A Semantic-Agent Framework for PaaS Interoperability

Suchismita Hoare, Na Helian, and Nathan Baddoo, 'A Semantic-Agent Framework for PaaS Interoperability', in Proceedings of the The IEEE International Conference on Cloud and Big Data Computing, Toulouse, France, 18-21, July 2016. DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0126 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud Platform as a Service (PaaS) is poised for a wider adoption by its relevant stakeholders, especially Cloud application developers. Despite this, the service model is still plagued with several adoption inhibitors, one of which is lack of interoperability between proprietary application infrastructure services of public PaaS solutions. Although there is some progress in addressing the general PaaS interoperability issue through various devised solutions focused primarily on API compatibility and platform-agnostic application design models, interoperability specific to differentiated services provided by the existing public PaaS providers and the resultant disparity owing to the offered services’ semantics has not been addressed effectively, yet. The literature indicates that this dimension of PaaS interoperability is awaiting evolution in the state-of-the-art. This paper proposes the initial system design of a PaaS interoperability (IntPaaS) framework to be developed through the integration of semantic and agent technologies to enable transparent interoperability between incompatible PaaS services. This will involve uniform description through semantic annotation of PaaS provider services utilizing the OWL-S ontology, creating a knowledgebase that enables software agents to automatically search for suitable services to support Cloud-based Greenfield application development. The rest of the paper discusses the identified research problem along with the proposed solution to address the issue.Submitted Versio

Adposition and Case Supersenses v2.5: Guidelines for English

This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 50 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github.com/nert-gu/streusle/; version 4.3 tracks guidelines version 2.5). Though the SNACS inventory aspires to be universal, this document is specific to English; documentation for other languages will be published separately. Version 2 is a revision of the supersense inventory proposed for English by Schneider et al. (2015, 2016) (henceforth "v1"), which in turn was based on previous schemes. The present inventory was developed after extensive review of the v1 corpus annotations for English, plus previously unanalyzed genitive case possessives (Blodgett and Schneider, 2018), as well as consideration of adposition and case phenomena in Hebrew, Hindi, Korean, and German. Hwang et al. (2017) present the theoretical underpinnings of the v2 scheme. Schneider et al. (2018) summarize the scheme, its application to English corpus data, and an automatic disambiguation task

Crypto-ransomware Detection through Quantitative API-based Behavioral Profiling

With crypto-ransomware's unprecedented scope of impact and evolving level of sophistication, there is an urgent need to pinpoint the security gap and improve the effectiveness of defenses by identifying new detection approaches. Based on our characterization results on dynamic API behaviors of ransomware, we present a new API profiling-based detection mechanism. Our method involves two operations, namely consistency analysis and refinement. We evaluate it against a set of real-world ransomware and also benign samples. We are able to detect all ransomware executions in consistency analysis and reduce the false positive case in refinement. We also conduct in-depth case studies on the most informative API for detection with context

Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all preposition tokens in a 55,000-word corpus of English. In an attempt to resolve problematic cases in English and apply the scheme to adpositions and case markers in other languages, we reconsider the assumption that an adposition’s lexical contribution is equivalent to the role/relation that it mediates, embracing the potential for construal to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition\u27s lexical function, and discuss how it would allow for a simpler inventory of labels

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant

Pneumomediastinum Due to Intractable Hiccup as the Presenting Symptom of Multiple Sclerosis

Pneumomediastinum and subcutaneous emphysema generally occurs following trauma to the esophagus or lung. It also occurs spontaneously in such situations of elevating intrathoracic pressure as asthma, excessive coughing or forceful straining. We report here on the rare case of a man who experienced the signs of pneumomediastinum and subcutaneous emphysema after a prolonged bout of intractable hiccup as the initial presenting symptoms of multiple sclerosis

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

CeBi has an intricate magnetic phase diagram whose fully-polarized state has recently been suggested as a Weyl semimetal, though the role of $f$ states in promoting strong interactions has remained elusive. Here we focus on the less-studied, but also time-reversal symmetry-breaking ferrimagnetic phase of CeBi, where our density functional theory (DFT) calculations predict additional Weyl nodes near the Fermi level $E_\mathrm{F}$. We use spin-polarized scanning tunneling microscopy and spectroscopy to image the surface ferrimagnetic order on the itinerant Bi $p$ states, indicating their orbital hybridization with localized Ce $f$ states. We observe suppression of this spin-polarized signature at $E_\mathrm{F}$, coincident with a Fano line shape in the conductance spectra, suggesting the Bi $p$ states partially Kondo screen the $f$ magnetic moments, and this $p-f$ hybridization causes strong Fermi-level band renormalization. The $p$ band flattening is supported by our quasiparticle interference (QPI) measurements, which also show band splitting in agreement with DFT, painting a consistent picture of a strongly interacting magnetic Weyl semimetal

Alterations in plasma soluble vascular endothelial growth factor receptor-1 (sFlt-1) concentrations during coronary artery bypass graft surgery: relationships with post-operative complications

<p>Abstract</p> <p>Background</p> <p>Plasma concentrations of sFlt-1, the soluble form of the vascular endothelial growth factor receptor (VEGF), markedly increase during coronary artery bypass graft (CABG) surgery with extracorporeal circulation (ECC). We investigated if plasma sFlt-1 values might be related to the occurrence of surgical complications after CABG.</p> <p>Methods</p> <p>Plasma samples were collected from the radial artery catheter before vascular cannulation and after opening the chest, at the end of ECC just before clamp release, after cross release, after weaning from ECC, at the 6^thand 24^thpost-operative hour. Thirty one patients were investigated. The presence of cardiovascular, haematological and respiratory dysfunctions was prospectively assessed. Plasma sFlt-1 levels were measured with commercially ELISA kits.</p> <p>Results</p> <p>Among the 31 investigated patients, 15 had uneventful surgery. Patients with and without complications had similar pre-operative plasma sFlt-1 levels. Lowered plasma sFlt-1 levels were observed at the end of ECC in patients with haematological (p = 0.001, ANOVA) or cardiovascular (p = 0.006) impairments, but not with respiratory ones (p = 0.053), as compared to patients with uneventful surgery.</p> <p>Conclusion</p> <p>These results identify an association between specific post-CABG complication and the lower release of sFlt-1 during ECC. sFlt-1-induced VEGF neutralisation might, thus, be beneficial to reduce the development of post-operative adverse effects after CABG.</p