The paper focuses on how to construct student models in the online virtual educational community. First, we analyze social interaction between students in the community, and present some algorithms to model students&#...
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The paper focuses on how to construct student models in the online virtual educational community. First, we analyze social interaction between students in the community, and present some algorithms to model students' (personal and shared) needs, preferences or knowledge structures in interaction activities. Then, the paper proposes an integrated student model combined with student modeling in information services and task processes, and emphasizes that it builds up a foundation to personalize information services and customize online educational programs.
As a novel system description and problem solving method, agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent interactions. Current research about agent org...
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As a novel system description and problem solving method, agent organization can potentially decrease the difficulty of problem solving and reduce the complexity of agent interactions. Current research about agent organization are mostly being undertaken in an agent organization model, organization structure, organization rules, organization formation and evolution, so it is necessary to extend the research to analyze mental states and their relations. In the paper, the mental states of commitments in agent organization are defined and analyzed including internal commitment, social commitment, group commitment and organization commitment. The semantics and properties of different commitments are given so advancing the works associated with agent organization.
The idea of performing client-server computing by transmission of executable programs between clients and servers has become highly popular among researchers and developers who are engaged in intelligent network servi...
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The idea of performing client-server computing by transmission of executable programs between clients and servers has become highly popular among researchers and developers who are engaged in intelligent network services. computing based on mobile agents is an important aspect of this idea. This paper focuses on researching the migration process of agents. A model based on modules is devised for constructing agents. A concurrent schedule method is presented, with which the agent migration can be easily implemented. Most of the unnecessary transmission of codes and data can be avoided by module reuse. Consequently, the executing period of mobile agents is reduced and their efficiency is improved. Additionally, a fault-tolerance mechanism is designed in the system to ensure that the agent can work even when some faults occur in the network or in the host.
Food image generation holds promising application prospects in food design, advertising, and food education. However, the existing methods rely on information such as recipes, ingredients, or food names, which leads t...
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Food image generation holds promising application prospects in food design, advertising, and food education. However, the existing methods rely on information such as recipes, ingredients, or food names, which leads to generated food images with less intra-class diversity. When recipes, ingredients and food names are identical for the same food, the real-world images may vary significantly in appearance. The question of how to simultaneously ensure the quality and diversity of the generated images is a key issue. To this end, we employ pre-trained diffusion model and Transformer to propose a method for generating diverse and high-quality images of both Chinese and Western food, named CW-Food. Different from previous works that utilize an overall food feature to generate new images, CW-Food first decouples the food images to obtain common intra-class features and private instance features. Additionally, we design a Transformer-based feature fusion module to integrate the common and private features, in order to avoid the shortcomings of conventional methods. Moreover, we also utilize a pre-trained diffusion model as our backbone, which is fine-tuned using LoRA with the fused multi-variate features. Extensive experiments on four datasets demonstrate the advantages of our proposed method, producing diverse and high-quality food images encompassing both Chinese and Western cuisines. To the best of our knowledge, our work is the first attempt to generate Chinese food images using only food names.
Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capa...
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Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.
In this paper, we present a novel indirect convergent Jacobi spectral collocation method for fractional optimal control problems governed by a dynamical system including both classical derivative and Caputo fractional...
Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have use...
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Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have used two-dimensional (2D) emotional stimulus materials. Virtual reality (VR) has been utilized in many fields, and its superiority over 2D in emotion recognition has been proven. However, relevant research on VR scenes is lacking in the field of personality recognition. In this study, based on the psychological principle that emotional arousal can expose an individual’s personality, we attempt to explore the feasibility and effect of using electrocardiogram (ECG) signals in response to VR emotional stimuli for personality identification. For this purpose, a VR-2D emotion-induction experiment was conducted in which ECG signals were collected, and physiological datasets of emotional personalities were constructed through preprocessing and feature extraction. Statistical analysis of the emotion scale scores and ECG features of the participants showed that the VR group had a higher number of significantly correlated features. Meanwhile, VR- and 2D-based personality recognition models were constructed using machine learning algorithms. The results showed that the VR-based personality recognition model achieved better results for the four personality dimensions, with a maximum accuracy of 79.76%. These findings indicate that VR not only enhances the physiological correlation between emotion and personality but also improves the classification accuracy of personality recognition.
In this paper, we develop spectral collocation method for a class of fractional diffusion differential equations. Since the solutions of these fractional differential equations usually exhibit singularities at the end...
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