The rapid development of large artificial intelligence (AI) models—large language models (LLMs), multimodel large language models (MLLMs) and vision–language models (VLMs)—has enabled instruction-driven visual understanding, where a...
In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data c...
In this article, we extend a recently introduced kinetic model for consensus-based segmentation of images. In particular, we will interpret the set of pixels of a 2D image as an interacting particle system that evolves in time in view of a consensus-...
Manual classification of algorithmic problems is costly and inconsistent, although it remains essential for curriculum sequencing, recommendation systems, and quality control on competitive programming platforms. To address this limitation, this stud...
Backdoor attacks pose a critical security risk to pre-trained language models (PLMs) by utilizing concealed triggers to manipulate model outputs. Existing defense strategies largely depend on statistical thresholds, which often struggle to identify s...
Sustainability in transportation is vital for lessening environmental effects and ensuring the long-term viability of the transportation industry. The aviation sector, a significant player in sustainable transport, has encountered obstacles in reduci...
In social network-based group decision-making, achieving consensus often entails costs, leading to an inherent trade-off between cost and consensus. To address this issue, we propose a dual-semantic, multi-objective consensus optimization model that...
Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the w...
The Nervos CKB (Common Knowledge Base) is a public permissionless blockchain designed for the Nervos ecosystem. The CKB consensus protocol is the key protocol of the Nervos CKB, which improves the limit of the consensus’s performance for Bitcoin. In...
Non-consensus phenomena are widely observed in human society, but more attention is paid to consensus phenomena. One famous consensus model is the DeGroot model, and there are a series of outstanding works derived from it. By introducing the cognitio...
Achieving consensus in group decision-making is both essential and challenging, especially in which non-cooperative behaviors can significantly hinder the process under uncertainty. These behaviors may distort consensus outcomes, leading to increased...
Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared vie...
Mixed-integer mathematical programming has been widely used to model and solve challenging optimization problems. One interesting feature of this technique is the ability to prove the optimality of the achieved solution, for many practical scenarios...
In group decision making (GDM), experts from a variety of fields collaborate to select the best alternative. Due to external influences or a lack of sufficient knowledge, experts may sometimes offer low-quality opinions on alternatives. In existing G...
Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus...
Artificial intelligence (AI) helps people improve decision-making in uncertain environments. When there is an AI decision maker (DM) in a large-scale group, how can consensus be reached? We address this problem by developing a hybrid human–AI c...
Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted c...
In this work, we address the question of the role of the influence of group size on the emergence of various collective social phenomena, such as consensus, polarization and social hysteresis. To answer this question, we study the three-state noisy q...
Complex conflict environments often involve composite conflicting parties composed of multiple individuals with divergent assessments, posing significant challenges for preference elicitation and conflict analysis in the Graph Model for Conflict Reso...
To address challenges in complex group decision-making (GDM)—specifically preference fuzziness, intricate subgroup segmentation, and non-cooperative behavior—this study proposes an adaptive consensus model based on probabilistic linguisti...
In this study, the distributed consensus control and model predictive control (MPC)-based formation strategies for quadrotors are proposed. First, the formation-control problem is decoupled into horizontal and vertical motions. The distributed consen...
The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing...
Large language models (LLMs) are increasingly deployed in second-language (L2) writing assessment. However, substantial disparities persist in how different architectures operationalize shared scoring criteria. This study investigates model-specific...
Cooperative control of multi-agent systems (MASs) is essential in engineering applications. However, malicious attacks and uncertainties can drive MASs to failure. Regrettably, prior work on resilient control of MASs rarely addresses uncertainties an...
Reliable road pothole detection remains challenging in complex environments, where low contrast, shadows, water films, and strong background textures cause frequent false alarms, missed detections, and boundary instability. Thin rims and adjacent obj...
This article aims to investigate relationships between the different realms of PCK by referring to the refined consensus model of PCK. We conducted a pre–post study with an intervention using a video-based simulated learning environment to meas...
Science education researchers have developed a refined understanding of the structure of science teachers’ pedagogical content knowledge (PCK), but how to develop applicable and situation-adequate PCK remains largely unclear. A potential proble...
In this article, we analyse potential filters that moderate the transformation process between the realms of PCK defined in the refined consensus model of pedagogical content knowledge. We tested 58 preservice biology teachers in a 15-week one-group...
Background/Objective: This study aimed to develop a predictive model to classify and rank highly active compounds that inhibit HIV-1 integrase (IN). Methods: A total of 2271 potential HIV-1 inhibitors were selected from the ChEMBL database. The most...
A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready...
In this article, we analyse how to improve pre-service biology teachers’ pPCK (personal Pedagogical Content Knowledge), professional values and motivational orientations in the field of academic and scientific language. On the basis of the theo...
Background: The reliable integration of large language models (LLMs) into neuroimaging data extraction workflows remains unresolved. Prior benchmarking shows that exact-match accuracy underestimates LLM extraction performance, but whether inter-model...
Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to...
Background: Adoption of surgical robotic systems is accelerating globally, driven by advances in clinical capability, ergonomics, and workflow efficiency. However, robotic surgery programs introduce complex organizational challenges related to patien...
The traditional consensus algorithm based on the Internet of Vehicles (IoV) system has the disadvantages of high latency, low reliability, and weak fault tolerance, and it cannot make real-time adjustments according to the actual environment, making...
We study the effect of time-fluctuating social influences on the formation of polarization and consensus in a three-party community consisting of two types of voters (“leftists” and “rightists”) holding extreme opinions, and m...
Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills...
While predictive accuracy is often prioritized in machine learning (ML) models, interpretability remains essential in scientific and high-stakes domains. However, diverse interpretability algorithms frequently yield conflicting explanations, highligh...
Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by mu...
A blockchain has been applied in many areas, such as cryptocurrency, smart cities and digital finance. The consensus protocol is the core part of the blockchain network, which addresses the problem of transaction consistency among the involved partic...
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 tw...
This paper is concerned with consensus problem of a class of second-order multi-agent systems subjecting to external disturbance generated from some unknown exosystems. In comparison with the case where the disturbance is generated from some known ex...
Although ensemble clustering methods based on the co-association (CA) matrix have achieved considerable success, they still face the following challenges: (1) in the label space, the noise within the connective matrices and the structural differences...
With the advancement of information technology, social media has become increasingly prevalent. The complex networks of social relationships among decision-makers (DMs) have given rise to the problem of social network group decision-making (SNGDM), w...
The polarization of opinions and difficulties in reaching a consensus are central problems of many modern societies. Understanding the dynamics governing those processes is, therefore, one of the main aims of sociophysics. In this work, the Sznajd mo...
Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combus...
Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually cons...
In recent years, the frequent occurrence of public emergencies has often triggered the rapid spread and amplification of social emotions. The accumulation and intensification of negative emotions can lead to collective behaviors and even pose a threa...
Affected by inter-annotator cognitive differences, fatigue effects, and data poisoning, training data inevitably contains a certain proportion of noise, which severely impairs model performance. Traditional manual verification is costly and inefficie...
Language ability reflects the developmental level and social communication in children with autism spectrum disorder (ASD), making scalable and explainable assessment essential. Existing large language model (LLM)-based methods mainly rely on single-...
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