Houses in a Landscape: Memory and Everyday Life in Mesoamerica

In Houses in a Landscape, Julia A. Hendon examines the connections between social identity and social memory using archaeological research on indigenous societies that existed more than one thousand years ago in what is now Honduras. While these societies left behind monumental buildings, the remains of their dead, remnants of their daily life, intricate works of art, and fine examples of craftsmanship such as pottery and stone tools, they left only a small body of written records. Despite this paucity of written information, Hendon contends that an archaeological study of memory in such societies is possible and worthwhile. It is possible because memory is not just a faculty of the individual mind operating in isolation, but a social process embedded in the materiality of human existence. Intimately bound up in the relations people develop with one another and with the world around them through what they do, where and how they do it, and with whom or what, memory leaves material traces. Hendon conducted research on three contemporaneous Native American civilizations that flourished from the seventh century through the eleventh CE: the Maya kingdom of Copan, the hilltop center of Cerro Palenque, and the dispersed settlement of the Cuyumapa valley. She analyzes domestic life in these societies, from cooking to crafting, as well as public and private ritual events including the ballgame. Combining her findings with a rich body of theory from anthropology, history, and geography, she explores how objects—the things people build, make, use, exchange, and discard—help people remember. In so doing, she demonstrates how everyday life becomes part of the social processes of remembering and forgetting, and how “memory communities” assert connections between the past and the present.https://cupola.gettysburg.edu/books/1050/thumbnail.jp

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory