435 research outputs found
On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing
Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc
failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new
theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results
Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment
This paper reports on experience with using semantically-enabled network
resource models to construct an operational multi-domain networked
infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded
through NSF's GENI project. A defining property of NIaaS is the deep
integration of network provisioning functions alongside the more common storage
and computation provisioning functions. Resource provider topologies and user
requests can be described using network resource models with common base
classes for fundamental cyber-resources (links, nodes, interfaces) specialized
via virtualization and adaptations between networking layers to specific
technologies.
This problem space gives rise to a number of application areas where semantic
web technologies become highly useful - common information models and resource
class hierarchies simplify resource descriptions from multiple providers,
pathfinding and topology embedding algorithms rely on query abstractions as
building blocks.
The paper describes how the semantic resource description models enable
ExoGENI to autonomously instantiate on-demand virtual topologies of virtual
machines provisioned from cloud providers and are linked by on-demand virtual
connections acquired from multiple autonomous network providers to serve a
variety of applications ranging from distributed system experiments to
high-performance computing
Sublinear expectation linear regression
Nonlinear expectation, including sublinear expectation as its special case,
is a new and original framework of probability theory and has potential
applications in some scientific fields, especially in finance risk measure and
management. Under the nonlinear expectation framework, however, the related
statistical models and statistical inferences have not yet been well
established. The goal of this paper is to construct the sublinear expectation
regression and investigate its statistical inference. First, a sublinear
expectation linear regression is defined and its identifiability is given.
Then, based on the representation theorem of sublinear expectation and the
newly defined model, several parameter estimations and model predictions are
suggested, the asymptotic normality of estimations and the mini-max property of
predictions are obtained. Furthermore, new methods are developed to realize
variable selection for high-dimensional model. Finally, simulation studies and
a real-life example are carried out to illustrate the new models and
methodologies. All notions and methodologies developed are essentially
different from classical ones and can be thought of as a foundation for general
nonlinear expectation statistics
A Linux Real-Time Packet Scheduler for Reliable Static SDN Routing
In a distributed computing environment, guaranteeing the hard deadline for real-time messages is essential to ensure schedulability of real-time tasks. Since capabilities of the shared resources for transmission are limited, e.g., the buffer size is limited on network devices, it becomes a challenge to design an effective and feasible resource sharing policy based on both the demand of real-time packet transmissions and the limitation of resource capabilities. We address this challenge in two cooperative mechanisms. First, we design a static routing algorithm to find forwarding paths for packets to guarantee their hard deadlines. The routing algorithm employs a validation-based backtracking procedure capable of deriving the demand of a set of real-time packets on each shared network device, and it checks whether this demand can be met on the device. Second, we design a packet scheduler that runs on network devices to transmit messages according to our routing requirements. We implement these mechanisms on virtual software-defined network (SDN) switches and evaluate them on real hardware in a local cluster to demonstrate the feasibility and effectiveness of our routing algorithm and packet scheduler
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis
Mitigating Denial-of-Service (DoS) attacks is vital for online service
security and availability. While machine learning (ML) models are used for DoS
attack detection, new strategies are needed to enhance their performance. We
suggest an innovative method, combinatorial fusion, which combines multiple ML
models using advanced algorithms. This includes score and rank combinations,
weighted techniques, and diversity strength of scoring systems. Through
rigorous evaluations, we demonstrate the effectiveness of this fusion approach,
considering metrics like precision, recall, and F1-score. We address the
challenge of low-profiled attack classification by fusing models to create a
comprehensive solution. Our findings emphasize the potential of this approach
to improve DoS attack detection and contribute to stronger defense mechanisms.Comment: 6 pages, 3 figures, IEEE CN
Dissecting the chloride–nitrate anion transport assay
A systematic study of chloride vs. nitrate selectivity across six anion transporters has revealed a good correlation between the selectivities of their anion binding and membrane transport properties. This work reveals the limitations of the chloride–nitrate exchange assay and shows how new approaches can be used to measure anion uniport.ARC, JSPS, University of Sydney, Kyushu Universit
Identification of miRNAs involved in pear fruit development and quality
BACKGROUND: MicroRNAs (miRNAs) are a class of small, endogenous RNAs that take part in regulating genes through mediating gene expressions at the post-transcriptional level in plants. Previous studies have reported miRNA identification in various plants ranging from model plants to perennial fruit trees. However, the role of miRNAs in pear (Pyrus bretschneideri) fruit development is not clear. Here, we investigated the miRNA profiles of pear fruits from different time stages during development with Illumina HiSeq 2000 platform and bioinformatics analysis. Quantitative real-time PCR was used to validate the expression levels of miRNAs. RESULTS: Both conserved and species-specific miRNAs in pear have been identified in this study. Total reads, ranging from 19,030,925 to 25,576,773, were obtained from six small RNA libraries constructed for different stages of fruit development after flowering. Comparative profiling showed that an average of 90 miRNAs was expressed with significant differences between various developmental stages. KEGG pathway analysis on 2,216 target genes of 188 known miRNAs and 1,127 target genes of 184 novel miRNAs showed that miRNAs are widely involved in the regulation of fruit development. Among these, a total of eleven miRNAs putatively participate in the pathway of lignin biosynthesis, nine miRNAs were identified to take part in sugar and acid metabolism, and MiR160 was identified to regulate auxin response factor. CONCLUSION: Comparative analysis of miRNAomes during pear fruit development is presented, and miRNAs were proved to be widely involved in the regulation of fruit development and formation of fruit quality, for example through lignin synthesis, sugar and acid metabolism, and hormone signaling. Combined with computational analysis and experimental confirmation, the research contributes valuable information for further functional research of microRNA in fruit development for pear and other species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-953) contains supplementary material, which is available to authorized users
Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling
Distributed Denial of Service (DDoS) attacks pose a significant threat to the
stability and reliability of online systems. Effective and early detection of
such attacks is pivotal for safeguarding the integrity of networks. In this
work, we introduce an enhanced approach for DDoS attack detection by leveraging
the capabilities of Deep Residual Neural Networks (ResNets) coupled with
synthetic oversampling techniques. Because of the inherent class imbalance in
many cyber-security datasets, conventional methods often struggle with false
negatives, misclassifying subtle DDoS patterns as benign. By applying the
Synthetic Minority Over-sampling Technique (SMOTE) to the CICIDS dataset, we
balance the representation of benign and malicious data points, enabling the
model to better discern intricate patterns indicative of an attack. Our deep
residual network, tailored for this specific task, further refines the
detection process. Experimental results on a real-world dataset demonstrate
that our approach achieves an accuracy of 99.98%, significantly outperforming
traditional methods. This work underscores the potential of combining advanced
data augmentation techniques with deep learning models to bolster
cyber-security defenses.Comment: 8 pages, 3 figure
The ‘responsibility’ factor in imagining the future of education in China
Design and creativity have been a considerable force for improving life conditions. A lot of effort has been invested in explaining the design process and creativity mainly through the design thinking methodology, but design accountability and responsible actions in the design process are, yet, to be fully explored. The concept of design ethics is now increasingly scrutinized on both the level of business organization and of the individual designer. A 4-day design workshop that involved creativity techniques provided the base to explore responsibility in the fuzzy front end of the design process. The future of education in 2030 was defined as the workshop's theme and fifty-six students from China were asked to create detailed alternative scenarios. A number of imagination exercises, implementation of technological innovations and macro-environment evolutions employed in the workshop are discussed. The aim was to incite moral and responsible actions among students less familiar with creative educational contexts of student-led discovery and collaborative learning. This paper reflects on the use of creativity methods to stimulate anticipation in (non)design students
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