Gaël richard

Gaël Richard, IMT-Académie des sciences Grand Prix

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Speech synthesis, sound separation, automatic recognition of instruments or voices… Gaël Richard‘s research at Télécom Paris has always focused on audio signal processing. The researcher has created numerous acoustic signal analysis methods, thanks to which he has made important contributions to his discipline. These methods are currently used in various applications for the automotive and music industries. His contributions to the academic community and technology transfer have earned him the 2020 IMT-Académie des sciences Grand Prix

Your early research work in the 1990s focused on speech synthesis: why did you choose this discipline?

Gaël Richard: I didn’t initially intend to become a researcher; I wanted to be a professional musician. After my baccalaureate I focused on classical music before finally returning to scientific study. I then oriented my studies toward applied mathematics, particularly audio signal processing. During my Master’s internship and then my PhD, I began to work on speech and singing voice synthesis. In the early 1990s, the first perfectly intelligible text-to-speech systems had just been developed. The aim at the time was to achieve a better sound quality and naturalness and to produce synthetic voices with more character and greater variability.

What research have you done on speech synthesis?

GR: To start with, I worked on synthesis based on signal processing approaches. The voice is considered as being produced by a source – the vocal cords – which passes through a filter – the throat and the nose. The aim is to represent the vocal signal using the parameters of this model to either modify a recorded signal or generate a new one by synthesis. I also explored physical modeling synthesis for a short while. This approach consists in representing voice production through a physical model: vocal cords are springs that the air pressure acts on. We then use fluid mechanics principles to model the air flow through the vocal tract to the lips.

What challenges are you working on in speech synthesis research today?

GR: I have gradually extended the scope of my research to include subjects other than speech synthesis, although I continue to do some work on it. For example, I am currently supervising a PhD student who is trying to understand how to adapt a voice to make it more intelligible in a noisy environment. We are naturally able to adjust our voice in order to be better understood when surrounded by noise. The aim of his thesis, which he is carrying out with the PSA Group, is to change the voice of a radio, navigation assistant (GPS) or telephone, initially pronounced in a silent environment, so that it is more intelligible in a moving car, but without amplifying it.

As part of your work on audio signal analysis, you developed different approaches to signal decomposition, in particular those based on “non-negative matrix factorization”. It was one of the greatest achievements of your research career, could you tell us what’s behind this complex term?

GR: The additive approach, which consists in gradually adding the elementary components of the audio signal, is a time-honored method. In the case of speech synthesis, it means adding simple waveforms – sinusoids – to create complex or rich signals. To decompose a signal that we want to study, such as a natural singing voice, we can logically proceed the opposite way, by taking the starting signal and describing it as a sum of elementary components. We then have to say which component is activated and at what moment to recreate the signal in time.

The method of non-negative matrix factorization allows us to obtain such a decomposition in the form of the multiplication of two matrices: one matrix represents a dictionary of the elementary components of the signal, and the other matrix represents the activation of the dictionary elements over time. When combined, these two matrices make it possible to describe the audio signal in mathematical form. “Non-negative” simply means that each element in these matrices is positive, or that each source or component contributes positively to the signal.

Why is this signal decomposition approach so interesting?

GR: This decomposition is very efficient for introducing initial knowledge into the decomposition. For example, if we know that there is a violin, we can introduce this knowledge into the dictionary by specifying that some of the elementary atoms of the signal will be characteristic of the violin. This makes it possible to refine the description of the rest of the signal. It is a clever description because it is simple in its approach and handling as well as being useful for working efficiently on the decomposed signal.

This non-negative matrix factorization method has led you to subjects other than speech synthesis. What are its applications?

GR: One of the major applications of this technique is source separation. One of our first approaches was to extract the singing voice from polyphonic music recordings. The principle consists in saying that, for a given source, all the elementary components are activated at the same time, such as all the harmonics of a note played by an instrument, for example. To simplify, we can say that non-negative matrix factorization allows us to isolate each note played by a given instrument by representing them as a sum of elementary components (certain columns of the “dictionary” matrix) which are activated over time (certain lines of the “activation” matrix). At the end of the process, we obtain a mathematical description in which each source has its own dictionary of elementary sound atoms. We can then replay only the sequence of notes played by a specific instrument by reconstructing the signal by multiplying the non-negative matrices and setting to zero all note activations that do not correspond to the instrument we want to isolate.

What new prospects can be considered thanks to the precision of this description?

GR: Today, we are working on “informed” source separation which incorporates additional prior knowledge about the sources in the source separation process. I currently co-supervise a PhD student who is using the knowledge of lyrics to help the separation of the isolate singing voices. There are multiple applications: from automatic karaoke generation by removing the detected voice, to music and movie sound track remastering or transformation. I have another PhD student whose thesis is on isolating a singing voice using the simultaneously recorded electroencephalogram (EEG) signal. The idea is to ask a person to wear an EEG cap and focus their attention on one of the sound sources. We can then obtain information via the recorded brain activity and use it to improve the source separation.

