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

Parallel Program Design and Generalized Weakest Preconditions

No abstract available

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