An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.     

A fine-grained privacy protection data aggregation scheme for outsourcing smart grid

Compared with the traditional power grid, smart grid involves many advanced technologies and applications. However, due to the rapid development of various network technologies, smart grid is facing the challenges of balancing privacy, security, efficiency, and functionality. In the proposed scheme, we design a privacy protection scheme for outsourcing smart grid aided by fog computing, which supports fine-grained privacy-protected data aggregation based on user characteristics. The fog server matches the encrypted characteristics in the received message with the encrypted aggregation rules issued by the service provider. Therefore, the service provider can get more fine-grained analysis data based on user characteristics. Different from the existing outsourcing smart grid schemes, the proposed scheme can achieve real-time pricing on the premise of protecting user privacy and achieving system fault tolerance. Finally, experiment analyses demonstrate that the proposed scheme has less computation overhead and lower transmission delay than existing schemes.     

LPPTE: A lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications

Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects.     

A review on the characteristics of cyber-physical systems for the future smart factories

The emergence of new technologies is providing new ways to compete in the current context of changeable and unpredictable market requirements. The focus of this paper is on Cyber-Physical Systems (CPSs), as one of the most promising transformative technological concept of such a context, thus considered by literature as the building blocks of future smart factories. However, CPSs are still in their conceptualization phase. To this end, much literature effort has been put on their technological characterization, while there is a lack of knowledge on the operations management characterization to manage such new systems. To contribute in this latter direction, this paper reviews literature in order to distinguish between technological characteristics of CPSs and operations management characteristics to build future CPS-based smart factories. This paper remarks the need for research on operations management characteristics as these may be the ones actually leading operations managers to the concrete implementation of CPS-based factories in manufacturing.     

基于相互认证和3DES加密的智能卡远程支付系统认证方案

针对现有基于智能卡支付系统的安全方案存在密码暴露、信息泄露和身份认证等问题,提出一种新的基于相互认证和3DES加密的智能卡远程支付系统认证方案。分析基于二次剩余的支付认证方案的不足,在注册、登录、身份认证和密码更改阶段对其进行改进,避免密码暴露攻击,提高密码更改阶段的安全性,同时结合3DES加密算法对支付信息进行加密处理。性能分析表明,该方案能有效抵御多种攻击,且用户能够自由地修改密码,同时可对用户信息进行匿名保护。与现有智能卡支付认证方案相比,该方案提高了支付系统的安全性能且具有较小的计算复杂度。     

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body’s metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.     

A study on negative binomial inspection for imperfect production systems

Production systems continuously deteriorate with age and usage due to corrosion, fatigue, and cumulative wear in production processes, resulting in an increasing possibility of producing defective products. To prevent selling defective products, inspection is usually carried out to ensure that the performance of a sold product satisfies the customer requirements. Nevertheless, some defective products may still be sold in practice. In such a case, warranties are essential in marketing products and can improve the unfavorable image by applying higher product quality and better customer service. The purpose of this paper is to provide manufacturers with an effective inspection strategy in which the task of quality management is performed under the considerations of related costs for production, sampling, inventory, and warranty. A Weibull power law process is used to describe the imperfection of the production system, and a negative binomial sampling is adopted to learn the operational states of the production process. A free replacement warranty policy is assumed in this paper, and the reworking of defective products before shipment is also discussed. A numerical application is employed to demonstrate the usefulness of the proposed approach, and sensitivity analyses are performed to study the various effects of some influential factors.     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.     

A systematic review of global research on natural user interface for smart home system

The smart home system (SHS) equipped with intelligent sensors and actuators aims at providing seamless services for users. The natural user interface (NUI) enables intuitive interaction between users and the SHS, significantly lowering the barrier to entry and enhancing user experience. However, a comprehensive evaluation of research on NUI for the SHS to provide a valuable synthesis of existing research and informing future research directions remains unavailable. This study attempts to address this gap by systematically evaluating 1979 relevant journal and conference articles from the Web of Science Core Collection and Scopus, and those articles with a weak connection to the topic and poor data integrity were excluded. Finally, 1330 articles were chosen to for analysis in terms of contributions, critical papers, co-occurrence, and clustering using scientometric techniques. It was revealed that, with the help of advancing technologies, the research field of NUI aims to enrich the variety of interactive device types, improve the accuracy of interpreting and responding to user behavior, and optimize the compatibility with diverse user demographics. Besides, a conceptual framework summarizing typical types of NUI for the SHS was proposed, and strengths and weaknesses of each NUI were discussed for investigating specific future directions. Furthermore, four general research directions (strategies for promoting effective multimodal NUI, adoption of artificial intelligence for better performance, exploration of mixed reality applications in the SHS, and standardization of protocols in the smart home ecosystem) were also identified for academics and practitioners in their future research and practice.     

A hybrid artificial immune system and Self Organising Map for network intrusion detection

Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks. However, many anomaly detection systems simply report the anomalous activity, rather than analysing it further in order to report higher-level information that is of more use to a security officer. On the other hand, misuse detection systems recognise known attack patterns, thereby allowing them to provide more detailed information about an intrusion. However, such systems cannot detect novel attacks.A hybrid system is presented in this paper with the aim of combining the advantages of both approaches. Specifically, anomalous network connections are initially detected using an artificial immune system. Connections that are flagged as anomalous are then categorised using a Kohonen Self Organising Map, allowing higher-level information, in the form of cluster membership, to be extracted. Experimental results on the KDD 1999 Cup dataset show a low false positive rate and a detection and classification rate for Denial-of-Service and User-to-Root attacks that is higher than those in a sample of other works.     