Your work allows you to identify specific sound sources through audio signal processing… to the point of automatic recognition?

GR: We have indeed worked on automatic sound classification, first of all through tests on recognizing emotion, particularly fear or panic. The project was carried out with Thales to anticipate crowd movements. Besides detecting emotion, we wanted to measure the rise or fall in panic. However, there are very few sound datasets on this subject, which turned out to be a real challenge for this work. On another subject, we are currently working with Deezer on the automatic detection of content that is offensive or unsuitable for children, in order to propose a sort of parental filter service, for example. In another project on advertising videos with Creaminal, we are detecting key or culminating elements in terms of emotion in videos in order to automatically propose the most appropriate music at the right time.

On the subject of music, is your work used for automatic song detection, like the Shazam application?

GR: Shazam uses an algorithm based on a fingerprinting principle. When you activate it, the app records the audio fingerprint over a certain time. It then compares this fingerprint with the content of its database. Although very efficient, the system is limited to recognizing completely identical recordings. Our aim is to go further, by recognizing different versions of a song, such as live recordings or covers by other singers, when only the studio version is saved in the memory. We have filed a patent on a technology that allows us to go beyond the initial fingerprint algorithm, which is too limited for this kind of application. In particular, we are using a stage of automatic estimation of the harmonic content, or more precisely the sequences of musical chords. This patent is at the center of a start-up project.

Your research is closely linked to the industrial sector and has led to multiple technology transfers. But you also have made several freeware contributions for the wider community.

GR: One of the team’s biggest contributions in this field is the audio extraction software YAAFE. It’s one of my most cited articles and a tool that is regularly downloaded, despite the fact that it dates from 2010. In general, I am in favor of the reproducibility of research and I publish the algorithms of work carried out as often as possible. In any case, it is a major topic of the field of AI and data science, which are clearly following the rise of this discipline. We also make a point of publishing the databases created by our work. That is essential too, and it’s always satisfying to see that our databases have an important impact on the community.

How to better track cyber hate: AI to the rescue

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The widescale use of social media, sometimes under cover of anonymity, has liberated speech and led to a proliferation of ideas, discussions and opinions on the internet. It has also led to a flood of hateful, sexist, racist and abusive speech. Confronted with this phenomenon, more and more platforms today are using automated solutions to combat cyber hate. These solutions are based on algorithms that can also introduce biases, sometimes discriminating against certain communities, and are still largely perfectible. In this context, French researchers are developing ever more efficient new models to detect hate speech and reduce the bias.

On September 16 this year, internet users launched a movement calling for a one-day boycott of Instagram. Supported by many American celebrities, the “Stop Hate for Profit” day aimed to challenge Facebook, the mother company of the photo and video sharing app, on the proliferation of hate, propaganda and misinformation on its platforms. Back in May 2019, during its bi-annual report on the state of moderation on its network, Facebook announced significant progress in the automated detection of hate content. According to the company, between January and April 2019, more than 65% of these messages were detected and moderated before users even reported them, compared with 38% during the same period in 2018.

Strongly encouraged to combat online hate content, in particular by the “Avia law” (named after the member of parliament for Paris, Lætitia Avia), platforms use various techniques such as detection by keywords, reporting by users and solutions based on artificial intelligence (AI). Machine learning allows predictive models to be developed from corpora of data. This is where biases can be damaging. “We realized that the automated tools themselves had biases against gender or the user’s identity and, most importantly, had a disproportionately negative impact on certain minority groups such as Afro-Americans,” explains Marzieh Mozafari, PhD student at Télécom SudParis. On Twitter, for example, it is difficult for AI-based programs to take into account the social context of tweets, the identity and dialect of the speaker and the immediate context of the tweet all at once. Some content is thus removed despite being neither hateful nor offensive.

So how can we minimize these biases and erroneous detections without creating a form of censorship? Researchers at Télécom SudParis have been using a public dataset collected on Twitter, distinguishing between tweets written in Afro-American English (AAE) and Standard American English (SAE), as well as two reference databases that have been annotated (sexist, racist, hateful and offensive) by experts and through crowdsourcing. “In this study, due to the lack of data, we mainly relied on cutting-edge language processing techniques such as transfer learning and the BERT language model, a pre-trained, unsupervised model”, explain the researchers.

Developed by Google, the BERT (Bidirectional Encoder Representations from Transformers) model uses a vast corpus of textual content, containing, among other things, the entire content of the English version of Wikipedia. “We were able to “customize” BERT [1] to make it do a specific task, to adjust it for our hateful and offensive corpus”, explains Reza Farahbakhsh, a researcher in data science at Télécom SudParis. To begin with, they tried to identify word sequences in their datasets that were strongly correlated with a hateful or offensive category. Their results showed that tweets written in AAE were almost 10 times more likely to be classed as racist, sexist, hateful or offensive compared to tweets written in SAE. “We therefore used a reweighting mechanism to mitigate biases based on data and algorithms,” says Marzieh Mozafari. For example, the number of tweets containing “n*gga” and “b*tch” is 35 times higher among tweeters in AAE than in SAE and these tweets will often be wrongly identified as racist or sexist. However, this type of word is common in AAE dialects and is used in everyday conversation. It is therefore likely that they will be considered hateful or offensive when they are written in SAE by an associated group.

“In fact, these biases are also cultural: certain expressions considered hateful or offensive are not so within a certain community or in a certain context. In French, too, we use certain bird names to address our loved ones! Platforms are faced with a sort of dilemma: if the aim is to perfectly identify all hateful content, too great a number of false detections could have an impact on users’ “natural” ways of expressing themselves,” explains Noël Crespi, a researcher at Télécom SudParis. After reducing the effect of the most frequently used words in the training data through the reweighting mechanism, this probability of false positives was greatly reduced. “Finally, we transmitted these results to the pre-trained BERT model to refine it even further using new datasets,” says the researcher.

Can automatic detection be scaled up?

Despite these promising results, many problems still need to be solved in order to better detect hate speech. These include the possibility of deploying these automated tools for all languages spoken on social networks. This issue is the subject of a data science challenge launched for the second consecutive year: the HASOC (Hate Speech and Offensive Content Identification in Indo-European Languages), in which a team from IMT Mines d’Alès is participating. “The challenge aims to accomplish three tasks: determine whether or not content is hateful or offensive, classify this content into one of three categories: hateful, offensive or obscene, and identify whether the insult is directed towards an individual or a specific group,” explains Sébastien Harispe, a researcher at IMT Mines Alès.

“We are mainly focusing on the first three tasks. Using our expertise in natural language processing, we have proposed a method of analysis based on supervised machine learning techniques that take advantage of examples and counter-examples of classes to be distinguished.” In this case, the researchers’ work focuses on small datasets in English, German and Hindi. In particular, the team is studying the role of emojis, some of which can have direct connotations with hate expressions. The researchers have also studied the adaptation of various standard approaches in automatic language processing in order to obtain classifiers able to efficiently exploit such markers.

They have also measured their classifiers’ ability to capture these markers, in particular through their performance. “In English, for example, our model was able to correctly classify content in 78% of cases, whereas only 77% of human annotators initially agreed on the annotation to be given to the content of the data set used,” explains Sébastien Harispe. Indeed, in 23% of cases, the annotators expressed divergent opinions when confronted with dubious content that probably needed to have been studied with account taken of the contextual elements.

What can we expect from AI? The researcher believes we are faced with a complex question: what are we willing to accept in the use of this type of technology? “Although remarkable progress has been made in almost a decade of data science, we have to admit that we are addressing a young discipline in which much remains to be developed from a theoretical point of view and, especially, for which we must accompany the applications in order to allow ethical and informed uses. Nevertheless, I believe that in terms of the detection of hate speech, there is a sort of glass roof created by the difficulty of the task as it is translated in our current datasets. With regard to this particular aspect, there can be no perfect or flawless system if we ourselves cannot be perfect.”

Besides the multilingual challenge, the researchers are facing other obstacles such as the availability of data for model training and the evaluation of results, or the difficulty in assessing the ambiguity of certain content, due for example to variations in writing style. Finally, the very characterization of hate speech, subjective as it is, is also a challenge. “Our work can provide material for the humanities and social sciences, which are beginning to address these questions: why, when, who, what content? What role does culture play in this phenomenon? The spread of cyber hate is, at the end of the day, less of a technical problem than a societal one” says Reza Farahbakhsh.

[1] M. Mozafari, R. Farahbakhsh, N. Crespi, “Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model”, PLoS ONE 15(8): e0237861. https://doi.org/10.1371/journal.pone.0237861

Anne-Sophie Boutaud

Also read on I’MTech

Algorithmic bias, discrimination and fairness
AI

AI for interoperable and autonomous industrial systems

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At Mines Saint-Étienne, researchers Olivier Boissier, Maxime Lefrançois and Antoine Zimmermann are using AI to tackle the issue of interoperability, which is essential to the industry of the future. The standardization of information in the form of knowledge graphs has allowed them to enable communication between machines that speak different languages. They then operate this system via a network of autonomous distributed agents on each machine to automate a production line.

Taking a train from France to Spain without interoperability means having to get off at the border since the rails are not the same in both countries. A train that hopes to cross over from one rail line to another is sure to derail. The same problem is posed on factory floors – which is why the interoperability of production lines is a key issue for the industry of the future. In an interoperable system, machines can communicate with one another in order to work together automatically, even if they don’t speak the same language. But this is not easy to implement. Factory floors are marked by a kind of cacophony of computer languages. And every machine has its own properties: a multitude of manufacturers, different applications, diverse ways of sending, measuring and collecting information etc. Such heterogeneity reduces the flexibility of production lines. During the Covid-19 crisis, for example, many companies had to reconfigure all of their machines by hand to set up new production operations, such as manufacturing masks. “As of now, on factory floors everything is coded according to an ideal world. Systems are incapable of adapting to change,” says Maxime Lefrançois, a specialist in web semantics. Interoperability also goes hand in hand with competition. Without it, ensuring that a factory runs smoothly would require investing in a single brand of equipment to be certain the various parts are compatible.  

