AI Insights

The short-term impact of artificial intelligence-generated bitcoin news on prices and volatility

2025-02-19T08:57:22+00:00

This research is essentially directed at investigating the immediate effect of AI-generated Bitcoin news on price and volatility. This paper, therefore, attempts to answer the following question: How do AI-generated news events affect Bitcoin’s market behavior in terms of fluctuations in price and volatility? In this regard, the present study integrates event study methodology with volatility analysis to study the relationship between AI-driven news and Bitcoin market dynamics from April 2022 to October 2024. Data is collected at a daily frequency, enabling the construction of a high-resolution picture of how the market responds to such specific news events. The findings from preliminary estimations show that AI-generated news significantly influences the short-term price movement of Bitcoin, increasing its volatility immediately after news releases. The obtained results contribute to the knowledge of the emerging relevance of AI on financial markets and provide useful information to traders, investors, and policymakers focusing on Bitcoin and other similar cryptocurrencies.

SSN filtering method with pre-trained models for entity matching in data washing machine

2025-03-25T09:19:50+00:00

Entity Resolution (ER) is a vital process in data integration and quality improvement, aimed at identifying and linking records that refer to the same real-world entity. As data volumes and diversity grow, traditional ER methods face challenges such as scalability, poor data quality, and difficulties in handling sparse or inconsistent records. To address these limitations, this research introduces the Proof-of-Concept Data Washing Machine (DWM), developed under the National Science Foundation, Data Analytics that are Robust and Trusted (NSF DART) Data Life Cycle and Curation research theme, which automates the detection and correction of data quality errors through unsupervised entity resolution. The study focuses on advancing ER by replacing traditional rule-based approaches with machine learning (ML) and deep learning techniques, particularly for the linking process. Deep learning models like Bidirectional Encoder Representations from Transformers (BERT) and its variants are employed to enhance similarity scoring within Cluster ER methods. By integrating these models into the DWM framework, the research leverages attention mechanisms to generate reference embeddings and compute similarity score vectors. Additionally, it addresses optimization in candidate pair reduction during the ER blocking process to improve efficiency. A novel method for managing sensitive data, such as Social Security Numbers (SSNs), is proposed to streamline pair reduction in the linking stage. Comparative analysis between Linking_with_ML and SSN_Filtering_with_ML methods across diverse file types reveals that SSN_Filtering_with_ML achieves higher precision while maintaining a balanced trade-off between precision and recall. These findings highlight its robustness and accuracy in entity matching, significantly enhancing the DWM’s capacity for accurate record linkage while reducing unnecessary comparisons. This research contributes to advancing data quality practices, enabling better decision-making across organizations by providing scalable and efficient solutions for complex entity resolution challenges.

Resources management and execution mechanisms for thinking operating system

2025-04-01T05:26:57+00:00

To achieve interpretable machine intelligence surpassing human cognitive levels and realize the ultimate objective of co-evolutionary human-computer interactions, this article analyzed various related aspects such as the human-computer interaction process, knowledge base construction, visual programming tool development, and thinking operating system design. This article proposed a method for simulating human thinking processes by computer: Firstly, it clarified the route by starting from the “teaching and learning” mode, which was the human-computer interaction computing mode, enabling the gradual accumulation of knowledge and data, and established the thinking knowledge base. Secondly, it established human thinking simulation mechanisms on the thinking operation system, including state perception, common sense judgment, error rollback, static logic structure analysis for the programs, and dynamic execution path analysis. Thirdly, it discussed the computer implementation methods of the thinking operation system and applications in detail, using mechanisms such as autonomous enumeration and rule induction of input data features, common sense judgment rollback, automatic error self-healing, online self-programming, and system adaptation (generalized pattern matching); all the above mechanisms were commonly used in human thinking. Finally, it summarized the whole article, and the future research directions were proposed by the authors.

Path planning for unmanned naval surface vehicles

2025-04-02T07:31:19+00:00

There nowadays is a myriad of approaches to real-time avoidance of fixed obstacles for unmanned surface vehicles (USVs) and, to a lesser extent, also the task of avoiding moving obstacles such as boats, ships, swimmers, and other USVs, but both topics still present challenges. This paper offers novel approaches to both of these problems. It uses a combination of a global path planner, which finds a path from a start point to a goal point that avoids fixed obstacles (given that their locations are known in advance), and a local path planner, which can circumnavigate a moving obstacle (as well as any previously unknown fixed obstacles). The global planner is novel in that it employs a combination of three path planners, one known in the literature as Grassfire, one that is a new modification of Grassfire, and one that is a new, and arguably more intuitive, version of the well-known Probabilistic Roadmap. The local planner is novel in that it employs a higher-level decision logic based on its observations regarding the direction of movement of the obstacle relative to the USVs global path. This logic enables the USV to determine the best strategy for avoiding the obstacle by systematically routing the vehicle behind the obstacle rather than running parallel to it until the opportunity to pass appears. Simulations are provided that validate these claims. For comparison with other systems, the simulations include an implementation of the well-known D* algorithm, and the discussion covers additional dynamic path planning systems, which, like D*, do not necessarily route the vehicle behind the moving obstacle.