Search Results (1,830)

Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis. Full article

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions. Full article

Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred and difficult to identify. The purpose of this study is to describe this concept, to draw attention to one of the “pandemics” of the 21st-century world, and to find methods by which we can defend ourselves against them. Materials and methods. A cross-sectional study was conducted based on a sample of 442 respondents. Results. For 77.8% of the people surveyed, the concept of “fake news” is important in Romania. Regarding trust in the mass media, a clear dominance (72.4%) was observed among participants who have little trust in the mass media. Although 98.2% of participants detect false information found on the internet, 78.5% are occasionally deceived by the information provided. Of the participants, 47.3% acknowledged their vulnerability to disinformation. The main source of disinformation is the internet, as 59% of the interviewed subjects believed. As the best measure against disinformation, the study group was divided almost equally according to the three possible answers, all of which were considered to be equally important: imposing legal restrictions and blocking the posting of certain news (35.4%), imposing stricter measures for authors (33.9%), and increasing vigilance among people (30.5%). Conclusions. According to the statistics based on the participants’ responses, the main purposes of disinformation are propaganda, manipulation, distracting attention from the truth, making money, and misleading the population. It can be observed that the main intention of disinformation, in the perception of the study participants, is manipulation. Full article

The development of Global Positioning System (GPS) technology has contributed in various ways to improving the physical condition of modern football players by enabling the quantification of physical load. Previous studies have reported that the running demands of matches vary depending on playing position and formation. Over the past decade, despite the widespread use of GPS technology, studies that have investigated the running performance of young football players within the 1-4-3-3 formation are particularly limited. Therefore, the aim of the present study was to create the match running profile of playing positions in the 1-4-3-3 formation among high-level youth football players. An additional objective of the study was to compare the running performance of players between the two halves of a match. This study involved 25 football players (Under-19, U19) from the academy of a professional football club. Data were collected from 18 league matches in which the team used the 1-4-3-3 formation. Positions were categorized as Central Defenders (CDs), Side Defenders (SDs), Central Midfielders (CMs), Side Midfielders (SMs), and Forwards (Fs). The players’ movement patterns were monitored using GPS devices and categorized into six speed zones: Zone 1 (0.1–6 km/h), Zone 2 (6.1–12 km/h), Zone 3 (12.1–18 km/h), Zone 4 (18.1–21 km/h), Zone 5 (21.1–24 km/h), and Zone 6 (above 24.1 km/h). The results showed that midfielders covered the greatest total distance (p = 0.001), while SDs covered the most meters at high and maximal speeds (Zones 5 and 6) (p = 0.001). In contrast, CDs covered the least distance at high speeds (p = 0.001), which is attributed to the specific tactical role of their position. A comparison of the two halves revealed a progressive decrease in the distance covered by the players at high speed: distance in Zone 3 decreased from 1139 m to 944 m (p = 0.001), Zone 4 from 251 m to 193 m (p = 0.001), Zone 5 from 144 m to 110 m (p = 0.001), and maximal sprinting (Zone 6) dropped from 104 m to 78 m (p = 0.01). Despite this reduction, the total distance remained relatively stable (first half: 5237 m; second half: 5046 m, p = 0.16), indicating a consistent overall workload but a reduced number of high-speed efforts in the latter stages. The results clearly show that the tactical role of each playing position in the 1-4-3-3 formation, as well as the area of the pitch in which each position operates, significantly affects the running performance profile. This information should be utilized by fitness coaches to tailor physical loads based on playing position. More specifically, players who cover greater distances at high speeds during matches should be prepared for this scenario within the microcycle by performing similar distances during training. It can also be used for better preparing younger players (U17) before transitioning to the U19 level. Knowing the running profile of the next age category, the fitness coach can prepare the players so that by the end of the season, they are approaching the running performance levels of the next group, with the goal of ensuring a smoother transition. Finally, regarding the two halves of the game, it is evident that fitness coaches should train players during the microcycle to maintain high movement intensities even under fatigue. Full article

Hemolin has been identified as a crucial immune gene in insect immune defense. The silkworm is susceptible to infections by pathogenic microorganisms when reared on artificial diets. In this study, through comparative analysis of the expression patterns of BmHemolin in silkworms fed on mulberry leaves and artificial diets, we found that the expression of BmHemolin was significantly upregulated in silkworms reared on artificial diets, and this upregulation was highly likely induced by pathogenic microorganisms. Further interaction analysis revealed that BmHemolin could bind to pathogenic microorganisms and form aggregates. Meanwhile, BmHemolin enhanced the melanization and aggregation of hemocytes. Subsequent in vitro antibacterial experiments showed that BmHemolin had the ability to inhibit the growth of Escherichia coli. In vivo clearance experiments demonstrated that BmHemolin facilitated the clearance of pathogens in the body. Moreover, CRISPR/Cas9-mediated knockout of the BmHemolin gene led to the downregulation of antimicrobial peptides and phagocytosis-related factors, while an excess of BmHemolin could enhance the expression of these genes, thereby improving the silkworm’s immune resistance to Enterococcus mundtii and increasing survival rates. In summary, our research demonstrates that BmHemolin played a pivotal role in both humoral and cellular immunity in the silkworm, thereby defending against pathogen invasion. Full article