There is no single method for making a system interoperable. Along with his colleagues at Mines Saint-Étienne, the researcher is addressing the issue of interoperability using an approach based on representing data about the machines (manufacturer, connection method, application, physical environment etc.) in a standardized way, meaning independent of the language inherent to a machine. This knowledge is then used by what is known as a multi-agent software system. The goal is to automate a production process based on the description of each machine.

Describing machines to automate decision-making

What does the automation of an industrial system imply? Service delegation, primarily. For example, allowing a machine to place an order for raw materials when it detects a low stock level, instead of going through a human operator. For this, the researchers are developing mechanisms for accessing and exchanging information between machines using the web of things. “On the web, we can set up a communication interface between the various devices via standardized protocols. These methods of interaction therefore reduce the heterogeneity of the language of connected devices,” explains Antoine Zimmermann, an expert in knowledge representation at Mines Saint-Étienne. All of the modeled data from the factory floor is therefore accessible to and understood by all the machines involved.

More importantly, these resources may then be used to allow the machines to cooperate with one another. To this end, the Mines Saint-Étienne team has opted for a flexible approach with local decision-making. In other words, an information system called an autonomous agent is deployed on each device and is able to interact with the agents on other machines. This results in a 4.0 word-of mouth system without loss of information. “An autonomous agent decides what to do based on what the machines upstream and downstream of its position are doing. This reasoning software layer allows the connected device to adjust its behavior according to current status of the system,” says Olivier Boissier, who specializes in autonomous agent systems at Mines Saint-Étienne. For example, a machine can stop a potentially dangerous process when it detects information indicating that a device’s temperature is too high. Likewise, it would no longer be necessary to redesign the entire system to add a component, since it is automatically detected by the other machines.

Read more on I’MTech: A dictionary for connected devices

Depending on the circumstances of the factory floor, a machine may also connect to different production lines to perform other tasks. “We no longer code a machine’s specific action, but the objective it must achieve. The actions are deduced by each agent using the data it collects. It therefore contributes to fulfilling a general mission,” adds the researcher. In this approach, no single agent can achieve this objective alone as each one has a range of action limited to its machine and possesses only part of the knowledge about the overall line. The key to success it therefore cooperation. This makes it possible to transition from producing cups to bottles, simply by changing the objective of the line, without reprogramming it from A to Z.

Towards industrial experiments

Last summer, the IT’m Factory technological platform, a simulated industrial space at Mines Saint-Étienne, hosted a case study for an interoperable and cooperative distributed system. This production line starts out with a first machine responsible for retrieving a cup in a storage area and placing it on a conveyor. A filling system then fills the cup with a liquid. When this second machine has run out of product to pour, it places a remote order with a supplier. At every step, several methods of cooperation are possible. The first is to send a message from one agent to another in order to notify it of the task it has just performed. A second method uses machine perception to detect the action performed by the previous machine. A certain method may be preferable depending on the objectives (production speed etc.).

The researchers have also shown that a robot in the middle of the line may be replaced by another. Interoperability made it possible for the line to adapt to hardware changes without impacting its production. This issue of flexibility is extremely important with a view towards integrating a new generation of nomadic robots. “In September 2020, we start the SIRAM industry of the future project, which should make it possible to deploy interoperable, adaptable information systems to control mobile robotic assistants,” says Maxime Lefrançois. In the future, these devices could be positioned at strategic locations in companies to assist humans or retrieve components at different parts of the production line. But to do so, they must be able to interact with the other machines on the factory floor.  

Anaïs Culot

brominated plastics

Innovative approaches to recycling brominated plastics

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Recovering untreated plastic materials and putting them back into the recycling loop through a decontamination line is the challenge of thesis research by Layla Gripon, a PhD student at IMT Lille Douai. These extraction methods contribute to a comprehensive approach to recovering plastic materials, in particular brominated plastics.

High consumption of electronic devices implies a significant amount of waste to be processed. While waste electrical and electronic equipment (WEEE) is often seen as a gold mine of silver and rare earths, plastic materials represent 18% of these deposits. This was equivalent to 143,000 tons in France in 2018 according to a report by Ademe (Ecological Transition Agency) published in 2019. But not all this plastic material is created equal. Some of it contains atoms of bromine – a chemical compound that is widely used in industrial flame retardants. These substances meet requirements for reducing flammability hazards in devices that may get hot while in use, such as computers or televisions. There’s just one problem: many of these substances are persistent organic pollutants (called POP). This means that they are molecules that can travel great distances without being transformed, and which are toxic to the environment and our health. The amount of these molecules contained in devices is therefore regulated in the design stage, as well as in the end-of-life processing stage. In 2019, the European Union set the threshold at which waste containing bromine can no longer be recycled at 2 grams per kilo. Beyond this limit, it is destroyed through incineration or used as fuel. But couldn’t it still be recycled, with the right processing? For her thesis co-supervised by researchers at IMT Lille Douai and The Alençon Institute of Plastics and Composites (ISPA), Layla Gripon has set out to identify a method for separating brominated flame retardants from plastics. “We seek to maximize recycling by limiting the loss of unrecoverable material, while complying with regulations,” says the PhD student.

Finding the right balance between extraction efficiency and respect for the environment

Approximately 13% of WEEE plastics are above the legal threshold for brominated flame retardants, which is equivalent to 17,500 tons in France. Samples tested in the laboratory reached a concentration of bromine up to 4 times higher than the legal threshold. In order to process them, Layla Gripon tested a number of methods that do not degrade the original plastic material. The first was highly efficient, removing 80% of the bromine. It was an extraction method used diethyl ether, an organic solvent. But since it uses a lot of solvent, it is not an environmentally-friendly solution. Another technique based on solvents is dissolution-precipitation. Through this technique, plastic is dissolved in the solvent, which retains the flame retardants. “In order to limit the environmental impact of this process, we subcontracted the German Fraunhofer Institute to carry out a test. Their patented process (CreaSolv) allows them to reuse the solvents. In the end, the bromine was no longer detected after processing and the environmental impact was reduced,” she explains.

In addition, a method that is more environmentally-friendly – but less efficient, for now – uses supercritical CO2, a green, non-toxic and non-flammable solvent. This process is already used in the agri-food industry, for example, to remove caffeine from coffee. In the supercritical state, carbon dioxide exists in an intermediate state between liquid and gas. To achieve this, the gas is heated and pressurized. In practice, the closed-loop system used by Layla Gripon is simple. Shredded plastic is placed inside an autoclave in which the supercritical fluid circulates continuously. When it leaves the autoclave, the recovered gas brings various additives with it, including a portion of the flame retardants.

To improve the yield of the second method, Layla Gripon planned to use a small amount of solvent. “The tests with ethanol improved the yield, with a rate of 44% of bromine removed, but this wasn’t enough,” says the PhD student. Other solvents could be considered in the future. “The supercritical CO2 method, on the other hand, works very well on the brominated flame retardant that is currently the most widely-used in industry (tetrabromobisphenol A – TBBPA),” she adds. But the most difficult brominated plastics to process are the ones that have been prohibited for a number of years. Although they are no longer available on the market, they are still accumulating as waste.

A large-scale approach to recovering recycled plastic  

These promising processing techniques must still evolve to respond fully to the needs of the recycling industry. “If these two processes are selected for applications beyond the laboratory, their environmental impact will have to be minimized,” says the PhD student. Such methods could therefore be incorporated in the pre-processing stage before the mechanical recycling of WEEE plastics.

At the same time, manufacturers are interested in the benefits of this research initiated through the Ecocirnov1 Chair. “They’ve joined this project because the laws are changing quickly and their products must take into account the need to recover materials,” explains Éric Lafranche, a researcher who specializes in plastic materials at IMT Lille Douai and is Layla Gripon’s thesis supervisor. The objective of maximizing recycling is combined with an ambition to create new products tailored to the properties of the recycled materials.  

Read more on I’MTech: A sorting algorithm to improve plastic recycling

“Recycling today is different than it was 10 years ago. Before, we sought to recover the material, reuse it with similar properties for an application identical to its original use. But the recycled product loses some of its properties. We have to find new applications to optimize its use,” says Éric Lafranche. For example, French industrial group Legrand, which specializes in electrical installations and information networks, seeks to use recycled plastic materials in its electrical protection products. In collaboration with researchers from IMT Lille Douai, the company has implemented a multilayer injection system based on recovered materials and higher-grade raw materials on the surface. This offers new opportunities for applications for recycled plastics – as long as their end-of-life processing is optimized.

By Anaïs Culot.

1 Circular economy and recycling chair created in 2015, bringing together IMT Lille Douai, and the Alençon Institute of Plastics and Composites and Armines.

LCA

What is life cycle analysis?

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Life cycle analysis (LCA) is increasingly common, in particular for eco-design or to obtain a label. It is used to assess the environmental footprint of a product or service by taking into account as many sources as possible. In the following interview, Miguel Lopez-Ferber, a researcher in environmental assessment at IMT Mines Alès, offers insights about the benefits and complexity of this tool.

What is life cycle analysis?

Miguel Lopez-Ferber: Life cycle analysis is a tool for considering all the impacts of a product or service over the course of its life, from design to dismantling of assemblies, and possibly recycling – we also refer to this as “cradle to grave.” It’s a multi-criteria approach that is as comprehensive as possible, taking into account a wide range of environmental impacts. This tool is crucial for analyzing performance and optimizing the design of goods and services.

Are there standards?

MLF: Yes, there are European regulations and today there are standards, in particular ISO standards 14040 and 14044. The first sets out the principles and framework of the LCA. It clearly presents the four phases of a LCA study: determining the objectives and scope of the study; the inventory phase; assessing the impact, and the interpretation phase. The ISO 14044 standard specifies the requirements and guidelines.

