1,720,983 research outputs found
“Textclass - Rel. 1.0"
No full textSistema informatico per analisi contenutistiche e catalogazione di testi scritt
Evaluation of strategies for the iterated prisoner's dilemma
No full textThe study aims to assess the effectiveness and adaptability of both classical and contemporary strategies.
It further explores the implications of strategic diversity and complexity on cooperation and stability within IPD tournaments.
Methods: Strategy tournaments were conducted with an extensive pool of participants, including main Axelrod? original strategies (Tit for Tat, Always Defect, etc.) and modern strategies.
Simulations were performed using custom-built software tested.
The study examined tournaments where strategies remain fixed throughout, by varying the set of participants and using different payoff matrices.
Matches were played with both fixed and variable durations, and in this case the average enrichment for each strategy was considered.
Results: The analysis revealed that Tit for Tat is not the sole dominant strategy. Different levels of strategic complexity and cooperation/defection often led to different winners, especially in environments with a strong Cooperation or Defection connotation. The impact of payoff structures was significant, with even small adjustments shifting the balance between cooperation and defection.
Conclusion: The findings offer an updated perspective on the IPD, highlighting the evolving interplay between strategic design and environmental factors. The results have broader implications for policy-making, algorithm design, and the study of cooperative dynamics across economic, biological, and artificial systems.
Future Directions: Future research could explore strategies developed through machine learning techniques and hybrid approaches combining memory and stochastic elements.
Additional studies could simulate diverse environments by systematically varying payoff matrices and incorporating performance metrics such as robustness and adaptability.
Furthermore, investigating strategies capable of modifying their behavior dynamically within matches would provide deeper insights into adaptive cooperation
Text Mining with Finite State Automata via Compound Words Ontologies
No full textThe paper introduces an efficient text mining method using finite automata to extract knowledge domains from textual documents. It focuses on identifying multi-word units within terminological ontologies.
Unlike simple words, multi-word units (credit card, for example) possess a monosemic nature and are relatively few and diverse from each other, precisely pinpointing a semantic area.
The algorithm, designed to handle challenges posed by even very long multi-word units composed of a variable number of simple words, integrates selected ontologies into a single finite automaton.
At runtime, it efficiently recognizes and outputs the knowledge domain associated with each multi-word unit, even when they partially or completely overlap.
Benefits of the system include minimal IT maintenance for ontologies, continuous updates without additional computational costs, and no need for software training.
The proposed approach demonstrates robust performance on both short and long documents, validated through tests on multiple textual documents, with a specific test outlined in the paper
Linguistic Text Mining
No full textThis paper explores the challenges of processing the increasing volume of natural language text, which often surpasses traditional methods' real-time processing abilities.
These texts are typically authored by individuals from diverse educational, cultural, and experiential backgrounds.
The paper highlights the main linguistic and semantic issues that arise in the analysis of natural language text.
Linguistic Text Mining is a computational approach that combines linguistic principles with computational techniques to extract high-quality information from natural language texts.
Despite the frequent mentions of ``Linguistic'' and ``Text-Mining'' in scientific literature, no formal definition exists; this paper proposes one. It further explores LTM’s potential in enhancing knowledge extraction by emphasizing linguistic features, such as multi-word units (MWUs).
Traditional text analysis relies heavily on statistical methods, focusing on simple words, which are often polysemous, or on aggregating words without semantic context and this limits systems' ability to interpret domain-specific semantics.
By contrast, MWUs, like ``credit card'', convey specific, unambiguous meanings, critical for identifying specialized domains.
MWUs are typically organized within ontologies that represent distinct knowledge domains.
Building on previous work, the study compares AUTOMETA, an ontology-based approach using finite automata for MWU identification, with large language model (LLM)-based and other ontology-driven Linguistic Text Mining methods.
Findings suggest that integrating linguistic frameworks significantly improves information extraction, offering a deeper understanding of complex language structures
Finite State Automata on Multi-Word Units for Efficient Text-Mining
No full textText mining is crucial for analyzing unstructured and semi-structured textual documents. This paper introduces a fast and precise text mining method based on a finite automaton to extract knowledge domains. Unlike simple words, multi-word units (such as credit card) are emphasized for their efficiency in identifying specific semantic areas due to their predominantly monosemic nature, their limited number and their distinctiveness. The method focuses on identifying multi-word units within terminological ontologies, where each multi-word unit is associated with a sub-domain of ontology knowledge. The algorithm, designed to handle the challenges posed by very long multi-word units composed of a variable number of simple words, integrates user-selected ontologies into a single finite automaton during a fast pre-processing step. At runtime, the automaton reads input text character by character, efficiently locating multi-word units even if they overlap. This approach is efficient for both short and long documents, requiring no prior training. Ontologies can be updated without additional computational costs. An early system prototype, tested on 100 short and medium-length documents, recognized the knowledge domains for the vast majority of texts (over 90%) analyzed. The authors suggest that this method could be a valuable semantic-based knowledge domain extraction technique in unstructured documents
La sorveglianza globale e le tecnologie dell’informazione e della comunicazione
No full textSi affronta qui l’analisi dell’impatto delle nuove tecnologie alla pratica della violazione della privacy. La società nella quale viviamo accumula informazioni sulle persone e ne traccia profili (classificazione) e segue, controlla, registra ogni azione se svolta in spazi aperti al pubblico (sorveglianza).
Nell’articolo viene innanzitutto descritto il più vasto sistema di spionaggio mai realizzato su scala planetaria, il sistema Echelon, e poi vengono descritti altri sistemi in grado di attentare alla privacy. Successivamente, vengono trattati alcuni metodi che permettono di limitare le intrusioni alla privacy. Nelle conclusioni vengono riportate alcune considerazioni su come e quanto i sistemi di sorveglianza globale contrastino con i diritti fondamentali dell’uomo, così come affermati dall’Unione Europea
Predictive Maintenance with Linguistic Text Mining
No full textThe escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime.
In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy.
The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance.
This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings.
The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution.
The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience.
Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine.
This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency
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