Learning path recommendation seeks to provide students with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relations, which present two major limitations: (1) Prerequisite relations between knowledge concepts are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. (2) Relying on a single sequentially dependent knowledge structure based on prerequisite relations implies that a confusing knowledge concept can disrupt subsequent learning processes, which is referred to as blocked learning. To address these two challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relations between knowledge concepts. Specifically, we introduce a knowledge structure graph generation module EDU-GraphRAG that constructs knowledge structure graphs for different educational datasets, significantly improving the applicability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module that utilizes similarity relations as fallback relations when prerequisite relations become ineffective, thereby alleviating the blocked learning. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also generates more effective and longer learning paths.

Large Vision--Language Models (LVLMs) frequently fail on knowledge-intensive educational video QA despite the presence of requisite visual evidence. Through region-level analysis, we identify a systematic evidence-suppression pattern: task-critical tokens (e.g., diagrams) exhibit lower representational energy than distractors at the encoder output, rendering them prone to persistent attention neglect during decoding. While a controlled study shows that performance is sensitive to the strength and amount of injected candidate evidence signals, we find that rigid heuristics are insufficient due to sensitivity to token quality. To address this, we propose Bi-level Active Evidence Injection (BAEI), a decoding-time intervention that keeps the LVLM backbone frozen. BAEI employs a lightweight Injection Policy Network (IPN), optimized via GRPO, to dynamically select candidate evidence tokens and predict their token-wise injection strengths. The framework operates on two levels: increasing the contribution of candidate evidence-related signals in shallow layers and performing adaptive correction in deep layers triggered by predictive entropy. Experiments on educational benchmarks demonstrate consistent gains, validating decoding-time evidence intervention as an effective solution for factual alignment. The code is available at https://github.com/diojojolc-cell/BAEI.

Abstraction is an important and useful concept in the field of artificial intelligence. To the best of our knowledge, there is no syntactic method to compute a sound and complete abstraction from a given low-level basic action theory and a refinement mapping. This paper aims to address this issue. To this end, we first present a variant of situation calculus, namely linear integer situation calculus, which serves as the formalization of high-level basic action theory. We then migrate Banihashemi, De Giacomo, and Lesperance’s abstraction framework to one from linear integer situation calculus to extended situation calculus. Furthermore, we identify a class of Golog programs, namely guarded actions, so as to restrict low-level Golog programs, and impose some restrictions on refinement mappings. Finally, we design a syntactic approach to computing a sound and complete abstraction from a low-level basic action theory and a restricted refinement mapping.

The Weber location problem is widely used in several artificial intelligence scenarios. However, the gradient of the objective does not exist at a considerable set of singular points. Recently, a de-singularity subgradient method has been proposed to fix this problem, but it can only handle the q-th-powered l_2-norm case (1

With the rapid growth of online education, the types and volumes of educational resources have increased significantly. Efficient classification of these resources can substantially reduce manual workload and enhance management effectiveness. However, existing models often struggle to accurately classify long-content educational content, particularly under imbalanced category distributions. To address these challenges, we propose PERC, a prior-guided framework for classifying long educational resources with imbalanced categories. To the best of our knowledge, PERC is the first framework to incorporate the foundational cognitive dimensions of Bloom's Taxonomy into educational resource classification. First, PERC leverages standardized pedagogical classification guidelines and maps the original label space into a semantically structured prior category space using a Structured State-Space Learning framework. Second, to handle the length and high information density of educational texts, we introduce a Dynamic Sliding Window Attention mechanism that captures both local and partial global dependencies, enabling the extraction of compact, semantically rich representations. Finally, a category-aware classifier integrates the prior representation of each category with the semantic representation of the resource to produce a category-aware embedding for final prediction. To evaluate PERC, we constructed two datasets: EduMix-24 and EduMath-24, comprising 18,799 educational resources manually annotated across 9 lesson types, 15 teaching modes, and 9 activity elements. Classification experiments on all three tasks consistently demonstrate that PERC outperforms state-of-the-art baselines.

The knowledge tracing (KT) model based on deep learning has been proven to be superior to the traditional knowledge tracing model, eliminating the need for artificial engineering features. However, there are still problems, such as insufficient interpretability of the learning and answering processes. To address these issues, we propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration, namely KVFKT. Firstly, the embedding representation module is responsible for embedding the questions and computing the attention vector of knowledge concepts (KCs) when students answer questions and when answer time stamps are collected. Secondly, the forgetting quantification module performs the pre-prediction update of the student’s knowledge state matrix. This quantification involves calculating the interval time and associated forgetting rate of relevant KCs, following the forgetting curve. Thirdly, the answer prediction module generates responses based on students’ knowledge status, guess coefficient, and question difficulty. Finally, the knowledge status update module further refines the students’ knowledge status according to their answers to the questions and the characteristics of those questions. In the experiment, four real-world datasets are used to test the model. Experimental results show that KVFKT better traces students’ knowledge state and outperforms state-of-the-art models.

Despite advancing language model (LM) alignment, direct preference optimization (DPO) falls short in LM reasoning with the free lunch from reinforcement learning (RL). As the breakthrough, this work proposes a new RL-free preference optimization method aiming to achieve DPO along with learning another LM, whose response generation policy holds the asymptotic equivalence with AlphaZero-like search, the apex of algorithms for complex reasoning missions like chess Go. While circumventing explicit value and reward modeling, the neural implicit tree search executed by the extra LM remains seeking to equip DPO with reasoning procedure technically akin to AlphaZero. Our experiments demonstrate that our methodology outperforms both regular DPO variants in human preference alignment, and MCTS-based LMs in mathematical reasoning and planning tasks.

Conformant planning is a computationally challenging task that generates an action sequence to achieve goal condition with uncertain initial states and non-deterministic actions. The generate-and-complete (in short, GC) approach shows superior performance on conformant planning, which iteratively enumerates the solution of a planning subproblem for a single initial state and attempts to extend it for all initial states until a conform solution is found. However, two major drawbacks of the GC approach hinder its performance: the computational overhead due to state exploration and the insertion of many redundant actions. To overcome the above drawbacks, we improve both verification and completion procedures. Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF.

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.

Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse learning capacities of students present a notable difficulty in recommending exercises. Collaborative filtering approaches frequently have difficulties in recommending suitable exercises, whereas deep learning methods lack explanation, which restricts their practical use. To address these issue, this paper proposes KG4EER, an explainable exercise recommendation with a knowledge graph. KG4EER facilitates the matching of various students with suitable exercises and offers explanations for its recommendations. More precisely, a feature extraction module is introduced to represent students' learning features, and a knowledge graph is constructed to recommend exercises. This knowledge graph, which includes three primary entities - knowledge concepts, students, and exercises - and their interrelationships, serves to recommend suitable exercises. Extensive experiments conducted on three real-world datasets, coupled with expert interviews, establish the superiority of KG4EER over existing baseline methods and underscore its robust explainability.

Adverse weather conditions such as haze and fog often significantly reduce the performance of crowd counting models. An intuitive solution is to preprocess degraded images by applying image restoration techniques prior to crowd counting. However, this solution introduces additional computational complexity and may produce restored images with noise and artifacts that is harmful to the subsequent crowd counting task. To mitigate the two issues, we integrate an image restoration module (IRM) into a unified framework to propose an effective network for crowd counting and localization in haze and rain. The lightweight IRM is designed to guide the network to learn haze-aware knowledge in feature space, which is removed in the inference phase without increasing the computational cost. In addition, two new datasets are constructed to evaluate the crowd counting methods in haze and rain. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed method. The code is available at https://github.com/lizhangray/Dehaze-P2PNet.

