Optical Memory and Neural Networks最新文献

Intelligence quotient (IQ) serves as a statistical gauge for evaluating an individual’s cognitive prowess. Measuring IQ is a formidable undertaking, mainly due to the intricate intricacies of the human brain’s composition. Presently, the assessment of human intelligence relies solely on conventional paper-based psychometric tests. However, these approaches suffer from inherent discrepancies arising from the diversity of test formats and language barriers. The primary objective of this study is to introduce an innovative, deep learning-driven methodology for IQ measurement using Electroencephalogram (EEG) signals. In this investigation, EEG signals are captured from participants during an IQ assessment session. Subsequently, participants' IQ levels are categorized into six distinct tiers, encompassing extremely low IQ, borderline IQ, low average IQ, high average IQ, superior IQ, and very superior IQ, based on their test results. An attention mechanism-based Convolution Neural Network-modified tanh Long-Short-term-Memory (CNN-MTLSTM) model has been meticulously devised for adeptly classifying individuals into the aforementioned IQ categories by using EEG signals. A layer named 'input enhancement layer' is proposed and incorporated in CNN-MTLSTM for enhancing its prediction accuracy. Notably, a CNN is harnessed to automate the process of extracting important information from the extracted EEG features. A new model, i.e., MTLSTM, is proposed, which works as a classifier. The paper’s contributions encompass proposing the novel MTLSTM architecture and leveraging attention mechanism to enhance the classification accuracy of the CNN-MTLSTM model. The innovative CNN-MTLSTM model, incorporating an attention mechanism within the MTLSTM network, attains a remarkable average accuracy of 97.41% in assessing a person’s IQ level.

We propose OVIGo-3DHSG method—Open-Vocabulary Indoor Grounding of objects using 3D Hierarchical Scene Graph. OVIGo-3DHSG represents an extensive indoor environment over a Hierarchical Scene Graph derived from sequences of RGB-D frames utilizing a set of open-vocabulary foundation models and sensor data processing. The hierarchical representation explicitly models spatial relations across floors, rooms, locations, and objects. To effectively address complex queries involving spatial reference to other objects, we integrate the hierarchical scene graph with a Large Language Model for multistep reasoning. This integration leverages inter-layer (e.g., room-to-object) and intra-layer (e.g., object-to-object) connections, enhancing spatial contextual understanding. We investigate the semantic and geometry accuracy of hierarchical representation on Habitat Matterport 3D Semantic multi-floor scenes. Our approach demonstrates efficient scene comprehension and robust object grounding compared to existing methods. Overall OVIGo-3DHSG demonstrates strong potential for applications requiring spatial reasoning and understanding of indoor environments. Related materials can be found at https://github.com/linukc/OVIGo-3DHSG.

Attainable on demand cloud computing makes it feasible to access a centralized shared pool of computing resources. Accurate estimation of cloud workload is necessary for optimal performance and effective use of cloud computing resources. Because cloud workloads are dynamic and unpredictable, this is a problematic problem. In this case, deep learning can provide reliable foundations for workload prediction in data centres when trained appropriately. In the proposed model, efficient workload prediction is executed out using novel deep learning. Efficient management of these hyperparameters may significantly improve the neural network model’s performance. Using the data centre’s workload traces at many consecutive time steps, the suggested approach is shown to be able to estimate Central Processing Unit (CPU) utilization. Collects raw data retrieved from the storage, including the number and type of requests, virtual machine (VMs) costs, and resource usage. Discover patterns and oscillations in the workload trace by preprocessing the data to increase the prediction efficacy of this model. During data pre-processing, the KCR approach, min max normalization, and data cleaning are used to select the important properties from raw data samples, eliminate noise, and normalize them. After that, a sliding window is used for deep learning processing to convert multivariate data into time series with supervised learning. Next, utilize a deep belief network based on green anaconda optimization (GrA-DBN) to attain precise workload forecasting. Comparing the suggested methodology with existing models, experimental results show that it provides a better trade-off between accuracy and training time. The suggested method provides higher performance, with an execution time of 28.5 s and an accuracy rate of 93.60%. According to the simulation results, the GrA-DBN workload prediction method performs better than other algorithms.

3D mapping in dynamic environments poses a challenge for modern researchers in robotics and autonomous transportation. There are no universal representations for dynamic 3D scenes that incorporate multimodal data such as images, point clouds, and text. This article takes a step toward solving this problem. It proposes a taxonomy of methods for constructing multimodal 3D maps, classifying contemporary approaches based on scene types and representations, learning methods, and practical applications. Using this taxonomy, a brief structured analysis of recent methods is provided. The article also describes an original modular method called M3DMap, designed for object-aware construction of multimodal 3D maps for both static and dynamic scenes. It consists of several interconnected components: a neural multimodal object segmentation and tracking module; an odometry estimation module, including trainable algorithms; a module for 3D map construction and updating with various implementations depending on the desired scene representation; and a multimodal data retrieval module. The article highlights original implementations of these modules and their advantages in solving various practical tasks, from 3D object grounding to mobile manipulation. Additionally, it presents theoretical propositions demonstrating the positive effect of using multimodal data and modern foundational models in 3D mapping methods. Details of the taxonomy and method implementation are available at https://yuddim.github.io/M3DMap.

