Following on the Foreign Corrupt Practices Act: The Dynamic Shareholder Derivative Suit

Corporations that have allegedly violated the Foreign Corrupt Practices Act (FCPA) increasingly face a new threat of liability: cases brought by private plaintiffs in follow-on derivative suits. These derivative suits for breaches of fiduciary duty focus on whether directors provided the necessary oversight through compliance systems designed to detect and prevent FCPA violations. The demand requirement, a procedural hurdle of derivative suits, has stymied plaintiffs that are unable to show that directors cannot disinterestedly assess whether to pursue a claim for violations. This Note proposes a framework that systematizes the factual scenarios under which the demand requirement could be excused. Using other instances of regulatory violations as a lens, courts can infer that directors knew of FCPA violations based on patterns of bribes and the importance of bribery to the overall business of the corporation. Only plaintiffs that have utilized procedural devices to inspect corporate books and records, however, can expect courts to reach this inference of director knowledge. Despite being much maligned, the follow-on derivative suit may actually clarify the duties of directors in FCPA compliance and advance the corporate governance reforms of corporations, separately from the deterrent effect of government enforcement

User-centric Privacy Engineering for the Internet of Things

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.Comment: 12 Page

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Digital Platforms and Antitrust Law

This Article is about “big data” and antitrust law. Big data, for my purposes, refers to digital platforms that enable the discovery and sharing of information by consumers, and the harvesting and analysis of consumer data by the platform. The obvious example of such a platform is Google. The big platforms owe their market dominance not to anticompetitive conduct but to economies of scale. This Article discusses three types of anticompetitive conduct associated with digital platforms: kill zone expropriation, acquisition of nascent rivals, and denial of access to data. There is nothing so unusual about digital platforms that would require a reform of the antitrust laws. Some are described as two-sided markets, but this designation, even after Ohio v. American Express Co., should not present an obstacle to the application of antitrust law. I. Introduction II. Platforms III. Competition Issues ... A. Kill Zone Expropriation ... B. Acquisition of Nascent Rivals ... C. Denial of Access to Data IV. Antitrust Law V. Conclusio