25,283 research outputs found
Symmetric Synchronous Collaborative Navigation
Synchronous collaborative navigation is a form of social navigation where users virtually share a web browser. In this paper, we present a symmetric, proxy-based architecture where each user can take the lead and guide others in visiting web sites, without the need for a special browser or other software. We show how we have applied this scheme to a problem-solving-oriented e-learning system
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
POTs: Protective Optimization Technologies
Algorithmic fairness aims to address the economic, moral, social, and
political impact that digital systems have on populations through solutions
that can be applied by service providers. Fairness frameworks do so, in part,
by mapping these problems to a narrow definition and assuming the service
providers can be trusted to deploy countermeasures. Not surprisingly, these
decisions limit fairness frameworks' ability to capture a variety of harms
caused by systems.
We characterize fairness limitations using concepts from requirements
engineering and from social sciences. We show that the focus on algorithms'
inputs and outputs misses harms that arise from systems interacting with the
world; that the focus on bias and discrimination omits broader harms on
populations and their environments; and that relying on service providers
excludes scenarios where they are not cooperative or intentionally adversarial.
We propose Protective Optimization Technologies (POTs). POTs provide means
for affected parties to address the negative impacts of systems in the
environment, expanding avenues for political contestation. POTs intervene from
outside the system, do not require service providers to cooperate, and can
serve to correct, shift, or expose harms that systems impose on populations and
their environments. We illustrate the potential and limitations of POTs in two
case studies: countering road congestion caused by traffic-beating
applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency
(FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this
work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was
presented at HotPETS 2018 and at PiMLAI 201
Psychological principles of successful aging technologies: A mini-review
Based on resource-oriented conceptions of successful life-span development, we propose three principles for evaluating assistive technology: (a) net resource release; (b) person specificity, and (c) proximal versus distal frames of evaluation. We discuss how these general principles can aid the design and evaluation of assistive technology in adulthood and old age, and propose two technological strategies, one targeting sensorimotor and the other cognitive functioning. The sensorimotor strategy aims at releasing cognitive resources such as attention and working memory by reducing the cognitive demands of sensory or sensorimotor aspects of performance. The cognitive strategy attempts to provide adaptive and individualized cuing structures orienting the individual in time and space by providing prompts that connect properties of the environment to the individual's action goals. We argue that intelligent assistive technology continuously adjusts the balance between `environmental support' and `self-initiated processing' in person-specific and aging-sensitive ways, leading to enhanced allocation of cognitive resources. Furthermore, intelligent assistive technology may foster the generation of formerly latent cognitive resources by activating developmental reserves (plasticity). We conclude that `lifespan technology', if co-constructed by behavioral scientists, engineers, and aging individuals, offers great promise for improving both the transition from middle adulthood to old age and the degree of autonomy in old age in present and future generations. Copyright (C) 2008 S. Karger AG, Basel
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