Cross-floor robotic navigation has become an increasingly critical capability for autonomous systems operating in multi-floor buildings. While 3D scene graphs have demonstrated promise for representing hierarchical spatial relationships, current approaches predominantly address cross-floor navigation by stairs, overlooking the practical challenges of elevator-mediated navigation in modern buildings. This paper presents ElevNav, a novel framework that bridges this gap through two key innovations: (1) automatic construction of semantically-rich 3D scene graphs from RGB-D sequences with estimated camera trajectories, and (2) task decomposition using large language models to translate natural language commands into executable action sequences. Our method addresses elevator interaction through specialized action primitives such as pressing buttons, entering and exiting the elevator, and moving toward target objects. We evaluate ElevNav in complex simulated environments built using Isaac Sim, demonstrating robust performance in multi-floor navigation scenarios. To facilitate further research, we release a new dataset containing elevator environments with corresponding scene graph representations, addressing a critical gap in existing 3D navigation benchmarks, which is open-sourced at: https://github.com/zhanghuzhenyu/elevnav.

Skin diseases are among the most frequent and pervasive conditions affecting individuals all over the world. The two primary causes of skin cancer are climate change and global warming. If skin conditions are not identified and treated promptly, they may become fatal. Advanced ML and DL approaches on skin diseases often face limitations such as insufficient data diversity, high variability in imaging quality, and challenges in accurately distinguishing between similar-looking conditions. These drawbacks can lead to reduce diagnostic accuracy and generalizability of the models. To overcome the aforementioned challenges, an improved segmentation and hybrid deep learning approach is used to identify numerous kinds of skin disease. Initially, raw images for input are collected from the skin disease image dataset. The collected image is pre-processed with resizing and a Hierarchical Noise Deinterlace Net (HNDN) to remove noise. The pre-processed images are then segmented into different parts or regions using the no new U-Network (nnU-Net). Here, the Marine Predator Algorithm (MPA) is used to choose the nnU-Net learning rate, and batch size optimally. Then, the segmented image is subjected to a hybrid Efficient-capsule network (E-cap Net) and Unified force operation network (UFO-Net) classifier predicting several types of skin disease. An analysis of proposed method’s simulation results indicates that it achieves 97.49% accuracy, 90.06% precision, and 98.56% selectivity. Thus, the proposed method is a most effective method for predicting the multi-type skin disease.

In this paper, we propose a novel learner cognitive feature model for personalized guidance and push (LCFLM) that traces the evolution of learners’ knowledge proficiency based on their exercising logs in online learning systems. Specifically, we introduce the exercise-aware dependency hierarchical graph of exercise dependency and pattern dependency that can establish a model of exercise dependency relationships. Additionally, we propose the implementation of a forget gating mechanism, which combines the forgetting features with the knowledge state features to predict a student’s learning performance. The experimental results clearly demonstrate that LCFLM achieves the new state-of-the-art performance, exhibiting an improvement of at least 3% in both AUC and ACC. Furthermore, the LCFLM model has the ability to autonomously uncover the fundamental concepts underlying exercises and provides a visual representation of a student’s evolving knowledge state.

Blockchain is a secure, decentralized ledger system that records transactions in immutable blocks. A smart contract is a self-executing piece of code on the blockchain that automatically enforces agreements when specific conditions are met. Additionally, once deployed, smart contracts are immutable, making it difficult to fix bugs or vulnerabilities without affecting the entire blockchain. Using machine learning and deep learning techniques, vulnerabilities in smart contract code have been effectively identified. The trained net model is tampered with since the algorithms' learning is not safe. Therefore, a fully homomorphic deep learning algorithm has been developed to detect vulnerabilities in smart contract systems for blockchain in order to safeguard user data. Initially, user data is stored on the blockchain based on a consensus algorithm that evaluates the operations of each node using a reputation model. Reputation-based Byzantine Fault Tolerance (RBFT) enhances security by assessing users' reputations to prevent malicious behaviour and ensure fault tolerance. Reputation values, ranging from 0 to 1, are crucial for establishing trust and reliability in the network. To further optimize RBFT performance, the Secretary Bird Optimization Algorithm is employed. Smart contract data is derived from source code, including the control flow graph and operation code. XLNet and Bi-LSTM are used to extract features from the control flow graph and operation code, which are then trained and tested using ElGamal cryptography with a Deep Belief Network to improve vulnerability detection and enhance security in blockchain-based smart contract systems. The proposed approach provides 98.40% accuracy, 95.40% positive predictive value (PPV), and 98.80% selectivity. This proposed approach enhances blockchain-based smart contract systems by improving vulnerability detection and ensuring robust encryption of sensitive data through advanced reputation models and cryptographic techniques.

The effect of the formation of microcylinders during laser treatment (λ = 532 nm) of the surface of a chromium–zirconium dioxide bilayer in a pulsed low-frequency mode is studied. An unusual formation of microcylinders was observed, which was explained by the effect of micro wrinkles. It was found that in this way it is possible to form a quasi-periodic matrix of microcylinders, the size of which is on the order of the diffraction limit, which is approximately 6 times smaller than the effective diameter of the laser spot. It is shown that the matrix of elements can have a fill factor of about 0.5, with a period up to 2.5 times smaller than the diameter of the laser spot. Numerical simulation of diffraction of Gaussian beams and optical vortices with circular polarization on arrays of subwavelength microcylinders has shown that a decrease in the diameter of microcylinders leads to a decrease in the size of the focal spot and light needle for a Gaussian beam, and an increase in height leads to the formation of the main intensity peaks inside the element for both the Gaussian beam and the Laguerre-Gauss mode. Based on the simulation results, a focusing meander matrix of microcylinders with a minimum element size of about 350 nm and a height of 0.21 λ was made.