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks greatly enhances the flexibility and efficiency of communication and distributed computation for ground mobile devices. Federated learning (FL) provides a privacy-preserving paradigm for device collaboration but remains highly vulnerable to poisoning attacks and is further challenged by the resource constraints and heterogeneous data common to UAV-assisted systems. Existing robust aggregation and anomaly detection methods often degrade in efficiency and reliability under these realistic adversarial and non-IID settings. To bridge these gaps, we propose FedULite, a lightweight and robust federated learning framework specifically designed for UAV-assisted environments. FedULite features unsupervised local representation learning optimized for unlabeled, non-IID data. Moreover, FedULite leverages a robust, adaptive server-side aggregation strategy that uses cosine similarity-based update filtering and dimension-wise adaptive learning rates to neutralize sophisticated data and model poisoning attacks. Extensive experiments across diverse datasets and adversarial scenarios demonstrate that FedULite reduces the attack success rate (ASR) from over 90% in undefended scenarios to below 5%, while maintaining the main task accuracy loss within 2%. Moreover, it introduces negligible computational overhead compared to standard FedAvg, with approximately 7% additional training time. Full article

This paper investigates perimeter defense strategies for multi-agent systems. Considering the complex scenario with multiple obstacles in the mission environment, a defense strategy based on prioritized path planning is proposed in this paper. The strategy employs a minimum weight matching method to solve the optimal task assignment for interception and determines the task priority based on the relative time window. Meanwhile, the swarm path planning is realized using particle swarm optimization with a designed cost function. Compared with the existing literature, the proposed method can handle large-scale agent-based perimeter defense while accounting for inter-defender collision avoidance and obstacle avoidance. The effectiveness of the strategy is verified through simulation in the mission scenario. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article

This study analyses the physical performance variables involved in different training tasks aimed at replicating the worst-case scenarios (WCSs) observed during official matches in professional football, with a focus on playing positions and occurrences within a 1 min period. Data were collected from 188 training sessions and 42 matches of a Spanish Second Division team during the 2021/2022 season. All data were reported on a per-player basis. GPS tracking devices were used to record physical variables such as total distance, high-speed running (HSR), sprints, accelerations, decelerations, and high metabolic load distance (HMLD). Players were grouped according to their match positions: central defenders, wide players, midfielders and forwards. The results showed that none of the training tasks fully replicated the physical demands of match play. However, task TYPEs 11 (Large-Sided Games) and 9 (small-sided games with orientation and transition) were the closest to match demands, particularly in terms of accelerations and decelerations. Although differences were observed across all variables, the most pronounced discrepancies were observed in sprint and HSR variables, where training tasksfailed to reach 60% of match demands. These findings highlight the need to design more specific drills that simulate the intensity of WCS, allowing for more accurate weekly training load planning. This study offers valuable contributions for optimising performance and reducing injury risk in professional footballers during the competitive period. Full article

Background/Objectives: Moso bamboo (Phyllostachys edulis), the most widely distributed bamboo species in China, is valued for both its shoots and timber. This species often faces challenges from high-temperature stress. To cope with this stress, Moso bamboo has evolved various adaptive mechanisms at the physiological and molecular levels. Although numerous studies have revealed that a large number of transcription factors (TFs) and genes play important roles in the regulatory network of plant heat stress responses, the regulatory network involved in heat responses remains incompletely understood. Methods: In this study, Moso bamboo was placed in a high-temperature environment of 42 °C for 1 h and 24 h, and transcriptome sequencing was carried out to accurately identify key molecules affected by high temperature and their related biological pathways. Results: Through a differential expression analysis, we successfully identified a series of key candidate genes and transcription factors involved in heat stress responses, including members of the ethylene response factor, HSF, WRKY, MYB, and bHLH families. Notably, in addition to traditional heat shock proteins/factors, multiple genes related to lipid metabolism, antioxidant enzymes, dehydration responses, and hormone signal transduction were found to play significant roles in heat stress responses. To further verify the changes in the expression of these genes, we used qRT-PCR technology for detection, and the results strongly supported their key roles in cellular physiological processes and heat stress responses. Conclusions: This study not only deepens our understanding of plant strategies for coping with and defending against extreme abiotic stresses but also provides valuable insights for future research on heat tolerance in Moso bamboo and other plants. Full article