基于二维码和信息隐藏的物流系统隐私保护方案

针对现有物流系统中的用户隐私信息容易泄露的问题,提出了一种基于二维码和信息隐藏的物流系统隐私保护方案。该方案使用信息隐藏技术将用户隐私信息嵌入快递面单上的二维码中,完成对隐私信息的访问权限控制。为了提升用户隐私信息的安全性,在该方案中设计了一种用于二维码的JPEG图像隐写算法。实验结果表明,该算法使在二维码图像中嵌入的用户隐私信息具有较高的抗检测性能,同时不影响载密二维码的正确扫描和解码。使用本文所提方案,在物流系统中具有访问权限的机构或个人才能够获取这些重要的隐私信息,既确保了快件的正确投递,也保障了用户的隐私安全。     

A binary ABC algorithm based on advanced similarity scheme for feature selection

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.     

A TRIZ-inspired knowledge-driven approach for user-centric smart product-service system: A case study on intelligent test tube rack design

With the advancement of information and communication technologies, smart product-service system (PSS) has attracted increasing research attention to integrate diverse providers/stakeholders and deliver value-added services. Many studies have thusly been conducted to design and develop the smart PSS, while i) how to identify multi-source requirements through reviewing the entire service interaction cycle, ii) how to define and deduce the innovative smart connected products (SCPs) features under contradictory interests, and iii) how to specify the functional and data service configuration solutions are still unclear. To tackle these problems, a knowledge-driven approach was introduced to better organize the multi-source design knowledge and deduce service recommendations for personalized user demands. Specifically, a user journey map-based method was proposed for knowledge elicitation of the specific user demands. To organize the user knowledge for better understanding, hierarchical personalized user requirements (PURs) analytics were proposed to quantitively transfer PURs into proper design engineering parameters. To inspire the problem-solving logic for design solution generation, TRIZ inventive principles were retrieved and referred to. With the generated design concept, the service logic diagrams were structured to define the personalized service content of SCPs. Having the service logic and SCPs, the overall service network of the user-centric smart PSS can be outlined. The proposed approach was demonstrated and validated in the digital health area via a case study on smart test tube rack (STTR) design, and it was validated from an economic feasibility perspective. It showed that the STTR was promising to reduce manual work, improve the nurses' performance, and enhance patients’ medical experience. Furthermore, it was suggested that the proposed approach can effectively guide the innovative SCPs creation in a straightforward and instructive manner, and is promising to process the service solution deduction and improve the efficiency of service creation in real practice.     

一种基于目标圆的有向强栅栏构建算法

K-栅栏覆盖是有向传感器网络覆盖控制的研究热点之一。提出一种基于目标圆的分布式有向强栅栏构建方法（DBCTC）。首次构建了节点目标圆和能耗比2个模型。以节点感知区域内横坐标最大的点为圆心,以感知半径为半径的圆就是目标圆。节点的运动能耗和栅栏增益的比值就是能耗比。前一个节点根据目标圆模型选择后一个节点的最佳目标位置。从附近移动节点中选择能耗比最少的移动节点构建有向强栅栏。仿真结果证明,该栅栏构建方法比其他算法降低60%左右的能耗。     

基于物联网的智能抄表高效TDMA协议设计

针对基于物联网的智能抄表系统节点众多、可靠性要求高、网络拓扑多变的特点,提出了一种用于智能抄表系统的高效时分多址E-TDMA协议。该协议基于Mesh网络架构,采用分布式协同调度的方式局部调整节点的时隙分配,并能进行网络拓扑调整。此外,E-TDMA还提供差异服务。采用NS2对协议进行仿真比较,结果显示,E-TDMA具有更好的性能,为基于物联网的智能抄表系统提供了高效的信道接入和可靠的质量保证。     

基于带有惩罚因子的阴性选择算法的恶意程序检测模型

提出了一个基于带有惩罚因子的阴性选择算法的恶意程序检测模型.该模型从指令频率和包含相应指令的文件频率两个角度出发,对指令进行了深入的趋向性分析,提取出了趋向于代表恶意程序的恶意程序指令库.利用这些指令,有序切分程序比特串,模型提取得到恶意程序候选特征库和合法程序类恶意程序特征库.在此基础上,文中提出了一种带有惩罚因子的阴性选择算法(negative selection algorithm with penalty factor,NSAPF),根据异体和自体的匹配情况,采用惩罚的方式,对恶意程序候选特征进行划分,组成了恶意程序检测特征库1(malware detection signature library 1,MDSL1)和恶意程序检测特征库2(MDSL2),以此作为检测可疑程序的二维参照物.综合可疑程序和MDSL1,MDSL2的匹配值,文中模型将可疑程序分类到合法程序和恶意程序.通过在阴性选择算法中引入惩罚因子C,摆脱了传统阴性选择算法中对自体和异体有害性定义的缺陷,继而关注程序代码本身的危险性,充分挖掘和调节了特征的表征性,既提高了模型的检测效果,又使模型可以满足用户对识别率和虚警率的不同要求.综合实验...