Search Results (86)

Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems. Full article

This paper presents a detailed analysis of signal data acquired from wearable sensors such as accelerometers and barometric altimeters for human activity recognition, with an emphasis on fall detection. This research addressed two types of activity recognition tasks: a binary classification problem between activities of daily living (ADLs) and simulated fall activities and a multiclass classification problem involving five different activities (running, walking, sitting down, jumping, and falling). By combining features derived from both sensors, traditional machine models such as random forest, support vector machine, XGBoost, logistic regression, and majority voter models were used for both classification problems. All of the aforementioned methods generally produced better results using combined features of both sensors compared to single-sensor models, highlighting the potential of sensor fusion approaches for fall detection. Full article

We investigate a variation of the classical voter model where the set of influencing agents depends on an individual’s current opinion. The initial population is made up of a random sample of equally sized sub-populations for each state, and two types of interactions are considered: (i) direct neighbors and (ii) second neighbors (friends of direct neighbors, excluding the direct neighbors themselves). The neighborhood size, reflecting regular network connectivity, remains constant across all agents. Our findings show that varying the interaction range introduces asymmetries that affect the probability of consensus and convergence time. At low connectivity, direct neighbor interactions dominate, leading to consensus. As connectivity increases, the probability of either state reaching consensus becomes equal, reflecting symmetric dynamics. This asymmetric effect on the probability of consensus is shown to be independent of network topology in small-world and scale-free networks. Asymmetry also influences convergence time: while symmetric cases display decreasing times with increased connectivity, asymmetric cases show an almost linear increase. Unlike the probability of reaching consensus, the impact of asymmetry on convergence time depends on the network topology. The introduction of stubborn agents further magnifies these effects, especially when they favor the less dominant state, significantly lengthening the time to consensus. We conclude by discussing the implications of these findings for decision-making processes and political campaigns in human populations. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced time series forecasting models (ARIMA, LSTM, and seasonal decomposition). Analyzing demographic and geographic data, we uncover significant turnout inequities, particularly for marginalized groups, and propose actionable reforms to enhance equitable voter participation. By integrating data-driven insights with theoretical perspectives, this study offers practical recommendations for campaigns and policymakers to counter polarization and foster inclusive democratic representation. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

This study proposes an AI-assisted governance framework to enhance decision-making within decentralized autonomous organizations (DAOs). By integrating chain-of-thought (CoT) reasoning with stakeholder-adaptive recommendations, the framework improves decision alignment, increases voter participation, and enhances governance transparency. Through simulations based on historical KlimaDAO data, the system achieved a 97% alignment with past decisions, a projected 40% increase in participation, and a 35% improvement in governance clarity. To support quantitative analysis in tokenomics, we developed a tailored CoT reasoning strategy, effectively reducing information asymmetry and generating structured, trustworthy recommendations. These results underscore the potential of AI to foster more inclusive and transparent DAO governance. Future work will explore deploying lightweight AI models and extending this approach to a broader range of DAO ecosystems. Full article

In many areas of operation, application-specific logic implemented in FPGAs (Field Programmable Gate Arrays) is critical. In these situations, various mitigation methods are used to reduce or completely eliminate malfunctions in the circuit resulting from undesired physical phenomena (e.g., ionizing radiation). Such phenomena may occur, among others, in medicine, the military, nuclear power, and space systems. One of the most popular methods is the use of triple modular redundancy (TMR). Here, the FPGA provides a good basis for building TMR-based safety-critical systems due to its concurrent processing. This paper presents an overview of the implementation of logic structures using TMR. In this paper, the authors focus on different concepts for the implementation of FSMs. The different concepts differ in the way TMR voters are attached and the extent of redundancy of the individual FSM components. The article compares the efficiency of the different solutions. In order to evaluate this efficiency, it is crucial to determine the logic utilization or the power consumption of a given implementation. In the experimental part of the article, the authors show the results of the synthesis of FSM benchmarks, for different mitigation models. The synthesis was carried out for both commercial and academic tools. Full article

Architectural ambiance refers to the mood perceived in a built environment, assessed through human reactions to virtual drawings of prospective spaces. This paper investigates the use of a ready-made artificial intelligence model to automate this task. Based on professional BIM models, videos of virtual tours of typical urban areas were built: a business district, a strip mall, and a residential area. GPT-4V was used to assess the aesthetic quality of the built environment based on keyframes of the videos and characterize these spaces shaped by subjective attributes. The spatial qualities analyzed through subjective human experience include space and scale, enclosure, style, and overall feelings. These factors were assessed with a diverse set of mood attributes, ranging from balance and protection to elegance, simplicity, or nostalgia. Human participants were surveyed with the same questions based on the videos. The answers were compared and analyzed according to these subjective attributes. Our findings indicate that, while GPT-4V demonstrates adequate proficiency in interpreting urban spaces, there are significant differences between the AI and human evaluators. In nine out of twelve cases, the AI’s assessments aligned with the majority of human voters. The business district environment proved more challenging to assess, while the green environment was effectively modeled. Full article

