Author: mguigon

Expert Group “Blockchain Technology in Interorganisational Collaboration” meeting 29.04.21

The 12th meeting of the expert group “Blockchain Technology in Interorganisational Collaboration” took place over lunch on the 29th of April.

First, the members were informed about the opportunities of the innovation process of the databooster. The innovation process gives the benefit of exploring and testing ideas together with experts in the field. Moreover, one can get support for project funding. The iterative process involves different steps such as scouting for ideas, setting up a call, shaping and re-shaping the challenge and ultimately setting up a deep-dive workshop (https://databooster.ch/expertise/).

After these introductory remarks, the expert group hosted Daniel Rutishauser from inacta AG to give an overview on the new DLT law and future trends in the blockchain sector. Daniel presented inacta’s hypotheses to four key areas of the blockchain space: crypto assets, token economies, DLT solutions, and DLT base layer. In particular in the area of crypto assets, Daniel explained that Switzerland has a competitive advantage due to the new favorable DLT law and he expects that many of the future crypto assets will be issued and traded in Switzerland. Besides many other topics, the current hype around NFTs (non-fungible tokens) and its effect on the digital art market gave rise to much discussion among the experts. The members could not agree on the fundamentals for the immense prices that this new market has realised. Considering the number of open questions, NFTs might be a topic that deserves a meeting for itself.

The online meeting was concluded without an apéro, but with many new insights on the blockchain sector gained.

Ask the Experts

New structure, new logo, new concept: the expert group “Machine Learning Clinic” is a unique pool of expert knowledge. Our last meeting aimed to connect experts willing to share their knowledge with companies in need of expertise to push AI-projects forward. Despite the hype around AI and deep learning of the last years, only a few deployed solutions are running in industry. Why is this? What are the missing bricks? One of the missions of the ML-Clinic is to overcome this gap between lab and real-world applications.

During registration we identified needs and experts on the following hot topics:

  • Data Management
  • Vision Inspection
  • Cloud Integration
  • Hardware / Edge-Processing

During a 90min virtual meeting we connected people, exchanged experience, and brainstormed about new ideas. With the new open-innovation initiative www.databooster.ch and the support from Innosuisse there are many possibilities to support companies on their ML-journey.

In a familiar round we discussed about real cases from Roche, Sulzer, SBB and others. One common issue is data quality, availability and working with rare scenarios. How to deal with missing, wrong or corrupted data. How to train robust neural networks based on such datasets. There is no easy solution but there are more and more ideas how to deal with these common industrial issues.

Beside the deep technology discussions another highlight was the “non-virtual apéro package” which all participants received before the event. Even though we only communicated through Bytes over a glass fibre everyone had a real chilled beer and some nuts in their hands – what beer would be better to stimulate the real neurons than the AI beer: DEEPER

Overall a successful event and we hope you tune in for the next get together of the ML-Clinic!

First Use-Case Talk of 2021

The first Use-Case Talk of the year the took place online on the 15th of March 2021. Industry, academic and individual members of the data innovation alliance came together to discuss data-driven innovation, confidential computing, and natural language processing for analytics.

The first speaker,  Lucas  Fiévet, Co-Founder and CEO of LogicFlow AG, presented an overview of  how machine learning will ease  the challenges of software test  maintenance, testing non-functional requirements and test diagnosis. The talk deep-dived into a use-case of auto encoders for anomaly detection in web application screenshots.

Our second speaker, Grégoire Devauchelle, Data Scientist at Elca Informatik AG, told us how ELCA developed an information extraction engine for legal documents using Azure services and a custom application. The work also focused on finding the right balance between process automation and error rate of the model.

These two interesting Use-Case talks sparked a lively and interesting discussion. In the Q&A session we exchanged ideas, challenges and information among the industry and academic experts.

This was the first online Use-Case Talk this year , however, we remain hopeful that the next ones will be take place  in person at Aspaara’s venue in Technopark Zurich .

The Use-Case Talks are part of a series taking place three times a year. If you are interested in sharing your AI stories and discussing them within the community,  you are warmly welcome to join us for our next Use-Case Talks taking place on 14th June 2021. If you are interested in presenting a Use-Case, please contact us by e-mail (nadine.furrer@aspaara.com).

About the Use-Case Talks

The Use-Case Talk Series allows participants to enjoy in-depth technical discussions and exchange information about interesting technical challenges amongst experts. The Use-Case Talk Series are organized by Aspaara Algorithmic Solutions AG on behalf of data innovation  alliance.

We look forward to seeing you soon!

Interview with Securaxis

Who is Securaxis is and what do you do ?

We are a start-up based in Geneva, Switzerland. We turn sounds into data, enabling cities to monitor urban noise in order to improve many aspects of city life. We combine Artificial Intelligence and the Internet of Things to hear our urban existence – this adds a missing layer to the smart city concept.

Glen Meleder

What is Securaxis background story?

Glen Meleder and Gaetan Vannay are the Co-founders of Securaxis. We both have experience working in difficult and sometimes unsafe environments. Glen Meleder is an IT engineer by training. He has worked in duty stations for an important international organization with focus on conflict resolution. Gaetan Vannay has worked extensively as a war correspondent. This is how we met. Our first project was to develop a tool to manage security and security information in an operational framework through an app on a web platform. This tool is currently used by international organizations, governments and private companies all around the world.

Since then, Securaxis has evolved into something very different.

The idea of combining AI and acoustic analysis (“to turn sound into information”) came out of a hackathon organized in 2018 by the CERN (European Center for Nuclear Research.) This made immediate sense to us. In many contexts of armed conflict you may hear a threat before you see it. Sound is very useful information!

As humans, we are used to understanding and transforming sound to guide our actions. At Securaxis we aim to transfer this ability to smart cities; hearing will add the missing layer to smart solutions. Initially, we focused on safety and security. However, discussing with authorities in different cities, we understood that the immediate traction of this concept is in the domain of road traffic monitoring; this enables traffic management, dynamic smart lighting, predictive road maintenance and real-time monitoring of the level of traffic noise.

Securaxis is also active in biodiversity monitoring. Scientists have shown that sound is a reliable indicator for monitoring ecosystem biodiversity. It provides information about species, habits, quality of life and habitat conditions. It can also help to determine the many and varied interactions between wildlife, human habitats and human built infrastructures. This solution can fulfil an important part of studies pertaining to the environmental impact of major construction projects. It is a surprise to us how far we have come from issues relating to people’s safety and security. But we are comfortable with this surprise!

Gaetan Vannay

Why are the projects important?

Road traffic causes 80% of noise pollution in cities. By 2050, 68% of the world population will live in cities or suburbs and road traffic will increase by more than 40%. It is well documented that exposure to excessive noise has an impact on people’s health. New tools to monitor and better understand noise in a city are needed urgently. There are also implications for privacy. People are wary of cameras that are currently the main solution for monitoring of traffic. With our system, the sounds never leave the street. There is no recording. Only specific sounds are detected and these sounds are processed at sensor level; only metadata are sent.

Who can profit from your services?

Our clients are OEMs (Original equipment manufacturers) and system integrators active in smart cities. At the end of the day, the people living in cities will profit from our approach. Cities can monitor and improve their urban environments whilst accommodating people’s concerns about privacy and sustainability. 

Can you give some further examples of your success stories?

We have initiated projects all over Europe from Finland to Portugal. Because of Covid-19 we had to delay some deployments of sensors. So far, we already have installations running in Switzerland, France and the United Kingdom. We will also soon have installations in Luxembourg. These projects are going well. What we could call a success story is that the idea of monitoring road traffic and noise traffic by a combination of acoustic sensors and AI is well understood and validated as an accurate and very cost-effective solution.

How do your customers find you?

Before Covid-19 we were mainly present at European fares and congresses. Today we participate in virtual events, but we increased our footprint in these. We sponsor the event and/or organize virtual workshops and panels. We reallocated budget for travel and accommodation to upgrade our participation in virtual fairs, exhibitions and congresses as a mere presence with a stand is not enough to attract prospects in this virtual world. Our potential customers find us at these events organized around “Mobility” or “Smart Cities”. Of course, we have our website (www.securaxis.com) and we work the phone. The good news: today clients are coming to us before we reach out to them. 

What are your biggest challenges?

Initially, we found that people’s thoughts about noise is first about recording or measuring decibels. We had to explain that what we are offering is very different. We had to show that cities can listen to themselves and that the technology that does this really works. The challenge today is to show that we comply GDPR (General Data Protection Regulation) and that our solution fully respects privacy. Our direct customers – OEMs and system integrators – understand our technology well but City authorities, policy makers, lobbyists etc. often need further clarification. Notably, in France we went through the process of having our solution validated by the CNIL (Commission nationale de l’informatique et des libertés), the official national body in charge of protecting personal data and preserving individual liberties.

How do you see the future of Securaxis and what is your long-term goal?

Our long-term goal is to become a reference for recognition and monitoring of sounds; especially for smart cities.

The Kistler Innovation Lab as a powerful Digitization Booster

On February 4, 2021, the expert group Smart Services got together online for another successful event in the series “Service Lunch”. Dr. Nikola Pascher from Kistler Instrumente AG presented the the Kistler Innovation Lab as a powerful Digitization Booster. The talk was accompanied by lively discussions among the more than 50 participants. Please find a summary of the talk here:

Kistler is the global leader for providing modular solutions in dynamic measurement technology for pressure, force, torque and acceleration measurements. The company looks back on a continuously growing business, selling hardware and system solutions in various markets. Headquartered in Winterthur, Switzerland, and with various locations worldwide, Kistler’s next step is a digital transformation to maintain steady growth within the digital age. This involves the creation of the Kistler Innovation Lab as a powerful digitization booster.


