Research and Practice of Cooperative Teaching System for Software Engineering Major

In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on the software engineering teaching team of the School of Software Engineering,Beijing University of Posts and Telecommunications,through the implementation of the collaborative education project of the Ministry of Education,and proposes the multi-course collaborative practice teaching system,through the reasonable cross-fusion of the practical links of the 5 software engineering courses in the college,realizes the multi-course collaborative education and reasonable cross-fusion of courses,shares practical project resources,introduces new enterprise technologies,and guides students’innovation and entrepreneurship provide a meaningful reference for the collaborative arrangement of teaching content and cross-disciplinary integration in the current university education system.

Design of Characteristic Curriculum on Software Engineering Major for Undergraduate-Graduate Education

The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-level and graduate-level courses,poor curriculum structure,lacking of industry characteristics.This paper proposes an integrated undergraduate-graduate education curriculum for Software Engineering Major,which is based on Software Engineering specialty knowledge system(C-SWEBOK)and focuses on the current national strategic demands.Additionally,the curriculum combines with the University’s transportation characteristics,and fuses the discipline of Software Engineering and Intelligent Transportation.The multi-level curriculum designed in this paper is with reasonable structure,complete system,progressive content,and salient feature,which provides the strong support for cultivating high-qualified software talents in line with national strategies and industry needs.

Research on the Construction of Application- Oriented Software Engineering Semester Training System

"Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the professional quality of students in an all-round way,then eventually achieve the goal of satisfactory employment for both students and enterprises.However,in order to achieve the above purpose,the design of traditional training project still has the following problems:the topic selection of traditional training is designed by teachers in college,which lacks the training of engineering ability aiming at practical problems;the content and technology of traditional project training are out of date,ignoring the urgent demand of software industry development for advanced technology application;the traditional project training inspects the mastery of knowledge in each semester Degree,ignores the incremental of a progressive training system.In view of the above problems,this study proposes an Application-Oriented Software Engineering Semester Training System.Practice has proved that the construction of the training system can effectively improve the quality of teaching,so as to further improve the comprehensive quality of students.

Reform and Practice on International Software Talent Training Mode

In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.

Hierarchical and probabilistic quantum information splitting of an arbitrary two-qubit state via two cluster states

Based on non-maximally entangled four-particle cluster states, we propose a new hierarchical information splitting protocol to probabilistically realize the quantum state sharing of an arbitrary unknown two-qubit state. In this scheme, the sender transmits the two-qubit secret state to three agents who are divided into two grades with two Bell-state measurements,and broadcasts the measurement results via a classical channel. One agent is in the upper grade and two agents are in the lower grade. The agent in the upper grade only needs to cooperate with one of the other two agents to recover the secret state but both of the agents in the lower grade need help from all of the agents. Every agent who wants to recover the secret state needs to introduce two ancillary qubits and performs a positive operator-valued measurement（POVM） instead of the usual projective measurement. Moreover, due to the symmetry of the cluster state, we extend this protocol to multiparty agents.

Ensuring the Authenticity and Non-Misuse of Data Evidence in Digital Forensics

In forensic investigations,it is vital that the authenticity of digital evidence should be ensured. In addition,technical means should be provided to ensure that digital evidence collected cannot be misused for the purpose of perjury. In this paper,we present a method to ensure both authenticity and non-misuse of data extracted from wireless mobile devices. In the method,the device ID and a timestamp become a part of the original data and the Hash function is used to bind the data together. Encryption is applied to the data,which includes the digital evidence,the device ID and the timestamp. Both symmetric and asymmetric encryption systems are employed in the proposed method where a random session key is used to encrypt the data while the public key of the forensic server is used to encrypt the session key to ensure security and efficiency. With the several security mechanisms that we show are supported or can be implemented in wireless mobile devices such as the Android,we can ensure the authenticity and non-misuse of data evidence in digital forensics.

