50 research outputs found
HVSTO: Efficient Privacy Preserving Hybrid Storage in Cloud Data Center
In cloud data center, shared storage with good management is a main structure
used for the storage of virtual machines (VM). In this paper, we proposed
Hybrid VM storage (HVSTO), a privacy preserving shared storage system designed
for the virtual machine storage in large-scale cloud data center. Unlike
traditional shared storage, HVSTO adopts a distributed structure to preserve
privacy of virtual machines, which are a threat in traditional centralized
structure. To improve the performance of I/O latency in this distributed
structure, we use a hybrid system to combine solid state disk and distributed
storage. From the evaluation of our demonstration system, HVSTO provides a
scalable and sufficient throughput for the platform as a service
infrastructure.Comment: 7 pages, 8 figures, in proceeding of The Second International
Workshop on Security and Privacy in Big Data (BigSecurity 2014
Multicloud-Based Evacuation Services for Emergency Management
A smart evacuation needs a scalable and flexible system to provide service in both emergency and normal situations. A single cloud service is usually limited to support scaling up requirements in an emergency, especially one with a large geographic scope. In this article, the authors propose MCES, a multicloud architecture that deploys smart evacuation services in multiple cloud providers and that can tolerant more pressure than single cloud-based services. This system maintains basic service to support monitoring, but during an emergency, visits to the service will scale up enormously, which means MDSE must support a rapid scaling up of service capacity in a short time. The authors use a three-layer cloud instance management to support rapid capacity scaling in MCES. By conducting extensive simulations, the authors demonstrate that their proposed MCES significantly outperforms single cloud solutions under various emergency settings
Supervised Knowledge May Hurt Novel Class Discovery Performance
Novel class discovery (NCD) aims to infer novel categories in an unlabeled
dataset by leveraging prior knowledge of a labeled set comprising disjoint but
related classes. Given that most existing literature focuses primarily on
utilizing supervised knowledge from a labeled set at the methodology level,
this paper considers the question: Is supervised knowledge always helpful at
different levels of semantic relevance? To proceed, we first establish a novel
metric, so-called transfer flow, to measure the semantic similarity between
labeled/unlabeled datasets. To show the validity of the proposed metric, we
build up a large-scale benchmark with various degrees of semantic similarities
between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical
class structure. The results based on the proposed benchmark show that the
proposed transfer flow is in line with the hierarchical class structure; and
that NCD performance is consistent with the semantic similarities (measured by
the proposed metric). Next, by using the proposed transfer flow, we conduct
various empirical experiments with different levels of semantic similarity,
yielding that supervised knowledge may hurt NCD performance. Specifically,
using supervised information from a low-similarity labeled set may lead to a
suboptimal result as compared to using pure self-supervised knowledge. These
results reveal the inadequacy of the existing NCD literature which usually
assumes that supervised knowledge is beneficial. Finally, we develop a
pseudo-version of the transfer flow as a practical reference to decide if
supervised knowledge should be used in NCD. Its effectiveness is supported by
our empirical studies, which show that the pseudo transfer flow (with or
without supervised knowledge) is consistent with the corresponding accuracy
based on various datasets. Code is released at
https://github.com/J-L-O/SK-Hurt-NCDComment: TMLR 2023 accepted paper. arXiv admin note: substantial text overlap
with arXiv:2209.0912
Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
The annotation scarcity of medical image segmentation poses challenges in
collecting sufficient training data for deep learning models. Specifically,
models trained on limited data may not generalize well to other unseen data
domains, resulting in a domain shift issue. Consequently, domain generalization
(DG) is developed to boost the performance of segmentation models on unseen
domains. However, the DG setup requires multiple source domains, which impedes
the efficient deployment of segmentation algorithms in clinical scenarios. To
address this challenge and improve the segmentation model's generalizability,
we propose a novel approach called the Frequency-mixed Single-source Domain
Generalization method (FreeSDG). By analyzing the frequency's effect on domain
discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the
single-source domain. Additionally, self-supervision is constructed in the
domain augmentation to learn robust context-aware representations for the
segmentation task. Experimental results on five datasets of three modalities
demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms
state-of-the-art methods and significantly improves the segmentation model's
generalizability. Therefore, FreeSDG provides a promising solution for
enhancing the generalization of medical image segmentation models, especially
when annotated data is scarce. The code is available at
https://github.com/liamheng/Non-IID_Medical_Image_Segmentation
NFC Secure Payment and Verification Scheme with CS E-Ticket
As one of the most important techniques in IoT, NFC (Near Field Communication) is more interesting than ever. NFC is a short-range, high-frequency communication technology well suited for electronic tickets, micropayment, and access control function, which is widely used in the financial industry, traffic transport, road ban control, and other fields. However, NFC is becoming increasingly popular in the relevant field, but its secure problems, such as man-in-the-middle-attack and brute force attack, have hindered its further development. To address the security problems and specific application scenarios, we propose a NFC mobile electronic ticket secure payment and verification scheme in the paper. The proposed scheme uses a CS E-Ticket and offline session key generation and distribution technology to prevent major attacks and increase the security of NFC. As a result, the proposed scheme can not only be a good alternative to mobile e-ticket system but also be used in many NFC fields. Furthermore, compared with other existing schemes, the proposed scheme provides a higher security
Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis
Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification