Evaluation of the Introduction of Florida Bass into an Oklahoma Reservoir Receiving a Heated Effluent

Zoolog

CrowdGuard: Federated Backdoor Detection in Federated Learning

Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data. However, FL is susceptible to backdoor (or targeted poisoning) attacks. These attacks are initiated by malicious clients who seek to compromise the learning process by introducing specific behaviors into the learned model that can be triggered by carefully crafted inputs. Existing FL safeguards have various limitations: They are restricted to specific data distributions or reduce the global model accuracy due to excluding benign models or adding noise, are vulnerable to adaptive defense-aware adversaries, or require the server to access local models, allowing data inference attacks. This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in FL and overcomes the deficiencies of existing techniques. It leverages clients' feedback on individual models, analyzes the behavior of neurons in hidden layers, and eliminates poisoned models through an iterative pruning scheme. CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback. The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios, including IID and non-IID data distributions. Additionally, CrowdGuard withstands adaptive adversaries while preserving the original performance of protected models. To ensure confidentiality, CrowdGuard uses a secure and privacy-preserving architecture leveraging Trusted Execution Environments (TEEs) on both client and server sides.Comment: To appear in the Network and Distributed System Security (NDSS) Symposium 2024. Phillip Rieger and Torsten Krau{\ss} contributed equally to this contribution. 19 pages, 8 figures, 5 tables, 4 algorithms, 5 equation

BayBFed: Bayesian Backdoor Defense for Federated Learning

Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of defenses have recently been proposed, which have primarily utilized intermediary states of the global model (i.e., logits) or distance of the local models (i.e., L2-norm) from the global model to detect malicious backdoors. However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies. In this paper, we introduce a novel and more generic backdoor defense framework, called BayBFed, which proposes to utilize probability distributions over client updates to detect malicious updates in FL: it computes a probabilistic measure over the clients' updates to keep track of any adjustments made in the updates, and uses a novel detection algorithm that can leverage this probabilistic measure to efficiently detect and filter out malicious updates. Thus, it overcomes the shortcomings of previous approaches that arise due to the direct usage of client updates; as our probabilistic measure will include all aspects of the local client training strategies. BayBFed utilizes two Bayesian Non-Parametric extensions: (i) a Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the clients' updates, and (ii) an adaptation of the Chinese Restaurant Process (CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure to detect and filter out malicious updates. We extensively evaluate our defense approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection, MNIST, and FMNIST, and show that it can effectively detect and eliminate malicious updates in FL without deteriorating the benign performance of the global model

ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks

IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive target for attackers. On the other hand, the large variability of different application scenarios and inherent heterogeneity of devices make it very challenging to reliably detect abnormal IoT device behaviors and distinguish these from benign behaviors. Existing approaches for detecting attacks are mostly limited to attacks directly compromising individual IoT devices, or, require predefined detection policies. They cannot detect attacks that utilize the control plane of the IoT system to trigger actions in an unintended/malicious context, e.g., opening a smart lock while the smart home residents are absent. In this paper, we tackle this problem and propose ARGUS, the first self-learning intrusion detection system for detecting contextual attacks on IoT environments, in which the attacker maliciously invokes IoT device actions to reach its goals. ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment. An unsupervised Deep Neural Network (DNN) is used for modeling the typical contextual device behavior and detecting actions taking place in abnormal contextual settings. This unsupervised approach ensures that ARGUS is not restricted to detecting previously known attacks but is also able to detect new attacks. We evaluated ARGUS on heterogeneous real-world smart-home settings and achieve at least an F1-Score of 99.64% for each setup, with a false positive rate (FPR) of at most 0.03%.Comment: To appear in the 32nd USENIX Security Symposium, August 2022, Anaheim CA, US

A pedagogic appraisal of the Priority Heuristic

We have explored how science and mathematics teachers made decisions when confronted with a dilemma in which a fictitious young woman, Deborah, may choose to have an operation that might address a painful spinal condition. We sought to explore the extent to which psychological heuristic models, in particular the Priority Heuristic, might successfully describe the decision-making process of these teachers and how an analysis of the role of personal and emotional factors in shaping the decision-making process might inform pedagogical design. A novel aspect of this study is that the setting in which the decision-making process is examined contrasts sharply with those used in psychological experiments. We found that to some extent, even in this contrasting setting, the Priority Heuristic could describe these teachers' decision-making. Further analysis of the transcripts yielded some insights into limitations on scope as well the richness and complexity in how personal factors were brought to bear. We see these limitations as design opportunities for educational intervention

Informing the development of Australia's national eating disorders research and translation strategy : a rapid review methodology

Background Eating disorders (EDs) are highly complex mental illnesses associated with significant medical complications. There are currently knowledge gaps in research relating to the epidemiology, aetiology, treatment, burden, and outcomes of eating disorders. To clearly identify and begin addressing the major deficits in the scientific, medical, and clinical understanding of these mental illnesses, the Australian Government Department of Health in 2019 funded the InsideOut Institute (IOI) to develop the Australian Eating Disorder Research and Translation Strategy, the primary aim of which was to identify priorities and targets for building research capacity and outputs. A series of rapid reviews (RR) were conducted to map the current state of knowledge, identify evidence gaps, and inform development of the national research strategy. Published peer-reviewed literature on DSM-5 listed EDs, across eight knowledge domains was reviewed: (1) population, prevalence, disease burden, Quality of Life in Western developed countries; (2) risk factors; (3) co-occurring conditions and medical complications; (4) screening and diagnosis; (5) prevention and early intervention; (6) psychotherapies and relapse prevention; (7) models of care; (8) pharmacotherapies, alternative and adjunctive therapies; and (9) outcomes (including mortality). While RRs are systematic in nature, they are distinct from systematic reviews in their aim to gather evidence in a timely manner to support decision-making on urgent or high-priority health concerns at the national level. Results Three medical science databases were searched as the primary source of literature for the RRs: Science Direct, PubMed and OVID (Medline). The search was completed on 31st May 2021 (spanning January 2009-May 2021). At writing, a total of 1,320 articles met eligibility criteria and were included in the final review. Conclusions For each RR, the evidence has been organised to review the knowledge area and identify gaps for further research and investment. The series of RRs (published separately within the current series) are designed to support the development of research and translation practice in the field of EDs. They highlight areas for investment and investigation, and provide researchers, service planners and providers, and research funders rapid access to quality current evidence, which has been synthesised and organised to assist decision-making

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis