The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models.Aiming to this,this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration.The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology,thereby the generalization performance and the convergence stability are improved.For the generator,a Flow-based network using Wavelet additive coupling(WAC)layers is implemented to extract multi-scale content features.Also,a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input images.To enhance the generated salient details,a loss of adaptive stroke edge is applied in both the global and local model training.The experimental results show that the proposed method improves PSNR by 5%,SSIM by 6.2%,and decreases Style Error by 29.4%over the existing models on the ChipPhi set.The competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization,thereby can be further applied to more APST scenes.

AN ENGINEERING MODEL FOR MULTICOMPONENT REVERSIBLE REACTION NETWORK

The treatment of a multicomponent reversible reaction network is extremely complicated because largenumber of rate constants must be precisely determined and because the calculation based on these rateconstants is tedious.In order to reduce the degrees of freedom of the process,the authors propose a methodin which the reactor and the separator are regarded as a whole.Based on this approach,an N-componentreversible reaction system can be dealt with as a two—component system.Consequently,a simple and ac-cessible way of the apparent rate determination is suggested.For fiist-order reactions,an explicit,simplifiedexpression has been derived for both lumped and distributed parameter reaction systems.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

A Neural Network Approach on Reverse Engineering Surface Reasoning in the Manufacturing Field

This paper applied the neural network technology to surface reasoning in reverse engineering and established the neural network computation model. One of the main advantages of reasoning solid surface using neural network is that no knowledge about surface is needed, and the limited measured points on the surface will do sufficiently. This paper listed the related reasoning cases, including the elementary analytical surfaces and freeform surfaces, discussed the various issues occurring during reasoning process and proved the feasibility and efficiency of this approach from theory and practical computing cases. The results show that a neural network is an excellent aided analysis means for surface reasoning in reversing engineering and possesses practical use for the surface that is complex, incomplete and partially worn out or damaged.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Neural network approach for modification and fitting of digitized data in reverse engineering~

Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

Logical Design and Control of Network in Local Mine Air-Reversing System

This paper sets up a mathematical model of switching network and switching function by utilizing graph theory to describe the logical function of different paths.The function varies with open and closed states of air doors in a complex mine air sub network, and the computer program for solving the switching function of complex networks are offered. It gives the method for discriminating a reversible branch in a complex network by means of the switching function, and the method of counter inverted logical control of airflow inversion by means of open and short circuit conversion of key branches. The research has solved the problem of the stablization of air flow for normal ventination and reversing ventination in a diagonal network.

Designing a Reverse Logistics Network Model for Waste Batteries

The logistics industry plays an important role in circular economy.Therefore,not only economic benefits,but also environmental protection factors have to be considered in reverse logistics.This paper uses the multi-objective 0-1 mixed integer programming to establish a reverse logistics network optimization model for waste batteries.The objective function is to minimize both,logistics costs and carbon dioxide emissions.The model considers the basic settings of reverse logistics(including recycling nodes,manufacturing,and processing nodes)and the material flow between different settings.In solving the model,Lingo 14.0 is used in this paper.An actual case of a waste battery reverse logistics enterprise verifies the effectiveness of the model in this paper.The results show that the application of this model can effectively improve the operating efficiency of waste battery reverse logistics enterprises.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Facility location model for a remanufacturing logistics network

First a remanufactming logistics network is con- structed, in which the structure of both the forward logistics and the reverse logistics are of two levels and all the logistics facilities are capacitated. Both the remanufactming products and the new products can be used to meet the demands of customers. Moreover, it is assumed that homogeneous facilities can be designed together into integrated ones, based on which a mixed integer nonlinear programming （MINLP） facility location model of the remanufacturing logistics network with six types of facilities to be sited is built. Then an algorithm based on enumeration for the model is given. The feasible combinations of binary variables are searched by enumeration, and the remaining sub-problems are solved by the LP solver. Finally, the validities of the model and the algorithm are illustrated by means of an example. The result of the sensitivity analysis of parameters indicates that the integration of homogeneous facilities may influence the optimal solution of the problem to a certain degree.

