Analogy-based software effort estimation using multi-objective feature selection

The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

NC-MACPABE: Non-centered multi-authority proxy re-encryption based on CP-ABE for cloud storage systems

The cloud storage service cannot be completely trusted because of the separation of data management and ownership, leading to the difficulty of data privacy protection. In order to protect the privacy of data on untrusted servers of cloud storage, a novel multi-authority access control scheme without a trustworthy central authority has been proposed based on CP-ABE for cloud storage systems, called non-centered multi-authority proxy re-encryption based on the cipher-text policy attribute-based encryption(NC-MACPABE). NC-MACPABE optimizes the weighted access structure(WAS) allowing different levels of operation on the same file in cloud storage system. The concept of identity dyeing is introduced to improve the users' information privacy further. The re-encryption algorithm is improved in the scheme so that the data owner can revoke user's access right in a more flexible way. The scheme is proved to be secure. And the experimental results also show that removing the central authority can resolve the existing performance bottleneck in the multi-authority architecture with a central authority, which significantly improves user experience when a large number of users apply for accesses to the cloud storage system at the same time.

Secure Big Data Storage and Sharing Scheme for Cloud Tenants

The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud.In this paper,we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers.Instead of protecting the big data itself,the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function.Analysis,comparison and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.

Grey systems for intelligent sensors and information processing

In a measurement system, new representation methods are necessary to maintain the uncertainty and to supply more powerful ability for reasoning and transformation between numerical system and symbolic system. A grey measurement system is discussed from the point of view of intelligent sensors and incomplete information processing compared with a numerical and symbolized measurement system. The methods of grey representation and information processing are proposed for data collection and reasoning. As a case study, multi-ultrasonic sensor systems are demonstrated to verify the effectiveness of the proposed methods.

Low-power task scheduling algorithm for large-scale cloud data centers

How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.

Reinforcement Learning from Algorithm Model to Industry Innovation Innovation: A Foundation Stone of Future Artificial Intelligence

Reinforcement learning(RL)algorithm has been introduced for several decades,which becomes a paradigm in sequential decision-making and control.The development of reinforcement learning,especially in recent years,has enabled this algorithm to be applied in many industry fields,such as robotics,medical intelligence,and games.This paper first introduces the history and background of reinforcement learning,and then illustrates the industrial application and open source platforms.After that,the successful applications from AlphaGo to AlphaZero and future reinforcement learning technique are focused on.Finally,the artificial intelligence for complex interaction(e.g.,stochastic environment,multiple players,selfish behavior,and distributes optimization)is considered and this paper concludes with the highlight and outlook of future general artificial intelligence.

CNN-RNN based method for license plate recognition

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public vehicle （taxis/cabs） have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks （CNN） and Recurrent Neural Networks namely, BLSTM （Bi-Directional Long Short Term Memory）, for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting （DCV）, which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.

Towards a Component Framework for Architecture-Based Self-Adaptive Applications

Self-adaptive software is an efficient way to cope with highly dynamic nature of the environment where it is situated. In this paper, from the perspective of software architecture, we propose a component framework for supporting the architecture-based design and development of self-adaptive applications. It captures some key elements of the research on software architecture and provides more flexible facilities to decouple interacting components. Based on that, a prototype is implemented to demonstrate its feasibility, and at last a case study is presented to illustrate our framework.

Multiway Dynamic Trust Chain Model on Virtual Machine for Cloud Computing

This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine monitor.Through dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.

Survey on the Application of Deep Reinforcement Learning in Image Processing

feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.

Genetic Algorithm-Based Approaches for Optimizing S-Boxes

Multi-objective genetic algorithm is much suitable for solving multi-objective optimization problems. By use of Genetic algorithm, the optimization of S-boxes is explored in this paper. Results of the experiments show that, with heuristic mutation strategy, the algorithm has high searching efficiency and fast convergence speed. Meanwhile, we also have take the avalanche probability of S-boxes into account, besides nonlinearity and difference uniformity. Under this method, an effective genetic algorithm for 6×6 S-boxes is provided and a number of S-boxes with good cryptographic capability can be obtained.

A Fuzzy Directed Graph-Based QoS Model for Service Composition

Web service composition lets developers create applications on top of service-oriented computing and its native description, discovery, and communication capabilities. This paper mainly focuses on the QoS when the concrete composition structure is unknown. A QoS model of service composition is presented based on the fuzzy directed graph theory. According to the model, a recursive algorithm is also described for calculating such kind of QoS. And, the feasibility of this QoS model and the recursive algorithm is verified by a case study. The proposed approach enables customers to get a possible value of the QoS before they achieve the service.

What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global crisis.With the development of online social media,people prefer to express their opinions and discuss the latest news online.We have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in time.It is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted online.In this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and experiments.We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by users.In order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about COVID-19.Experimental results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

KANTOROVICH THEOREM FOR VARIATIONAL INEQUALITIES

Kantorovich theorem was extended to variational inequalities by which the convergence of Newton iteration,the existence and uniqueness of the solution of the problem can be tested via computational conditions at the initial point.

A communication-aware scheduling algorithm for heterogeneous systems

Previous work mostly employs an ideal system model that assumes fully connected processors and concurrent communication, which leads to inaccurate and inegicient schedule results. In this paper, a new scheduling model which integrates the communication awareness was proposed. Then, a novel scheduling algorithm based on list heuristic was put forward for the new model, which can produce optimal schedule by serializing the communications edges. Experimental results show that the accuracy and efficiency of the new model and algorithm are significantly improved.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

A Function-Aware Mimic Defense Theory and Its Practice

In recent years,network attacks have been characterized by diversification and scale,which indicates a requirement for defense strategies to sacrifice generalizability for higher security.As the latest theoretical achievement in active defense,mimic defense demonstrates high robustness against complex attacks.This study proposes a Function-aware,Bayesian adjudication,and Adaptive updating Mimic Defense(FBAMD)theory for addressing the current problems of existing work including limited ability to resist unknown threats,imprecise heterogeneous metrics,and over-reliance on relatively-correct axiom.FBAMD incorporates three critical steps.Firstly,the common features of executors’vulnerabilities are obtained from the perspective of the functional implementation(i.e,input-output relationships extraction).Secondly,a new adjudication mechanism considering Bayes’theory is proposed by leveraging the advantages of both current results and historical confidence.Furthermore,posterior confidence can be updated regularly with prior adjudication information,which provides mimic system adaptability.The experimental analysis shows that FBAMD exhibits the best performance in the face of different types of attacks compared to the state-of-the-art over real-world datasets.This study presents a promising step toward the theo-retical innovation of mimic defense.

Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among vehicles.However,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users.Trust mechanisms serve as an effective solution to this issue.In recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area.In this paper,we propose a distributed vehicular network scheme based on trust scores.Specifically,the designed architecture partitions multiple vehicle regions into clusters.Then,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by vehicles.Additionally,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation model.Our proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among vehicles.Both theoretical and experimental analysis show that our scheme outperforms the compared schemes.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.