Eikosim

Eikosim improves the dialogue between prototyping and simulation

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Simulate. Prototype. Measure. Repeat. Developing an industrial part inevitably involves these steps. First comes the digital model. Then, its characteristics are assessed through simulation, after which, the first version of the part is built. The part must then be subject to mechanical stress to assess its resistance and be closely observed from every angle. The test results will be used to improve the modelling, which will produce a new prototype… and so the cycle continues until a satisfactory version is produced. But Renaud Gras and Florent Mathieu want to reduce the repetitions involved in this cycle, which is why they created Eikosim, a startup that has been incubating at Paristech Entrepreneurs for one year. They develop software specialized in helping engineers with these design stages.

So, what is the key to saving as much time as possible? Facilitating the comparison between the digital tests and the measurements. Eikosim meets this need by integrating the measurement results recorded during testing directly into the part’s digital model. Any deformation, cracking or change in the mechanical properties is therefore recorded in the digital version of the object. The engineers can then easily compare the changes measured during the tests with those predicted during simulation, and therefore automatically correct the simulation so that it better reflects reality. What this startup offers is a breakthrough solution, since the traditional alternative involves storing the real measurements in a data table, and creating algorithms for manually readjusting the part through simulation. A tedious and time-consuming process.

Another strength the startup has to offer: its software can optimize the measurements of prototypes, for example by facilitating the positioning of observation cameras. One of the challenges is to ensure their actual position is well calibrated to correctly record the movements. To achieve this, the cameras are usually positioned using an alignment jig and arranged using a complex procedure which, again, is time-consuming. But the Eikosim software makes it possible to directly record the cameras’ positions on a digital model of the part. Since an alignment jig is no longer needed, the calibration is much faster. The technology is therefore compatible with large-scale parts, such as the chassis of trains. These dimensions are too large for technology offered by competitors, which struggles to arrange many cameras around such enormous parts.

The startup’s solutions have won over manufacturers, especially in aeronautics. The sector innovates materials, but must constantly address safety constraints. The accuracy of the simulations is therefore essential. In this industry, 20% of engineers’ time is spent making comparisons between simulation and real tests. The powerful software developed by Eikosim is therefore represents an enormous advantage in reducing development times.

The founders

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Florent Mathieu - Eikosim

Florent Mathieu

Renaud Gras - Eikosim

Renaud Gras and Florent Mathieu founded Eikosim after completing a thesis at the ENS Paris-Saclay Laboratory of Mechanics and Technology. Equipped with their expertise in understanding the mechanical behavior of materials by instrumenting tests using imaging techniques, they now want to use their startup to pass these skills on to the manufacturing industry.

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passwords

Passwords: security, vulnerability and constraints

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Hervé Debar, Télécom SudParis – Institut Mines-Télécom, Université Paris-Saclay

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What is a password?

A password is a secret linked to an identity. It associates two elements, what we own (a bank card, badge, telephone, fingerprint) and what we know (password or code).

Passwords are very widely used, for computers, telephones, banking. The simplest form is the numerical code (PIN), with 4 to 6 numbers. Our smartphones therefore use two PIN codes, one to unlock the device, and another associated with the SIM card, to access the network. Passwords are most commonly associated with internet services (email, social networks, e-commerce, etc.).

Today, in practical terms, identity is linked to an email address. A website uses it to identify a person. The password is a secret, known by both the server and the user, making it possible to “prove” to the server that the identity provided is authentic. Since an email address is often public, knowing this address is not enough for recognizing a user. The password is used as a lock on this identity. Therefore, passwords are stored on the websites we log in to.

What is the risk associated with this password?

The main risk is password theft, in which the associated identity is stolen. A password must be kept hidden, so that it remains secret, preventing identity theft when incidents arise, such as the theft of Yahoo usernames.

Therefore, a website doesn’t (or shouldn’t) save passwords directly. It uses a hash function to calculate the footprint, such as the bcrypt function Facebook uses. With the password, it is very easy to calculate the footprint and verify that it is correct. On the other hand, it is very difficult mathematically to find the code if only the footprint is known.

Searching for a password by following the footprint

Unfortunately, technological progress has made brute force password search tools, like “John the Ripper” extremely effective. As a result, an attacker can find passwords fairly easily using footprints.

The attacker can therefore capture passwords, for example by tricking the user. Social engineering (phishing) causes users to connect to a website that imitates the one they intended to connect to, thus allowing the attacker to steal their login information (email and password).

Many services (social networks, shops, banks) require user identification and authentication. It is important be sure we are connecting to the right website, and that the connection is encrypted (lock, green color in the browser address bar), to prevent these passwords from being compromised.

Can we protect ourselves, and how?

For a long time, the main risk involved sharing computers. Writing your password on a post-it note on the desk was therefore prohibited. Today, in a lot of environments, this is a pragmatic and effective way of keeping the secret.

The main risk today involves to the fact that an email address is associated with the passwords. This universal username is therefore extremely sensitive, and naturally it is a target for hackers. It is therefore important to identify all the possible means an email service provider offers to protect this address and connection. These mechanisms can include a code being sent by SMS to a mobile phone, a recovery email address, pre-printed one-time use codes, etc. These methods control access to your email address by alerting you of attempts to compromise your account, and help you regain access if you lose your password.

For personal use

Another danger involves passwords being reused for several websites. Attacks on websites are very common, and levels of protection vary greatly. Reusing one password on several websites therefore very significantly increases the risk of it being compromised. Currently, the best practice is to therefore to use a password manager, or digital safe (like KeePass or Password Safe, free and open software), to save a different password for each website.

The automatic password generation function offered by these managers provides passwords that are more difficult to guess. This greatly simplifies what users need to remember and significantly improves security.

It is also good to keep the database on a flash drive, and to save it frequently. There are also cloud password management solutions. Personally, I do not use them, because I want to be able to maintain control of the technology. That could prevent me, for example, from using a smart phone in certain environments.

For professionals

Changing passwords frequently is often mandatory in the professional world. It is often seen as a constraint, which is amplified by the required length, variety of characters, the impossibility of using old passwords, etc. Experience has shown that too many constraints lead users to choose passwords that are less secure.

It is recommended to use an authentication token (chip card, USB token, OTP, etc.). At a limited cost, this offers a significant level of security and additional services such as remote access, email and document signature, and protection for the intranet service.

Important reminders to avoid password theft or limit its impact

Passwords, associated with email addresses, are a critical element in the use of internet services. Currently, the two key precautions recommended for safe use is to have one password per service (if possible generated randomly and kept in a digital safe) and to be careful to secure sensitive services, such as email addresses and login information (by using the protective measures provided by these services, including double authentication via SMS or recovery codes, and remaining vigilant if anything abnormality is detected). You can find more recommendations on the ANSSI website.

Hervé Debar, Head of the Telecommunications Networks and Services department at Télécom SudParis, Télécom SudParis – Institut Mines-Télécom, Université Paris-Saclay

The original version of this article was published in French on The Conversation France.

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facial expressions

Our expressions under the algorithmic microscope

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Mohamed Daoudi, a researcher at IMT Lille Douai, is interested in the recognition of facial expressions in videos. His work is based on geometrical analysis of the face and machine learning algorithms. They may pave the way for applications in the field of medicine.

 

Anger, sadness, happiness, surprise, fear, disgust. Six emotions which are represented in humans by universal facial expressions, regardless of our culture. This was proven in Paul Ekman’s work, published in the 60s and 70s. Fifty years on, scientists are using these results to automate the recognition of facial expressions in videos, using algorithms for analyzing shapes. This is what Mohamed Daoudi, a researcher at IMT Lille Douai, is doing, using computer vision.

We are developing digital tools which allow us to place characteristic points on the image of a face: in the corners of the lips, around the eyes, the nose, etc.” Mohamed Daoudi explains. This operation is carried out automatically, for each image of a video. Once this step is finished, the researcher has a dynamic model of the face in the form of points which change over time. The movements of these points, as well as their relative positions, give indications on the facial expressions. As each expression is characteristic, the way in which these points move over time corresponds to an expression.

The models created using points on the face are then processed by machine learning tools. “We train our algorithms on databases which allow them to learn the dynamics of the characteristic points of happiness or fear” Mohamed Daoudi explains. By comparing new measurements of faces with this database, the algorithm can classify a new video analysis of an expression into one of six categories.

This type of work is of interest to several industrial sectors. For instance, for observing customer satisfaction when purchasing a product. The FUI Magnum project has taken an interest in the project. By observing a customer’s face, we could detect whether or not his experience was an enjoyable one. In this case, it is not necessarily about recognizing a precise expression, but more about describing his state as either positive or negative, and to what extent. “Sometimes this is largely sufficient, we do not need to determine whether the person is sad or happy in this type of situation” highlights Mohamed Daoudi.

The IMT Lille Douai researcher highlights the advantages of such a technology in the medical field, for example: “in psychiatry, practitioners look at expressions to get an indication of the psychological state of a patient, particularly for depression.” By using a camera and a computer or smartphone to help analyze these facial expressions, the psychiatrist can make an objective evaluation of the medication administered to the patient. A rigorous study of the changes in their face may help to detect pain in some patients who have difficulty expressing it. This is the goal of work by PhD student Taleb Alashkar, whose thesis is funded by IMT’s Futur & Ruptures (future and disruptive innovation) program and supervised by Mohamed Daoudi and Boulbaba Ben Amor. “We have created an algorithm that can detect pain using 3D facial sequences” explains Mohamed Daoudi.

The researcher is careful not to present his research as emotional analysis. “We are working with recognition of facial expressions. Emotions are a step above this” he states. Although an expression relating to joy can be detected, we cannot conclude that the person is happy. For this to be possible, the algorithms would need to be able to say with certainty that the expression is not faked. Mohamed Daoudi explains that this remains a work in progress. The goal is indeed to introduce emotion into our machines, which will become increasingly intelligent.

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From 3D to 2D

To improve facial recognition in 2D videos, researchers incorporate algorithms used in 3D for detecting shape and movement. Therefore, to study faces in 2D videos more easily, Mohamed Daoudi is capitalizing on the results of the ANR project Face Analyser, conducted with Centrale Lyon and university figures in China. Sometimes the changes are so small they are difficult to classify. This therefore requires creating digital tools making it possible to amplify them. With colleagues at the University of Beihang, Mohamed Daoudi’s team has managed to amplify the subtle geometrical deformations of the face to be able to classify them better.[/box]

 

startup Footbar

IoT: How to find your market? Footbar’s story

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In the connected objects sector, the path to industrialization is rarely direct. Finding a market sometimes requires adapting the product, strategic repositioning, a little luck, or a combination of all three. Footbar is a striking example of how a startup can revise its original strategy to find customers while maintaining its initial vision. Sylvain Ract, one of the founders of the startup incubated at Télécom ParisTech, takes a look back at the story of his company.

 

Can you summarize the idea you had at the start of the Footbar project?

Sylvain Ract: My business partner and I wanted to make technology accessible to the entire soccer world. Professionals players have their statistics, but amateurs do not have much. The idea was to boost players’ enjoyment of the game by providing them with more information on their performance. My training in embedded systems at Télécom ParisTech was decisive in our choice to develop a connected object ourselves. This approach gave us more freedom than if we had started with an existing object, such as an activity tracker, and improved it with our own algorithms.

Where did you search for your first customers?

SR: When we started in 2015, we had a difficult time trying to sell our sensors to amateur clubs. The problem is, these organizations do not have much money. Outside of the professional level, clubs barely have the resources to purchase players’ jerseys and pay travel expenses. Another approach was to see the players as providing some of their own equipment; we could therefore directly target them as individuals. But mass-producing millions of sensors was too costly for a startup like ours.

How did you find your market?

SR: A little by chance. When we were just getting started we conducted a crowdfunding campaign. It was not successful because amateur players’ interest did not convert into financial contributions. This made us realize that the retail market was still immature. On the other hand, this campaign helped spread the word about our project. Later, the Foot à 5 Soccer Park network contacted us expressing interest in our sensors. The players who attend their centers are already used to an improved game experience since the matches are filmed. They were interested in going even further.

How did this meeting change things for you?

SR: The fact that Soccer Park films the players’ matches is a huge plus for us. This allowed us to create an enormous annotated database. We can also visually follow players who wear our device in their shin guards and clearly connect the facts observed during the game with the data from our devices’ accelerometers. We were therefore able to greatly improve our artificial intelligence algorithms. From a business perspective, we were able to expand our network to include other Foot à 5 centers in France and abroad, which gave us new perspectives.

What are your thoughts on this change of direction?

SR: Strangely enough, today we feel we are very much in line with our initial idea. Over the years we have changed our approach several times, whether from doubts or difficulties, but in the end, our current positioning is consistent with the idea of providing amateurs with this technology. We have a product that exists, customers who appreciate it and use it for enjoyment. What we are interested in is being involved in using digital technology to redefine how sports are experienced, in this case soccer. In the long-term, artificial intelligence will likely become increasingly prevalent in the competitive aspect, but the professional environment is not as big a market as one might think. Helping amateurs change the way they play is a challenge better suited to our startup.