A Logical Method for Policy Enforcement over Evolving Audit Logs

We present an iterative algorithm for enforcing policies represented in a first-order logic, which can, in particular, express all transmission-related clauses in the HIPAA Privacy Rule. The logic has three features that raise challenges for enforcement --- uninterpreted predicates (used to model subjective concepts in privacy policies), real-time temporal properties, and quantification over infinite domains (such as the set of messages containing personal information). The algorithm operates over audit logs that are inherently incomplete and evolve over time. In each iteration, the algorithm provably checks as much of the policy as possible over the current log and outputs a residual policy that can only be checked when the log is extended with additional information. We prove correctness and termination properties of the algorithm. While these results are developed in a general form, accounting for many different sources of incompleteness in audit logs, we also prove that for the special case of logs that maintain a complete record of all relevant actions, the algorithm effectively enforces all safety and co-safety properties. The algorithm can significantly help automate enforcement of policies derived from the HIPAA Privacy Rule.Comment: Carnegie Mellon University CyLab Technical Report. 51 page

Peeling Back the Onion Competitive Advantage Through People: Test of a Causal Model

Proponents of the resource-based view (RBV) of the firm have identified human resource management (HRM) and human capital as organizational resources that can contribute to sustainable competitive success. A number of empirical studies have documented the relationship between systems of human resource policies and practices and firm performance. The mechanisms by which HRM leads to firm performance, however, remain largely unexplored. In this study, we explore the pathways leading from HRM to firm performance. Specifically, we use structural equation modeling to test a model positing a set of causal relationships between high performance work systems (HPWS), employee retention, workforce productivity and firm market value. Within a set of manufacturing firms, results indicate the primary impact of HPWS on productivity and market value is through its influence on employee retention

HRM and Firm Productivity: Does Industry Matter?

Recent years have witnessed burgeoning interest in the degree to which human resource systems contribute to organizational effectiveness. We argue that extant research has not fully considered important contextual conditions which moderate the efficacy of these practices. Specifically, we invoke a contingency perspective in proposing that industry characteristics affect the relative importance and value of high performance work practices (HPWPs). We test this proposition on a sample of non-diversified manufacturing firms. After controlling for the influence of a number of other factors, study findings support the argument that industry characteristics moderate the influence of HPWPs on firm productivity. Specifically, the impact of a system of HPWPs on firm productivity is significantly influenced by the industry conditions of capital intensity, growth and differentiation

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly complex feature hierarchies from the image pixels. While such generalized automatic feature extraction methods have significantly reduced the burden of feature engineering in many domains, for niche tasks such as the one we consider in this paper, they result in two major problems. First, they use a very large number of features (that may or may not be relevant) and therefore training such models is computationally expensive. Further, more importantly, the large feature-space makes it very hard to interpret which features are truly important for predictions. Thus, a criticism of such methods is that learning algorithms pose opaque black boxes to its users, in this case, medical experts. The recommendation of such algorithms can be understood easily, but the reason for their recommendation is not clear. This is the problem of non-interpretability of the model, and the best-performing algorithms are usually the least interpretable. To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.Comment: Accepted in The 2020 International Joint Conference on Neural Networks (IJCNN 2020) At Glasgow (UK

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe From China, the disease started spreading to the rest of the world After China, Italy became the next epicentre of the virus and witnessed a very high death toll Soon nations like the USA became severely hit by SARS-CoV-2 virus The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2 To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19 In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19 It further focuses on the study of vaccinations to cope with the pandemic Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this stud

Parametric Optimization of Re-refining of Waste Lubricating Oil Using Bio-flocculant via Taguchi Approach

Over the past few decades, recycling used lubricants have drawn much attention as a cleaner technique. The current study focuses on the fabrication and application of bio flocculant (sodium alginate) from brown algae (Sargassum Muticum) for the refining of waste lubricating oil. Further the work illustrates on the optimization of the four process parameters like refining time, refining temperature, solvent-to-waste oil ratio, and flocculant dosage at three different levels (low, intermediate and high) using Taguchi approach during the process of refining of waste lubricating oil by clean and environmental friendly extraction flocculation method. The optimized parameters for maximization of the yield (91.31 %) were observed at refining time of 60 minutes, refining temperature of 80 ?, a solvent-to-waste oil ratio of 3:1, and a flocculant dosage of 1 g/kg of solvent. A good fit of the model could be achieved with a R2 of 0.9938 and p value of 0.018. The re-refined lubricating oil had a flash point, pour point, kinematic viscosity@40 ? and 100 ? of 234 ?, -33 ?,155.21 cSt and 17.11 cSt which are comparable to the virgin lubricating oil and hence refined oil can remarkably be used for specific purpose in automotive engine after addition of requisite amount of additives

Program Actions as Actual Causes: A Building Block for Accountability

Abstract-Protocols for tasks such as authentication, electronic voting, and secure multiparty computation ensure desirable security properties if agents follow their prescribed programs. However, if some agents deviate from their prescribed programs and a security property is violated, it is important to hold agents accountable by determining which deviations actually caused the violation. Motivated by these applications, we initiate a formal study of program actions as actual causes. Specifically, we define in an interacting program model what it means for a set of program actions to be an actual cause of a violation. We present a sound technique for establishing program actions as actual causes. We demonstrate the value of this formalism in two ways. First, we prove that violations of a specific class of safety properties always have an actual cause. Thus, our definition applies to relevant security properties. Second, we provide a cause analysis of a representative protocol designed to address weaknesses in the current public key certification infrastructure

Large-scale Graphitic Thin Films Synthesized on Ni and Transferred to Insulators: Structural and Electronic Properties

We present a comprehensive study of the structural and electronic properties of ultrathin films containing graphene layers synthesized by chemical vapor deposition (CVD) based surface segregation on polycrystalline Ni foils then transferred onto insulating SiO2/Si substrates. Films of size up to several mm's have been synthesized. Structural characterizations by atomic force microscopy (AFM), scanning tunneling microscopy (STM), cross-sectional transmission electron microscopy (XTEM) and Raman spectroscopy confirm that such large scale graphitic thin films (GTF) contain both thick graphite regions and thin regions of few layer graphene. The films also contain many wrinkles, with sharply-bent tips and dislocations revealed by XTEM, yielding insights on the growth and buckling processes of the GTF. Measurements on mm-scale back-gated transistor devices fabricated from the transferred GTF show ambipolar field effect with resistance modulation ~50% and carrier mobilities reaching ~2000 cm^2/Vs. We also demonstrate quantum transport of carriers with phase coherence length over 0.2 $\mu$m from the observation of 2D weak localization in low temperature magneto-transport measurements. Our results show that despite the non-uniformity and surface roughness, such large-scale, flexible thin films can have electronic properties promising for device applications.Comment: This version (as published) contains additional data, such as cross sectional TEM image