Knowledge Tracing (KT) aims to mine students’ evolving knowledge states and predict their future question-answering performance. Existing methods based on heterogeneous information networks (HINs) are prone to introducing noises due to manual or random selection of meta-paths and lack necessary quality assessment of meta-path instances. Conversely, recent large language models (LLMs)-based methods ignore the rich information across students, and both paradigms struggle to deliver consistently accurate and evidence-based explanations. To address these issues, we propose an innovative framework, HIN-LLM Synergistic Enhanced Knowledge Tracing (HISE-KT), which seamlessly integrates HINs with LLMs. HISE-KT first builds a multi-relationship HIN containing diverse node types to capture the structural relations through multiple meta-paths. The LLM is then employed to intelligently score and filter meta-path instances and retain high-quality paths, pioneering automated meta-path quality assessment. Inspired by educational psychology principles, a similar student retrieval mechanism based on meta-paths is designed to provide a more valuable context for prediction. Finally, HISE-KT uses a structured prompt to integrate the target student's history with the retrieved similar trajectories, enabling the LLM to generate not only accurate predictions but also evidence-backed, explainable analysis reports. Experiments on four public datasets show that HISE-KT outperforms existing KT baselines in both prediction performance and interpretability.

Vision-language models (VLMs) have gained widespread attention for their strong zero-shot capabilities across numerous downstream tasks. However, these models assume that each test image’s class label is drawn from a predefined label set and lack a reliable mechanism to reject samples from emerging unknown classes when only unlabeled data are available. To address this gap, open-set domain adaptation methods retrain models to push potential unknowns away from known clusters. Yet, some unknown samples remain stably anchored to specific known classes in the VLM feature space due to semantic relevance, which is termed as Semantic Affinity Anchoring (SAA). Forcibly repelling these samples unavoidably distorts the native geometry of VLMs and degrades performance. Meanwhile, existing score‑based unknown detectors use simplistic thresholds and suffer from threshold sensitivity, resulting in sub‑optimal performance. To address aforementioned issues, we propose VLM-OpenXpert, which comprises two training‑free, plug‑and‑play inference modules. SUFF performs SVD on high-confidence unknowns to extract a low-rank "unknown subspace". Each sample’s projection onto this subspace is weighted and softly removed from its feature, suppressing unknown components while preserving semantics. BGAT corrects score skewness via a Box–Cox transform, then fits a bimodal Gaussian mixture to adaptively estimate the optimal threshold balancing known-class recognition and unknown-class rejection. Experiments on 9 benchmarks and three backbones (CLIP, SigLIP, ALIGN) under Source-Free OSDA settings show that our training-free pipeline matches or outperforms retraining-heavy state-of-the-art methods, establishing a powerful lightweight inference calibration paradigm for open-set VLM deployment.

Knowledge Tracing (KT) is essential for tracking students' evolving knowledge states and predicting their future performance.While current graph-based methods focus on exerciseconcept relations, they often overlook the inherent group structures among students.Similarly, emerging LLM-based approaches rely on individual histories, lacking the broader context of group references and contrastive evidence.As a result, existing individual-isolation paradigms fail to provide stable predictions and evidencebased explanations.To bridge this gap, we propose Micro-Community Knowledge Tracing (MicroC-KT), a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT.MicroC-KT identifies latent learning communities via hypergraph modeling and generates dual-granular summaries to facilitate community matching and peer retrieval.By extracting contrastive group evidence, the model prompts an LLM to generate both accurate answer predictions and verifiable analysis reports.Experiments on four public datasets demonstrate that MicroC-KT significantly outperforms state-of-the-art baselines in predictive performance while providing more reliable and evidence-based explanations.

Large language models (LLMs) require continual knowledge updates to keep pace with the evolving world.While various model editing methods have been proposed, most face critical challenges in the context of lifelong learning due to two fundamental limitations: (1) Edit Overshooting -parameter updates intended for a specific fact spill over to unrelated regions, causing interference with previously retained knowledge; and (2) Knowledge Entanglement -polysemantic neurons' overlapping encoding of multiple concepts makes it difficult to isolate and edit a single fact.In this paper, we propose MicroEdit, a neuron-level editing method that performs minimal and controlled interventions within LLMs.By leveraging a sparse autoencoder (SAE), MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates.This targeted design enables fine-grained control over the editing scope, effectively mitigating interference and preserving unrelated knowledge.Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaMA 1 and Mistral 2 .

Graph anomaly detection methods based on contrastive learning have been widely proposed due to their powerful unsupervised feature learning capabilities. However, most existing methods overlook the loss and degradation of original graph information caused by the random data augmentation. In addition, the impact of anomalous nodes is usually ignored during the training phase. To address these issues, we propose a collaborative contrastive self-supervised graph anomaly detection model, namely, CCS-GAD. The core idea lies in the collaboration and mutual constraints among its submodules. Specifically, we design a view collaboration module to seamlessly integrate contrastive-based and reconstruction-based frameworks, capturing node patterns from both local and global views simultaneously to alleviate the graph information gaps caused by data augmentation in conventional self-supervised paradigms. Additionally, we introduce a filtering collaboration method to identify and filter out potential anomalies exhibiting abnormal behaviors from other nodes, thereby reducing their interference with the model’s learning of normal patterns. Finally, anomalies are detected based on local consistency and global reconstruction errors. Extensive experiments on six real-world datasets with seven state-of-the-art(SOTA) baselines demonstrate the superior adaptability of our model in graph anomaly detection. Specifically, our model achieves an average improvement of 16.43% in AUC, 5.83% in Precision, and 9.48% in F1-score.

The cooperation in mixed cooperative-competitive tasks has drawn significant attention in multi-agent deep reinforcement learning. Agents need to cooperate with their teammates while competing against their opponents. However, most existing works treat the cooperative agents and competitive agents equally as they perform the same operation on all the agents. As a result, without distinguishing between cooperative and competitive agents, they may suffer from information disorder in learning an optimally cooperative policy and struggle to decide on the next step action. To address the above issues, we decompose the final Q-value into a weighted combination of three parts: the Q-values of the cooperative agents, the competitive group, and the current agent. A theoretical proof of the correctness of the decomposition is provided. With this decomposition, we are able to consider cooperative and competitive agents separately. Accordingly, we propose a multi-agent actor-critic algorithm called actor-double-attention-critic (ADAC) under centralized training and decentralized execution according to the decomposition. In ADAC, networks with group-specific attention and an attentional weighting network are specially designed. With the designed double-attention structure, ADAC can capture the distributions from different agents and improve cooperation performance. Extensive experiments are conducted in three scenarios with nine settings against six representative methods. The results demonstrate the superiority of the proposed ADAC model against state-of-the-art methods in various mixed cooperative-competitive tasks. The code is available at https://github.com/CrazyBayes/ADAC

Compared to block precoding (BP), interference exploitation symbol-level precoding (IESLP) can effectively reduce transmit power in multiantenna wireless communication systems. However, because most existing IESLP methods are symbol-level optimizations of zero-forcing (ZF) BP which is usually not the best BP, the performance and application range of IESLP are restricted. In this article, we investigate the symbol-level optimization of any given BP method to reduce transmit power. We first define generalized constructive interference (GCI) as a new constructive interference metric for any received BP signals, where the interference is constructive as long as it will not impair the symbol error rate (SER). Then, convex GCI regions (GCIRs) of received BP signals for both phase shift keying (PSK) and quadrature amplitude modulation (QAM) constellations are designed. With the aid of GCIR, we propose BP-based symbol-level precoding (BPSLP) methods to reduce transmit power. Moreover, due to the difficulty of obtaining perfect channel state information (CSI), we also propose robust BPSLP (RBPSLP) methods to reduce transmit power for PSK and QAM constellations, where outage probability (OP) is constrained for robustness. Numerical results show that compared to existing precoding methods, our proposed precoding methods effectively reduce transmit power. It is worth noting that due to the bounded or even one-point constructive interference region (CIR), there are usually no feasible solutions for existing robust IESLP methods under QAM constellations, but our proposed RBPSLP method for QAM constellations is feasible as long as a feasible robust BP matrix exists.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

A redundancy-based strategy is widely employed by assigning each task to multiple workers and then inferring the correct answer (called truth) for each task in crowdsourcing. Most existing truth inference methods are designed for the situation with a fairly big number of answers for each task (referred to as high redundancy). However, the high redundancy unavoidably leads to a high cost. In this work, we propose a novel truth inference approach called TWLR based on worker representations for the situation with a small number of answers for each task (referred to as low redundancy). We develop a deep model to learn the representations of workers considering both answers and worker-task relations. For each task, we identify the worker with the highest quality, and select his/her answer as the predicted answer. To the best of our knowledge, this is the first work to perform truth inference by utilizing deep learning techniques to deal with the low redundancy situation in crowdsourcing. We have conducted a set of experiments against 7 real-world datasets to show the accuracy improvement of our truth inference approach by comparing with 11 baseline methods.

