Jean Hennebert

HES-SO University of Applied Sciences and Arts Western Switzerland · iCoSys — Institut des systèmes complexes

Radon is a noble, natural, and radioactive gas coming mainly from the ground which might accumulate indoors and lead each year to 200-300 deaths from lung cancer in Switzerland. A brand new and innovative living lab will be built as of 2023 in Fribourg (Switzerland) which will allow to tackle the built environment and the relationship with its occu...

This paper presents an innovative methodology for enhancing energy efficiency assessment procedures in the built environment, with a focus on the Switzerland’s Energy Strategy 2050. The current methodology necessitates intensive expert surveys, leading to substantial time and cost implications. Also, such a process can’t be scaled to a large number...

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature....

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile por...

The modern built environment is now connected. Multiple software and protocols are used in buildings of many kinds, thus creating a fascinating and heterogeneous environment. Within this context, applied research can be complicated and would benefit from a single data location across projects and users. The first version of BBData tried to solve th...

With the fourth generation of district heating networks in sight, opportunities are rising for better services and optimized planning of energy production. Indeed, the more intensive data collection is expected to allow for load prediction, customer profiling, etc. In this context, our work aims at a better understanding of customer profiles from t...

After the success of the two first editions of the “Arabic Text in Videos Competition—AcTiVComp”, we are proposing to organize a new edition in conjunction with the 25th International Conference on Pattern Recognition (ICPR’20). The main objective is to contribute in the research field of text detection and recognition in multimedia documents, with...

Deep excavations are today mainly designed by manually optimising the wall’s geometry, stiffness and strut or anchor layout. In order to better automate this process for sustained excavations, we are exploring the possibility of approximating key values using a machine learning (ML) model instead of calculating them with time-consuming numerical si...

In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial process by using machine learning (ML) to detect anomalies of production machines. The main advantages of ML are in the ability to (1) capture non-linear phenomena, (2) adapt to many different processes without human intervention and (3) learn incremental...

This paper presents SwissCrawl, the largest Swiss German text corpus to date. Composed of more than half a million sentences, it was generated using a customized web scraping tool that could be applied to other low-resource languages as well. The approach demonstrates how freely available web pages can be used to construct comprehensive text corpor...

Deep Learning Library (DLL) is a library for machine learning with deep neural networks that focuses on speed. It supports feed-forward neural networks such as fully-connected Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). Our main motivation for this work was to propose and evaluate novel software engineering strategie...

Purpose: To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open angle glaucoma (POAG) and healthy (H) eyes. Design: Development and evaluation of a diagnostic test with machine learning. Subjects:...

Deep Learning Library (DLL) is a new library for machine learning with deep neural networks that focuses on speed. It supports feed-forward neural networks such as fully-connected Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). It also has very comprehensive support for Restricted Boltzmann Machines (RBMs) and Convolutio...

This study presents a novel approach for Arabic video text recognition based on recurrent neural networks. In fact, embedded texts in videos represent a rich source of information for indexing and automatically annotating multimedia documents. However, video text recognition is a non-trivial task due to many challenges like the variability of text...

Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This pap...

Future buildings will more and more rely on advanced Building Management Systems (BMS) connected to a variety of sensors, actuators and dedicated networks. Their objectives are to observe the state of rooms and apply automated rules to preserve or increase comfort while economizing energy. In this work, we advocate for the inclusion of a dedicated...

Electricity load monitoring in residential buildings has become an important task allowing for energy consumption understanding, indirect human activity recognition and occupancy modelling. In this context, Non Intrusive Load Monitoring (NILM) is an approach based on the analysis of the global electricity consumption signal of the habitation. Curre...

More than 80 million people worldwide suffer from glaucoma, an asymptomatic and irreversible disease of the optic nerve leading to blindness unless intra-ocular pressure (IOP) is controlled. IOP is the only controllable risk factor to stabilize patients, and various therapeutic options exist to reduce IOP. IOP follows, however, individual nycthemer...

Although Graphics Processing Units (GPUs) seem to currently be the best platform to train machine learning models, most research laboratories are still only equipped with standard CPU systems. In this paper, we investigate multiple techniques to speedup the training of Restricted Boltzmann Machine (RBM) models and Convolutional RBM (CRBM) models on...

To spot keywords on handwritten documents, we present a hybrid keyword spotting system, based on features extracted with Convolutional Deep Belief Networks and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer his...

Benchmark datasets and their corresponding evaluation protocols are commonly used by the computer vision community, in a variety of application domains, to assess the performance of existing systems. Even though text detection and recognition in video has seen much progress in recent years, relatively little work has been done to propose standardiz...

This paper presents the iFLUX middleware, designed to provide a lightweight integration solution for Smart City applications. Based on three core abstractions, namely event sources, action targets and rules, iFLUX makes it very easy to expose sensors and actuators through REST APIs so that they can be integrated in application-level workflows. Sens...

In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segment- ation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefu...

We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As...

The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to...

We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hidden Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (10-1 Hz) and are sourced from a specific database...

In this paper, we propose a new dataset and a ground-truthing methodology for layout analysis of historical documents with complex layouts. The dataset is based on a generic model for ground-truth presentation of the complex layout structure of historical documents. For the purpose of extracting uniformly the document contents, our model defines fi...

To detect and quantify eye blinks during 24-hour intraocular pressure (IOP) monitoring with a contact lens sensor (CLS). A total of 249 recordings of 24-hour IOP patterns from 202 participants using a CLS were included. Software was developed to automatically detect eye blinks, and wake and sleep periods. The blink detection method was based on det...

We present ACS-F2, a new electric consumption signature database acquired from domestic appliances. The scenario of use is appliance identification with emerging applications such as domestic electricity consumption understanding, load shedding management and indirect human activity moni-toring. The novelty of our work is to use low-end electricity...

