Deep Reinforcement Learning-based Image Captioning with Embedding Reward

Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network" and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics

Discussion on Emergency Renewal Design of Indoor Space of Multi-Storey Residential Building under Public Health Emergency

In order to deal with public health emergencies, five residential areas in the main urban area of Handan were selected for on-the-spot measurement and questionnaire survey, and the survey data were sorted out and sequenced regression analysis. it is summarized that the main problems of indoor space in emergency are poor spatial independence, unreasonable layout, low flexibility and poor natural lighting effect. On the basis of this, the corresponding optimization strategy is put forward in order to help to update and improve the emergency design of interior space in multi-storey residential buildings

Ghost translation

Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector's signal will be `translated' into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.Comment: 10 pages, 8 figure

Boosting the Discriminant Power of Naive Bayes

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. The proposed stack auto-encoder consists of two auto-encoders for different purposes. The first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. The second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. The proposed method is evaluated on a set of machine-learning benchmark datasets. The experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.Comment: Accepted by 2022 International Conference on Pattern Recognitio

Influence of Acetaldehyde Induction on Monomeric and Polymeric Polyphenols in Wine using the Polyphenol/Protein-binding Model

Polyphenols make a substantial contribution to the sensory properties of wine, and their evolution is affected by the acetaldehyde present during fermentation and ageing. In this work, five typical monomeric phenolic standards and three different polymeric flavanol fractions separated from wine were tested for polyphenol/protein binding by means of circular dichroism measurement and fluorescence spectrum assay in the presence or absence of acetaldehyde, and the formation of new oligomeric compounds linked by ethyl bridges was observed through HPLC-MS analyses. The results show that the protein-binding ability of these monomers was in the order of gallic acid > caffeic acid > quercetin > (+)-catechin > (-)-epicatechin, while acetaldehyde exerted a stronger effect on (+)-catechin and (-)-epicatechin monomers. Moreover, different wine fractions had different responses when reacted with proteins with the participation of acetaldehyde, while the polymeric proanthocyanidins produced the largest value (84.67%) of the salivary protein precipitation index and the strongest fluorescence-quenching effect

Fruit Classification Based on Improved YOLOv7 Algorithm

With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability

Does a higher minimum wage accelerate labour division in agricultural production? Evidence from the main riceplanting area in China

Agricultural production outsourcing, a new means of agricultural production, can optimise the allocation of resources, reduce agricultural production costs, and improve agricultural productivity. However, farmers’ outsourcing behaviours are strongly interfered with by many factors such as economics, technology and institutions. Using a farmer-level data set from 2014 to 2018 in China, we examine the effects of the minimum wage increase on rice farmers’ production outsourcing behaviours. Our study relies on a Logit regression framework and uses the control function (C.F.) approach to address potential endogeneity concerns. Results show that the minimum wage increase significantly reduces the probability of farmers conducting production outsourcing. We also examine the heterogeneous effects of the minimum wage increase, and find that compared with other outsourcing services, the adverse effects on harvesting outsourcing are the strongest; the negative effects on production outsourcing are stronger for rice farmers with higher education. Our results provide new insights into understanding how labour regulation affects labour division in agricultural production