Crowdsourcing platforms are widely used for requesters to find workers for general tasks. The answers to general tasks are usually open and not constrained by multiple choices. For the general tasks, the worker performance prediction models can facilitate the task assignment process in crowdsourcing. Worker performance prediction is affected by the three roles: the worker, the requester, and the task. The existing worker performance prediction models mainly consider the features of tasks and workers. However, these models rarely consider the features of requesters. And the existing worker performance prediction models for multiple-choice tasks are not suitable for general tasks as they are built based on the workers' accuracy on choices. In this work, we propose a worker performance prediction model by taking account of features of workers, tasks, and requesters to help requesters select workers for their general tasks on crowdsourcing platforms. We design a relationship learning module to learn the low dimension relationship representations of workers, tasks, and requesters. Furthermore, we design a performance learning model to predict workers' performance based on the features and relationship representations of workers, tasks, and requesters. A set of experiments against the realworld dataset from the Zhubajie platform has been conducted. Experimental results show that the proposed approach has better prediction results than the existing baseline methods.

In recent years, Internet of Things (IoT) has become one of the most important technologies in academia and industry. However, in many application scenarios, smart devices are distributed over a remote area without terrestrial communication systems. Considering the power of smart devices is limited, it is infeasible for terrestrial access networks to effectively receive the data of smart devices. Therefore, as a supplement to the terrestrial networks, Space-Air-Ground networks are critical to the Internet of Remote Things (IoRT). In this paper, we use high-altitude platforms (HAPs) to assist data transmission from smart devices to low earth orbit (LEO) satellites. Aiming at minimizing system power consumption, we propose an algorithm that jointly optimizes the resource allocation scheme and the HAP relay deployment. Since the problem is a mix-integer non-convex programming which is prohibitive to solve, our proposed algorithm divides it into two sub-problems to find a near-optimal solution with low computational complexity. Simulation results show that compared with average resource allocation scheme, our proposed algorithm can significantly reduce the system power consumption while satisfying the rate requirements of smart devices.

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.

General tasks on crowdsourcing platforms attract more and more workers with different skills and experiences. Existing approaches only leverage the information from tasks with feedback to evaluate worker ability. However, there are millions of tasks without feedback on the platforms. The participating behavior of workers involved in these tasks has not been exploited. In this work, we propose a worker ability model PB-Worker to support general tasks on crowdsourcing platforms. We model the worker latent relation and task latent relation by exploiting the worker participating behavior. To the best of our knowledge, this is the first work to consider the worker participating behavior. Our model is a semi-supervised model that can cover tasks with feedback and tasks without feedback. We employ the ladder network to generate the representations of workers and employ the neural network to predict the worker ability scores. A set of experiments against the real-world dataset from the Zhubajie platform has been conducted. Experimental results show that the output quality of the proposed approach is better than the existing baseline methods.

As the development of wireless communication system, high altitude platform(HAP) plays an important role in forwarding signal in the stratosphere and Ka-band attracts more and more attention because of larger transmission bandwidth. However, ka-band is more sensitive to the troposphere weathers. For Ka-band based HAP communication system, considering the scenario where the user moves over a large area and moves for a long time, the multi-states statistical channel model affected by the troposphere weathers and ground environment is proposed in this paper. Assuming that terrestrial terminal is in three scenarios, there are three state fading which are modeled by a Markov chain in each scenario. Firstly, the probability distribution function (PDF) of the received signal is analyzed; Secondly, a step-by-step methodology generating time-series of proposed channel model is presented; Finally, based on the proposed channel model, the time-series and the bit error rate (BER) performance are obtained.

Low Earth Orbit (LEO) satellite networks play a major role to provide communication support for the regions beyond the coverage of terrestrial network systems. The positions of gateway stations have influence on the time delay, power consumption and throughput of LEO satellite networks, making it important to deploy the gateway stations at the best position. Aiming at minimizing the inter-satellite hop count, we are the first to study the deployment problem of gateway stations. The gateway deployment problem is formulated as a non-convex optimization problem. According to the characteristics of the deployment problem, we propose an improved genetic algorithm (GA) and an improved simulated annealing algorithm (SA) not only to get the optimal deployment location, but also to greatly reduce the computational complexity. Simulation results and analyses show that compared with random deployment, the optimal locations of the gateway stations obtained by the proposed algorithms can decrease 42.6% average hop count of inter-satellite links and increase 4.4% average capacity of feeder links.

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.