Modern society largely depends on various network systems such as computer networks, communications, and power networks, it is crucial to exactly compute the reliability of these network systems. The exact computation of network reliability is known to be #P-Complete problem. The state-of-the-art exact computation method is based on binary decision diagrams (BDDs). Sentential decision diagrams (SDDs) are a new canonical representation of Boolean functions which is a strict superset of BDD. In both theoretical and practical perspectives, SDDs are a more compact representation than BDDs. In addition, it maintains canonicity and polynomial-time operations on Boolean functions. In this paper, we propose a top-down compilation algorithm based on SDD for representing the set of subgraphs that makes the network active. Then the exact computation of network reliability is based on the constructed SDD. The experiments show that in most real networks and all synthetic networks, the resulting SDD is smaller than the BDD representing the same set of subgraphs. Thus, SDD-based method to exact computation of network reliability is more efficient than BDD-based.

Multi-Modal Large Language Models (MM-LLMs) have demonstrated powerful reasoning abilities in various visual question-answering tasks. However, they face the challenge of lacking rigorous reasoning and precise arithmetic, when solving geometry questions. To address this challenge, we propose a novel prompting method, namely Reason-and-Execute (R&E), to enhance the accuracy of solving geometry questions by MM-LLMs. Specifically, the R&E method includes two templates: reasoning template and execution template. We first adopt a reverse-thinking approach to construct a rigorous reasoning template so that it guides MM-LLMs to start reasoning from the most relevant domain knowledge of the question and ultimately identify the arithmetic requirements. We then make use of program-assisted thought to construct execution template in order to guide MM-LLMs to understand the arithmetic requirements from reasoning template and generate executable code block. The answer is finally obtained by executing the code block. We evaluate our prompting method on 9 models in answering questions on 6 datasets (including four geometry datasets and two science datasets) compared to Chain-of-Thought (CoT) and Program-Aided Language (PAL) prompting methods. R&E method shows up to 12.8% improvement compared to CoT and PAL, proving strong reasoning and arithmetic abilities for solving geometry questions of our method. Moreover, we further analyze the answering accuracy from the different perspectives on solving geometric questions, including domain knowledge, geometry shapes, question length, and language. Through multiple analysis, our method is able to enhance the ability of MM-LLMs to solve geometry questions.

Trend representation has been attracting more and more attention recently in portfolio optimization (PO) via machine learning methods. It adopts concepts and phenomena from the field of empirical and behavioral finance when little prior knowledge is obtained or strict statistical assumptions cannot be guaranteed. It is used mostly in estimating the expected asset returns, but hardly in measuring risk. To fill this gap, we propose a novel multitrend conditional value at risk (MT-CVaR), which embeds multiple trends and their influences in CVaR. Besides, we propose a novel PO model with this MT-CVaR as the risk metric and then design a solving algorithm based on the interior point method to compute the portfolio. Extensive experiments on six benchmark datasets from diverse financial markets with different frequencies show that MT-CVaR achieves the state-of-the-art investing performance and risk management.

Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.

In order to improve the security of IoT information systems, this paper proposes the Blockchain-based Framework for Securing IoT Information (BFSII), which is built on consortium blockchain and the edge IoT architectures. This paper addresses data security in smart hotels as a research scenario. The majority of data generated by IoT devices in smart hotels contains users' private information, which is susceptible to alteration and leakage during transmission and storage. The BFSII solution leverages the decentralized nature of blockchain to enhance data traceability and tamper-proof capabilities. And it uses edge IoT architecture and consortium blockchain to improve system operational efficiency. Sensitive data generated by IoT devices are protected in BFSII. The experiment's findings show that BFSII can boost smart hotel system security while maintaining operational effectiveness. The information management system of smart hotels is provided with an inventive and secure solution by the BFSII framework.