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

An Integrated Multi-Echelon Model for a Sustainable Closed Loop Supply Chain Network Design

The integration of entire supply and value chain into a closed loop network is gaining more importance in recent times in order to ensure a business to be economically and environmentally sustainable with the changing trends in business and social environments, growing environmental consciousness in the society and government legislations to protect the environment as well as the business. In this context, this paper considers a multi-echelon closed loop supply chain network design with forward and reverse logistics components. An attempt has been made to develop a mixed integer non-linear programming model for this problem with different costs so that the sum of the total cost is minimized subject to different constraints pertaining to capacities of the entities of the system, demands of first customers and second customers. A generalized model is presented and then its application is illustrated using an example problem by solving the model using LINGO14. This model forms as a tool to compare future meta-heuristics to check the closeness of their solutions with corresponding optimal solutions.

Mesh Generation from Dense 3D Scattered Data Using Neural Network

An improved self-organizing feature map (SOFM) neural network is presented to generate rectangular and hexagonal lattic with normal vector attached to each vertex. After the neural network was trained, the whole scattered data were divided into sub-regions where classified core were represented by the weight vectors of neurons at the output layer of neural network. The weight vectors of the neurons were used to approximate the dense 3-D scattered points, so the dense scattered points could be reduced to a reasonable scale, while the topological feature of the whole scattered points were remained.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Deep Learning Based Model Predictive Control for a Reverse Osmosis Desalination Plant

Reverse Osmosis (RO) desalination plants are highly nonlinear multi-input-multioutput systems that are affected by uncertainties, constraints and some physical phenomena such as membrane fouling that are mathematically difficult to describe. Such systems require effective control strategies that take these effects into account. Such a control strategy is the nonlinear model predictive (NMPC) controller. However, an NMPC depends very much on the accuracy of the internal model used for prediction in order to maintain feasible operating conditions of the RO desalination plant. Recurrent Neural Networks (RNNs), especially the Long-Short-Term Memory (LSTM) can capture complex nonlinear dynamic behavior and provide long-range predictions even in the presence of disturbances. Therefore, in this paper an NMPC for a RO desalination plant that utilizes an LSTM as the predictive model will be presented. It will be tested to maintain a given permeate flow rate and keep the permeate concentration under a certain limit by manipulating the feed pressure. Results show a good performance of the system.

A Modified SOFM Method for Point Cloud Segmentation in Reverse Engineering

The purpose of reverse engineering is to convert a large point cloud into a CAD model. In reverse engineering, the key issue is segmentation, i.e. studying how to subdivide the point cloud into smaller regions, where each of them can be approximated by a single surface. Segmentation is relatively simple, if regions are bounded by sharp edges and small blends; problems arise when smoothly connected regions need to be separated. In this paper, a modified self-organizing feature map neural network （SOFM） is used to solve segmentation problem. Eight dimensional feature vectors （3-dimensional coordinates, 3-dimensional normal vectors, Gaussian curvature and mean curvature） are taken as input for SOFM. The weighted Euclidean distance measure is used to improve segmentation result. The method not only can deal with regions bounded by sharp edges, but also is very efficient to separating smoothly connected regions. The segmentation method using SOFM is robust to noise, and it operates directly on the point cloud. An examples is given to show the effect of SOFM algorithm.

Secure vehicular data communication in Named Data Networking

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data communications.This paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure rates.This paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure rates.SCD constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse paths.Only the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are achieved.SCD is evaluated,and the data results demonstrate that SCD achieves the above objectives.

EDSM-Based Binary Protocol State Machine Reversing

Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols are emerging in endlessly various network environments.Herein,relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection.Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols,most existing methods have proven to be time-consuming with limited efficiency,especially when applied on unknown protocol state machines.This paper proposes a state merging algorithm based on EDSM(Evidence-Driven State Merging)to infer the transition rules of unknown protocols in form of state machines with high efficiency.Compared with another classical state machine inferring method based on Exbar algorithm,the experiment results demonstrate that our proposed method could run faster,especially when dealing with massive training data sets.In addition,this method can also make the state machines have higher similarities with the reference state machines constructed from public specifications.