This study evaluates the link between economic voting and electoral behavior in the 2024 European Parliament (EP) elections. This study is grounded in both selective perception and economic voting theories, examining how different independent factors could interact with electoral behavior. In this regard, the research aims to achieve several research directions: (i) the evaluation of the statistical differences in voters’ turnout in 2024 EP elections by geographical regions; (ii) the analysis of the interaction between voters’ perceptions of the current or future economic situations and voter turnout; (iii) the analysis of the interaction between objective economic conditions and electoral behavior. Using both multiple linear regression and logistic models, the study highlights that voter turnout and incumbent party reelection are significantly related to voters’ perceptions of the current or future state of the national economy. The results reveal that regional differences in voter turnout are largely explained by significant differences in voters’ economic perceptions, while the decision to vote for the incumbent party is driven by future economic expectations. The empirical findings underscore the pivotal role played by subjective perceptions in shaping electoral behavior, illustrating that political attitudes and behaviors are derived from personal interpretation of the national economic situations. Beyond theoretical perspectives that highlight the link between psychological processes and voting, the paper might have several practical implications for academics or decision makers interested in the field of electoral behavior. Full article

In electronic voting systems, a large number of voters are often required to vote. It is also necessary to ensure the security of the voters and the fairness of the vote. The use of ring signatures is very suitable for e-voting systems because of their special anonymity. Among the many types of ring signatures, certificateless ring signature (CRS) stands out because it does not require certificates and avoids the need to completely trust the key generation center (KGC). In this paper, we propose a certificateless ring signature based on the special structure of the number theory research unit (NTRU) lattice, which utilizes the Merkle tree and seed tree to split commitments and integrate them again to generate signatures. At the same time, we embed the NTRU small integer solution (NTRU-SIS) problem and provide a detailed proof of security under the random oracle model (ROM). In efficiency, the Merkle tree makes the signature size logarithmically increase with the ring scale. In the era of big data explosion, this feature enables the proposed scheme to maintain a comparatively short signature size even when the number of ring members N is very large. When $N=8$, the signature size is 61.08 KB; when N increases to 512, the size is 65.02 KB. From the data, we can observe that the signature size grows slowly, by only 4 KB when N grows exponentially, which is much slower than ring signatures with linear growth. Full article

The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of concern. This is because AI language models are far from neutral or objective; they inherit biases from their training data and the individuals who design and utilize them, which can sway voter decisions and affect global elections and democracy. In this research paper, we explore how AI can directly impact election outcomes through various techniques. These include the use of generative AI for disseminating false political information, favoring certain parties over others, and creating fake narratives, content, images, videos, and voice clones to undermine opposition. We highlight how AI threats can influence voter behavior and election outcomes, focusing on critical areas, including political polarization, deepfakes, disinformation, propaganda, and biased campaigns. In response to these challenges, we propose a Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time. It leverages the transparency of blockchain technology to reinforce electoral integrity. Finally, we also propose comprehensive countermeasures, including enhanced legislation, technological solutions, and public education initiatives, to mitigate the risks associated with AI in electoral contexts, proactively safeguard democracy, and promote fair elections. Full article

The integrity of democratic processes relies on secure and reliable election systems, yet achieving this reliability is challenging. This paper introduces the Post-Quantum Secured Multiparty Computed Hierarchical Authoritative Consensus Blockchain (PQMPCHAC-Bchain), a novel e-voting system designed to overcome the limitations of current Biometric Electronic Voting Machine (EVM) systems, which suffer from trust issues due to closed-source designs, cyber vulnerabilities, and regulatory concerns. Our primary objective is to develop a robust, scalable, and secure e-voting framework that enhances transparency and trust in electoral outcomes. Key contributions include integrating hierarchical authorization and access control with a novel consensus mechanism for proper electoral governance. We implement blockchain sharding techniques to improve scalability and propose a multiparty computed token generation system to prevent fraudulent voting and secure voter privacy. Post-quantum cryptography is incorporated to safeguard against potential quantum computing threats, future-proofing the system. Additionally, we enhance authentication through a deep learning-based face verification model for biometric validation. Our performance analysis indicates that the PQMPCHAC-Bchain e-voting system offers a promising solution for secure elections. By addressing critical aspects of security, scalability, and trust, our proposed system aims to advance the field of electronic voting. This research contributes to ongoing efforts to strengthen the integrity of democratic processes through technological innovation. Full article

This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system’s redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system’s dynamics—the input observer and the system model—which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheeled omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach’s efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults. Full article

Digital transformation and new technologies have made people’s lives easier and led to great results in most areas of business and society. Implementing blockchain technology is one of the best tools for establishing sustainable smart cities and societies. In terms of sustainable governance sophisticated and secure voting systems are necessary to achieve high integrity and transparency and null election fraud, and, in environmental sustainability, e-voting systems eliminate the mass waste of paper and transportation gas emissions; namely, e-voting systems are eco-friendly with high democratic outcomes. Blockchain technology can revolutionize e-voting by increasing the security and transparency of the voting process. Integrating artificial intelligence (AI) and machine learning (ML) into blockchain-based e-voting systems further augments their effectiveness. AI algorithms can analyze voting patterns and detect irregularities, supporting the prevention of fraudulent activities and coercion. ML procedures can enhance voter authentication processes, improve accessibility for diverse demographics, and optimize the productivity of blockchain networks during peak voting periods. This study focuses on understanding citizen perceptions of blockchain-based e-voting in a smart city context using the Technology Acceptance Model (TAM). The study’s results indicate that perceived ease of use and perceived usefulness are important factors in determining citizens’ intentions to use blockchain-based e-voting. Furthermore, trust in the technology and perceived security were found to influence the usefulness of blockchain-based e-voting positively. This study provides important insights for policymakers and technologists seeking to promote the adoption of blockchain-based e-voting systems in smart cities. The findings of the research supported the research model with positive results. In conclusion, our research model encourages the adoption of a blockchain-based e-voting system to enhance the future voting environment. Full article