AbstractÐElectronic commerce technology offers the opportunity to integrate and optimize the global production and distribution supply chain. The computers of the various corporations, located throughout the world, will communicate with each other to determine the availability of components, to place and confirm orders, and to negotiate delivery timescales. In this paper, we describe MAgNET, a system for networked electronic trading that is based on the Java mobile agent technology, called aglets. Aglets are dispatched by the buyer to the various suppliers, where they negotiate orders and deliveries, returning to the buyer with their best deals for approval. MAgNET handles the deep supply chain, where a supplier may need to contact further suppliers of subcomponents in order to respond to an enquiry. Experimental results demonstrate the feasibility of using the Java aglet technology for electronic commerce. Fig. 2. A mobile aglet runs on server A and is dispatched to server B. Before the aglet leaves server A, its onDispatching method is invoked. Similarly, the aglet's onArrival method is invoked before the aglet starts to run on server B. P. Michael Melliar-Smith received a PhD degree in computer science from the University of Cambridge, England. He is now a professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research interests include faulttolerant distributed systems and high-speed communication networks and protocols. He is a member of the ACM, the IEEE, and the IEEE Computer Society.

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Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.

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Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource exchange in P2P networks does not involve monetary transactions. This makes P2P systems vulnerable to problems including the free-rider problem that enables users to acquire resources without contributing anything, collusion between groups of users to incorrectly promote or malign other users, and zero-cost identity that enables nodes to obliterate unfavorable history without incurring any expenditure. Previous research addresses these issues using user-reputation, referrals, and shared history based techniques. Here, we describe a multi-agent based reciprocity mechanism where each user's agent makes the decision to share a resource with a requesting user based on the amount of resources previously provided by the requesting user to the providing user and globally in the system. A robust reputation mechanism is proposed to avoid the differential exploitations by the free-riders and to prevent collusion. Experimental results on a simulated P2P network addresses the problems identified above and shows that users adopting the reciprocative mechanism outperform users that do not share resources in the P2P network. Hence, our proposed reciprocative mechanism effectively suppresses free-riding.

Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks where a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable to use and leave critical systems vulnerable to cybersecurity attacks. Our paper provides a detailed survey of the state-of-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning-based systems more robust and reliable for cybersecurity tasks.

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