Informatica

One of the most recent innovations in the field of fuzzy sets has been continuous intuitionistic fuzzy sets (CINFUSs), where membership and non-membership degrees are defined by nonlinear functions, as a direct extension of intuitionistic fuzzy sets (IFSs). The membership and non-membership degrees of CINFUSs can account for uncertainty at every point since they are represented by continuous structures that change based on how the decision-maker responds to uncertainty. On the other hand, Pythagorean fuzzy sets (PFSs) allow for a more accurate representation of the data and a better way to handle uncertainty in decision issues by reflecting the hesitations of decision-makers over a larger range. Due to these superior advantages of CINFUSs and the fact that PFSs are more comprehensive than IFSs, in this study, continuous Pythagorean fuzzy sets (CPFUSs) have been aimed at introducing to define uncertainty more broadly and accurately by representing PFSs with a continuous structure as in IFSs. In this study, firstly, the basic principles and mathematical operators of CPFUSs have been developed and presented. Then, multi-attribute decision-making (MADM) models have been developed by considering different aggregation operators to indicate the feasibility and effectiveness of the continuous Pythagorean fuzzy (CPFU) extension. The developed CPFU-MADM models have been implemented to the solution of two different decision problems: green supplier selection and waste disposal site selection problems. In addition, sensitivity analyses have been conducted on criterion weights, expert weights and parameters in order to demonstrate the reliability and stability of the developed models. Furthermore, the validity and superiority of the developed models have been indicated by the comparative analysis conducted with IFSs and PFSs-based MADM models in the literature. MADM applications have shown that continuous Pythagorean fuzzy sets can successfully represent the expert decisions with different attitudes within the same model. It has been observed that the rankings of alternatives according to aggregation operators do not change even when there are differences in the score values of the alternatives.

The Autonomous Vehicle (AV) industry is constantly growing, thus analysing its perspectives is essential. However, for this analysis a sophisticated approach is necessary which considers the ambiguity of decision-makers, and different objectives and criteria related to stakeholders. In this paper a new model is proposed based on Decomposed Fuzzy Sets and the Best-Worst Method to deal with possible non-reciprocity of pairwise comparisons and different preferences of stakeholders in the AV industry. The main advantage of the model is that it is capable of considering optimistic and pessimistic attitudes along with the different objectives and criteria of the involved groups. The results show that users require short travel time, while operators, manufacturers and legislators expect mainly the increase of revenues from the AV implementation. Among the most important criteria, our analysis indicates the need of regulatory and safety issues are the most essential obstacles of expanding the AV industry. The new model can also be applied for evaluating the perspectives of other emerging technologies and industrial sectors.

Establishing secure keys over untrusted networks is one of the most fundamental cryptographic tasks. While two-party key establishment protocols are available for many scenarios, even offering resistance to potential adversaries equipped with quantum computing resources, the multi-party scenario is not as well understood. In particular, there is a need to find designs that can make the most of the technologies available to each party involved in a cooperative n-party key establishment.

This paper focuses on the aggregation or scoring methods to evaluate the alternatives in Multiple Attribute Decision Making problems (MADM), e.g. Weighted Sum Model (WSM) and Weighted Product Model (WPM). The paper deals with the incorporation of the two concepts into the scoring methods, which has not been studied yet. These concepts are decision maker’s Indifference Thresholds (IT) and Yearning Thresholds (YT) on the decision making criteria. Reviewing the related literature reveals that the existent scoring methods do not have a suitable structure to involve the IT, and there is no scoring method which addresses a way to take the YT into account. The paper shows that there is an important drawback to the famous Aspiration Level (AL) concept. Hence, the YT idea is given to resolve the AL limitation. Based on the IT and YT concepts, two new scoring methods are developed: Extended WPM (EWPM) and Extended WSM (EWSM). The EWPM and EWSM are compared with the other scoring methods using a set of simulation analysis. A real-world case extracted from Exploration and Production (E&P) companies in oil industry is examined.

