Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly pro...
详细信息
ISBN:
(纸本)9781450377607
Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.
Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot resea...
详细信息
ISBN:
(数字)9781728194813
ISBN:
(纸本)9781728194820
Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.
The security and performance of a cryptographic algorithm can be reduced when implemented on an embedded system, such as a 32-bit microcontroller, due to the limitation of hardware resources, such as computational pre...
详细信息
The security and performance of a cryptographic algorithm can be reduced when implemented on an embedded system, such as a 32-bit microcontroller, due to the limitation of hardware resources, such as computational precision, processing power, and memory. This paper introduces the Integer Reversible Discrete Dual-Hahn Transform (IRDDHT), a novel integer-based transform designed for secure image encryption in resource-constrained embedded systems. The IRDDHT ensures lossless encryption by eliminating rounding errors common in floating-point transforms, maintaining image fidelity during encryption and decryption. We then propose a lightweight encryption algorithm based on IRDDHT, which introduces implicit diffusion and relies on parameter sensitivity to enhance security. When implemented on the ESP32 microcontroller, the proposed algorithm occupies less than 4 % of SRAM and 8.5 % of Flash memory, achieving a throughput of 170.67 kbps at the device’s maximum clock frequency. It is energy-efficient, capable of encrypting up to 129,025 grayscale images of size 256 × 256 pixels on a standard 3300 mAh battery. The algorithm ensures perfect decryption with no loss of image fidelity and demonstrates strong resistance to statistical attacks with near-ideal correlation values, as well as brute-force attacks with a large key space of 2 199 . These results indicate that the IRDDHT-based encryption method is an effective solution for secure image encryption in embedded systems.
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets ...
详细信息
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challen...
详细信息
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a
With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D...
With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D reconstruction and reduce the possibility of other algorithms falling into local optimal solutions, attempting to get better and faster performance, a new parallax calculation based on symmetric continuous optimization is proposed in this paper. The algorithm is proposed here will be tested respectively in the same data, in order to certificate the algorithm applied is better than traditional minimize El algorithm.
Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various cont...
详细信息
Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in deve...
Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algo...
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient.
Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and t...
详细信息
ISBN:
(数字)9781728114194
ISBN:
(纸本)9781728104966
Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering.
暂无评论