Diagnosing the glaucoma is a very difficult task for healthcare professionals. High intraocular pressure (IOP) remains the main treatable symptom of this degenerative disease which leads to blindness. Nowadays, new types of wearable sensors, such as the contact lens sensor Triggerfish®, provide an automated recording of 24-hour profile of ocular di...

In this paper, we propose a method to detect and recognize a Sudoku puzzle on images taken from a mobile camera. The lines of the grid are detected with a Hough transform. The grid is then recomposed from the lines. The digits position are extracted from the grid and finally, each character is recognized using a Deep Belief Network (DBN). To test o...

In this paper, we present a semi-automatic news video annotation tool. The tool and its algorithms are dedicated to artificial Arabic text embedded in video news in the form of static text as well as scrolling one. It is performed at two different levels. Including specificities of Arabic script, the tool manages a global level which concerns the e...

In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segment- ation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefu...

The automatic recognition of appliances through the monitoring of their electricity consumption finds many applications in smart buildings. In this paper we discuss the use of Hidden Markov Models (HMMs) for appliance recognition using so-called intrusive load monitoring (ILM) devices. Our motivation is found in the observation of electric signatur...

Offices, factories and even private housings are more and more endowed with building management systems (BMS) targeting an increase of comfort as well as lowering energy costs. This expansion is made possible by the progress realized in pervasive computing, providing small sized and affordable sensing devices. However, current BMS are often based o...

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current research...

Data storage has become a major topic in sensor networks as large quantities of data need to be archived for future processing. In this paper, we present a cloud storage solution benefiting from the available memory on smart things becoming data nodes. In-network storage reduces the heavy traffic resulting of the transmission of all the data to an...

Internet-of-Things (IoT) devices, especially sensors are producing large quantities of data that can be used for gathering knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to execute machine learning algorithms within the sensor n...

Objective: This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (...

In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Var...

In this paper we present a novel text line segmentation method for historical manuscript images. We use a pyramidal approach where at the first level, pixels are classified into: text, background, decoration, and out of page, at the second level, text regions are split into text line and non text line. Color and texture features based on Local Bina...

We present the evaluation of a product identification task using the LIRe system and SURF (Speeded-Up Robust Features) for content-based image retrieval (CBIR). The evaluation is performed on the Fribourg Product Image Database (FPID) that contains more than 3’000 pictures of consumer products taken using mobile phone cameras in realistic condition...

Nowadays, pervasive application scenarios relying on sensor networks are gaining momentum. The field of smart buildings is a promising playground where the use of sensors allows a reduction of the overall energy consumption. Most of current applications are using the classical DNS which is not suited for the Internet-of-Things because of requiring...

The emerging concept of Smart Building relies on an intensive use of sensors and actuators and therefore appears, at first glance, to be a domain of predilection for the IoT. However, technology providers of building automation systems have been functioning, for a long time, with dedicated networks, communication protocols and APIs. Eventually, a m...

Nowadays, our surrounding environment is more and more scattered with various types of sensors. Due to their intrinsic properties and representation formats, they form small islands isolated from each other. In order to increase interoperability and release their full capabilities, we propose to represent devices descriptions including data and ser...

In ecological networks, niche-overlap graphs are considered as complex systems. They represent the competition between two predators that share common resources. The purpose of this paper is to investigate the structural properties of these graphs considered as weighted networks and compare their measures with the ones calculated for the binary net...

The structure of networks has always been interesting for researchers. Investigating their unique architecture allows to capture insights and to understand the function and evolution of these complex systems. Ecological networks such as food-webs and niche-overlap graphs are considered as complex systems. The main purpose of this work is to compare...

We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to...

Building management systems (BMS) are nowadays present in new and renovated buildings, relying on dedicated networks. The presence of various building networks leads to problems of heterogeneity, especially for developing BMS. In this paper, we propose to leverage on the Web-of-Things (WoT) framework, using well-known standard technologies of the W...

This paper describes the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 12th International Conference on Document Analysis and Recognition (ICDAR'2013), during August 25-28, 2013, Washington DC, United States of America. This competition has used the freely available Arabic Printed Text I...

Due to increasing energy costs and the importance of the comfort, smart buildings tend to democratize both in new and renovated constructions, based on management systems relying on dedicated networks. Network heterogeneity leads to complex building management systems having to implement all the protocols of the building networks, resulting in low...

The Web-of-Things or WoT offers a way to standardize the access to services embedded on everyday objects, leveraging on well accepted standards of the Web such as HTTP and REST services. The WoT offers new ways to build mashups of object services, notably in smart buildings composed of sensors and actuators. Many things are now taking advantage of...

Smart buildings tend to democratize both in new and renovated constructions aiming at minimizing energy consumption and maximizing comfort. They rely on dedicated networks of sensors and actuators orchestrated by management systems. Building networks are often heterogeneous, leading to complex management systems having to implement all the availabl...

Leveraging on the Web-of-Things (WoT) allows standardizing the access of things from an application level point of view. The protocols of the Web and especially HTTP are offering new ways to build mashups of things consisting of sensors and actuators. Two communication protocols are now emerging in the WoT domain for event-based data exchang, namel...

We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applicat...

We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided int...

In this paper, we propose a new font and size identification method for ultra-low resolution Arabic word images using a stochastic approach. The literature has proved the difficulty for Arabic text recognition systems to treat multi-font and multi-size word images. This is due to the variability induced by some font family, in addition to the inher...

In the last few years the studies on complex networks have gained extensive research interests. Significant impacts are made by these studies on a wide range of different areas including social networks, technology networks, biological networks and others. Motivated by understanding the structure of ecological networks we introduce in this paper a...