Four-layer cache strategy of edge computing in education environment

The rapid development of MEC and 5G technology has aroused huge repercussions and aroused the attention of education and industry.The storage of educational resources,energy consumption and delays in use have become urgent issues.In response to the above problems,this paper proposes a three-tier education network model and abstracts the network modeling.At the same time,the DQN method in reinforcement learning is used to solve the modeling method and simulation.Finally,the simulation results show that this method can effectively improve the cache hit rate and reduce the network load.

Reform and Practice on Cultivation of Data Thinking oriented Course Construction of Introduction to Big Data

With rapidly increasing massive data,mankind is entering a new era of big data.This brings new opportunities and challenges to modern society,and big data techniques also play an important role in advancing techniques of computer science(CS)and software engineering(SE).To keep pace with developments in technology,more and more university universities are incorporating big data into their curriculum courses.However,it is distinguished from traditional CS/SE courses and big data courses.This paper attempts to think deeply about the course construction of introduction to big data from the cultivation of data thinking,and further explore the implementation result and existing problems of the course,so as to better carry out the teaching of big data course.

Reform and Practice of Operating System Course

In recent years,with the changes in the international competitive environment,the development strategic focuses of the national software industry have also shifted.To adapt to these changes,we analyze and summarize the problems existing in the teaching content and methods of the traditional“Operating System”course and carry out the reform and practice of this core course for software engineering majors.First,we design and integrate domestic operating system examples into the course to meet the needs of national strategic development,thus cultivating operating system software talents in line with China’s national conditions and needs.Second,we reconstruct the teaching content of the course,strengthen the assessment of comprehensive ability,and establish a 3-level progressive teaching structure.Third,we carry out the deep integration of university and enterprise to realize the combination of theory and practice and establish a comprehensive and whole process university-enterprise course co-construction mode.Through the above reform measures,students show a strong interest in learning the operating system course.The operating system course has won the honor of the first-class course of Beijing Jiaotong University and has been approved for 4 industry-education cooperation projects of the Ministry of Education.

Reform and Practice on Course Construction of Deep Learning

In recent years,as one of the main technical ways to realize artificial intelligence,deep learning has attracted more and more attention of researchers from universities,research institutions and Internet enterprises.As a result,more and more universities are incorporating deep learning into their curriculum planning.However,there is a big difference between deep learning and previous courses.This paper attempts to think about the course construction of deep learning,and discusses the implementation and existing problems of the course,so as to better carry out the teaching of deep learning.

Teaching Reform and Practice on Course Construction of Discrete Mathematics

Discrete mathematics studies the respective structures,laws and interrelationships of different discrete quantities,which has a wide range of applications in computer science and software engineering.It has the characteristics of research,practice and innovation.The teaching goals of the course of discrete mathematics include not only the understanding and mastery of knowledge,but also the study of methodology.However,current teaching mechanism has limitations in outdated course contents,rare real project examples and nonscientific scoring modes.This paper attempts to think about the course construction of discrete mathematics to improve the teaching effects and cultivate students’practical ability.

Research and Practice on Teaching Reform of University Computer Foundation

In order to solve the problems of students’basic difference,more course content and less class hours,it is difficult for students to apply what they have learned.This paper puts forward the teaching reform scheme of university computer foundation course.From the optimization of teaching content,the construction of case scenarios,the improvement of teaching philosophy,the adjustment of teaching mode and assessment mode,etc.,we should give prominence to teaching students in accordance with their aptitude,apply what they have learned,and fully mobilize the subjective initiative and enthusiasm of students.According to the implementation and feedback of two consecutive years,the reform effect of the course is good.

Reform of Human-Computer Interaction Course based on Mobile Application Interaction Design

With the popularity of new intelligent mobile devices in people’s lives,the development of mobile applications has paid increasing attention to the interactive experience of users.As the content of traditional Human-Computer Interaction(HCI)course and teaching material is out of date,it cannot meet the needs of mobile application interaction design and enterprises for students.Therefore,we need a new generation HCI course based on intelligent mobile devices to study the relationship between users and systems.The HCI course not only teaches students HCI theory and model,but also needs to cultivate students’interaction-oriented design practical ability.This paper proposes a set of HCI teaching material design and teaching methods for improving HCI class quality on mobile application interaction design,so as to make students more suitable for the employment requirements of enterprises.

