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.

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.

A Framework for the Service Model of NFC Stack

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.

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things(IoT),a secure and robust authentication mechanism is urgently required as a critical architectural component.The IoT has improved the quality of everyday life for numerous people in many ways.Owing to the predominantly wireless nature of the IoT,connected devices are more vulnerable to security threats compared to wired networks.User authentication is thus of utmost importance in terms of security on the IoT.Several authentication protocols have been proposed in recent years,but most prior schemes do not provide sufficient security for these wireless networks.To overcome the limitations of previous schemes,we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme(COBBAS).The proposed scheme is based on biometric data,and uses lightweight operations to enhance the efficiency of the network in terms of time,storage,and battery consumption.A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication.Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks.Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing,impersonation,stolen verifier attacks,denial-of-service attacks,and errors in biometric recognition.This protocol also provides user anonymity,confidentiality,integrity,and biometric recovery in acceptable time with reasonable computational cost.

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.

Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

Knowledge Graph and Knowledge Reasoning:A Systematic Review

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

Mobile Web Middleware of NFC Context-Awareness Applications

With the development of wireless networks and mobile computing,more advanced applications with context-awareness and adaptability to their changing context are needed.However,building context-aware applications is difficult due to the lack of adequate infrastructure support.In this paper,a web middleware architecture for the development of context-awareness applications using near field communication(NFC)is proposed.Based on it,the efficient support for acquiring,interpreting,and accessing context is provided,and the user’s quality of experience is improved.Moreover,a mobile web middleware for the testing and full realization of NFC context-awareness applications has been developed together with two application examples.

A Sandwich Control System with Dual Stochastic Impulses

Dear editor,Along with the progress of science and technology and the development of social civilization,control system brings an increasingly significant function in daily life.The application field of control system is very wide,for instance,in mobile technology[1],artificial earth satellite[2],pest control[3],etc.Ribeiro[4]first put forward the concept of random pulse in 1967.At present,impulsive control is used in networked control[5],secure communication[6],etc.In the 21st century,the impulsive control has been used in synchronization of coupled system,intelligent fault identification,image encryption.

Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

Optimal Formation Control for Second-Order Multi-Agent Systems With Obstacle Avoidance

Dear Editor,The optimal formation control design problem is studied for a class of second-order multi-agent systems(MASs) with obstacle avoidance.Based on the actor-critic framework, an optimized formation controller is proposed by constructing a novel performance index function. Furthermore, the stability of MAS is proved by constructing the Lyapunov function. The simulation results are provided to depict the effectiveness of the proposed strategies.

An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism learning.In order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

Optimal Static Partition Configuration in ARINC653 System

ARINC653 systems, which have been widely used in avionics industry, are an important class of safety-critical applications. Partitions are the core concept in the Arinc653 system architecture. Due to the existence of partitions, the system designer must allocate adequate time slots statically to each partition in the design phase. Although some time slot allocation policies could be borrowed from task scheduling policies, no existing literatures give an optimal allocation policy. In this paper, we present a partition configuration policy and prove that this policy is optimal in the sense that if this policy fails to configure adequate time slots to each partition, nor do other policies. Then, by simulation, we show the effects of different partition configuration policies on time slot allocation of partitions and task response time, respectively.

Formative Assessment of Programming Language Learning Based on Peer Code Review: Implementation and Experience Report

In programming courses, the traditional assessment approach tends to evaluate student performance by scoring one or more project-level summative assignments. This approach no longer meets the requirements of a quality programming language education. Based on an upgraded peer code review model, we propose a formative assessment approach to assess the learning of computer programming languages, and develop an online assessment system(OOCourse) to implement this approach. Peer code review and inspection is an effective way to ensure the high quality of a program by systematically checking the source code. Though it is commonly applied in industrial and open-source software development, it is rarely taught and practiced in undergraduate-level programming courses. We conduct a case study using the formative assessment method in a sophomore level Object-Oriented Design and Construction course with more than 240 students. We use Moodle(an online learning system) and some relevant plugins to conduct peer code review. We also conduct data mining on the running data from the peer assessment activities. The case study shows that formative assessment based on peer code review gradually improved the programming ability of students in the undergraduate class.

Heart-Rate Analysis of Healthy and Insomnia Groups with Detrended Fractal Dimension Feature in Edge

Insomnia,whether situational or chronic,affects over a third of the general population in today’s society.However,given the lack of non-contact and non-inductive quantitative evaluation approaches,most insomniacs are often unrecognized and untreated.Although Polysomnographic(PSG)is considered as one of the assessment methods,it is poorly tolerated and expensive.In this paper,with the recent development of Internet-of-Things devices and edge computing techniques,we propose a detrended fractal dimension(DFD)feature for the analysis of heart-rate signals,which can be easily acquired by many wearables,of good sleepers and insomniacs.This feature was derived by calculating the fractal dimension(FD)of detrended signals.For the trend component removal,we improved the null space pursuit algorithm and proposed an adaptive trend extraction algorithm.The experimental results demonstrated the efficacy of the proposed DFD index through numerical statistics and significance testing for healthy and insomnia groups,which renders it a potential biomarker for insomnia assessment and management.

Cross-Layer Design Based Optimized Link State Routing Protocol for Wireless Mesh Networks

A proactive routing protocol CL-OLSR (cross-layer based optimized link state routing) by using a brand-new routing metric CLM (cross-layer metric) is proposed. CL-OLSR takes into account four link quality impact factors in route calculation through the cross-layer operation mechanism: the node available bandwidth, the node load, the link delivery rate, and the link interference, and thus the effect of route selection is optimized greatly. The simulation results show that the proposed CL-OLSR protocol can not only improve the network throughput to a large extent, but also reduce the end-to-end delay, while achieving load balance route results.

Efficient Publication of Distributed and Overlapping Graph Data Under Differential Privacy

Graph data publication has been considered as an important step for data analysis and mining.Graph data,which provide knowledge on interactions among entities,can be locally generated and held by distributed data owners.These data are usually sensitive and private,because they may be related to owners’personal activities and can be hijacked by adversaries to conduct inference attacks.Current solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed manners.Therefore,this work proposes a novel framework for distributed graph publication.In this framework,differential privacy is applied to justify the safety of the published contents.It includes four phases,i.e.,graph combination,plan construction sharing,data perturbation,and graph reconstruction.The published graph selection is guided by one data coordinator,and each graph is perturbed carefully with the Laplace mechanism.The problem of graph selection is formulated and proven to be NP-complete.Then,a heuristic algorithm is proposed for selection.The correctness of the combined graph and the differential privacy on all edges are analyzed.This study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

A Trust Value Sharing Scheme in Heterogeneous Identity Federation Topologies

Recent developments in heterogeneous identity federation systems have heightened the need for the related trust management system.The trust management system evaluates,manages,and shares users’trust values.The service provider(SP)members of the federation system rely on users’trust values to determine which type and quality of service will be provided to the users.While identity federation systems have the potential to help federated users save time and energy and improve service experience,the benefits also come with significant privacy risks.So far,there has been little discussion about the privacy protection of users in heterogeneous identity federation systems.In this paper,we propose a trust value sharing scheme based on a proxy ring signature for the trust management system in heterogeneous identity federation topologies.The ring signature schemes can ensure the validity of the data and hide the original signer,thereby protecting privacy.Moreover,no group manager participating in the ring signature,which naturally matches with our decentralized heterogeneous identity federation topologies.The proxy signature can reduce the workload of the private key owner.The proposed scheme shortens the calculation time for verifying the signature and then reduces the overall time consumption in the process of trust sharing.Our studies prove that the proposed scheme is privacy-preserving,efficient,and effective.

MACHINE LEARING FOR MOBILE EDGE COMPUTING

In recent years,mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages(such as low latency,computation efficiency and privacy)caused by its local model of providing storage and computation resources.

Next Generation Mobile Video Networking

The most recent Cisco Visual Networking Index(VNI) fore-casts that more than three-fourths of the world's mobile datatraffic, which is expected to be 49 exabytes per month by2021, will be video, i.e., a 9-fold increase between 2016