User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated...
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User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured ***-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user ***,these technologies only extract common features in historical records and cannot represent changes in user ***,sequential feature can express the user’s interests and intentions that change time by *** the sequential recommendation resultsbased on the user representation of the item lack the interpretability of preference *** address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer ***,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for *** sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s ***,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of *** extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation *** addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.
The early diagnosis of Alzheimer's disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and...
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The early diagnosis of Alzheimer's disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and suitable treatment. We investigate microwave sensing for low-cost, non-intrusive early detection and assessment of Alzheimer's disease. Thisstudy isbased on the emerging evidence that the electromagnetic properties of cerebrospinal fluid are affected by abnormal concentrations of proteins recognized as early-stage biomarkers. We design a conformal six-element antenna array placed on the upper portion of the head, operating in the 500 MHz to 6.5 GHz band. It measuresscattering response due to changes in the dielectric properties of intracranial cerebrospinal fluid. A multi-layer perceptron network extracts the diagnostic information. Data classification consists of two steps: binary classification to identify the disease presence and multi-class classification to evaluate itsstage. The algorithm is trained and validated through controlled experiments mimicking various pathological severities with an anthropomorphic multi-tissue head phantom. Resultssupport the feasibility of the proposed method using only amplitude data and lay the foundation for more extensive studies on microwave sensing for early Alzheimer's detection.
The development of the Internet of Things(IoT)technology is leading to a new era of smart applicationssuch assmart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled ...
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The development of the Internet of Things(IoT)technology is leading to a new era of smart applicationssuch assmart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by varioussmart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.
Federated learning(FL)is a decentralized machine learning paradigm,which hassignificant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attac...
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Federated learning(FL)is a decentralized machine learning paradigm,which hassignificant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].
The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient *** address this limitatio...
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The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient *** address this limitation,we propose the salient Feature sampling Points-based QEM(sFsP-QEM)—also referred to as the Deep learning-basedsalient Feature-Preserving Algorithm for Mesh simplification—which incorporates a salient Feature-Preserving Point sampler(sFsP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during *** results demonstrate that sFsP-QEM significantly outperforms traditional QEM in preserving geometric ***,for general models from the stanford 3D scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using sFsP-QEM is reduced by an average of 46.58% compared to those simplified using traditional *** customized modelssuch as the Zigong Lantern used in cultural heritage preservation,sFsP-QEM achieves an average reduction of 28.99% in Hausdorff ***,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric *** results demonstrate that sFsP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.
1 Introduction onMultimodal learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directi...
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1 Introduction onMultimodal learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspotssuch as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LsTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.
Multi-omics data analysis combines different and complementary information content to provide novel insights into several bioinformaticsscenarios. In this context, an important objective of microbiomics and metabolom...
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A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation *** method breaks the traditional idea that the ro...
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A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation *** method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in *** this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the ***,the physical human-robot cooperation model,including the role factor is ***,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is *** role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated ***,the roles adjustment rule established above continuously improves the comprehensive *** of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been *** resultsshow that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.
In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheu...
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The significance of sTEM (science, Technology, Engineering, and Mathematics) education in equipping students for the demands of the 21st century is widely recognized in the rapidly evolving field of education. Althoug...
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