Can Digital Speech Loosen the Gordian Knot of Reputation Law?

Can Digital Speech Loosen the Gordian Knot of Reputation Law

Reputational Privacy and the Internet: A Matter for Law?

Reputation - we all have one. We do not completely comprehend its workings and are mostly unaware of its import until it is gone. When we lose it, our traditional laws of defamation, privacy, and breach of confidence rarely deliver the vindication and respite we seek due, primarily, to legal systems that cobble new media methods of personal injury onto pre-Internet laws. This dissertation conducts an exploratory study of the relevance of law to loss of individual reputation perpetuated on the Internet. It deals with three interrelated concepts: reputation, privacy, and memory. They are related in that the increasing lack of privacy involved in our online activities has had particularly powerful reputational effects, heightened by the Internet’s duplicative memory. The study is framed within three research questions: 1) how well do existing legal mechanisms address loss of reputation and informational privacy in the new media environment; 2) can new legal or extra-legal solutions fill any gaps; and 3) how is the role of law pertaining to reputation affected by the human-computer interoperability emerging as the Internet of Things? Through a review of international and domestic legislation, case law, and policy initiatives, this dissertation explores the extent of control held by the individual over her reputational privacy. Two emerging regulatory models are studied for improvements they offer over current legal responses: the European Union’s General Data Protection Regulation, and American Do Not Track policies. Underscoring this inquiry are the challenges posed by the Internet’s unique architecture and the fact that the trove of references to reputation in international treaties is not making its way into domestic jurisprudence or daily life. This dissertation examines whether online communications might be developing a new form of digital speech requiring new legal responses and new gradients of personal harm; it also proposes extra-legal solutions to the paradox that our reputational needs demand an overt sociality while our desire for privacy has us shunning the limelight. As we embark on the Web 3.0 era of human-machine interoperability and the Internet of Things, our expectations of the role of law become increasingly important

Think twice about nebulizers for asthma attacks

MDIs with spacers are as effective as nebulizers for delivering beta-agonists and less likely to cause adverse effects. Practice changer: Stop ordering nebulizers to deliver beta-agonists to patients over age 2 with mild or moderate asthma exacerbations. A metered-dose inhaler (MDI) with a spacer produces the same benefits with fewer adverse effects

Another good reason to recommend low-dose aspirin

Evidence shows that daily low-dose aspirin during pregnancy can safely lower the risk of preeclampsia and other adverse outcomes. Practice changer: Prescribe low-dose aspirin (eg, 81 mg/d) to pregnant women who are at high risk for preeclampsia because it reduces the risk of this complication, as well as preterm birth and intrauterine growth restriction

Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data

Background: DNA microarray gene expression classification poses a challenging task to the machine learning domain. Typically, the dimensionality of gene expression data sets could go from several thousands to over 10,000 genes. A potential solution to this issue is using feature selection to reduce the dimensionality. Aim The aim of this paper is to investigate how we can use feature quality information to improve the precision of microarray gene expression classification tasks. Method: We propose two evolutionary machine learning models based on the eXtended Classifier System (XCS) and a typical feature selection methodology. The first one, which we call FS-XCS, uses feature selection for feature reduction purposes. The second model is GRD-XCS, which uses feature ranking to bias the rule discovery process of XCS. Results: The results indicate that the use of feature selection/ranking methods is essential for tackling high-dimensional classification tasks, such as microarray gene expression classification. However, the results also suggest that using feature ranking to bias the rule discovery process performs significantly better than using the feature reduction method. In other words, using feature quality information to develop a smarter learning procedure is more efficient than reducing the feature set. Conclusion: Our findings have shown that extracting feature quality information can assist the learning process and improve classification accuracy. On the other hand, relying exclusively on the feature quality information might potentially decrease the classification performance (e.g., using feature reduction). Therefore, we recommend a hybrid approach that uses feature quality information to direct the learning process by highlighting the more informative features, but at the same time not restricting the learning process to explore other features

A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem

Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems