Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images

Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis as it delivers important morphological properties such as shapes and volumes. In this paper, we propose a region proposal rectification (RPR) module to address this challenging incomplete segmentation problem. In particular, we offer a progressive ROIAlign module to introduce neighbor information into a series of ROIs gradually. The ROI features are fed into an attentive feed-forward network (FFN) for proposal box regression. With additional neighbor information, the proposed RPR module shows significant improvement in correction of region proposal locations and thereby exhibits favorable instance segmentation performances on three biological image datasets compared to state-of-the-art baseline methods. Experimental results demonstrate that the proposed RPR module is effective in both anchor-based and anchor-free top-down instance segmentation approaches, suggesting the proposed method can be applied to general top-down instance segmentation of biological images. Code is available

Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival

The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the scheduling agent. The DRL method is adaptive to the dynamic changes of the production environment in a flexible job shop, which contributes to the establishment of a flexible scheduling system with self-learning, real-time optimization and intelligent decision-making

Does Informatization Cause the Relative Substitution Bias of Agricultural Machinery Inputs for Labor Inputs? Evidence from Apple Farmers in China

The change of information scenario may change the market transaction cost of different factors, thus changing the relative price of factors and inducing the substitution of production factors, but there is no research to prove this. Therefore, this study takes labor-saving technology (mechanical substitution of labor) as an example, evaluates informatization from three aspects of information technology access, information technology application and information literacy comprehensively, and uses the probit model and CMP method to analyze whether informatization causes the substitution of agricultural machinery inputs for labor inputs and its heterogeneity. The results show that informatization has a significant negative impact on farmers' choice of labor-saving technology, and the result is robust at the regional level, but the negative impact of informatization on farmers' choice of labor-saving technology in the eastern region is smaller than that in the western region. The level of information literacy has the largest negative impact on farmers' choice of labor-saving technology, followed by the level of access to information technology, and the level of application of information technology has the smallest impact. The study concludes that informatization has not led to the significant substitution of labor by machinery in apple production. Thus, the results are important for enriching the theory of induced change in agricultural technology in the context of informatization

Malcertificate: Research and Implementation of a Malicious Certificate Detection Algorithm Based on GCN

Encryption is widely used to ensure the security and confidentiality of information. Because people trust in encryption technology, a series of attack methods based on certificates have been derived. Malicious certificates protect many malicious behaviors and threaten data security. To counter this threat, machine learning algorithms are widely used in malicious certificate detection. However, the detection efficiency of such algorithms largely depends on whether the extracted features can effectively represent the data. In contrast, graph convolutional networks (GCNs) can automatically extract useful features. GCNs are powerful at fitting graph data, which can improve the effectiveness of learning systems by efficiently embedding prior knowledge in an end-to-end manner. In this paper, we propose an algorithm for detecting malicious digital certificates with GCNs. Firstly, we transform the digital certificate dataset with pem document structure into a corpus of graph structure based on attribute co-occurrence and document attribute relations. Then, we put the graph structure certificate dataset into a GCN for training. The results of the experiment show that GCN is very effective in certificate classification and outperforms traditional machine learning algorithms and extant neural network algorithms. The accuracy of our algorithm to detect malicious certificates is 97.41%. This shows that our algorithm is very effective

ETCNLog: A System Log Anomaly Detection Method Based on Efficient Channel Attention and Temporal Convolutional Network

The scale of the system and network applications is expanding, and higher requirements are being put forward for anomaly detection. The system log can record system states and significant operational events at different critical points. Therefore, using the system log for anomaly detection can help with system maintenance and avoid unnecessary loss. The system log has obvious timing characteristics, and the execution sequence of the system log has a certain dependency relationship. However, sometimes the length of sequence dependence is long. To handle the problem of longer sequence logs in anomaly detection, this paper proposes a system log anomaly detection method based on efficient channel attention and temporal convolutional network (ETCNLog). It builds a model by treating the system log as a natural language sequence. To handle longer sequence logs more effectively, ETCNLog uses the semantic and timing information of logs. It can automatically learn the importance of different log sequences and detect hidden dependencies within sequences to improve the accuracy of anomaly detection. We run extensive experiments on the actual public log dataset BGL. The experimental results show that the Precision and F1-score of ETCNLog reach 98.15% and 98.21%, respectively, both of which are better than the current anomaly detection methods

Malcertificate: Research and Implementation of a Malicious Certificate Detection Algorithm Based on GCN

Encryption is widely used to ensure the security and confidentiality of information. Because people trust in encryption technology, a series of attack methods based on certificates have been derived. Malicious certificates protect many malicious behaviors and threaten data security. To counter this threat, machine learning algorithms are widely used in malicious certificate detection. However, the detection efficiency of such algorithms largely depends on whether the extracted features can effectively represent the data. In contrast, graph convolutional networks (GCNs) can automatically extract useful features. GCNs are powerful at fitting graph data, which can improve the effectiveness of learning systems by efficiently embedding prior knowledge in an end-to-end manner. In this paper, we propose an algorithm for detecting malicious digital certificates with GCNs. Firstly, we transform the digital certificate dataset with pem document structure into a corpus of graph structure based on attribute co-occurrence and document attribute relations. Then, we put the graph structure certificate dataset into a GCN for training. The results of the experiment show that GCN is very effective in certificate classification and outperforms traditional machine learning algorithms and extant neural network algorithms. The accuracy of our algorithm to detect malicious certificates is 97.41%. This shows that our algorithm is very effective

Hierarchical Reinforcement Learning for Multi-Objective Real-Time Flexible Scheduling in a Smart Shop Floor

With the development of intelligent manufacturing, machine tools are considered the “mothership” of the equipment manufacturing industry, and the associated processing workshops are becoming more high-end, flexible, intelligent, and green. As the core of manufacturing management in a smart shop floor, research into the multi-objective dynamic flexible job shop scheduling problem (MODFJSP) focuses on optimizing scheduling decisions in real time according to changes in the production environment. In this paper, hierarchical reinforcement learning (HRL) is proposed to solve the MODFJSP considering random job arrival, with a focus on achieving the two practical goals of minimizing penalties for earliness and tardiness and reducing total machine load. A two-layer hierarchical architecture is proposed, namely the combination of a double deep Q-network (DDQN) and a dueling DDQN (DDDQN), and state features, actions, and external and internal rewards are designed. Meanwhile, a personal computer-based interaction feature is designed to integrate subjective decision information into the real-time optimization of HRL to obtain a satisfactory compromise. In addition, the proposed HRL framework is applied to multi-objective real-time flexible scheduling in a smart gear production workshop, and the experimental results show that the proposed HRL algorithm outperforms other reinforcement learning (RL) algorithms, metaheuristics, and heuristics in terms of solution quality and generalization and has the added benefit of real-time characteristics

Chemical Composition in Kernels of Ten Grafted Pecan (<i>Carya illinoensis</i>) Varieties in Southeastern China

As woody oil crop, pecan [Carya illinoinensis (Wangenh.) K. Koch] may be a solution to the shortage of edible oil in the future. In this study, fruit traits, kernel nutrition and fatty acid composition of 10 pecan varieties were determined to assess the potential of pecans for exploitation as edible oil, as well as to further screen varieties that could be used as edible oil resources and to understand their development prospects for cultivation in mountainous hills. The study showed that all the fruit trait indicators measured, including green-fruit weight (mean 28.47 g), nut weight (10.33 g), kernel weight (5.25 g), nut percentage (36.83%) and kernel percentage (50.50%), showed highly significant differences among the 10 varieties. Among the main nutritional indicators of the kernels, the crude fat content was stable (mean 70.01%) with non-significant differences, while protein (67.50 mg·g−1), soluble sugar (10.7 mg·g−1) and tannin (6.07 mg·g−1) showed highly significant differences between varieties. The oil percentage of nuts (kernel percentage * crude fat) averaged 35.36%, with highly significant differences between varieties. The fatty acid composition was dominated by unsaturated fatty acids (mean 91.82%), with unsaturated fatty acids being 11.24 times more abundant than saturated fatty acids. Among the monounsaturated fatty acids, oleic acid was the highest (mean 70.02%), with highly significant differences between varieties, followed by cis-11-eicosanoic acid (0.25%), with non-significant differences between varieties; among the polyunsaturated fatty acids, linoleic acid was the highest (19.58%), followed by linolenic acid (0.97%), both of which showed highly significant differences between varieties; monounsaturated fatty acids were 2.42 times more abundant than polyunsaturated fatty acids. Compared to other oilseed crops, pecan has the potential to produce “nutritious, healthy and stable” edible oil, while its wide habitat and good productivity benefits offer broad prospects for development in the hills and mountains of subtropical China