For more than two decades, Lithuanian speech recognition has been researched solely in Lithuania due to the need for deep knowledge of Lithuanian. AI advancements now allow high-quality speech-to-text systems to be built without native knowledge, given sufficient annotated data is available. This study evaluated as many as 18 Lithuanian speech transcribers using a small piece of recording; 7 best ones were selected and evaluated using extensive data. The top system achieved a WER of 5.1% for Lithuanian words, with three others showing 8.7–9.2%. For other word-size tokens, such as numbers, speech disfluencies, abbreviations, foreign words, a classification adapted to the Lithuanian language was proposed. Different processing strategies for tokens of these classes were examined and it was assessed which transcribers tend to follow which strategies.

In this paper, we focus on the problem of whether efficient Lithuanian large language models (LLMs) can be achieved from Llama2 LLMs, which lack Lithuanian-specific components. Although the Llama2 architecture was previously successfully utilised to derive various regional LLMs, we propose and describe the first open Llama2 LLMs for the Lithuanian language (7 and 13 billion parameter versions), an accompanying question/answer (Q/A) dataset, and translations of popular language understanding benchmarks (Arc, Belebele, Hellaswag, MMLU, TruthfulQA, and Winogrande), which contribute to the standardisation of Lithuanian LLM evaluation. We empirically evaluate the proposed models by investigating their perplexity and performance in the translated language understanding benchmarks. The perplexity experiments show that it decreases consistently during pretraining, reflecting enhanced next-token prediction capabilities. Benchmarking the proposed LLMs against language understanding tasks reveals that high-quality pretraining datasets may be essential to achieve models that perform efficiently on these benchmarks. Comparison of the proposed LLMs with the latest open multilingual LLM shows that our model with 13 billion parameters is ranked 4th of 8 models in tasks such as Arc, Hellaswag, and Winogrande, but is generally outperformed in other tasks. These benchmarks allow us to hypothesise that from recent LLMs more efficient Lithuanian language models can be derived in the future. The complete realisations of the LLMs and other contributed components are available in the accompanying open repository https://huggingface.co/neurotechnology.

Electricity demand estimation is vital for the optimal design and operation of microgrids, especially in isolated, unelectrified, or partially electrified areas where demand patterns evolve with electricity adoption. This study proposes a causal model for electricity demand estimation that explicitly considers the electrification process along with key factors such as hour, month, weekday/weekend distinction, temperature, and humidity, effectively capturing both temporal and environmental demand patterns.

A public key signcryption with equality test (PKSCET) scheme is a public key signcryption (PKSC) scheme with the property of equality test. However, all the existing PKSCET schemes are vulnerable to a new kind of security threats, called side-channel attacks, which could potentially lead to the unauthorized disclosure of sensitive information or even the compromise of secret keys, undermining the overall confidentiality and integrity of the system. Therefore, this study aims to propose the first leakage-resilient PKSCET (LR-PKSCET) scheme that achieves resistance to side-channel attacks. Moreover, the proposed LR-PKSCET scheme is demonstrated to possess four security properties, namely, leakage resilience, indistinguishability, one-wayness, and existential unforgeability. Based on the proposed LR-PKSCET scheme, an anti-scam system (application) is presented to mitigate the ongoing occurrence of a myriad of scam cases.

Smart Farming (SF) has garnered interest from computer science researchers for its potential to address challenges in Smart Farming and Precision Agriculture (PA). This systematic review explores the application of Fuzzy Logic (FL) in these areas. Using a specific anonymous search method across five scientific web indexing databases, we identified relevant scholarly articles published from 2017 to 2024, assessed through the PRISMA methodology. Out of 830 selected papers, the review revealed four gaps in using FL to manage imprecise data in Smart Farming. This review provides valuable insights into FL for potential applications and areas needing further investigation in SF.