations show that the proposed approach results in reduction in the attack reporting time and victim service downtime by providing isolated and timely resources to ensure availability of other critical services

Learned Visual Features to Textual Explanations

Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, we propose a novel method that leverages the capabilities of large language models (LLMs) to interpret the learned features of pre-trained image classifiers. Our method, called TExplain, tackles this task by training a neural network to establish a connection between the feature space of image classifiers and LLMs. Then, during inference, our approach generates a vast number of sentences to explain the features learned by the classifier for a given image. These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier. Our method, for the first time, utilizes these frequent words corresponding to a visual representation to provide insights into the decision-making process of the independently trained classifier, enabling the detection of spurious correlations, biases, and a deeper comprehension of its behavior. To validate the effectiveness of our approach, we conduct experiments on diverse datasets, including ImageNet-9L and Waterbirds. The results demonstrate the potential of our method to enhance the interpretability and robustness of image classifiers

First measurement of the Head-Tail directional nuclear recoil signature at energies relevant to WIMP dark matter searches

We present first evidence for the so-called Head-Tail asymmetry signature of neutron-induced nuclear recoil tracks at energies down to 1.5 keV/amu using the 1m^3 DRIFT-IIc dark matter detector. This regime is appropriate for recoils induced by Weakly Interacting Massive Particle (WIMPs) but one where the differential ionization is poorly understood. We show that the distribution of recoil energies and directions induced here by Cf-252 neutrons matches well that expected from massive WIMPs. The results open a powerful new means of searching for a galactic signature from WIMPs.Comment: 4 pages, 6 figures, 1 tabl

Low Energy Electron and Nuclear Recoil Thresholds in the DRIFT-II Negative Ion TPC for Dark Matter Searches

Understanding the ability to measure and discriminate particle events at the lowest possible energy is an essential requirement in developing new experiments to search for weakly interacting massive particle (WIMP) dark matter. In this paper we detail an assessment of the potential sensitivity below 10 keV in the 1 m^3 DRIFT-II directionally sensitive, low pressure, negative ion time projection chamber (NITPC), based on event-by-event track reconstruction and calorimetry in the multiwire proportional chamber (MWPC) readout. By application of a digital smoothing polynomial it is shown that the detector is sensitive to sulfur and carbon recoils down to 2.9 and 1.9 keV respectively, and 1.2 keV for electron induced events. The energy sensitivity is demonstrated through the 5.9 keV gamma spectrum of 55Fe, where the energy resolution is sufficient to identify the escape peak. The effect a lower energy sensitivity on the WIMP exclusion limit is demonstrated. In addition to recoil direction reconstruction for WIMP searches this sensitivity suggests new prospects for applications also in KK axion searches