What is LCA used for?

MLF: The main benefit is that it allows us to compare different technologies or methods to guide decision-making. It’s a tremendous tool for companies looking to improve their products or services. For example, the LCA will immediately pinpoint the components of a product with the biggest impact. Possible substitutes for this component may then be explored, while studying the impacts these changes could lead to. And the same goes for services. Another advantage of the “life cycle” view is that it takes impact transfer into account. For example, in order to lower the impact of an oven’s power consumption, we can improve its insulation. But that will require more raw material and increase the impact of production. The LCA allows us to take these aspects into account and compare the entire lifetime of a product. The LCA is a very powerful tool for quickly detecting these impact transfers.

How is this analysis carried out?

MLF: The ISO 14040 and 14044 standards clearly set out the procedure. Once the framework of the study and objectives have been identified, the inflows and outflows associated with the product or service must be determined – this is the inventory phase. These flows must be brought back to flows from the environment. To do so, there are growing databases, with varying degrees of ease of access, containing general or specialized information. Some focus on agricultural products and their derivatives, others on plastics or electricity production. This information about flows is collected, assembled and related to the flow for a functional unit (FU) that makes it possible to make comparisons. There is also accounting software to help compile the impacts of various stages of a product or service.  

The LCA does not directly analyze the product, but its function, and it is able to compare very different technology. So we will define a FU that focuses on the service provided. Take two shoe designs, for example. Design A is of very high quality so it requires more material to be produced, but lasts twice as long as Design B. Design A may have greater production impacts, but it will be equivalent to two Design Bs over time. For the same service provided, Design A could ultimately have a lower impact.

What aspects are taken into account in the LCA?

MLF: The benefit of life cycle analysis is that it has a broad scope, and therefore takes a wide range of factors into account. This includes direct as well as indirect impacts, consumption of resources such as raw material extraction, carbon footprint, and pollution released. So there is a temporal aspect, since the entire lifetime of a good or service must be studied, a geographical aspect, since several sites are taken into consideration, and the multi-criteria aspect, meaning all the environmental compartments. 

Who conducts the LCA?

MLF: When they are able to, and have the expertise to do so, companies have them done in-house. This is increasingly common. Otherwise, they can hire a consulting firm to conduct them. In any case, if the goal is to share this information with the public, the findings must be made available so that they can be reviewed, verified and validated by outside experts.

What are the current limitations of the tool?

MLF: There is the question of territoriality. For example, power consumption will not have the same impact from one country to another. In the beginning, we used global averages for LCA. We now have continental, and even national averages, but not yet regional ones. The more specific the data, the more accurate the LCA will be.  

Read more on I’MTech: The many layers of our environmental impact

Another problem is additional or further impacts. We operate under the assumption that impacts are cumulative and linear, meaning that manufacturing two pens doubles the impacts of a single pen. But this isn’t always the case. Imagine if a factory releases a certain amount of pollutants – this may be sustainable if it is alone, but not if three other companies are also doing so. After a certain level, the environmental impact may increase.  

And we’re obviously limited by our scientific knowledge. Environmental and climate impacts are complex and the data changes in response to scientific advances. We’re also starting to take social aspects into consideration, which is extremely complex but very interesting.

By Tiphaine Claveau

digital intelligence

Digital transformation: how to avoid missing out on the new phase of work that has begun

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Aurélie Dudézert, Institut Mines-Télécom Business School and Florence Laval, IAE de Poitiers

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[dropcap]A[/dropcap]fter a lockdown that has helped reveal how far along companies are in their digital transformation, the easing of lockdown measures has ushered in a new phase marked by a desire to return to “normal” activities, which is impossible due to changing health restrictions.

Some organizations have therefore tried to use the health context as a pretext for regaining control over informal exchanges and adjustments that are impossible to control in a remote work environment (employees clocking in on site vs. remotely; identifying who is working with whom, at what time, etc.).

The organizational agility required for the goal of digital transformation and implemented in teams during the lockdown has been undermined by attempts to standardize work and return to uniform processes for the entire organization.

Mask-wearing has also become a point of tension. Besides being uncomfortable, masks conceal faces after what was often a period of remote work – in which it was difficult to perceive others’ emotions – and therefore complicate relationships. We must learn to read emotions differently and communicate with others differently.

These challenges are compounded by uncertainty over changing health restrictions. Highly adaptive ways of working must be put in place. Periods of site closures are followed by periods of hybrid work with employees taking turns working on site to comply with health restrictions.

Designing the transformation

After learning how to work together remotely, employees and managers must once again learn how to work together constantly. 

To respond to this situation, three strategies, which we explain in the collective work L’impact de la crise sur le management (The Impact of the Crisis on Management, Éditions EMS) seem to be useful to help get through this second wave of the crisis and continue the digital transformation of working methods.

The first is to work with teams on emerging stress and tensions by seeing them not as symptoms of individuals’ inability/incompetence to cope with the situation, but as drivers for developing appropriate ways to coordinate work.

