Reliable Mechanism Research Articles

Call Rewinding: Efficient Backward Edge Protection

The prevalence of memory-unsafe software prompts significant efforts by the research community to mitigate memory corruption bugs. This endeavor is crucial for safeguarding critical systems against security threats. Specifically, there is a focus to protect against code-reuse attacks through enforcing control-flow integrity (CFI). This paper introduces call rewinding, a novel microarchitecture-level mechanism for protection of return addresses. It is based on a property of the calling convention that is common to major architectures such as x86, ARM and RISC-V, which states that all return instructions transfer control to a valid call site. Call rewinding consists of jumping to the instruction preceding the return target for each return instruction and checking if the instruction at this address is a call or not. On systems equipped with return address prediction, a commonly employed optimization, the security check is performed only on mispredicted return addresses. The proposed protection mechanism demonstrates negligible impact on both area and performance. We implement call rewinding on the CV64A6, a RISC-V CPU with consequent branch prediction support. Our evaluation validates the effectiveness of call rewinding, both in bare-metal and in a Linux operating system (OS) environment. It triggers no false positives in bare-metal and is functional with the OS extended with a custom exception handler. Furthermore, our findings indicate that call rewinding successfully detects unauthorized return addresses, highlighting its potential as a reliable and efficient security mechanism.

Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework

<span lang="EN-US">With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.</span>

Transition from Human to AI-Judge - The Future Automated Adjudication Mechanism in India

In the current technological landscape, artificial intelligence is rapidly evolving and reshaping the way we operate. Nevertheless, AI also presents challenges that necessitate human involvement and unique problem-solving approaches. This study explores the potential future impact of Human to AI Judges. It begins by global approach on AI as automated decisionmaking authority, the acceptance models that helps to adopt such advanced technology in India and also examining AI's legal personality. The submission of evidence is a critical aspect of any adjudication process. Therefore, it is essential to authenticate such evidence through a reliable mechanism to ensure the secure collection of evidence. The use of chain of custody and blockchain technology has been widespread for this purpose, although countries like China have differing methods for utilizing such technology. However, India currently lacks such efficiency. Predictive analysis has been employed to gather data and develop algorithms. However, AI lacks the cognitive ability to evaluate cases based on emotions by relying solely on these algorithms. The paper also delves into the inability of AI judges to detect emotions in criminal and family cases. The appeals process has been evaluated, as individuals who disagree with a judge's decision typically lodge an appeal. The issue of accountability is a significant concern, prompting discussions about whether the programmer or AI should bear the responsibility. This research paper also explores the appealability of decisions made by AI judges, offering a viable solution to address the research question.

Alignment of words and actions? Government environmental attention and enterprise digital transformation

This study employs a unique dataset of A-share listed companies from 2007 to 2021 to investigate the impact of government environmental attention on enterprise digital transformation (EDT) and its underlying mechanisms. Empirical findings document that in response to upholding organizational legitimacy, government environmental attention remarkably favors EDT. This core conclusion remains robust after addressing endogeneity concerns and alternative robustness tests. Mechanism analysis unveils that intensified government environmental attention propels companies to expedite green technology innovation, alleviate financing constraints, and enhance human capital quality, all accelerating EDT. Subsequent investigations indicate that heightened government environmental attention notably impacts EDT more for larger plants and highly polluting industries. In regions with lower financial development, government environmental attention serves as a reliable signaling mechanism, motivating EDT. These findings guide plants in accelerating EDT for enhanced sustainable development while shedding light on the evolving mechanisms of government attention allocation and EDT, which offers insights for future research on the correlation between government actions and corporate environmental governance.

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

On a Stable Multiplicative Calculus-Based Hybrid Parallel Scheme for Nonlinear Equations

Fractional-order nonlinear equation-solving methods are crucial in engineering, where complex system modeling requires great precision and accuracy. Engineers may design more reliable mechanisms, enhance performance, and develop more accurate predictions regarding outcomes across a range of applications where these problems are effectively addressed. This research introduces a novel hybrid multiplicative calculus-based parallel method for solving complex nonlinear models in engineering. To speed up the method’s rate of convergence, we utilize a second-order multiplicative root-finding approach as a corrector in the parallel framework. Using rigorous theoretical analysis, we illustrate how the hybrid parallel technique based on multiplicative calculus achieves a remarkable convergence order of 12, indicating its effectiveness and efficiency in solving complex nonlinear equations. The intrinsic stability and consistency of the approach—when applied to nonlinear situations—are clearly indicated by the symmetry seen in the dynamical planes for various parameter values. The method’s symmetrical behavior indicates that it produces accurate findings under a range of scenarios. Using a dynamical system procedure, the ideal parameter values are systematically analyzed in order to further improve the method’s performance. Implementing the aforementioned parameter values using the parallel approach yields very reliable and consistent outcomes. The method’s effectiveness, reliability, and consistency are evaluated through the analysis of numerous nonlinear engineering problems. The analysis provides a detailed comparison with current techniques, emphasizing the benefits and potential improvements of the novel approach.

Event-Driven Data Pipelines : A Cloud-Based Approach to Real-Time Data Processing

This article comprehensively analyzes event-driven data pipelines in cloud computing environments, examining their architecture, implementation considerations, and real-world applications. The article explores the fundamental components of event-driven systems, from event sources and message brokers to processing layers, while evaluating their performance characteristics and reliability mechanisms. Through detailed analysis of system architectures, we investigate the integration of various cloud services and their role in enabling scalable, real-time data processing. The article demonstrates how modern event-driven architectures achieve sub-millisecond processing times and handle millions of events per second while maintaining system resilience. Our findings reveal significant improvements in operational efficiency across various industries, including financial services, marketing automation, and IoT monitoring solutions. The article also addresses critical implementation aspects, including security frameworks, integration protocols, and best practices for system optimization. These insights provide valuable guidance for organizations leveraging event-driven architectures in their digital transformation initiatives. The article concludes that event-driven data pipelines represent a crucial advancement in data processing technology, offering unprecedented capabilities in handling real-time data streams while maintaining scalability, reliability, and cost-effectiveness.

Reliability analysis under bias stress and elevated temperature of dual-gate IGZO TFT

Indium–gallium–zinc oxide (IGZO) as a star material has been broadly applied in multiple functional devices, including planar displays, flexible electronic devices, and photoelectronics. In recent years, the development of artificial intelligence and great data also extends the application of IGZO-based thin film transistors (TFTs) to memory, memristors, and neuromorphic computing. Thus, the research of high performance and reliable IGZO TFTs attracts tremendous attention worldwide. Herein, a high-quality IGZO thin film was deposited via atomic layer deposition, and a dual-gate (DG) IGZO TFT was fabricated. Comprehensive electrical characterization was conducted on the fabricated DG IGZO TFTs, revealing that DG IGZO TFTs exhibited good performance for reliability analysis. The reliability and degradation mechanism of the DG IGZO TFT was systematically investigated under bias stress at room temperature and elevated temperatures of 350, 400, and 450 K by the comprehensive electrical characterization. Results indicate that the defects of the dual gate insulators and the interfaces majorly influence the degradation under bias stresses, while the carrier scattering of the IGZO channel is the major degradation mechanism under elevated temperatures. These findings highlight the degradation mechanism of the DG IGZO TFT under bias stress and elevated temperatures from a novel viewpoint, which provides a utilizable reference to its application in circuits.

Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation

Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a significant concern for real-world clinical applications, necessitating reliable detection mechanisms. This paper introduces a comprehensive benchmarking framework aimed at evaluating failure detection methodologies within medical image segmentation. Through our analysis, we identify the strengths and limitations of current failure detection metrics, advocating for the risk-coverage analysis as a holistic evaluation approach. Utilizing a collective dataset comprising five public 3D medical image collections, we assess the efficacy of various failure detection strategies under realistic test-time distribution shifts. Our findings highlight the importance of pixel confidence aggregation and we observe superior performance of the pairwise Dice score (Roy et al., 2019) between ensemble predictions, positioning it as a simple and robust baseline for failure detection in medical image segmentation. To promote ongoing research, we make the benchmarking framework available to the community.

Sustained release of heparin from PLLA micropartricles for tissue engineering applications

Heparin holds promise for cardiac tissue engineering, but challenges such as hematoma or bleeding and accumulation in tissue caused by excessive release, and short half-life persist. The present study aimed to introduce a reliable mechanism for the prolonged heparin release from a biocompatible polymer carrier. The designed system must ensure that heparin retains its bioactivity over time while preventing premature release. Heparin was encapsulated within poly (L-lactic acid) microparticles using the double emulsion method, with polyvinyl alcohol employed as the stabilizer. The encapsulation efficiency of heparin in the microparticles was calculated as 25.56 %. The functionality of the design was evaluated using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, and Energy Dispersive X-ray Spectroscopy. Drug release and microparticle degradation studies were conducted alongside cell viability tests. The particle sizes ranged from 5 to 10 ± 2.53 μm, with evidence suggesting that heparin promotes the smaller particle formation. The system demonstrated a consistent drug release profile over six weeks with a release rate of 54 % by week two and 97.65 % by week six. The degradation of heparin-loaded microparticles reached less than 50 % by week six, and the loading of heparin did not significantly affect the degradation behavior of the PLLA microparticles in PBS. Furthermore, heparin concentrations between 200 and 400 μg/ml enhanced the viability of Placenta-derived Mesenchymal Stem Cells and H9c2. These findings suggest that the system could be considered as an effective vehicle for sustained heparin delivery across a spectrum of biological applications, particularly in cardiac tissue engineering.