The primary aim of cognitive diagnosis is to predict students' performance and knowledge structures by analyzing their learning behavior and answering results, thereby enabling educators can provide personalized instruction. Scholars have proposed many cognitive diagnostic models. However, most of the models do not fully extract and utilize the relevant data and parameters of cognitive diagnosis. Moreover, some models only rely on artificially designed simple functions to analyze the cognitive process of students, which cannot fully capture the complex relationship between students and the exercises. To address these limitations, this paper proposes a neural cognitive diagnostic model named NCDFSA, which focuses on students' attention to knowledge concepts. The model utilizes neural networks to diagnose students' knowledge and considers students' implicit relationships with knowledge concepts. We introduce the concept of attention matrix(AM) and define the importance of knowledge concepts by the frequency of usage of knowledge concepts to improve the prediction effect. This paper compares NCDFSA with existing classical models on four real datasets and finds that the model has higher accuracy and rationality in predicting student performance.

The Knowledge Tracing (KT) task aims to trace the changes of students' knowledge state in real time according to students' historical learning behavior, and predict students' future learning performance. The modern KT models have two problems. One is that these KT models can't reflect students' actual knowledge level. Most KT models only judge students' knowledge state based on their performance in exercises, and poor performance will lead to a decline in knowledge state. However, the essence of students' learning process is the process of acquiring knowledge, which is also a manifestation of learning behavior. Even if they answer the exercises incorrectly, they will still gain knowledge. The other problem is that many KT models don't pay enough attention to the impact of students' forgetting behavior on the knowledge state in the learning process. In fact, learning and forgetting behavior run through students' learning process, and their effects on students' knowledge state shouldn't be ignored. In this paper, based on educational psychology theory, we propose a knowledge tracing model with learning and forgetting behavior (LFBKT). LFBKT comprehensively considers the factors that affect learning and forgetting behavior to build the knowledge acquisition layer, knowledge absorption layer and knowledge forgetting layer. In addition, LFBKT introduces difficulty information to enrich the information of the exercise itself, while taking into account other answering performances besides the answer. Experimental results on two public datasets show that LFBKT can better trace students' knowledge state and outperforms existing models in terms of ACC and AUC.

Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which aims to diagnose the strengths and weaknesses of participants. Existing cognitive diagnosis methods only consider which of knowledge concepts are involved in the knowledge components of exercises, but ignore the fact that different knowledge concepts have different effects on practice scores in actual learning situations. Therefore, researchers need to reshape the learning scene by combining the multi-factor relationships between knowledge components. In this paper, in order to more comprehensively simulate the interaction between students and exercises, we developed a neural network-based CDMFKC model for cognitive diagnosis. Our method not only captures the nonlinear interaction between exercise characteristics, student performance, and their mastery of each knowledge concept, but also further considers the impact of knowledge concepts by designing the difficulty and discrimination of knowledge concepts, and uses multiple neural layers to model their interaction so as to obtain accurate and interpretable diagnostic results. In addition, we propose an improved CDMFKC model with guessing parameter and slipping parameter designed by knowledge concept proficiency and student proficiency vectors. We validate the performance of these two diagnostic models on six real datasets. The experimental results show that the two models have better effects in the aspects of accuracy, rationality and interpretability.

Strategy representation and reasoning has recently received much attention in artificial intelligence. Impartial combinatorial games (ICGs) are a type of elementary and fundamental games in game theory. One of the challenging problems of ICGs is to construct winning strategies, particularly, generalized winning strategies for possibly infinitely many instances of ICGs. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on the linear integer arithmetic fragment of numeric part of PDDL. We then propose an approach to generating the winning formula that exactly captures the states in which the player can force to win. Furthermore, we compute winning strategies for ICGs based on the winning formula. Experimental results on several games demonstrate the effectiveness of our approach.

In short-term portfolio optimization (SPO), some financial characteristics like the expected return and the true covariance might be dynamic. Then there are only a small window size $w$ of observations that are sufficiently close to the current moment and reliable to make estimations. $w$ is usually much smaller than the number of assets $d$, which leads to a typical undersampled problem. Worse still, the asset price relatives are not likely subject to any proper distributions. These facts violate the statistical assumptions of the traditional covariance estimates and invalidate their statistical efficiency and consistency in risk measurement. In this paper, we propose to reconsider the function of covariance estimates in the perspective of operators, and establish a rank-one covariance estimate in the principal rank-one tangent space at the observation matrix. Moreover, we propose a loss control scheme with this estimate, which effectively catches the instantaneous risk structure and avoids extreme losses. We conduct extensive experiments on $7$ real-world benchmark daily or monthly data sets with stocks, funds and portfolios from diverse regional markets to show that the proposed method achieves state-of-the-art performance in comprehensive downside risk metrics and gains good investing incomes as well. It offers a novel perspective of rank-related approaches for undersampled estimations in SPO.

