Towards correct-by-construction product variants of a software product line: GFML, a formal language for feature modules

Software Product Line Engineering (SPLE) is a software engineering paradigm that focuses on reuse and variability. Although feature-oriented programming (FOP) can implement software product line efficiently, we still need a method to generate and prove correctness of all product variants more efficiently and automatically. In this context, we propose to manipulate feature modules which contain three kinds of artifacts: specification, code and correctness proof. We depict a methodology and a platform that help the user to automatically produce correct-by-construction product variants from the related feature modules. As a first step of this project, we begin by proposing a language, GFML, allowing the developer to write such feature modules. This language is designed so that the artifacts can be easily reused and composed. GFML files contain the different artifacts mentioned above.The idea is to compile them into FoCaLiZe, a language for specification, implementation and formal proof with some object-oriented flavor. In this paper, we define and illustrate this language. We also introduce a way to compose the feature modules on some examples.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301

Bringing Clout to the Masses: An In-Depth Look at the “Legal Fake” Phenomenon

A snaking line of customers that wraps around the block leading to a minimalist, yet iconoclastic store can only mean one thing: drop day. Rain or shine, devoted fans of brands such as Supreme, Palace, and Off-White, among others, are willing to spend their time and money for the opportunity to cop the latest and most exclusive items. In recent years, the rise of streetwear has projected once-underground skater labels to the forefront of youth culture, mainstream society, and high fashion. Not only has this movement affected niche designers and traditional luxury names, but streetwear has also reshaped the consumer experience. However, the continued evolution and globalization of fashion, fueled by the near-instantaneous speed of the internet and social media, has brought the seemingly novel issue of legal fakes to the forefront. In reality, legal fakes are a face- lifted version of counterfeiting and traditional trademark squatting. By “legally” registering a stolen trademark, impostor companies run their entire business under the guise of a well-known brand. To address this threat, this Note examines the intricacies of a typical legal fake scheme, from its shady origins, to widespread distribution of fake products, to its eventual demise in litigation. This Note further proposes a solution requiring multinational cooperation in order to seal the cracks in international trademark law through which legal fakes have slipped

\u3ci\u3eMalone v. Brincat\u3c/i\u3e: The Fiduciary Disclosure Duty of Corporate Directors under Delaware Law

In Malone v. Brincat, the Supreme Court of Delaware significantly broadened the fiduciary disclosure duty of corporate directors under Delaware law. Malone allows shareholders to bring either a direct or a derivative action against directors for the public release of misleading financial statements reported to the Securities Exchange Commission, regardless of whether the alleged misstatements were made in connection with a request for shareholder action. The court also held that a federal preemption statute, the Securities Litigation Uniform Standards Act of 1998, did not preempt the shareholders\u27 action in Delaware state court. This Note argues that the Supreme Court of Delaware failed to provide precise limits to its new disclosure rule and thereby rendered unclear the elements of the cause of action by which directors may now be accountable for a breach of the disclosure duty. In addition, the court\u27s erroneous grant of a direct cause of action creates problematic overlaps between state and federal causes of action regarding corporate disclosures, particularly in light of the new federal preemption statute. In conclusion, this Note argues that Malone ultimately contributes little to the understanding of the fiduciary disclosure duty and unnecessarily disrupts the balance between state and federal regulation of corporate disclosures

Sustainable cooperation in small groups:dynamic interaction and the emergence of norms

In three empirical experiments, this dissertation studies how cooperative norms emerge over time in small, interactive groups. It also explores the impact that different pathways to social identification have on cooperation. We show that both social identification pathways (bottom-up or top-down) can lead to similarly high or low levels of cooperation – albeit through different trajectories. We also find that groups tend to form cooperative norms based on similar decision making rules, regardless of the social identity pathway. In other words, decision-making behavior regarding cooperation is an emergent property of the group. While cooperation is high in the first two studies, in the third study we find that changing the societal context, as well as the monetary outcome of the experiment, leads to a sizable reduction in cooperation. Taken together, the results of the decision rules for how much to cooperate as well as the content of the messages participants send their groups, suggests that static factors – such as macro-level variables that can explain differences between societies – may predict initial contributions to the Public Good. However, dynamic factors – which can only come into play through interaction and communication over time – subsequently direct how cooperation further evolves. Crucial to explaining this variability in cooperation among groups, it seems, is the nature of social interaction – specifically, whether groups coordinate activity at the level of “us”, while also promoting group solidarity. Social interaction among group members appears to be the foundation for the emergence of social norms that maximize cooperation

Newcastle Business School Principles of Responsible Management Education Project (NBS PRIME)

The world is changing rapidly and new demands face business leaders to deal with the planet and environment more sustainably, to deal with the numerous societies their organisations operate in more equitably and with greater cultural understanding, and to be more open, transparent and responsible with respect to their stakeholders. Recent events such as the credit and banking crisis alongside general global corporate social responsibility and sustainability concerns, have led to questions as to whether current management education is adequate to equip and develop future leaders with the requisite skills to meet these new demands (Colby, Ehrlich, Sullivan, Dolle, & Shulman, 2011; Datar, Garvin, & Cullen, 2010; Weybrecht, 2010). For these reasons it is essential that universities and business schools seek to embrace principles of sustainability and responsible management into their teaching, research and enterprise activities. Newcastle Business school is ideally placed to make a significant contribution to social, environmental and economic well being through its global reputation for delivering some of the best business management education in the UK

Clustering Multiple Sclerosis Medication Sequence Data with Mixture Markov Chain Analysis with covariates using Multiple Simplex Constrained Optimization Routine (MSiCOR)

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that causes neurodegeneration. While disease-modifying therapies (DMTs) reduce inflammatory disease activity and delay worsening disability in MS, there are significantly varying treatment responses across people with MS (pwMS). pwMS often receive serial monotherapies of DMTs. Here, we propose a novel method to cluster pwMS according to the sequence of DMT prescriptions and associated clinical features (covariates). This is achieved via a mixture Markov chain analysis with covariates, where the sequence of prescribed DMTs for each patient is modeled as a Markov chain. Given the computational challenges to maximize the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes and shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the Expectation-Maximization (EM) algorithm based method for clustering sequence data without covariates. Based on the analysis, we divided MS patients into 3 clusters: inferon-beta dominated, multi-DMTs, and natalizumab dominated. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters. This method may guide the DMT prescription sequence based on clinical features