Safe DreamerV3: Safe Reinforcement Learning with World Models

The widespread application of Reinforcement Learning (RL) in real-world situations is yet to come to fruition, largely as a result of its failure to satisfy the essential safety demands of such systems. Existing safe reinforcement learning (SafeRL) methods, employing cost functions to enhance safety, fail to achieve zero-cost in complex scenarios, including vision-only tasks, even with comprehensive data sampling and training. To address this, we introduce Safe DreamerV3, a novel algorithm that integrates both Lagrangian-based and planning-based methods within a world model. Our methodology represents a significant advancement in SafeRL as the first algorithm to achieve nearly zero-cost in both low-dimensional and vision-only tasks within the Safety-Gymnasium benchmark. Our project website can be found in: https://sites.google.com/view/safedreamerv3

HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods

Cluster-Based Structural Redundancy Identification for Neural Network Compression

The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically compress models based on importance, removing unimportant filters. This paper reconsiders model pruning from the perspective of structural redundancy, claiming that identifying functionally similar filters plays a more important role, and proposes a model pruning framework for clustering-based redundancy identification. First, we perform cluster analysis on the filters of each layer to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we propose a pruning scheme that automatically determines the pruning rate of each layer. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework

Innovative Deep Neural Network Modeling for Fine-Grained Chinese Entity Recognition

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks

Inhibiting the Progression of Human Retinoblastoma Cell by Downregulation of MMP-2/MMP-9 Using Short Hairpin RNAs (shRNAs) In Vitro

Objective. To investigate the effect of downregulated matrix metalloproteinases (MMPs) gene on the proliferation, apoptosis, cell cycle, migration, and invasion of human retinoblastoma (RB) cell line in vitro. Methods. Small hairpin RNA (shRNA) targeting MMP-2/MMP-9 was designed and transfected into WER1-Rb-1 cells. 48 hours after transfection, qRT-PCR and western blot technique were used to investigate the inhibitory effect of MMP-2 and MMP-9 shRNAs. Cell viability was examined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cycle arrest was detected using a flow cytometer while apoptosis was tested with Annexin V/PI kit. Transwell chamber assay was performed to detect the migration and invasion ability of the WER1-Rb-1 cells. Results. After transfection of MMP-2/MMP-9 shRNA, there was a significant decrease in the expressions of both mRNA and protein in the shRNA groups compared with the negative and vector controls. The results of MTT assay suggested that the cell viability was significantly decreased in shRNA groups (p<0.05). Cell apoptosis also increased significantly in shRNA groups compared with the negative and vector controls (p<0.05). The flow cytometer analysis proved that the proportion of the G1 phase increased and the proportion of the G0 phase reduced significantly by the transfection of MMP-2/MMP-9 shRNA (p<0.05). The migration and invasion ability were also significantly decreased in the groups of MMP-2/MMP-9 shRNA (p<0.05). Conclusions. Cell viability, migration, and invasion ability of RB cells are inhibited, and apoptosis is induced after downregulation of MMP-2/MMP-9 through RNA interference. MMP-2 and MMP-9 may be potential targets in the gene therapy of RB