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset's performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system.

Modal logics are primary formalisms for multi-agent systems but major reasoning tasks in such logics are intractable, which impedes applications of multi-agent modal logics such as automatic planning. One technique of tackling the intractability is to identify a fragment called a normal form of multiagent logics such that it is expressive but tractable for reasoning tasks such as entailment checking, bounded conjunction transformation and forgetting. For instance, DNF of propositional logic is tractable for these reasoning tasks. In this paper, we first introduce a notion of logical separability and then define a novel disjunctive normal form SDNF for the multiagent logic Kn, which overcomes some shortcomings of existing approaches. In particular, we show that every modal formula in Kn can be equivalently casted as a formula in SDNF, major reasoning tasks tractable in propositional DNF are also tractable in SDNF, and moreover, formulas in SDNF enjoy the property of logical separability. To demonstrate the usefulness of our approach, we apply SDNF in multi-agent epistemic planning. Finally, we extend these results to three more complex multi-agent logics Dn, K45n and KD45n.

The Sentential Decision Diagram (SDD) is a compact and canonical representation of Boolean functions that generalizes the Ordered Binary Decision Diagrams (OBDDs). A variant of SDDs, namely Zero-suppressed Sentential Decision Diagrams (ZSDDs), was proposed recently by using different trimming rules. SDDs are suitable for functions where adjacent input assignments have the same outcome, while ZSDDs are more compact for spare functions. In this paper, we introduce a novel canonical SDD variant, called the Tagged Sentential Decision Diagrams (TSDDs). The key insight of TSDDs is to combine both trimming rules of SDDs and ZSDDs. With both characteristics of SDDs and ZSDDs, the TSDD representation is at least as small as the SDD or ZSDD representation for any Boolean functions. This is also shown in our experimental evaluation.

We propose a short-term sparse portfolio optimization (SSPO) system based on alternating direction method of multipliers (ADMM). Although some existing strategies have also exploited sparsity, they either constrain the quantity of the portfolio change or aim at the long-term portfolio optimization. Very few of them are dedicated to constructing sparse portfolios for the short-term portfolio optimization, which will be complemented by the proposed SSPO. SSPO concentrates wealth on a small proportion of assets that have good increasing potential according to some empirical financial principles, so as to maximize the cumulative wealth for the whole investment. We also propose a solving algorithm based on ADMM to handle the $\ell^1$-regularization term and the self-financing constraint simultaneously. As a significant improvement in the proposed ADMM, we have proven that its augmented Lagrangian has a saddle point, which is the foundation of the iterative formulae of ADMM but is seldom addressed by other sparsity strategies. Extensive experiments on $5$ benchmark data sets from real-world stock markets show that SSPO outperforms other state-of-the-art systems in thorough evaluations, withstands reasonable transaction costs and runs fast. Thus it is suitable for real-world financial environments.

Belief change is an important research topic in AI. It becomes more perplexing in multi-agent settings, since the action of an agent may be partially observable to other agents. In this paper, we present a general approach to reasoning about actions and belief change in multi-agent settings. Our approach is based on a multi-agent extension to the situation calculus, augmented by a plausibility relation over situations and another one over actions, which is used to represent agents' different perspectives on actions. When an action is performed, we update the agents' plausibility order on situations by giving priority to the plausibility order on actions, in line with the AGM approach of giving priority to new information. We show that our notion of belief satisfies KD45 properties. As to the special case of belief change of a single agent, we show that our framework satisfies most of the classical AGM, KM, and DP postulates. We also present properties concerning the change of common knowledge and belief of a group of agents.