Prior elicitation in Bayesian quantile regression for longitudinal data

© 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original auhor and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.In this paper, we introduce Bayesian quantile regression for longitudinal data in terms of informative priors and Gibbs sampling. We develop methods for eliciting prior distribution to incorporate historical data gathered from similar previous studies. The methods can be used either with no prior data or with complete prior data. The advantage of the methods is that the prior distribution is changing automatically when we change the quantile. We propose Gibbs sampling methods which are computationally efficient and easy to implement. The methods are illustrated with both simulation and real data.This article is made available through the Brunel Open Access Publishing Fund

Sentiment Mining of Consumer Reviews: Evidence from Guangxi Fresh Fruit Supply on China's JD.com E-commerce Platform

This study identifies the product supply chain management issues based on significant data analysis methods by considering user evaluation data from JD.com e-commerce platform, a central e-commerce platform in China. Fresh fruit contributes to substantial sales in e-commerce platforms due to the particular characteristics of freshness. The quality perception of fresh fruits directly affects consumers' trust and willingness to repurchase. Therefore, studying the perceived value of online shopping for fresh fruit consumers in Guangxi, China, is significant to the operation and marketing of fresh fruit e-commerce. In view of these perspectives, we developed a user portrait model for fresh fruits in the Guangxi province based on three key dimensions: user information, fruit category details, and user evaluation information. Utilizing Rost CM software to analyze the content of user comments, including keyword word frequency analysis, semantic network analysis and consumers' shopping sentiment mining, so as to find out the existing problems, and provide decision-making reference for the improvement of the supply chain system of fresh fruits in Guangxi

A finite deformation coupled plastic-damage model for simulating fracture of metal foams

Metal foams are a novel class of lightweight materials with unique mechanical, thermal, and acoustical properties. The low ductility of metal foams hinders the possibilities of applying secondary forming techniques to shape metal foam sandwich panels into desired industrial components. An important factor is the limited studies on their macroscopic damage and fracture behavior under complex loading conditions. There exist numerous mechanistic micromechanics models describing the fracture behavior of metal foams at the strut level, but very few work have been done on modeling their macroscopically coupled plasticity-damage constitutive behavior. The objective of this study is to develop a continuum finite deformation elasto-plastic-damage mechanics-based constitutive model for metal foams. The constitutive model is implemented in a user-defined material subroutine (UMAT) in the finite element software ABAQUS/Standard. The elasto-plastic part is implemented using backward-Euler return algorithm within Deshpande–Fleck constitutive framework. Continuum damage mechanics framework is formulated for the development of damage evolution equations. These damage evolution equations take into consideration the various key degradation mechanisms that lead to the macroscopic fracture of metal foams under various loading conditions. The model is calibrated and validated based on experimental data on aluminum foams under different loading paths, strain rates, and temperatures

Cloud Forensics Investigations Relationship: A Model And Instrument

Cloud computing is one of the most important advances in computing in recent history. cybercrime has developed side by side and rapidly in recent years. Previous studies had confirmed the existing gap between cloud service providers (CSPs) and law enforcement agencies (LEAs), and LEAs cannot work without the cooperation of CSPs. Their relationship is influenced by legal, organisational and technical dimensions, which affect the investigations. Therefore, it is essential to enhance the cloud forensics relationship between LEAs and CSPs. This research addresses the need for a unified collaborative model to facilitate proper investigations and explore and evaluate existing different models involved in the relationship between Omani LEAs and local CSPs as a participant in investigations. Further, it proposes a validated research instrument that can be cloud forensics survey. It can also be used as an evaluation tool to identify, measure, and manage cloud forensic investigations

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Towards Sustainable Middle Eastern Cities: A Local Sustainability Assessment Framework

للمساعدة في جعل مدن الشرق الأوسط أكثر استدامة، يعتبر إطار عمل منهجي توجيهي لتقييم الاستدامة المحلية هو المفتاح لتحقيق مستقبل مستدام. تبحث هذه الورقة في الأطر المتاحة وتطور مقاربة لتقييم الاستدامة المحلية، من خلال بناء إطار منهجي باستخدام مزيج من منهج (من أسفل إلى أعلى) ومن (أعلى إلى أسفل). هذا يسهل صياغة واختيار وترتيب أولويات المؤشرات الرئيسية التي يمكن بعد ذلك توجيه تقييم استدامة المدينة على المستوى المحلي في الشرق الأوسط. تطبق الورقة أخيرًا إطار المنهجية على مدينة الحلة العراقية وتنجح في صياغة وتصنيف 57 مؤشراً صالحاً للاستدامة.To assist in making Middle Eastern cities more sustainable a guiding methodological framework for local sustainability assessment is key to achieving a sustainable future. This paper investigates available frameworks and develops an approach to local sustainability assessment (LSA), by constructing a methodological framework utilising a combination of (bottom-up) and (top-down) approaches. This facilitates the formulation, selection and prioritisation of key indicators, which can then guide the assessment of a city’s sustainability at a local level in the Middle East. The paper finally applies the LSA methodological framework to the Iraqi city of Hilla and succeeds in formulating and ranking 57 useful and valid sustainability indicators

BayesImposter: Bayesian Estimation Based .bss Imposter Attack on Industrial Control Systems

Over the last six years, several papers used memory deduplication to trigger various security issues, such as leaking heap-address and causing bit-flip in the physical memory. The most essential requirement for successful memory deduplication is to provide identical copies of a physical page. Recent works use a brute-force approach to create identical copies of a physical page that is an inaccurate and time-consuming primitive from the attacker's perspective. Our work begins to fill this gap by providing a domain-specific structured way to duplicate a physical page in cloud settings in the context of industrial control systems (ICSs). Here, we show a new attack primitive - \textit{BayesImposter}, which points out that the attacker can duplicate the .bss section of the target control DLL file of cloud protocols using the \textit{Bayesian estimation} technique. Our approach results in less memory (i.e., 4 KB compared to GB) and time (i.e., 13 minutes compared to hours) compared to the brute-force approach used in recent works. We point out that ICSs can be expressed as state-space models; hence, the \textit{Bayesian estimation} is an ideal choice to be combined with memory deduplication for a successful attack in cloud settings. To demonstrate the strength of \textit{BayesImposter}, we create a real-world automation platform using a scaled-down automated high-bay warehouse and industrial-grade SIMATIC S7-1500 PLC from Siemens as a target ICS. We demonstrate that \textit{BayesImposter} can predictively inject false commands into the PLC that can cause possible equipment damage with machine failure in the target ICS. Moreover, we show that \textit{BayesImposter} is capable of adversarial control over the target ICS resulting in severe consequences, such as killing a person but making it looks like an accident. Therefore, we also provide countermeasures to prevent the attack