For instance, if mask-wearing is a source of tension, bringing teams together to discuss what is causing the tension could provide an opportunity to create a new working arrangement that is more effective and better-suited to the new digital environment. This means that the manager must acknowledge employees’ experiences and perceptions and take them seriously so they can be revealed as expectations, such as creativity, or as the rejection of the organization and its goals.

The second strategy is to develop reflexive management, which takes an objective look at the work methods put in place in the current adaptation phase. It is quite clear today that work practices are moving towards a hybridization between working from the office/remotely and synchronous/asynchronous.

Rather than seeing the successive changes in health regulations as constraints, which make it difficult to do business and seamlessly continue their digital transformation, organizations would benefit from taking advantage of these periodic adjustments to gain insight into the pros and cons of this hybrid system.  

This objective look could provide an opportunity to characterize which activities specific to each team are indisputably more productive in person than remotely, or to determine how to manage teams working both from home and on-site.

The third strategy is to “encourage digital intelligence”, meaning working with the team to determine the requirements and uses of digital technology, depending on working methods. For example, it may not be necessary to upgrade employees’ skills to increase their proficiency in an entire collaborative work if the goal is simply to enable them to work together via web conference.

Overstretching employees at such an uncertain and strange time is an additional risk that could undermine the digital transformation process. Going back to the basic uses of digital technology in order to carry out tasks seems to be much more useful and effective.

Aurélie Dudézert, Full Professor, IMT BS, Institut Mines-Télécom Business School and Florence Laval, Lecturer at IAE de Poitiers

This article has been republished from The Conversation under a Creative Commons. Read the original article (in French).

EuHubs4data

Data and AI: fostering innovation at European level

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EuHubs4data, a project funded by the European Union, seeks to make a big contribution to the growth of European SMEs, start-ups and web-based companies in the global data economy. How? By providing them with a European catalogue of data-driven solutions and services in an effort to foster innovation in this field. The project was launched on 1 September 2020 and will run for three years, with a budget of €12.5 Million. It brings together 12 Digital Innovation Hubs (DIH) across 9 European Union countries. One of these innovation hubs is TeraLab, IMT’s big data and artificial intelligence platform. An interview with Natalie Cernecka, Head of Business Development at TeraLab.

What are the goals of the EuHubs4data project, in which TeraLab is a partner?

Natalie Cernecka The goal of the project is to bring together services provided by European big data hubs to take full advantage of the benefits offered by the various members of the network. 

There are nearly 200 digital innovation hubs (DIH) in Europe. Some of them are specialized in data. Individually, these hubs are highly effective: they provide various services related to data and act as a link between SMEs in the digital sector and other companies and organizations. At the European level, however, interconnection between these hubs is sorely lacking, which is why the idea of a unified network is so important.

The  project will foster collaboration and exchange between existing hubs and provide a solid foundation for a European data economy to meet the growing data needs of SMEs in the digital sector and start-ups that work with technologies such as artificial intelligence (AI). The focus will be on system interoperability and data sharing. The project is therefore an important step towards implementing the European Commission plan to strengthen Europe’s data economy and digital sovereignty.

How will you achieve these goals?

NC: The project focuses on two areas: supply and demand. On the supply side, we’ll be working on the catalogue of services and datasets and on providing training opportunities in big data and AI. On the demand side, we’ll be carrying out experiments, with three open call sessions, along with an extensive awareness program aimed at getting hundreds of companies and organizations involved and encouraging them to innovate with data.

EuHubs4data offers a catalogue of services for SMEs, start-ups and web companies. Could you give us some concrete examples of such services?

NC: The goal is to propose a single catalogue presenting the various services offered by project partners and their respective ecosystems. For example, TeraLab could provide access to its data sharing platform, while a second partner could offer access to datasets, and a third could provide data analysis tools or training opportunities. The companies will benefit from a comprehensive catalogue and may in turn offer their customers innovative services.

12 digital innovation hubs located in 9 European countries are partners in this project. How will this federation be structured?

NC: The structuring of this federation will be specified over the course of the project. The consortium is headed by the Instituto Tecnológico de Informática in Valencia, Spain and includes DIHs and major European players in the field of big data – such as the Big Data Value Association, in which IMT is a member, and the International Data Spaces Association, which is at the center of GAIA-X and includes IMT as the French representative. A number of initiatives focus on structuring and expanding this ecosystem. The structure has to be flexible enough to incorporate new members, whether over the course of the project or in the distant future.

What will TeraLab’s specific role be?

NC: TeraLab’s role in the project is threefold. First, it is responsible for the work package in charge of establishing the catalogue of services, the central focus of the project. Second, TeraLab will provide its big data and AI platform along with its expertise in securing data. And third, as a DIH, TeraLab will accompany experiments and open calls, which will use the catalogue of services and datasets.  

Read more on I’MTech | Artificial Intelligence: TeraLab becomes a European digital innovation hub

What are some important steps coming up for the project?

