Large language models (LLMs) are increasingly used to generate self-explanations alongside their predictions, a practice that raises concerns about the faithfulness of these explanations, especially in low-resource languages. This study evaluates the faithfulness of LLM-generated explanations in the context of emotion classification in Persian, a low-resource language, by comparing the influential words identified by the model against those identified by human annotators. We assess faithfulness using confidence scores derived from token-level log-probabilities. Two prompting strategies, differing in the order of explanation and prediction (Predict-then-Explain and Explain-then-Predict), are tested for their impact on explanation faithfulness. Our results reveal that while LLMs achieve strong classification performance, their generated explanations often diverge from faithful reasoning, showing greater agreement with each other than with human judgments. These results highlight the limitations of current explanation methods and metrics, emphasizing the need for more robust approaches to ensure LLM reliability in multilingual and low-resource contexts.

The Bitcoin Lightning Network, as a second-layer solution for enhancing the scalability of Bitcoin transactions, facilitates transactions through payment channels between nodes. However, the rapid growth of the network and rising transaction volumes have exacerbated the challenge of managing payment channel imbalances. Payment channel imbalance, characterized by the concentration of liquidity in one direction, leads to a decrease in payment success rates, a reduction in the effective lifespan of payment channels, and a decline in the network’s overall efficiency and throughput. This study introduces a graph neural network-based recommendation strategy designed to enhance the Lightning Network’s autopilot system. The proposed approach proactively mitigates channel imbalances by optimizing channel recommendations, enabling dynamic and scalable liquidity management for network users. Simulations conducted using the CLoTH tool demonstrate a 45% increase in payment success rates, a 46% reduction in imbalanced channels, and a 14% increase in the lifespan of payment channels across the network compared to the existing autopilot recommendation strategies, and when compared with the commonly adopted circular rebalancing method, the proposed strategy achieves a 27% improvement in payment success rates. Additionally, we offer a comparative topological analysis between two snapshots of the LN, taken in November 2021 and August 2023, to facilitate unsupervised learning tasks. The results highlight an increase in network centralization alongside a decrease in the number of network size, emphasizing the growing need for decentralization strategies in the LN, such as the approach proposed in this study.

In recent years, the increase in cyber threats has raised many concerns about security and privacy in the digital world. However, new attack methods are often limited to a few core techniques. Here, in order to detect new threat patterns, the authors use an attack graph structure to model unprecedented network traffic. This graph for the unknown attack is matched to a pre‐known threat database, which contains attack graphs related to each known threat. The main challenge is to associate unknown traffics to a family of known threats. For this, the authors utilise random walks and pattern theorem. The authors utilise the pattern theorem and apply it to a set of proposed algorithms for detecting new generations of malicious traffics. Under the assumption of having a proper threat database, the authors argue that for each unknown threat, which belongs to a family of threats, it is possible to find at least one matching pattern with high matching rate and sensitivity.

Reducing the size of exclusion zones (EZs) in spectrum sharing is vital for efficient utilization of fallow spectrum as well as for the economic viability of spectrum sharing itself. In this paper, we explore two approaches for reducing the size of EZs. We show that multi-tiered EZs can be used to improve spectrum utilization efficiency by implementing the concept of differential spectrum access hierarchy. Also, we provide quantitative results that show the impact of using a point-to-point mode terrain profile in calculating an EZ's contour. Such a terrain profile captures the effects of propagation losses due to area-specific topography, which are not considered by the F-curves, a common method of calculating an EZ's boundary. Our results indicate that the use of such a terrain profile results in a noticeable decrease in the size of an EZ.

When different stakeholders share a common resource, such as the case in spectrum sharing, security and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as cognitive radios, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security and enforcement. In this paper, we review some of the critical security and privacy threats that impact spectrum sharing. We propose a taxonomy for classifying the various threats, and describe representative examples for each threat category. We also discuss threat countermeasures and enforcement techniques, which are discussed in the context of two different approaches: ex ante (preventive) and ex post (punitive) enforcement.

This paper focuses on the problem of spectrum sharing between secondary networks that access spectrum opportunistically in TV spectrum. Compared to the coexistence problem in the ISM (Industrial, Scientific and Medical) bands, the coexistence situation in TV whitespace (TVWS) is potentially more complex and challenging due to the signal propagation characteristics in TVWS and the disparity of PHY/MAC strategies employed by the systems coexisting in it. In this paper, we propose a novel decision making algorithm for a system of coexistence mechanisms, such as an IEEE 802.19.1-compliant system, that enables coexistence of dissimilar TVWS networks and devices. Our algorithm outperforms existing coexistence decision making algorithms in terms of fairness, and percentage of demand serviced.

A specific class of differential cryptanalytic approach, named as impossible differential attack, has been successfully applied to several symmetric cryptographic primitives in particular encryption schemes such as Advanced Encryption Standard (AES). Such attacks exploit differences that are impossible at some intermediate state of the cipher algorithm. The best-known impossible differential attack against AES-128 has applied to six rounds. An attack on AES-128 up to seven rounds is proposed. The proposed attack requires 2115.5 chosen plaintexts and 2109 bytes of memory and performs 2119 seven-round AES encryptions. This is also the best-known attack on a reduced version of the AES-128 till now.