ARIES WP3 – Needs and Requirements Analyses

Information and communication technologies have increasingly influenced and changed our daily life. They allow global connectivity and easy access to distributed applications and digital services over the Internet. This report analysis security requirements on trust establishment and trust evaluation based on two different use case scenarios: "Trusted Communication using COTS" and "Trust Establishment for Cross-organizational Crises Management". A systematic needs analysis is performed on both scenarios which haver resulted in a large and well documented set of requirements. This is the first step in a large effort to define a security architecture for the two use case scenarios.

From Monologue to Dialogue: Natural Language Generation in OVIS

This paper describes how a language generation system that was originally designed for monologue generation, has been adapted for use in the OVIS spoken dialogue system. To meet the requirement that in a dialogue, the system's utterances should make up a single, coherent dialogue turn, several modifications had to be made to the system. The paper also discusses the influence of dialogue context on information status, and its consequences for the generation of referring expressions and accentuation

A Verified Information-Flow Architecture

SAFE is a clean-slate design for a highly secure computer system, with pervasive mechanisms for tracking and limiting information flows. At the lowest level, the SAFE hardware supports fine-grained programmable tags, with efficient and flexible propagation and combination of tags as instructions are executed. The operating system virtualizes these generic facilities to present an information-flow abstract machine that allows user programs to label sensitive data with rich confidentiality policies. We present a formal, machine-checked model of the key hardware and software mechanisms used to dynamically control information flow in SAFE and an end-to-end proof of noninterference for this model. We use a refinement proof methodology to propagate the noninterference property of the abstract machine down to the concrete machine level. We use an intermediate layer in the refinement chain that factors out the details of the information-flow control policy and devise a code generator for compiling such information-flow policies into low-level monitor code. Finally, we verify the correctness of this generator using a dedicated Hoare logic that abstracts from low-level machine instructions into a reusable set of verified structured code generators

Estimating residual value of heavy construction equipment using ensemble learning

Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012

Venture Capital on the Downside: Preferred Stock and Corporate Control

This Article takes the occasion of the simultaneous collapse of the high technology stock market and the failure of the dot-coin startups, along with the subsequent retrenchment of the venture capital business, to examine the law and economics of downside arrangements in venture capital contracts. The subject matter implicates core concerns of legal and economic theory of the firm. Debates about the separation of ownership and control, relational investing, takeover policy, the law and economics of debt capitalization, and bankruptcy reform, all grapple with the downside problem of controlling and terminating unsuccessful managers for the benefit of outside debt and equity investors (and the related upside problem of incentivizing effective but fallible managers). The factors motivating these debates also bear on venture capital contracting. But venture capital presents a special puzzle for solution. Convertible preferred stock is the dominant financial contract in the venture capital market, at least in the United States. This contrasts with other contexts in corporate finance, where preferred stock is thought to be a financing vehicle long in decline. The only mature firms that finance with preferred, which once was ubiquitous in American capital structures, tend to be firms in regulated industries having little choice in the matter. Tax rules favoring debt finance provide the primary explanation for preferred\u27s decline. But many corporate law observers would suggest dysfunctional downside contracting as a concomitant cause. Simply, preferred performs badly on the downside, where senior security contracts supposedly are at their most effective. Preferred stockholders routinely have been victimized in distress situations by opportunistic issuers who strip them of their contract rights, transferring value to the junior equity holders who control the firm\u27s management. The cumulation of bad experiences adds impetus to a wider trend in favor of debt as the mode of senior participation

A randomized controlled trial of folic acid intervention in pregnancy highlights a putative methylation-regulated control element at ZFP57

Table S1. Pyrosequencing and transcriptional primer sets used in this study. Pyroassay primers are given as bisulfite converted sequence. The same primers were used for both RT-PCR and RT-qPCR. (DOCX 15 kb

Unpacking Ambiguity in Building Requirements to Support Automated Compliance Checking

In the architecture, engineering, and construction (AEC) industry, manual compliance checking is labor-intensive, time-consuming, expensive, and error-prone. Automated compliance checking (ACC) has been extensively studied in the past 50 years to improve the productivity and accuracy of the compliance checking process. While numerous ACC systems have been proposed, these systems can only deal with requirements that include quantitative metrics or specified properties. This leaves the remaining 53% of building requirements to be checked manually, mainly due to the ambiguity embedded in them. In the literature, little is known about the ambiguity of building requirements, which impedes their accurate interpretation and automated checking. This research thus aims to address this issue and establish a taxonomy of ambiguity. Building requirements in health building notes (HBNs) are analyzed using an inductive approach. The results show that some ambiguous clauses in building requirements reflect regulators’ intention while others are unintentional, resulting from the use of language, tacit knowledge, and ACC-specific reasons. This research is valuable for compliance-checking researchers and practitioners because it unpacks ambiguity in building requirements, laying a solid foundation for addressing ambiguity appropriately

Generating natural language specifications from UML class diagrams

Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semistructured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet,a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study