In line with the objective of the PRE-SEED call, the proposed project - A Corrective Biofeedback Mirror for Musicians, hereafter REFORM - aims to fund the establishment and first steps of BioSense, a new digital health startup. The focus of REFORM is the commercialisation of an innovative biofeedback mirror system for preventing musculoskeletal playing-related disorders in musicians. The proposed project builds upon a funded Marie Sklodowska-Curie (MSCA) project, through which a prototype system was developed toward this goal, albeit using high-end hardware. REFORM will transform the existing prototype into a commercial product that runs on affordable custom-made hardware, targeting the consumer market. The project capitalizes on the unique combination of skills and experience of the PI as both a Musician and a Neuroscientist, and the technical and business expertise of the rest of the team. In line with the scope of the call, REFORM proposes a business idea with a high degree of novelty and prospects of rapid growth at the international level. REFORM is aligned with the Health priority as presented in the S3Cy 2023-2023 document on smart specialization sectors in Cyprus. Specifically, it matches well with sector 7.2., which covers the development of digital health tools to improve health services, use of health data, and decision making. Within this sector, REFORM matches best with focus area 7.2.1 that refers to the development of products and services for, among others, health management and simulations.

Cyprus, as well as many other European counties, face significant environmental risks due to the accumulation of dumping in areas not designated for dumping disposal. This illegal practice has an impact also on tourism, which is one of the top-three sources of income for the Cypriot economy. The general aim of DumpMapper is to develop a reliable service which detects and identifies illegal dumping activities from satellite photos on a weekly basis, by using artificial intelligence and computer vision, helping cleaning agencies, municipalities and local communities to spot and clean the areas affected. The general objectives of the project are: a) Detect illegal dumping accurately and as fast as possible, within few days from the occurrence of the activity; b) Prepare a reliable and correct service, offering online services to clients (e.g. municipalities, communities, cleaning services) to observe those dumping locations and monitor cleaning operations; and c) Alleviate the environmental impact of illegal dumping, by understanding where illegal dumping occurs and when, taking cleaning measures and monitoring the measures taken, fighting dumping before it becomes a source of pollution and contamination. To our knowledge, there exist only few relevant services around the world, mainly outside European Union. DumpMapper is an original and novel solution to reduce pollution and contamination, facilitating more informed decision making much faster than with current practices. The DumpMapper service constitutes the product of research and development that involves novel concepts, approaches and methods. The DumpMapper solution aspires to improve the quality of life of humans, by reducing environmental risks (contamination of water and soils, bad smell) and health-related hazards (e.g. food safety issues, dissemination of diseases via rodents) etc. The project’s outcomes will stimulate economic growth, promoting circular economy and waste reuse.

PoultryFI aims to advance the proof-of-concept technology for poultry farm monitoring previously developed by Algolysis Ltd (HO) by leveraging the Artificial Intelligence and Machine Learning expertise of CYENS (Partner) in AgriTech to devise a next-generation affordable farm intelligence platform, composed of a combination of hardware and software. The predictive power of AI/ML will be strategically utilised to provide farmers with tools to better understand how environmental conditions in their farms are, as well as how their management of resources and everyday actions and decisions affect production quality, productivity and animal welfare. A key innovation this platform will bring about is the ability of governmental authorities to monitor, control and manage this agricultural sector more easily. This would result in a multitude of benefits for the government, the producers, the animals and consumers.

3DMotaaS aims to advance proof of concept technology for 3D motion search and retrieval through a strategic collaboration between Algolysis Ltd (SME/Host) and the CYENS - Centre of Excellence (RO/Partner). By leveraging the two Partners’ complementary expertise, know-how and Intellectual Property, the consortium aims to develop a 3D motion data search engine, which will in turn power a novel state-of-the-art Motion-as-a-Service prototype that will be validated in a relevant environment (TRL5). Algolysis Ltd brings into the project its proof-of-concept motion search and retrieval technology (TRL3) and know-how, while CYENS invests in applying its expertise in Machine Learning and makes available its significant Motion Capture infrastructure that is necessary for the success of this endeavour. One of the key features of this novel search engine is the ability to query-by-example, i.e. to search within a corpus of motion data using a motion sample, instead of keywords. To unlock the potential of this technology and benefit a multitude of sectors the motion similarity engine can be used programmatically by third party developers/applications, making it possible to embed motion search and retrieval in software targeted for entertainment (game development, film production), sports, health, robotics, biomechanics, etc.

Given the potential of algorithmic systems to influence the social world, by amplifying or abating bias and potential discrimination, KeepA(n)I develops a structured, methodological approach to aid developers and machine learning practitioners to detect social bias at the input datasets and output data of the application. In contrast to existing methods posed by the Fair ML community, which evaluate group and individual fairness in datasets and algorithmic results, in an attempt to reduce/mitigate the effect of bias, KeepA(n)I takes a different approach. The project focuses on the expression of social stereotypes (e.g., based on gender, race or socio-economic status) and how those are reflected in biases shared by groups of people interacting in different ways with the system. KeepA(n)I is envisioned as a human-in-the-loop approach, methodically exposing social stereotypes and reducing the negative impact or even enhancing people’s access to opportunities and resources when interacting with both high and low risk AI applications. By engaging humans in the evaluation process (i.e., through crowdsourcing), KeepA(n)I will achieve a diverse (e.g., across cultures) and dynamic (e.g., across contexts and time) evaluation of social norms, according to the objective of the evaluated application. The project will focus on computer vision applications that analyse people-related media (e.g., image content analysis or “tagging,” gender or age recognition from a profile photo) with significant implications for high-risk applications (e.g., screening job applicant profiles or dating applications).