Associative learning mechanism for drug‐target interaction prediction

As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction model,which enhances the consistency of the probability distribution between actual and predicted values.The source code of proposed method is available at https://github.com/Lamouryz/Code/tree/main/MT‐DTA.

Multi-focus image fusion via morphological similarity-based dictionary construction and sparse representation

Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.

Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation

Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

As one chemical composition,nicotine content has an important influence on the quality of tobacco leaves.Rapid and nondestructive quantitative analysis of nicotine is an important task in the tobacco industry.Near-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely used.In this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud environment.This 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy data.It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches.This research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.

Video-based Person Re-identification Based on Distributed Cloud Computing

Person re-identification has been a hot research issues in the field of computer vision.In recent years,with the maturity of the theory,a large number of excellent methods have been proposed.However,large-scale data sets and huge networks make training a time-consuming process.At the same time,the parameters and their values generated during the training process also take up a lot of computer resources.Therefore,we apply distributed cloud computing method to perform person re-identification task.Using distributed data storage method,pedestrian data sets and parameters are stored in cloud nodes.To speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution process.Moreover,we assign different layers of the re-identification network to different nodes to complete the training in the way of model parallelism.By comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

Blockchain and Artificial Intelligence Applications

The emergence and popularity of blockchain,distributed ledger technology distributed computing,and network security and trust techniques are significantly changing the operation and management of computing and communication systems,as these techniques have the potential to disrupt any domain involving coordination among autonomous resources without trusted third parties.These techniques and their applications include finance and payments(e.g.,Facebook Libra),but also networks(e.g.,power grids or telecom networks),computing(e.g.,brokering of edge resources),Internet of Things(e.g.,supply chain or industry 4.0),and service platforms(e.g.,identity management).The market capitalization,investor appetite,and institutional coverage for cryptocurrency(as well as bitcoin and blockchain)have all jumped exponentially.The total market capitalization of the cryptocurrency market has significantly increased in the past three years.The applications of blockchain exhibit a variety of complicated problems and new requirements,which brings more open issues and challenges for artificial intelligence(AI)and related research.

Tenant-based access control model for multi-tenancy and sub-tenancy architecture in Software-as-a-Service

Software-as-a-Service （SaaS） introduces multi- tenancy architecture （MTA）. Sub-tenancy architecture （STA）, is an extension of MTA, allows tenants to offer services for subtenant developers to customize their applications in the SaaS infrastructure. In a STA system, tenants can create sub- tenants, and grant their resources （including private services and data） to their subtenants. The isolation and sharing re- lations between parent-child tenants, sibling tenants or two non-related tenants are more complicated than those between tenants in MTA. It is important to keep service components or data private, and at the same time, allow them to be shared, and support application customizations for tenants. To ad- dress this problem, this paper provides a formal definition of a new tenant-based access control model based on administra- tive role-based access control （ARBAC） for MTA and STA in service-oriented SaaS （called TMS-ARBAC）. Autonomous areas （AA） and AA-tree are proposed to describe the auton- omy of tenants, including their isolation and sharing relation- ships. Authorization operations on AA and different resource sharing strategies are defined to create and deploy the access control scheme in STA models. TMS-ARBAC model is ap- plied to design a geographic e-Science platform.