The Innovation Lab follows the general vision “Turning data into value”. This means, that we build on the vast amounts of data created with Kistler’s sensor technology and create value by using digital methods, rooted in data science, mathematics and signal processing. Digital initiatives are pursued in a protected framework at a higher speed than possible in the general corporate context. To accomplish this, the Innovation Lab stands on three pillars: With the co-creation platform, we connect different fields of expertise, share knowledge and data and provide digital know-how. The digital technology incubator is a professional framework for quick experiments and ideas with the ultimate goal to pursue proof of concept projects for digital services and solutions based on Kistler sensor data. With the digital training center, we want to empower the Kistler team and our partners to identify digital business opportunities.


In the first part of this talk, we report on the general digital transformation mechanism at Kistler with a focus on the ramp-up of the Innovation Lab within the corporate context. Despite the challenges, which are imposed by the Covid-19 pandemic, the Innovation Lab turned out to be a powerful tool, delivering first proof of Kistler’s data-based capabilities and strengthening the credibility towards our team, customers and partners.


In the second part of the talk, we focus on technical aspects of data-based services and solutions. All initiatives build on a powerful and scalable technology stack, which allows the quick set-up and deployment of cloud-based APIs. We report on first projects within the co-creation platform and the digital technology incubator. These projects aim at the fast creation of data-based services and solutions. In a co-creation project with our in-house sensor production, we aimed at optimizing a metal machining process inside a turning lathe. Together with the Kistler-internal machine shop, we made an important step towards a predictive maintenance and quality forecasting service. In a second project, we analyzed data from our weigh in motion (WIM) systems and realized, that roughly 30% of all trucks are driving empty. With the help of a machine learning model, we can forecast the flows of empty and full trucks with high accuracy.

Interview with Kitro SA

Can you shortly tell us what Kitro does and who you are?

Kitro aims to reduce food waste in the hospitality industry. We do this by analysing the food that’s being thrown away in large kitchens. Our solution is a fully automated IoT devise that consists of two parts, it’s a hardware with a scale and a camera. The scale automatically detects when something is added to the bin and this triggers the camera. The captured image is uploaded to the cloud where it is analysed. The results of the analysis are uploaded to an online dashboard where our customers have access to it 24/7. Based on this, our customers can decide how to reduce their waste. Our customer service can also help to understand the dashboard and the data in order to make better decisions. 

We are currently developing our “best practices” page to serve as an inspiration for our customers. The idea of the “best practices” page is to have a collection of options on ways to reduce food waste.

What is Kitro’s background story?

The company was founded by Naomi MacKenzie and Anastasia Hofmann. They studied at the École hôtelière de Lausanne. During their education they gained work experience in kitchens and services, where they saw how much food was thrown away all the time. They wanted to tackle this issue and came up with the idea for Kitro. Kitro was founded in 2017.  As neither Naomi and Anastasia had a tech background, they got out outside talent to develop the product and are themselves managing the business side.

Why is it important that Kitro exists?

Globally, along the supply chain, one third of all food is wasted. In the food and beverage industry, two thirds of all the food that is thrown away is a waste that could be avoided; it’s still edible but is still being disposed. This has a large environmental impact and is also a big cost for the restaurants. With our product we hope to help restaurants reduce food waste and consequently reduce the environmental impact and save money.

Who can profit from your product?

We mainly work with larger schools, hospitals and canteens. We also work with restaurants and hotels, but they have to be of a certain size for it to be advantageous for them to use our product. We offer a subscription and the idea is that they save more than they invest.

For now, our product is not useful for individual persons who want to reduce their food waste – but that would be a cool idea! 

Can you give some examples of your success stories?

From a tech perspective we have built a huge data set with all the data that we collected. We were able to train machine learning models based on this data that helped with the analysis and made the process more efficient and reduced costs.

We have also had customers from really early on in the project; the product has already been tested with customers for three years.

A big milestone is also that we grew the team to 12 people.

How do the customers find you?

In the beginning we took part in many competitions, such as start-up competitions, which gave us visibility. This helped the customers find us. We also went to conferences. Now we have also started to approach potential customers through cold calls and the like.

What are your biggest challenges?

From a tech perspective it’s challenging to automate the processes. We want to basically reduce or remove the human from the labelling in order to reduce costs and make the company profitable.

It has also been a challenge to create a clean data set that can be used for machine learning. This is still an ongoing process – it takes a lot of work. These are the biggest challenges.

How do you see the future of Kitro and what is your long-term goal?

The data that we collect could be interesting also for the government, not only for restaurants and food services. It could be interesting for them to get a better picture of the amount and kind of food that is thrown away, and to set up some kind of rules or guidelines in the future.

Food waste is also a problem in retail and on farms, not only in commercial kitchens and that is an area where we could make an impact.

In the future we also want to support our customers more with the decision making – with our “best practices” website and with predictive measures – based on the data collected. A first version is already online, but currently it is only visible to our customers on their dashboard.

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