Deep Learning Methods Used in Remote Sensing Images: A Review

Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many others.Traditional machine learning algorithms are given less and less attention in the era of big data.Recently,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision.The number of relevant articles has nearly doubled every year since 2015.Advances in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern applications.However,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact performance.In this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.

A Portable Fall Detection and Alerting System Based on k-NN Algorithm and Remote Medicine

This paper presents information on a portable fall detection and alerting system mainly consisting of a custom vest and a mobile smart phone. A wearable motion detection sensor integrated with tri-axial accelerometer, gyroscope and Bluetooth is built into a custom vest worn by elderly. The vest can capture the reluctant acceleration and angular velocity about the activities of daily living(ADLs) of elderly in real time. The data via Bluetooth is then sent to a mobile smart phone running a fall detection program based on k-NN algorithm. When a fall occurs the phone can alert a family member or health care center through a call or emergent text message using a built in Global Positioning System. The experimental results show that the system discriminates falls from ADLs with a sensitivity of 95%, and a specificity of 96.67%. This system can provide remote monitoring and timely help for the elderly.

Semi-Fragile Audio Watermarking Algorithm in DWT Domain

A novel semi-fragile audio watermarking algorithm in DWT domain is proposed in this paper.This method transforms the original audio into 3-layer wavelet domain and divides approximation wavelet coefficients into many groups.Through computing mean quantization of per group,this algorithm embeds the watermark signal into the average value of the wavelet coefficients.Experimental results show that our semi-fragile audio watermarking algorithm is not only inaudible and robust against various common images processing,but also fragile to malicious modification.Especially,it can detect the tampered regions effectively.

Weighted Time-Based Global Hierarchical Path Planning in Dynamic Environment

A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid-or graph-based modeling is performed and the environment is divided into a set of grids or nodes. Then two time-based features of time interval and time cost are presented. The time intervals for each grid are built, during each interval the condition of the grid remains stable, and a time cost of passing through the grid is defined and assigned to each interval. Furthermore, the weight is introduced for taking both time and distance into consideration, and thus a sequence of multiscale paths with total time cost can be achieved. Experimental results show that the proposed method can handle the complex dynamic environment, obtain the global time optimal path and has the potential to be applied to the autonomous robot navigation and traffic environment.

AABN: Anonymity Assessment Model Based on Bayesian Network With Application to Blockchain

Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity of blockchain has caused widespread concern.In this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian Network.Firstly,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment schemes.Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail.Finally,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.

Term-Based Pooling in Convolutional Neural Networks for Text Classification

To achieve good results in convolutional neural networks(CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models(we named TBCNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters(convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority.

DIV： Dynamic Integrity Validation Framework for Detecting Compromises on Virtual Machine Based Cloud Services in Real Time

with the increasing popularity of cloud services,attacks on the cloud infrastructure also increase dramatically.Especially,how to monitor the integrity of cloud execution environments is still a difficult task.In this paper,a real-time dynamic integrity validation(DIV) framework is proposed to monitor the integrity of virtual machine based execution environments in the cloud.DIV can detect the integrity of the whole architecture stack from the cloud servers up to the VM OS by extending the current trusted chain into virtual machine's architecture stack.DIV introduces a trusted third party(TTP) to collect the integrity information and detect remotely the integrity violations on VMs periodically to avoid the heavy involvement of cloud tenants and unnecessary information leakage of the cloud providers.To evaluate the effectiveness and efficiency of DIV framework,a prototype on KVM/QEMU is implemented,and extensive analysis and experimental evaluation are performed.Experimental results show that the DIV can efficiently validate the integrity of files and loaded programs in real-time,with minor performance overhead.