Structure and Precedent

The standard model of vertical precedent is part of the deep structure of our legal system. Under this model, we rarely struggle with whether a given decision of a court within a particular hierarchy is potentially binding at all. When Congress or the courts alter the standard structure and process offederal appellate review, however, that standard model of precedent breaks down. This Article examines several of these unusual appellate structures and highlights the difficulties associated with evaluating the precedential effect of decisions issued within them. For instance, when Congress consolidates challenges to agency decision making in a single federal circuit, is the decision that ultimately issues binding on just the deciding court, or is it binding nationwide? The lack of wellaccepted answers to this and similar questions undermines the work of practitioners, courts, and Congress. This Article uses these nonstandard processes and institutions to emphasize a rarely stated observation that will ensure a more careful and rational discussion of precedential rules in the future: the structure of the court system within which decisions are made-the structure of the appellate universe-is critical to defining the rules of binding precedent. After discussing this relationship between structure and precedent, this Article identifies, and argues in favor of a Clear-Statement Approach to determining the precedential effect of decisions in nonstandard appellate structures. This approach encourages Congress to pay attention to the precedential effect of its structural decisions, and highlights the degree to which Congress controls rules of precedent through its control over the structure of the federal judiciary

The Inherent and Supervisory Power

Parties to litigation expect courts to operate bothpredictably and fairly. A core part of this expectation isthe presence of codified rules of procedure, which ensurefairness while constraining, and making morepredictable, the ebb and flow of litigation.Within the courts of this country, however, there is afont of authority over procedure that courts often turn toin circumstances when they claim that there is nowritten guidance. This authority, referred to as the“inherent” or “supervisory” power of courts, is an almostpure expression of a court’s exercise of discretion in thatit gives courts the ability to do “all things reasonablynecessary” for the administration of justice. Thesweeping nature of this power requires us to examine therole of discretion in courts’ decisions and to ask whetherprocedural goals of fairness, notice, and predictabilitycan be met in circumstances when courts rely on theirinherent powers. As a first step in this examination, thisArticle begins by considering and characterizing the useof inherent power by both federal and state courts, aswell as its roots in common law judicial authority.While the unconstrained exercise of inherent power isever-less acceptable in a legal system that is increasinglymoving toward written rules, the absence of suchauthority would present its own difficulties. This Articletherefore concludes by suggesting that although courtsshould not be barred from using their inherent power,they should do so only after making two explicitfindings: (1) why inherent power should be exercised,particularly in light of relevant positive law, and (2) what standards the court will use to determinewhether to apply that power in a given case. Throughthese findings on whether and how the inherent powershould be used, lower courts retain the proceduralflexibility of inherent power while being discouragedfrom its unconstrained use. At the same time, appellatecourts are given the tools they need to fully test the properapplication of this otherwise sweeping power in futurecases

Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems

A Mobile Lifelogging Platform to Measure Anxiety and Anger During Real-Life Driving

The experience of negative emotions in everyday life, such as anger and anxiety, can have adverse effects on long-term cardiovascular health. However, objective measurements provided by mobile technology can promote insight into this psychobiological process and promote self-awareness and adaptive coping. It is postulated that the creation of a mobile lifelogging platform can support this approach by continuously recording personal data via mobile/wearable devices and processing this information to measure physiological correlates of negative emotions. This paper describes the development of a mobile lifelogging system that measures anxiety and anger during real-life driving. A number of data streams have been incorporated in the platform, including cardiovascular data, speed of the vehicle and first-person photographs of the environment. In addition, thirteen participants completed five days of data collection during daily commuter journeys to test the system. The design of the system hardware and associated data streams are described in the current paper, along with the results of preliminary data analysis

Knowledge Extraction Using Probabilistic Reasoning: An Artificial Neural Network Approach

The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internet-enabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of ‘understanding’ the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively