In upcoming sixth generation (6G) networks, it is a critical challenge to support a plethora of innovative services across wide-area networks. To realize the dedicated QoS provisioning and meet the diverse quality of ...
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computer-generated aesthetic patterns arewidely used as design materials in various fields. Themost common methods use fractals or dynamicalsystems as basic tools to create various patterns. Toenhance aesthetics and c...
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computer-generated aesthetic patterns arewidely used as design materials in various fields. Themost common methods use fractals or dynamicalsystems as basic tools to create various patterns. Toenhance aesthetics and controllability, some researchershave introduced symmetric layouts along with thesetools. One popular strategy employs dynamical systemscompatible with symmetries that construct functionswith the desired symmetries. However, these aretypically confined to simple planar symmetries. Theother generates symmetrical patterns under theconstraints of tilings. Although it is slightly moreflexible, it is restricted to small ranges of tilingsand lacks textural variations. Thus, we proposed anew approach for generating aesthetic patterns bysymmetrizing quasi-regular patterns using general kuniformtilings. We adopted a unified strategy toconstruct invariant mappings for k-uniform tilings thatcan eliminate texture seams across the tiling ***, we constructed three types of symmetriesassociated with the patterns: dihedral, rotational, andreflection symmetries. The proposed method can beeasily implemented using GPU shaders and is highlyefficient and suitable for complicated tiling with regularpolygons. Experiments demonstrated the advantages of our method over state-of-the-art methods in terms offlexibility in controlling the generation of patterns withvarious parameters as well as the diversity of texturesand styles.
In order to improve the conversion rate of users’ purchase and the personalisation of marketing push, the application of AI technology in upgrading traditional marketing methods of retailers was studied. Firstly, it ...
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APTs (Advanced Persistent Threats) have caused serious security threats worldwide. Most existing APT detection systems are implemented based on sophisticated forensic analysis rules. However, the design of these rules...
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APTs (Advanced Persistent Threats) have caused serious security threats worldwide. Most existing APT detection systems are implemented based on sophisticated forensic analysis rules. However, the design of these rules requires in-depth domain knowledge and the rules lack generalization ability. On the other hand, deep learning technique could automatically create detection model from training samples with little domain knowledge. However, due to the persistence, stealth, and diversity of APT attacks, deep learning technique suffers from a series of problems including difficulties of capturing contextual information, low scalability, dynamic evolving of training samples, and scarcity of training samples. Aiming at these problems, this paper proposes APT-KGL, an intelligent APT detection system based on provenance data and graph neural networks. First, APT-KGL models the system entities and their contextual information in the provenance data by a HPG (Heterogeneous Provenance Graph), and learns a semantic vector representation for each system entity in the HPG in an offline way. Then, APT-KGL performs online APT detection by sampling a small local graph from the HPG and classifying the key system entities as malicious or benign. In addition, to conquer the difficulty of collecting training samples of APT attacks, APT-KGL creates virtual APT training samples from open threat knowledge in a semi-automatic way. We conducted a series of experiments on two provenance datasets with simulated APT attacks. The experiment results show that APT-KGL outperforms other current deep learning based models, and has competitive performance against state-of-the-art rule-based APT detection systems. IEEE
Most existing Human-Object Interaction (HOI) detection methods focus on supervised learning, but labeling all interactions is costly because of the enormous possible combinations of objects and verbs. Zero-shot HOI de...
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A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic...
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The rapid growth of IoT has enabled diverse applications using Wireless Sensor Networks across various fields. A significant challenge in Wireless Sensor Networks is the efficient deployment of sensors to ensure cover...
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To address the problem that low-dose SPECT imaging will lead to poor-quality projection images, we propose a network structure based on the conditional generative adversarial network and add a convolutional LSTM modul...
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This paper presents the novel spatial and temporal fusion model (STFM), an effective approach for Autism Spectrum Disorder (ASD) detection and classification tasks using foundational machine learning models. Utilizing...
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The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and con...
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