This paper aims to provide a comprehensive introduction to lattices constructed based on polar-like codes and demonstrate some of their key properties, such as AWGN-goodness. We first present polar lattices directly f...
Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. S...
详细信息
There is increasing attention on remanufacturing, with the primary focus in the industry on recovering reusable products to generate profit. In the process of optimizing remanufacturing, addressing the balance issue i...
详细信息
ISBN:
(数字)9798350376739
ISBN:
(纸本)9798350376746
There is increasing attention on remanufacturing, with the primary focus in the industry on recovering reusable products to generate profit. In the process of optimizing remanufacturing, addressing the balance issue in the operation of the disassembly line is crucial. Therefore, this study proposes the use of an improved Dingo Optimization Algorithm (IDOA) and designs a mathematical model for human-robot collaboration to explore how to achieve higher profits under these conditions. Finally, the improved IDOA is experimentally compared with the commercial optimization solver CPLEX and another intelligent optimization method—the Battle Royale Optimization (BRO). The results demonstrate that this method offers high quality and efficiency.
Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illne...
详细信息
Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia.
In recent years, the gaming industry has witnessed exponential growth, with an increasing focus on enhancing player experience and engagement. To achieve this, we propose a method that recognizes game experience trait...
In recent years, the gaming industry has witnessed exponential growth, with an increasing focus on enhancing player experience and engagement. To achieve this, we propose a method that recognizes game experience traits of players using Electroencephalography (EEG). The proposed method aims to comprehensively assess player engagement with neural measurements from EEG recordings. The MUSE EEG headband is used to capture data while playing a game. Data is preprocessed to minimize the unwanted noise in the EEG data. Different frequency domain features are extracted and Three different classifiers (Random Forest, K-nearest neighbour and Naive Bayes) are used to detect the existence (High/Low) of two gaming experience traits: tension and challenge. A highest accuracy of 86.6% and 88.8% is achieved for challenge and tension game trait recognition respectively using Random Forest classifier. Notably, the study unveiled that different game traits can be recognized using EEG, suggesting the potential for personalized game design.
Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach fo...
详细信息
Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.
In this paper, an adaptive controller design method is proposed for chaotic systems with unknown actuator dead-zone. First, the terminal sliding mode (TSM) manifold is proposed to ensure exponential stability as well ...
详细信息
ISBN:
(纸本)9781665473705
In this paper, an adaptive controller design method is proposed for chaotic systems with unknown actuator dead-zone. First, the terminal sliding mode (TSM) manifold is proposed to ensure exponential stability as well as faster finite-time stability. In addition, a neural network (NN) is introduced to estimate the partially unknown nonlinear dynamic behavior of the object, and according to the implicit function theorem, the unknown asymmetric dead zone of the actuator is overcome by another static neural network. Furthermore, a robust terminology updated online deals with refactoring errors and external interferences of neural networks. Finally, the system can stably track any smooth target trajectory online is rigidly proved via Lyapunov analysis, and numerical simulation shows its effectiveness and feasibility.
While Large Language Models (LLMs) adapt well to downstream tasks after fine-tuning, this adaptability often compromises prompt robustness, as even minor prompt variations can significantly degrade performance. To add...
Equilibrium optimizer (EO) is a new proposed meta-heuristic algorithm by utilizing the mass balance model of the control volume. In order to solve the binary appli-cations, this paper proposes a binary version of equi...
详细信息
Conditional privacy-preserving authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users' privacy while resisting malicious users and false messages. However...
Conditional privacy-preserving authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users' privacy while resisting malicious users and false messages. However, existing schemes suffer from various disadvantages, such as unavailable batch verification, unrenewable user public keys/certificates, and untimely revocation. In this work, we design an efficient conditional privacy-preserving authentication scheme with the on-chain key management (EAKM) for VANETs. To achieve lightweight authentication, we design an efficient Signature of Knowledge (SoK) and a batch verification algorithm. Moreover, we use the hash chain technology to update users' anonymous public keys. In addition, based on the blockchain technology and smart contract, we could manage users' anonymous public keys efficiently and transparently. Security analysis and simulation results show that EAKM ensures conditional privacy with less authentication overhead.
暂无评论