Reorienting Investment Dispute Resolution in Indonesia: Towards a Fair and Efficient System

In the era of Industry 4.0, the surge in foreign investment and the proliferation of global trade agreements have intensified the need for more reliable dispute resolution mechanisms. While the Investor-State Dispute Settlement (ISDS) system has long been the standard, it faces widespread criticism for its lack of transparency, fairness, and absence of an appellate mechanism. In response, the Investment Court System (ICS) has emerged as a novel alternative, introducing significant reforms such as the appointment of more qualified arbitrators, greater neutrality, enhanced transparency, and, crucially, a structured appeals process that offers stronger legal certainty. Although no disputes have yet been resolved through ICS, raising questions about its efficiency, the system represents a promising advancement in creating a more equitable and trustworthy framework. The appellate mechanism of ICS, in particular, addresses a critical shortcoming of ISDS, where arbitration awards are often challenged in national courts, leading to legal uncertainty. By balancing investor protection with state sovereignty to regulate in the public interest, ICS has the potential to enhance legal clarity, foster public confidence, and create a more stable and inclusive global investment environment.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent.

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks in the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.

Beyond Transparency Statements: Explaining the Impact of Efficiency and Auditing on the Disclosure of Public Information

Transparency has become a key element for public entities as a mechanism of reliability and accountability. In addition, the continuous auditing of these entities, as well as the efficient provision of public services, are important elements that can have an impact on a greater degree of transparency. In this paper, the aim is to measure how these factors influence the level of municipal transparency through an analysis of a sample of 143 Spanish municipalities with more than 50,000 inhabitants using the generalized model of moments. The results show that an efficient and highly audited municipality offers a higher level of public information and influences other political and socioeconomic factors.

Activation of peroxomonosulfate by manganese-containing non-homogeneous catalysts prepared via a soft template calcination strategy for efficient degradation of carbamazepine

Manganese (Mn)-containing inhomogeneous catalysts have demonstrated potential to activate peroxymonosulfate (PMS), but may be constrained by low efficiency and poor resistance to environmental disturbances. According to the soft template calcination strategy, g-C3N4 with Mn doping was synthesized to improve PMS activation through an effective Mn(II)/Mn(III)/Mn(IV) cycle. The constructed Mn25@CN/PMS system achieved complete and rapid removal of carbamazepine (CBZ) at 0.3 g/L catalyst and 2 mM PMS. Its corresponding rate constant of pseudo-first-order was 0.3424 min−1, and it was 3424 and 16.15 times higher than that of pure g-C3N4/PMS system and CN/PMS system, respectively. More strikingly, the catalysts exhibited excellent resistance to environmental impacts, with CBZ removal rates exceeding 95% under the influence of different anions, cations, aqueous pH values, and humic acid (HA) concentrations. Subsequently, it was proposed a reliable catalytic mechanism for CBZ removal and PMS activation derived from electron paramagnetic resonance (EPR) tests and quenching experiments. The O2·- and 1O2 played a major role in the CBZ removal process. This research provides a perspective soft template strategy for PMS activation using highly efficient catalysts with high resistance to environmental impacts, which provides new ideas to alleviate environmental issues.

Innovative tools from WHO Europe to protect children from digital marketing of unhealthy products

Abstract Children in the WHO European Region are extensively exposed to marketing of unhealthy products, including tobacco, alcohol, HFSS foods, and gambling, which negatively influences their dietary choices, behaviors, and perceptions of a norm, threatening their health rights. Recognizing the urgency to protect children’s developmental potential and rights, WHO has actively addressed this issue both within Europe and globally. In 2010, WHO issued recommendations for Member States on marketing foods and beverages to children. Building on these efforts, the CLICK framework for monitoring digital marketing to children was introduced in 2018. In 2023, WHO released guidelines urging countries to adopt comprehensive, mandatory policies to protect children from HFSS food marketing. Despite political commitments and policies, children and adolescents continue to be exposed to unhealthy product marketing, increasing their risk of NCDs as online activity rises. At the same time, while efforts have focused on restricting specific products, rapid changes in digital marketing strategies necessitate broader measures. Emerging trends include promoting brand loyalty among young children through brand characters and logos. Tools and support are urgently needed to monitor and implement WHO recommendations in online settings, including brand marketing. Despite WHO recommendations, few countries have implemented mandatory policies. Reliable monitoring mechanisms are essential. To address this gap, the WHO Regional Office for Europe developed the KidAd platform to monitor children’s exposure to digital advertising.

