Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification for Lung Cancer Prediction

One of the major causes of cancer-related mortality worldwide is lung tumors. An earlier prediction of lung tumors is crucial since it may severely increase the death rates. For this reason, genomic profiles have been considered in many advanced microarray technology schemes. Amongst, an Improved Dragonfly optimization Algorithm (IDA) with Boosted Weighted Optimized Neural Network Ensemble Classification (BWONNEC) has been developed which extracts most suitable features and fine-tunes the weights related to the ensemble neural network classifiers. But, its major limitations are the number of learning factors in neural network and computational difficulty. Therefore in this article, a Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification (BWOCNNEC) algorithm is proposed to lessen the number of learning factors and computation cost of neural network. In this algorithm, the boosting weights are combined into the CNN depending on the least square fitness value. Then, the novel weight values are assigned to the features extracted by the IDA. Moreover, these weight values and the chosen features are processed in different CNN structures within the boosted classifier. Further, the best CNN structure in each iteration i.e., CNNs having the least weighted loss is selected and ensemble to predict and diagnose the lung tumors effectively. Finally, the investigational outcomes exhibit that the IDA-BWOCNNEC achieves better prediction efficiency than the existing algorithms.

SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices

Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed. Based on YOLOv4-Tiny, the network incorporates the Inception V3 structure, replaces two CSPBlock modules with the RES-SEBlock modules to reduce the number of parameters and computational difficulty, and adds a convolutional block attention module and a squeeze-and-excitation module to extract key feature information. Moreover, a modified ReLU (M-ReLU) activation function is introduced to replace the original Leaky_ReLU function. The experimental results show that SAI-YOLO reduces the number of network parameters and calculation difficulty and improves the detection speed of the network while maintaining certain recognition accuracy. The mean average precision (mAP) for face-mask-wearing detection reaches 86% and the average precision (AP) for mask-wearing normative detection reaches 88%. In the resource-constrained device Raspberry Pi 4B, the average detection time after acceleration is 197 ms, which meets the actual application requirements.

Can longer gaze duration determine risky investment decisions? An interactive perspective

Can longer gaze duration determine risky investment decisions? Recent studies have tested how gaze influences people’s decisions and the boundary of the gaze effect. The current experiment used adaptive gaze-contingent manipulation by adding a self-determined option to test whether longer gaze duration can determine risky investment decisions. The results showed that both the expected value of each option and the gaze duration influenced people’s decisions. This result was consistent with the attentional diffusion model (aDDM) proposed by Krajbich et al. (2010), which suggests that gaze can influence the choice process by amplify the value of the choice. Therefore, the gaze duration would influence the decision when people do not have clear preference.The result also showed that the similarity between options and the computational difficulty would also influence the gaze effect. This result was inconsistent with prior research that used option similarities to represent difficulty, suggesting that both similarity between options and computational difficulty induce different underlying mechanisms of decision difficulty.

EOESGC: Predicting miRNA−disease Associations Based on Embedding of Embedding and Simplified Graph Convolutional Network

Background: A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA−disease associations could provide us a root cause understanding on the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time consuming and costly. Therefore, we come up with more efficient models to solve this challenge. Results: In this work, we propose a deep learning model called EOESGC to predict potential miRNA−disease associations based on embedding of embedding and simplified convolutional network. Firstly, a coupled heterogeneous graph is constructed by using the integrated disease similarity, integrated miRNA similarity and miRNA−disease association networks where parts of the connected edges with less similarity values are removed to simplify the graph structure. The initial feature representation of nodes in the graph is learned using the embedding of embedding model(EOE) based on the principle that the nodes with associations are close to each other and the nodes without association are far from each other. The use of EOE can effectively learn the positional information among nodes and protect the graph structure information to some extent. Then the initial features of the nodes are fed into the simplified graph convolutional network(SGC), and in this step we only use miRNA−disease association network to further simplify the graph structure and thus reduce the computational complexity. Finally, feature embeddings of both miRNA and disease spliced into the MLP for prediction. The two graph simplifications of our model effectively reduce the computational difficulty, and the experimental results show that our model can indeed predict the potential miRNA−disease associations effectively. Compared with the latest published models, our model shows better results. On EOESGC evaluation part, the AUC, AUPR and F1 of our model are 0.9658, 0.8543 and 0.8644 by 5−fold cross validation respectively. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion: The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA−disease associations.

