Imagined communities: Reflections on the origin and spread of nationalism

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The power of feedback.

The measurement of meaning

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

/pdf/urban-computing-concepts-methodologies-and-applications-1wix0tsb38.pdf

Urban Computing: Concepts, Methodologies, and Applications

Mobile devices, such as mobile phones and personal digital assistants, have gained wide-spread popularity. These devices will increasingly be networked, thus enabling the construction of distributed applications that have to adapt to changes in context, such as variations in network bandwidth, battery power, connectivity, reachability of services and hosts, etc. In this paper, we describe CARISMA, a mobile computing middleware which exploits the principle of reflection to enhance the construction of adaptive and context-aware mobile applications. The middleware provides software engineers with primitives to describe how context changes should be handled using policies. These policies may conflict. We classify the different types of conflicts that may arise in mobile computing and argue that conflicts cannot be resolved statically at the time applications are designed, but, rather, need to be resolved at execution time. We demonstrate a method by which policy conflicts can be handled; this method uses a microeconomic approach that relies on a particular type of sealed-bid auction. We describe how this method is implemented in the CARISMA middleware architecture and sketch a distributed context-aware application for mobile devices to illustrate how the method works in practice. We show, by way of a systematic performance evaluation, that conflict resolution does not imply undue overheads, before comparing our research to related work and concluding the paper.

/pdf/carisma-context-aware-reflective-middleware-system-for-35oflhnitm.pdf

CARISMA: context-aware reflective middleware system for mobile applications

A survey of the use of crowdsourcing in software engineering

xlinkit is a lightweight application service that provides rule-based link generation and checks the consistency of distributed Web content. It leverages standard Internet technologies, notably XML, XPath, and XLink. xlinkit can be used as part of a consistency management scheme or in applications that require smart link generation, including portal construction and management of large document repositories. In this article we show how consistency constraints can be expressed and checked. We describe a novel semantics for first-order logic that produces links instead of truth values and give an account of our content management strategy. We present the architecture of our service and the results of two substantial case studies that use xlinkit for checking course syllabus information and for validating UML models supplied by industrial partners.

/pdf/xlinkit-a-consistency-checking-and-smart-link-generation-99pxgl0m20.pdf

xlinkit: a consistency checking and smart link generation service

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.

/pdf/temporal-diversity-in-recommender-systems-2p8zlw0dws.pdf

Licia Capra

Papers

Urban Computing: Concepts, Methodologies, and Applications

CARISMA: context-aware reflective middleware system for mobile applications

A survey of the use of crowdsourcing in software engineering

xlinkit: a consistency checking and smart link generation service

Temporal diversity in recommender systems