Related papers: PLUTUS Open Source -- Breaking Barriers in Algorit…
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users…
Autonomous trading robots have been studied in artificial intelligence area for quite some time. Many AI techniques have been tested for building autonomous agents able to trade financial assets. These initiatives include traditional neural…
Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running…
Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for…
The identity problem today is a data-sharing problem. Today the fixed attributes approach adopted by the consumer identity management industry provides only limited information about an individual, and therefore is of limited value to the…
The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to…
With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques…
The TELOS Collaboration is committed to producing and analysing lattice data reproducibly, and sharing its research openly. In this document, we set out the ways that we make this happen, where there is scope for improvement, and how we…
Trading data through blockchain platforms is hard to achieve \textit{fair exchange}. Reasons come from two folds: Firstly, guaranteeing fairness between sellers and consumers is a challenging task as the deception of any participating…
GitHub, a central hub for collaborative software development, has revolutionized the open-source software (OSS) ecosystem through its GitHub Marketplace, a platform launched in 2017 to host automation tools aimed at enhancing the efficiency…
Digital computational outputs are now ubiquitous in the research workflow and the way in which these data are stored and cataloged is becoming more standardized across fields of research. However, even with accessible data and code, the…
The consensus protocol is a critical component of distributed ledgers and blockchains. Achieving consensus over a decentralized network poses challenges to transaction finality and performance. Currently, the highest-performing consensus…
The rise of algorithmic pricing in online retail platforms has attracted significant interest in how autonomous software agents interact under competition. This article explores the potential emergence of algorithmic collusion -…
The use of standard platforms in the field of humanoid robotics can lower the entry barrier for new research groups, and accelerate research by the facilitation of code sharing. Numerous humanoid standard platforms exist in the lower size…
Algorithm Operating System (AlgOS) is an unopinionated, extensible, modular framework for algorithmic implementations. AlgOS offers numerous features: integration with Optuna for automated hyperparameter tuning; automated argument parsing…
Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making money. This work…
Our analysis of recent AI4H publications reveals that, despite a trend toward utilizing open datasets and sharing modeling code, 74% of AI4H papers still rely on private datasets or do not share their code. This is especially concerning in…
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions…
Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest…
The rapid adoption of open source machine learning (ML) datasets and models exposes today's AI applications to critical risks like data poisoning and supply chain attacks across the ML lifecycle. With growing regulatory pressure to address…