Constructing the 2-Element AGDS Protocol based on the Discrete Logarithm Problem

It is considered a group signature scheme in frame of which different sets of signers sign electronic documents with hidden signatures and the head of the signing group generates a group signature of fixed size. A new mechanism for imbedding the information about signers into a group signature is proposed. The method provides possibilities for reducing the signature size and to construct collective signature protocols for signing groups. New group signature and collective signature protocols based on the computational difficulty of discrete logarithm are proposed.

Numerical Simulations of Red-Blood Cells in Fluid Flow: A Discrete Multiphysics Study

In this paper, we present a methodological study of modelling red blood cells (RBCs) in shear-induced flows based on the discrete multiphysics (DMP) approach. The DMP is an alternative approach from traditional multiphysics based on meshless particle-based methods. The proposed technique has been successful in modelling multiphysics and multi-phase problems with large interfacial deformations such as those in biological systems. In this study, we present the proposed method and introduce an accurate geometrical representation of the RBC. The results were validated against available data in the literature. We further illustrate that the proposed method is capable of modelling the rupture of the RBC membrane with minimum computational difficulty.

Quantum-Resistant Network for Classical Client Compatibility

Quantum computing is no longer a thing of the future. Shor’s algorithm proved that a quantum computer couldtraverse key of factoring problems in polynomial time. Because the time-complexity of the exhaustive keysearch for quantum computing has not reliably exceeded the reasonable expiry of crypto key validity, it is believedthat current cryptography systems built on top of computational security are not quantum-safe. Quantumkey distribution fundamentally solves the problem of eavesdropping; nevertheless, it requires quantumpreparatory work and quantum-network infrastructure, and these remain unrealistic with classical computers.In transitioning to a mature quantum world, developing a quantum-resistant mechanism becomes a stringentproblem. In this research, we innovatively tackled this challenge using a non-computational difficulty schemewith zero-knowledge proof in order to achieve repellency against quantum computing cryptanalysis attacks foruniversal classical clients.

Robust optimization approach to emergency mobile facility routing

Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people’s lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing model is proposed to maximize the total served demand by the available mobile facilities. Based on the uninterruptible feature of emergency services, the model abstracts emergency events act as a combination of multiple uncertain variables. To overcome the computational difficulty, a robust optimization approach and genetic algorithm are employed to obtain solutions. Illustrative examples show that it provides an effective method for solving the emergency mobile facility routing problem, and that the risk factor and penalty factor of the model can further guide decision-making.

The Python’s Lunch: geometric obstructions to decoding Hawking radiation

According to Harlow and Hayden [arXiv:1301.4504] the task of distilling information out of Hawking radiation appears to be computationally hard despite the fact that the quantum state of the black hole and its radiation is relatively un-complex. We trace this computational difficulty to a geometric obstruction in the Einstein-Rosen bridge connecting the black hole and its radiation. Inspired by tensor network models, we conjecture a precise formula relating the computational hardness of distilling information to geometric properties of the wormhole — specifically to the exponential of the difference in generalized entropies between the two non-minimal quantum extremal surfaces that constitute the obstruction. Due to its shape, we call this obstruction the ‘Python’s Lunch’, in analogy to the reptile’s postprandial bulge.

Blind signature protocols based on hidden discrete logarithm problem

Introduction: The progress in the development of quantum computing has raised the problem of constructing post-quantum two-key cryptographic algorithms and protocols, i.e. crypto schemes resistant to attacks from quantum computers. Based on the hidden discrete logarithm problem, some practical post-quantum digital signature schemes have been developed. The next step could be the development of post-quantum blind signature protocols. Purpose: To develop blind signature protocols based on the computational difficulty of the hidden discrete logarithm problem. Method: The use of blinding factors introduced by the client during the blind signature protocol when the parameters necessary for the blind signature formation are passed to the signatory. Results: It has been proposed to use blinding multipliers of two different types: left-sided and right-sided ones. With them, you can develop blind signature protocols on the base of schemes with a verification equation defined in non-commutative algebraic structures. New blind signature protocols have been developed, based on the computational difficulty of the hidden discrete logarithm problem. As the algebraic carrier for the developed protocols, finite non-commutative associative algebras of two types are used: 1) those with a global two-sided unit, and 2) those with a large set of global left units. Practical relevance: The proposed protocols have a high performance and can be successfully implemented either in software or in hardware.