Sustainable Mining in the Era of Artificial Intelligence

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread use.Over-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

Parallel Vision ■ Image Synthesis/Augmentation

Dear Editor,Scene understanding is an essential task in computer vision.The ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans do.Parallel vision is a research framework that unifies the explanation and perception of dynamic and complex scenes.

Research on the Cultivation of Innovative Talents in Software Engineering Based on the OBE Concept

Under the background of New Engineering,focusing on talent cultivation,this article explores the teaching reform and innovation of software engineering majors.Starting from the goal of cultivating software engineering talents in the context of New Engineering,the concept of outcome-based education is introduced to study and explore the construction of innovative talent training models for software engineering majors in universities.Through recent years’application practice,initial results have been achieved,which can provide feasible methods and ideas for the cultivation of innovative talents in other majors.

Teaching Reform Based on the Competency Model for Software Engineering-related Courses

This paper explores the reform and practice of software engineering-related courses based on the competency model of the Computing Curricula,and proposes some measures of teaching reform and talent cultivation in software engineering.The teaching reform emphasizes student-centered education,and focuses on the cultivation and enhancement of students’knowledge,skills,and dispositions.Based on the three elements of the competency model,specific measures of teaching reform are proposed for some professional courses in software engineering,to strengthen course relevance,improve knowledge systems,reform practical modes with a focus on skill development,and cultivate good dispositions through student-centered education.The teaching reform’s attempts and practice are conducted in some courses such as Advanced Web Technologies,Software Engineering,and Intelligent Terminal Systems and Application Development.Through the analysis and comparison of the implementation effects,significant improvements are observed in teaching effectiveness,students’mastery of knowledge and skills are noticeably improved,and the expected goals of the teaching reform are achieved.

Exploration on 5E Teaching Principle-based Education for the Object-oriented Software Construction Course

Most current object-oriented programming courses offered by domestic colleges and universities generally focus on the object-oriented programming language itself,i.e.,the programming grammar of the language,but ignore the design pattern.However,design patterns are essential to software engineering because they can solve common problems in software design and improve code reuse,readability,extensibility,and reliability.Our Object-oriented Software Construction Course is creative since it aims at cultivating students’object-oriented thinking as well as basic abilities required to construct high-quality,object-oriented software.Specifically,we exploit the 5E teaching principle during the education of this course,and present the whole pipeline in the paper.We also provide one case of the factory pattern to further demonstrate the implementation of the 5E teaching principle in the course.The effect of the 5E teaching principle has also been demonstrated.

On the Assessment of Generative AI in Requirements Analysis and Modeling Tasks with UML:An Exploratory Study

Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.

Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations.When transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each other.If one party refuses to do so,the channel is unstable.A stable channel is thus required.Because nodes may show uncooperative behavior,they may have a negative impact on the stability of such channels.In order to address this issue,this work proposes a dynamic evolutionary game model based on node behavior.This model considers various defense strategies'cost and attack success ratio under them.Nodes can dynamically adjust their strategies according to the behavior of attackers to achieve their effective defense.The equilibrium stability of the proposed model can be achieved.The proposed model can be applied to general channel networks.It is compared with two state-of-the-art blockchain channels:Lightning network and Spirit channels.The experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable state.Thus its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

A Framework for the Service Model of NFC Stack

Abstract--Near field communications （NFC） is a newly thrived technology in recent years. This technology has been installed on many kinds of mobile phone systems, especially the Android. However, there is no unified and complete framework to access NFC so far. The current software stack of NFC merely implements data obtaining features, ignoring the post-processing of data and lacking a certain security mechanism for NFC, which results in inefficiency and inconvenience for software development and maintenance. Above all, security problems could be caused due to the absence of the security mechanism. To propose a solution, this paper presents a brand-new framework for NFC utilization by analyzing and constructing a service model. Thus, the proposed framework encapsulates the current NFC stack on Android, formulating a three-layer structure after implementing the encapsulation and parsing of NFC records, which ultimately enables an XML document to describe the configuration of NFC and its related service flow. Simultaneously, a context-awareness model is proposed and built in this paper to equip the framework with the capability of adapting to different＇physical environment.

The Research about the Role and influence of Teacher emotional support in Online Learning Environment

At the beginning of 2020,the“COVID-19”came out.Affected by the outbreaks,the universities have to carry out online teaching.Online learning provides students with full freedom and personalized learning space,but at the same time,it also brings problems such as weak feelings between teachers and students and lack of learning experience.To solve these problems,this paper adopts the methods of questionnaire survey,experimental control and behavioral modeling.This paper studies how teachers’emotional support behavior affects students’learning process and learning emotion in online learning environment,and proposes that teachers’emotional support behavior is appealed and desired by students.Positive teachers’emotional support behavior can promote students’learning process and improve students’learning emotion.

Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

As wafer circuit width shrinks down to less than ten nanometers in recent years,stringent quality control in the wafer manufacturing process is increasingly important.Thanks to the coupling of neighboring cluster tools and coordination of multiple robots in a multi-cluster tool,wafer production scheduling becomes rather complicated.After a wafer is processed,due to high-temperature chemical reactions in a chamber,the robot should be controlled to take it out of the processing chamber at the right time.In order to ensure the uniformity of integrated circuits on wafers,it is highly desirable to make the differences in wafer post-processing time among the individual tools in a multicluster tool as small as possible.To achieve this goal,for the first time,this work aims to find an optimal schedule for a dual-arm multi-cluster tool to regulate the wafer post-processing time.To do so,we propose polynomial-time algorithms to find an optimal schedule,which can achieve the highest throughput,and minimize the total post-processing time of the processing steps.We propose a linear program model and another algorithm to balance the differences in the post-processing time between any pair of adjacent cluster tools.Two industrial examples are given to illustrate the application and effectiveness of the proposed method.

Teaching Reform and Practice of Software Engineering Graduate Students by Implanting Scientific Research Literacy into Classroom Teaching Content

How to cultivate and improve graduate students’innovation and practical abilities in software engineering through the curriculum and teaching mode reform is an important issue.In this paper,a research literacy-driven teaching mode is proposed.It assists in the reform of the curriculum system.Then,a curriculum system construction framework is proposed,which involves the integration of research literacy into classroom teaching content.It assists in the cultivation of research abilities of graduate students in software engineering.The effectiveness of the curriculum reform is demonstrated through questionnaire surveys and research outcomes of the project team.The results show that the methods explored in this paper can serve as valuable references for future course design and teaching practice in computer-related courses for graduates.

An efficient non-iterative smoothed particle hydrodynamics fluid simulation method with variable smoothing length

In classical smoothed particle hydrodynamics(SPH)fluid simulation approaches,the smoothing length of Lagrangian particles is typically constant.One major disadvantage is the lack of adaptiveness,which may compromise accuracy in fluid regions such as splashes and surfaces.Attempts to address this problem used variable smoothing lengths.Yet the existing methods are computationally complex and non-efficient,because the smoothing length is typically calculated using iterative optimization.Here,we propose an efficient non-iterative SPH fluid simulation method with variable smoothing length(VSLSPH).VSLSPH correlates the smoothing length to the density change,and adaptively adjusts the smoothing length of particles with high accuracy and low computational cost,enabling large time steps.Our experimental results demonstrate the advantages of the VSLSPH approach in terms of its simulation accuracy and efficiency.

Digital Transformation of Higher Education:History and Consensus,Trends and Experiences,Issues and Challenges

The rapid development of new-generation information technology has triggered the evolution of education and teaching towards digitalization,accelerating the digital transformation of higher education and bringing an important opportunity for the high-quality development of higher education.Firstly,we give an overview of the digital development of higher education and discuss how information technology is reshaping the teaching and learning of higher education.Secondly,we explain the consensus on the digital development of higher education,and focus on summarizing the digital achievements of higher education in China and analyzing the successful experience through the introduction of the digital development trend of higher education in the world.Finally,we point out the current problems and challenges and make a preliminary discussion.Digital empowerment has arrived,and in the era of digitization,the transformation and development of higher education will lead to systemic changes in universities.This is an inevitable stage in the process of higher education development.Digital transformation will drive higher education to be more competitive,inclusive,and accessible,enabling universities to unleash their digital vitality in various service functions and contribute to the construction of a digital China.

Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network

Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimization(PPO) and long short-term memory(LSTM).