Project Funding Agency: Republic of Cyprus

KeepA(n)I: A Methodological Approach for Identifying Social Stereotypes in Artificial Intelligence Applications

KeepA(n)I (pronounced “Keep an eye and/or Keep AI ”) aspires to play a key role in aiding towards achieving Trustworthy AI, promoting a novel solution that will lay the groundwork for detecting bias in AI applications. The project considers high-risk applications of AI and aims at creating an active approach in detecting social stereotypes in the input and output of the AI application, as to inform the developer of the social behavior of the developed application. Consortium partners will perform state-of-the-art research, positioning them at the forefront of the area of FAIR / Trustworthy AI, and addressing the challenges brought about by the “democratization of AI” within Europe.

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Utilizing Efficient Reads for ATomic Objects in 3D Networked Virtual Environments

The advent of fast highly-available network connectivity in combination with affordable 3D hardware (GPUs, VR/AR HMDs, etc.) has enabled making Networked Virtual Environments (NVEs) possible and available to multiple simultaneous end-users beyond the confines of expensive purpose-built 3D facilities and laboratories. However, the algorithms making possible the NVEs of today are already reaching their limits, proving unreliable, suffer asynchronies and deployed over an inherently fault-prone network infrastructure. Thus, new scalable, robust, and responsive strategies that build on top of unreliable, asynchronous, and fault-prone network infrastructure, must be devised in order to support the needs of the NVEs of the future.
Current developments of distributed architectures handle concurrency by either providing weak consistency guarantees (e.g. eventual consistency), or by relying on the bounded life span of inconsistent states. However, recent scientific works are shifting the viewpoint around the practicality of strongly consistent distributed storage spaces by proposing latency-efficient algorithms of atomic read/write Distributed Shared Memory (DSM) with provable consistency guarantees.

Our approach is to combine a DSM implementation of ERATO with a hybrid approach leveraging the simplicity of the client-server architecture. This, allows us to take the best features of the Multi-Server architecture and combine them with a low-level Distributed Shared Memory algorithm offering (A) ease of integration with existing VEs and implementation of new ones, since the DSM promises to take care of synchronization and consistency across servers, without application-specific interventions at client nodes and no special hand-off mechanisms between servers for users joining and leaving; and (B) reduced system complexity, and in turn faster development time and lower deployment and maintenance costs.

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MARI-Sense

MARI-Sense employs smart management, spatial planning, and agile response measures to help preserve the natural environment, securing society, and ensuring economic growth. Its cognitive systems work in synergy with human operators to “make sense” of the maritime environment

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Collaborate

Distributed Storage Systems (DSS) encompass the technology powering modern cloud data storage services such as DropBox and Google Drive that are used by millions of users as networked platforms for collaborative applications and data storage. Algorithms for DSS ensure data availability and survivability by replicating data in geographically dispersed network locations. However, a major problem with data distribution is consistency, especially when the storage is accessed concurrently by multiple processes; a key to enabling collaboration. Numerous strategies have been devised to mitigate these issues, however a robust and efficient solution remains elusive.
Collaborate (https://projects.algolysis.com/collaborate/), proposes a novel atomic Distributed Storage System built on top of asynchronous message-passing, failure-prone, commodity devices, and providing tight consistency guarantees when the storage is accessed concurrnelty by different processes. Atomicity enables the most natural consistency guarantee as it provides the illusion of a centralised sequentially accessed storage. To enhance the practicality of our atomic DSS, Collaborate will develop and combine the following services: (i) Fragmentation, (ii) Reconfiguration, and (iii) Failure Prediction.

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DriveNest

Storage devices, such as hard disk and solid state drives, are getting smaller and faster, but are still prone to faults, often resulting in data loss and costly downtime of important technical infrastructure. Despite recent technological advances in hardware design and manufacturing, it is still unclear what the exact causes of those failures are and how to detect an imminent failure; enabling proactive mitigation, such as data replication and backup. Moreover, the storage retail market is flooded with devices of dubious quality, and often times performance and robustness specifications are exaggerated by their manufacturers, hindering the ability of the end-users to make an informed choice of storage devices.
DriveNest (www.drivenest.com) is a crowd-monitoring platform of storage devices which are distributed anywhere around the globe. The objective is to collect reliability indicators from devices deployed ‘in-the-wild’ (not in controlled data center environments), and provide users with statistics and failure forecasting based on predictive analytics. Statistics across a global population of storage devices will enable end-users to purchase storage devices based on hard data, while failure prediction will assist end-users, system administrators, and backup software designers to proactively act upon imminent hardware failures.

The project is under constant development and thus constructive feedback is important to us

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