NC: The open calls! The first will be launched in December 2020; that means that the first iteration of the catalogue should be ready at that time. The experiments will begin in spring 2021. TeraLab will follow them very closely and accompany several participating companies, to better understand their needs in terms of services, data and the use of the catalogue, in order to improve its use.

Learn more about the EUHubs4Data project :

Interview by Véronique Charlet

OSO-AI

When AI keeps an ear on nursing home residents

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The OSO-AI start-up has recently completed a €4 million funding round. Its artificial intelligence solution that can detect incidents such as falls or cries for help has convinced investors, along with a number of nursing homes in which it has been installed. This technology was developed in part through the work of Claude Berrou, a researcher at IMT Atlantique, and the company’s co-founder and scientific advisor.

OSO-AI, a company incubated at IMT Atlantique, is the result of an encounter between Claude Berrou, a researcher at the engineering school, and Olivier Menut, an engineer at STMicroelectronics. Together, they started to develop artificial intelligence that can recognize specific sounds. After completing a €4 million funding round, the start-up now plans to fast-track the development of its product: ARI (French acronym for Smart Resident Assistant), a solution designed to alert staff in the event of an incident inside a resident’s room.

The device takes the form of an electronic unit equipped with high-precision microphones. ARI’s goal is to “listen” to the sound environment in which it is placed and send an alert whenever it picks up a worrying sound. Information is then transmitted via wi-fi and processed in the cloud.

“Normally, in nursing homes, there is only a single person on call at night,” says Claude Berrou. “They hear a cry for help at 2 am but don’t know which room it came from. So they have to go seek out the resident in distress, losing precious time before they can intervene – and waking up many residents in the process. With our system, the caregiver on duty receives a message such as, ‘Room 12, 1st floor, cry for help,’ directly on their mobile phone.” The technology therefore saves time that may be life-saving for an elderly person, and is less intrusive than a surveillance camera so it is better accepted. Especially since it is paused whenever someone else enters the room. Moreover, it helps relieve the workload and mental burden placed on the staff.

OSO-AI is inspired by how the brain works

But how can an information system hear and analyze sounds? The device developed by OSO-AI relies on machine learning, a branch of artificial intelligence, and artificial neural networks. In a certain way, this means that it tries to imitate how the brain works. “Any machine designed to reproduce basic properties of human intelligence must be based on two separate networks,” explains the IMT Atlantique researcher. “The first is sensory-based and innate: it allows living beings to react to external factors based on the five senses. The second is cognitive and varies depending on the individual: it supports long-term memory and leads to decision-making based on signals from diverse sources.”

How is this model applied to the ARI unit and the computers that receive the preprocessed signals? A first “sensory-based” layer is responsible for capturing the sounds, using microphones, and turning them into representative vectors. These are then compressed and sent to the second “cognitive” layer, which then analyzes the information, relying in particular on neural networks, in order to decide whether or not to issue an alert. It is by comparing new data to that already stored in its memory that the system is able to make a decision. For example, if a cognitively-impaired resident tends to call for help all the time, it must be able to decide not to warn the staff every time.

The challenges of the learning phase

Like any self-learning system, ARI must go through a crucial initial training phase to enable it to form an initial memory, which will subsequently be increased. This step raises two main problems.

First of all, it must be able to interpret the words pronounced by residents using a speech-to-text tool that turns a speaker’s words into written text. But ARI’s environment also presents certain challenges. “Elderly individuals may express themselves with a strong accent or in a soft voice, which makes their diction harder to understand,” says Claude Berrou. As such, the company has tailored its algorithms to these factors.

Second, what about other sounds that occur less frequently, such as a fall? In these cases, the analysis is even more complex. “That’s a major challenge for artificial intelligence and neural networks: weakly-supervised learning, meaning learning from a limited number of examples or too few to be labeled,” explains the IMT Atlantique researcher. “What is informative is that it’s rare. And that which is rare is not a good fit for current artificial intelligence since it needs a lot of data.” OSO-AI is also innovative in this area of weakly-supervised learning.

Data is precisely a competitive advantage on which OSO-AI intends to rely. As it is installed in a greater number of nursing homes, the technology acquires increasingly detailed knowledge of sound environments. And little by little, it builds a common base of sounds (falls, footsteps, doors etc.) which can be reused in many nursing homes.

Read more on I’MTech: In French nursing homes, the Covid-19 crisis has revealed the detrimental effects of austerity policies

From nursing homes to home care

As of now, the product has completed its proof-of-concept phase, and approximately 300 devices have been installed in seven nursing homes, while the product has started to be marketed. The recent funding round will help fast-track the company’s technological and business development by tripling its number of employees to reach a staff of thirty by the end of 2021.

The start-up is already planning to deploy its system to help elderly people remain in their homes, another important societal issue. Lastly, according to Claude Berrou, one of OSO-AI’s most promising future applications is to monitor well-being, in particular in nursing home residents. In addition to situations of distress, the technology could detect unusual signs in residents, such as a more pronounced cough. In light of the current situation, there is no doubt that such a function would be highly valued.