On Reject and Refine Options in Multicategory Classification

In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred. In recent years, there has been much development for binary classification with a reject option. Yet, little progress has been made for the multicategory case. In this article, we propose margin-based multicategory classification methods with a reject option. In addition, and more importantly, we introduce a new and unique refine option for the multicategory problem, where the class of an observation is predicted to be from a set of class labels, whose cardinality is not necessarily one. The main advantage of both options lies in their capacity of identifying error-prone observations. Moreover, the refine option can provide more constructive information for classification by effectively ruling out implausible classes. Efficient implementations have been developed for the proposed methods. On the theoretical side, we offer a novel statistical learning theory and show a fast convergence rate of the excess $\ell$-risk of our methods with emphasis on diverging dimensionality and number of classes. The results can be further improved under a low noise assumption. A set of comprehensive simulation and real data studies has shown the usefulness of the new learning tools compared to regular multicategory classifiers. Detailed proofs of theorems and extended numerical results are included in the supplemental materials available online.Comment: A revised version of this paper was accepted for publication in the Journal of the American Statistical Association Theory and Methods Section. 52 pages, 6 figure

A privacy-preserving, decentralized and functional Bitcoin e-voting protocol

Bitcoin, as a decentralized digital currency, has caused extensive research interest. There are many studies based on related protocols on Bitcoin, Bitcoin-based voting protocols also received attention in related literature. In this paper, we propose a Bitcoin-based decentralized privacy-preserving voting mechanism. It is assumed that there are n voters and m candidates. The candidate who obtains t ballots can get x Bitcoins from each voter, namely nx Bitcoins in total. We use a shuffling mechanism to protect voter's voting privacy, at the same time, decentralized threshold signatures were used to guarantee security and assign voting rights. The protocol can achieve correctness, decentralization and privacy-preservings. By contrast with other schemes, our protocol has a smaller number of transactions and can achieve a more functional voting method.Comment: 5 pages;3 figures;Smartworld 201

Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error. The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics. In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information. In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding

Space optimisation in multi-dimensional data visualisation

University of Technology Sydney. Faculty of Engineering and Information Technology.Multi-dimensional Data Visualisation (MDV) is the technique to generate visual presentations of datasets with more than three features (or attributes). These graphic representations of data and associated data features can facilitate human comprehension, extraction of implicit patterns and discovery of the relationships among numerous data items for visual data analysis. Although many optimisation methods have been proposed in the past to improve the visual data processing, not many have been applied to MDV. In particular, little research work has been done in the field of display space optimisation. This thesis focuses on the optimisation of two popular Multi-dimensional visualisation methods: 1) scatterplot matrix and 2) parallel coordinate plots, visualisation by using unique approaches to achieve display space optimisation and interaction. The first contribution of the thesis is proposing a new visualisation approach named the Spaced Optimised Scatterplot Matrices that achieves the maximization of the display space utilisation through position transferring. Breaking through the limitation of discovering the pairwise variable relationships, the new method is able to explore the influences of a single variable towards others. In addition, our algorithms improve the efficiency of interactive Multi-dimensional data visualisation significantly, through the reduction of the computational cost. The second contribution of the thesis is to improve the parallel coordinate plots and apply it to the computer forensic investigation. As we are living in a big data era, it is much harder for the researchers to provide accurate evidence for victims within a certain time frame. Our research shows that visualisation techniques can improve the working efficiency of investigations in certain cases. To conclude, we propose a concept of a space optimised Scatterplot Matrix(SPM) visualisation technique considering the shortcomings of the exsited SPM and parallel coordinates in Multi-dimensional visualisation research area. In the meantime, to demonstrate the necessity of our research methodologies, we apply them into computer forensics, which is an area needed analyzing abilities with higher accuracies and efficiencies. By the tests on using Parallel Coordinates and DOITrees, the forensic specialists can easily discover the necessary information in different cases. In the future, we plan to improve our space optimised scatterplot matrix technique from technological optimisation and the broaden application aspects. For example, dealing with the visualisation coolusion problem, non-trivial computation time issue, etc; We will also do the investigations to enlarge the development and availability of Multi-dimensional visualisation techniques

Evolutionary Game for Mining Pool Selection in Blockchain Networks

In blockchain networks adopting the proof-of-work schemes, the monetary incentive is introduced by the Nakamoto consensus protocol to guide the behaviors of the full nodes (i.e., block miners) in the process of maintaining the consensus about the blockchain state. The block miners have to devote their computation power measured in hash rate in a crypto-puzzle solving competition to win the reward of publishing (a.k.a., mining) new blocks. Due to the exponentially increasing difficulty of the crypto-puzzle, individual block miners tends to join mining pools, i.e., the coalitions of miners, in order to reduce the income variance and earn stable profits. In this paper, we study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies. We identify the hash rate and the block propagation delay as two major factors determining the outcomes of mining competition, and then model the strategy evolution of the individual miners as an evolutionary game. We provide the theoretical analysis of the evolutionary stability for the pool selection dynamics in a case study of two mining pools. The numerical simulations provide the evidence to support our theoretical discoveries as well as demonstrating the stability in the evolution of miners' strategies in a general case.Comment: Submitted to IEEE Wireless Communication Letter

A Hierarchical Game with Strategy Evolution for Mobile Sponsored Content and Service Markets

In sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator, to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers' grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence, and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm, and demonstrate how sponsoring helps improve both the providers' profits and the users' experience