Profiling the real world potential of neural network compression

Abstract—Many real world computer vision applications are required to run on hardware with limited computing power, often referred to as ”edge devices”. The state of the art in computer vision continues towards ever bigger and deeper neural networks with equally rising computational requirements. Model compression methods promise to substantially reduce the computation time and memory demands with little to no impact on the model robustness. However, evaluation of the compression is mostly based on theoretic speedups in terms of required floating-point operations. This work offers a tool to profile the actual speedup offered by several compression algorithms. Our results show a significant discrepancy between the theoretical and actual speedup on various hardware setups. Furthermore, we show the potential of model compressions and highlight the importance of selecting the right compression algorithm for a target task and hardware. The code to reproduce our experiments is available at https://hub.datathings.com/papers/2022-coins

The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems andInternet of Things applications. Instead, to make smart deci-sions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units.These micro learning units are modeled together with and at the same level as the domain data. We show, based on asmart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning

Industrial defect detection on the edge with deep learning over scarcely labeled and extremely imbalanced data

Abstract—Reliable automated defect detection is an integral part of modern manufacturing and improved performance can provide a competitive advantage. Despite the proven capabilities of convolutional neural networks (CNNs) for image classification, application on real world tasks remains challenging due to the high demand for labeled and well balanced data of the common supervised learning scheme. Semi-supervised learning (SSL) promises to achieve comparable accuracy while only requiring a small fraction of the training samples to be labeled. However, SSL methods struggle with data imbalance and existing benchmarks do not reflect the challenges of real world applications. In this work we present a CNN-based defect detection unit for thermal sensors. We describe how to collect data from a running process and release our dataset of 1k labeled and 293k unlabeled samples. Furthermore, we investigate the use of SSL under this challenging real world task

Privacy Challenges in Ambient Intelligence Systems

peer reviewedToday, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the “right to be left alone” to being a “commodity” that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The human- centred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg

Building social policies in Lebanon

Texte en français, en anglais et en arab

Building social policies in Lebanon

Texte en français, en anglais et en arab

CalcGraph: taming the high costs of deep learning using models

Models based on differential programming, like deep neural networks, are well established in research and able to outperform manually coded counterparts in many applications. Today, there is a rising interest to introduce this flexible modeling to solve real-world problems. A major challenge when moving from research to application is the strict constraints on computational resources (memory and time). It is difficult to determine and contain the resource requirements of differential models, especially during the early training and hyperparameter exploration stages. In this article, we address this challenge by introducing CalcGraph, a model abstraction of differentiable programming layers. CalcGraph allows to model the computational resources that should be used and then CalcGraph’s model interpreter can automatically schedule the execution respecting the specifications made. We propose a novel way to efficiently switch models from storage to preallocated memory zones and vice versa to maximize the number of model executions given the available resources. We demonstrate the efficiency of our approach by showing that it consumes less resources than state-of-the-art frameworks like TensorFlow and PyTorch for single-model and multi-model execution

Towards Ambient Intelligent Applications Using [email protected] And Machine Learning For Context-Awareness

Ambient Intelligence (AmI) constitutes a new paradigm of interaction among humans, smart objects and devices. AmI systems are expected to support humans in their every day tasks and activities. In order to achieve this goal, these systems require augmenting the environment with sensing, computing, communicating, and reasoning capabilities. Due to advances in technology, sensors are getting more powerful, cheaper and smaller, which stimulated large scale development and production. These sensors will generate a big amount of data and can easily lead to millions of values in a short amount of time, which can quickly reach the computation and storage limits when it comes to structuring and processing the data. For this problem, we propose a concept of continuous models that can handle highly-volatile data, and represent the continuous nature of sensor data in an efficient and compact way. We show on various AmI datasets that this can significantly improve storage and efficiency. One important goal of AmI systems is to transform living and working environments into intelligent spaces able to adapt to their users’ needs and desires in real-time. In this sense, we call AmI applications context-aware, meaning that they use environmental information to adaptively provide more relevant and better services to the user. However, AmI systems are composed from heterogeneous components, operating in an open and dynamic environment. Each of these components can have different storage and computation capabilities. They might not have all the information needed to derive context information, and they might not be reachable all the time for various reasons. In this thesis, we present a contextual reasoning solution adapted for component based platforms. Our solution can derive contextual information in a distributed way and can handle inconsistencies when contradictory information is received from several components. Other than the storage and computation efficiency, several qualities need to be satisfied according to the different contexts. Privacy is one of these qualities. AmI services will rely more and more on personal data that is vastly collected, stored, and exchanged with other third parties in order to provide added-value services. Such data are sensitive and often related to personal activities and therefore can lead to privacy risks, especially when data is shared with high precision and frequency. However, this privacy quality can be relaxed in some contexts, for example in an emergency situation in order to increase utility or efficiency. This leads to the need of developing an adaptive solution that is able to react to context changes in real-time and involve optimizing conflicting objectives. For this challenge, we propose to use blurring components as our main privacy preservation elements. The idea behind this approach is that, by gradually decreasing the data quality, a blurring component is able to hide some of the personal data delivered by sensors while still keeping the necessary information for the services to work. In order to find a good trade-off between these different conflicting objectives, we adapt a multi-objective evolutionary algorithm to run directly on top of domain specific models. We then apply it as our main optimization engine on [email protected] to keep adapting the different qualities, when the context change. Finally, AmI services are expected to be tailored for different users’ needs in a seamless and unobtrusive way. Meaning that they should be able to detect contexts and learn habits automatically with the least possible intervention of users. In order to achieve this, machine learning (ML) techniques need to be merged at the core of reasoning models. These techniques offer powerful tools to automatically detect patterns, categorize contexts, build usage profiles, represent data with compact mathematical hypothesis and provide statistical information vital for the intelligent aspect of AmI. This dissertation ends up by opening new directions on how to model and adapt machine learning techniques to fit for AmI platforms. Overall, this thesis provides solutions for the next leap of technology, where sensors become ubiquitous. Our solutions, implemented in an open source framework KMF, allow to create efficient and distributed, data and component models for IoT, adaptable at runtime leveraging multi-objective optimization to find good tradeoff between qualities for the current context, and machine learning techniques to derive contextual rules, profile and learn habits automatically