Müqavilənin standart şərtləri

The article examines the institution of "standard terms of contract", which is an important parameter of today's civil law sphere. The place of the concept in question in the modern legal system is discussed. In relation to this issue, the legislator's position, court practice and approaches available in the doctrine are analyzed. Important nuances such as the understanding of the standard terms of the contract, their place in the legal system of the Republic of Azerbaijan, their elements, verification, reliability, and defense mechanisms formed by the legislator against abuses that may arise from it are touched upon. In particular, the imperative requirements regulating this institution are considered in the legislation. It is clarified whether this type of condition is necessary and desirable from the perspective of civil law. Key words: contract, standard, condition, element, validity, check, mechanism, court, legislator, imperative, interpretation, content

Auto encoder-based defense mechanism against popular adversarial attacks in deep learning.

Convolutional Neural Network (CNN)-based models are prone to adversarial attacks, which present a significant hurdle to their reliability and robustness. The vulnerability of CNN-based models may be exploited by attackers to launch cyber-attacks. An attacker typically adds small, carefully crafted perturbations to original medical images. When a CNN-based model receives the perturbed medical image as input, it misclassifies the image, even though the added perturbation is often imperceptible to the human eye. The emergence of such attacks has raised security concerns regarding the implementation of deep learning-based medical image classification systems within clinical environments. To address this issue, a reliable defense mechanism is required to detect adversarial attacks on medical images. This study will focus on the robust detection of pneumonia in chest X-ray images through CNN-based models. Various adversarial attacks and defense strategies will be evaluated and analyzed in the context of CNN-based pneumonia detection. From earlier studies, it has been observed that a single defense mechanism is usually not effective against more than one type of adversarial attack. Therefore, this study will propose a defense mechanism that is effective against multiple attack types. A reliable defense framework for pneumonia detection models will ensure secure clinical deployment, facilitating radiologists and doctors in their diagnosis and treatment planning. It can also save time and money by automating routine tasks. The proposed defense mechanism includes a convolutional autoencoder to denoise perturbed Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) adversarial images, two state- of-the-art attacks carried out at five magnitudes, i.e., ε (epsilon) values. Two pre-trained models, VGG19 and VGG16, and our hybrid model of MobileNetV2 and DenseNet169, called Stack Model, have been used to compare their results. This study shows that the proposed defense mechanism outperforms state-of-the-art studies. The PGD attack using the VGG16 model shows a better attack success rate by reducing overall accuracy by up to 67%. The autoencoder improves accuracy by up to 16% against PGD attacks in both the VGG16 and VGG19 models.

Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks.

The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks.

Reliable Finite‐Time Control for Nonlinear Chaotic Semi‐Markov Jump Systems With Incomplete Transition Rates

ABSTRACTThis article studies the finite‐time control problem for a class of nonlinear chaotic semi‐Markov jump systems with incomplete transition rates described by the T‐S fuzzy model approach. As a means to depict the dynamical properties pertaining to the examined system, parametric uncertainties, faults, external disturbances and input saturation are taken into consideration. The foremost objective of this study is to come up with a composite control mechanism that effectively rejects and attenuates the repercussions of faults and disturbances. In particular, we primarily built a disturbance observer in order to obtain a precise estimation of the external disturbances. After which, the fault diagnosis observer is designated to effectively estimate the faults. Specifically, a composite reliable control mechanism is developed by fusing the output of the constructed observers with the mode‐dependent fuzzy‐rule based state feedback controller. By employing suitable Lyapunov functions in conjunction with the linear matrix inequality technique, a set of mode‐dependent conditions is derived to ensure the finite‐time stochastic boundedness of the underlying closed‐loop and estimation error systems. Following this, the anticipated controller and observer gain matrices are elicited by resolving the established linear matrix inequalities. Thereafter, intending to ascertain the efficacy and usefulness of accrued theoretical findings, simulation results performed on Chua's circuit system is endowed.