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, associated with high recurrence and amputation rates. Adherence to offloading devices is critical for wound healing but remains inadequately monitored in real-world settings. This study evaluates the SmartBoot edge-computing system—a wearable, real-time remote monitoring solution integrating an inertial measurement unit (Sensoria Core) and smartwatch—for its validity in quantifying cadence and step count as digital biomarkers of frailty, and for detecting adherence. Twelve healthy adults wore two types of removable offloading boots (Össur and Foot Defender) during walking tasks at varied speeds; system outputs were validated against a gold-standard wearable and compared with staff-recorded adherence logs. Additionally, user experience was assessed using the Technology Acceptance Model (TAM) in healthy participants (n = 12) and patients with DFU (n = 81). The SmartBoot demonstrated high accuracy in cadence and step count across conditions (bias < 5.5%), with an adherence detection accuracy of 96% (Össur) and 97% (Foot Defender). TAM results indicated strong user acceptance and perceived ease of use across both cohorts. These findings support the SmartBoot system’s potential as a valid, scalable solution for real-time remote monitoring of adherence and mobility in DFU management. Further clinical validation in ongoing studies involving DFU patients is underway. Full article

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (${10}^{-2}$ probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article

Standard arguments for open borders draw on cosmopolitan premises. By contrast, statism as a theory of global justice seems to be at odds with open borders. If states are only responsible for protecting the autonomy of citizens and do not owe foreigners equal consideration of their claims, it appears to follow that they may legitimately exclude unwanted immigrants as long as their human rights are not at stake. In this article, I argue that one can be a statist and still defend open borders. Even though moral equality gives rise to demands of distributive justice only in the context of shared subjection to the authority of the state, such that foreigners are not entitled to equal treatment, moral equality demands that the state shows equal respect for their autonomy. Immigration restrictions that are not aimed at protecting the autonomy of citizens are incompatible with equal respect for foreigners’ autonomy, since they subject the latter to unilateral coercion without it being necessary for the former to lead autonomous lives. Full article

Legal actors often discuss emotion-based decisions and reasoned evaluation of the facts as distinct and opposite methods through which jurors can reach conclusions. However, research suggests that emotion can have an indirect effect on juror decisions by changing the way that jurors evaluate the facts of the case. In three studies (N = 713, N = 677, N = 651), we tested whether mock jurors’ negative moral emotions towards the defendant predicted their evaluations of unrelated case evidence and in turn their case judgments and whether judicial rehabilitation could reduce this effect. Participants read a civil case and were randomly assigned to either receive judicial rehabilitation or not. Then, they completed measures relating to their negative moral emotions towards the defendant, their agreement with plaintiff and defense evidence, and case judgments. When participants reported increased negative emotions towards the defendant, they agreed more with unrelated plaintiff evidence and less with unrelated defense evidence. In turn, they voted liable more often and awarded more in damages. Judicial rehabilitation did not reduce this effect. This research provides support for the idea that there is a more complicated relationship between emotion and decisions than legal actors suggest. Specifically, negative emotions towards the defendant are associated with a pro-plaintiff evaluation of evidence and pro-plaintiff judgments. Full article

This article reframes sustainability transitions by positioning the deliberate deactivation of fossil fuel infrastructures—such as coal plants, oil fields, and pipelines—as a central mechanism of systemic change. While prevailing approaches often emphasize renewable energy and innovation, they tend to neglect how existing fossil systems are actively maintained by powerful networks. We argue that sustainability transitions require not only building alternatives but also deactivating entrenched fossil infrastructures. To address this gap, we propose an analytical framework that conceptualizes deactivation as a contested socio-political process shaped by antagonistic interactions between fossil blocs—coalitions of incumbent agents defending fossil infrastructures—and emerging deactivation networks working to disable and dismantle them. Drawing on six illustrative cases from diverse contexts, we examine the legal, institutional, narrative, and spatial mechanisms through which deactivation is either enabled or obstructed. We also introduce an interdisciplinary methodology that combines path tracing, social network analysis, and qualitative comparison to analyze how these dynamics between fossil blocs and deactivation networks evolve over time. This article contributes to the sustainability transition literature by demonstrating that the deactivation of fossil infrastructures is a political, material, and justice-oriented process, one that is essential to ending fossil fuel dependency and enabling sustainable futures. Full article