Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

Deep Reinforcement Learning-Based Job Shop Scheduling of Smart Manufacturing

Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems.One of the key aspects of these systems is a production planning,specifically,Scheduling operations on the machines.To cope with this problem,this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm(DRLAC).We model the Job-Shop Scheduling Problem(JSSP)as a Markov Decision Process(MDP),represent the state of a JSSP as simple Graph Isomorphism Networks(GIN)to extract nodes features during scheduling,and derive the policy of optimal scheduling which guides the included node features to the best next action of schedule.In addition,we adopt the Actor-Critic(AC)network’s training algorithm-based reinforcement learning for achieving the optimal policy of the scheduling.To prove the proposed model’s effectiveness,first,we will present a case study that illustrated a conflict between two job scheduling,secondly,we will apply the proposed model to a known benchmark dataset and compare the results with the traditional scheduling methods and trending approaches.The numerical results indicate that the proposed model can be adaptive with real-time production scheduling,where the average percentage deviation(APD)of our model achieved values between 0.009 and 0.21 comparedwith heuristic methods and values between 0.014 and 0.18 compared with other trending approaches.

Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.