143 research outputs found
A Tutorial Introduction to Mosaic Pascal
In this report we describe a Pascal system that has been developed for programming Mosaic multi-
computers. The system that we discuss runs on our Sun workstations, and we assume some familiarity
with the use thereof. We assume the reader to be also familiar with programming in Pascal, and with
message-passing programs. We describe how the Pascal language has been extended to perform message
passing. We discuss a few implementation aspects that are relevant only to those users who have a need
(or desire) to control some machine-specific aspects. The latter requires some detailed knowledge of the
Mosaic system
Weakest Preconditions for Progress
Predicate transformers that map the postcondition and all intermediate conditions of a command to a precondition are introduced. They can be used to specify certain progress properties of sequential programs
Multi-task Self-Supervised Learning for Human Activity Detection
Deep learning methods are successfully used in applications pertaining to
ubiquitous computing, health, and well-being. Specifically, the area of human
activity recognition (HAR) is primarily transformed by the convolutional and
recurrent neural networks, thanks to their ability to learn semantic
representations from raw input. However, to extract generalizable features,
massive amounts of well-curated data are required, which is a notoriously
challenging task; hindered by privacy issues, and annotation costs. Therefore,
unsupervised representation learning is of prime importance to leverage the
vast amount of unlabeled data produced by smart devices. In this work, we
propose a novel self-supervised technique for feature learning from sensory
data that does not require access to any form of semantic labels. We learn a
multi-task temporal convolutional network to recognize transformations applied
on an input signal. By exploiting these transformations, we demonstrate that
simple auxiliary tasks of the binary classification result in a strong
supervisory signal for extracting useful features for the downstream task. We
extensively evaluate the proposed approach on several publicly available
datasets for smartphone-based HAR in unsupervised, semi-supervised, and
transfer learning settings. Our method achieves performance levels superior to
or comparable with fully-supervised networks, and it performs significantly
better than autoencoders. Notably, for the semi-supervised case, the
self-supervised features substantially boost the detection rate by attaining a
kappa score between 0.7-0.8 with only 10 labeled examples per class. We get
similar impressive performance even if the features are transferred from a
different data source. While this paper focuses on HAR as the application
domain, the proposed technique is general and could be applied to a wide
variety of problems in other areas
Distributed Fault Detection in Smart Spaces Based on Trust Management
AbstractApplication performance in a smart space is affected by faulty behaviours of nodes and communication networks. Detection of faults helps diagnosis of problems and maintenance can be done to restore performance, for example, by replacing or reconfiguring faulty parts. Fault detection methods in the literature are too complex for typical low-resource devices and they do not perform well in detecting intermittent faults. We propose a fully distributed fault detection method that relies on evaluating statements about trustworthiness of aggregated data from neighbors. Given one or more trust statements that describe a fault-free state, the trustor node determines for each observation coming from the trustee whether it is an outlier or not. Several fault types can be explored using different trust statements whose parameters are assessed differently. The trustor subsequently captures the observation history of the trustee node in only two evidence variables using evidence update rules that give more weight to recent observations. The proposed method detects not only permanent faults but also intermittent faults with high accuracy and low false alarm rate
Infinitely-fast diffusion in Single-File Systems
We have used Dynamic Monte Carlo (DMC) methods and analytical techniques to
analyze Single-File Systems for which diffusion is infinitely-fast. We have
simplified the Master Equation removing the fast reactions and we have
introduced a DMC algorithm for infinitely-fast diffusion. The DMC method for
fast diffusion give similar results as the standard DMC with high diffusion
rates. We have investigated the influence of characteristic parameters, such as
pipe length, adsorption, desorption and conversion rate constants on the
steady-state properties of Single-File Systems with a reaction, looking at
cases when all the sites are reactive and when only some of them are reactive.
We find that the effect of fast diffusion on single-file properties of the
system is absent even when diffusion is infinitely-fast. Diffusion is not
important in these systems. Smaller systems are less reactive and the occupancy
profiles for infinitely-long systems show an exponential behavior.Comment: 8 pages, 5 figure
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth
of data that cannot be accumulated in a centralized repository for learning
supervised models due to privacy, bandwidth limitations, and the prohibitive
cost of annotations. Federated learning provides a compelling framework for
learning models from decentralized data, but conventionally, it assumes the
availability of labeled samples, whereas on-device data are generally either
unlabeled or cannot be annotated readily through user interaction. To address
these issues, we propose a self-supervised approach termed
\textit{scalogram-signal correspondence learning} based on wavelet transform to
learn useful representations from unlabeled sensor inputs, such as
electroencephalography, blood volume pulse, accelerometer, and WiFi channel
state information. Our auxiliary task requires a deep temporal neural network
to determine if a given pair of a signal and its complementary viewpoint (i.e.,
a scalogram generated with a wavelet transform) align with each other or not
through optimizing a contrastive objective. We extensively assess the quality
of learned features with our multi-view strategy on diverse public datasets,
achieving strong performance in all domains. We demonstrate the effectiveness
of representations learned from an unlabeled input collection on downstream
tasks with training a linear classifier over pretrained network, usefulness in
low-data regime, transfer learning, and cross-validation. Our methodology
achieves competitive performance with fully-supervised networks, and it
outperforms pre-training with autoencoders in both central and federated
contexts. Notably, it improves the generalization in a semi-supervised setting
as it reduces the volume of labeled data required through leveraging
self-supervised learning.Comment: Accepted for publication at IEEE Internet of Things Journa
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