Incorporating accountability mechanisms in online services requires effective trust management and immutable, traceable source of truth for transaction evidence. The emergence of the blockchain technology brings in high hopes for fulfilling most of those requirements. However, a major challenge is to find a proper consensus protocol that is applicable to the crowdsourcing services in particular and online services in general. Building upon the idea of using blockchain as the underlying technology to enable tracing transactions for service contracts and dispute arbitration, this paper proposes a novel consensus protocol that is suitable for the crowdsourcing as well as the general online service industry. The new consensus protocol is called “Proof-of-Trust” (PoT) consensus; it selects transaction validators based on the service participants' trust values while leveraging RAFT leader election and Shamir's secret sharing algorithms. The PoT protocol avoids the low throughput and resource intensive pitfalls associated with Bitcoin's “Proof-of-Work” (PoW) mining, while addressing the scalability issue associated with the traditional Paxos-based and Byzantine Fault Tolerance (BFT)-based algorithms. In addition, it addresses the unfaithful behaviors that cannot be dealt with in the traditional BFT algorithms. The paper demonstrates that our approach can provide a viable accountability solution for the online service industry.

Early classification of time series aims to predict the class value of a sequence accurately as early as possible, not wait for the full-length data, which is significant in many time-sensitive applications and has attracted great interest in recent years. For instance, early diagnosis can help patients get early treatment and even save their lives. The problem of early classification is how to determine whether the collected data are sufficient to output the class value. Moreover, in practical applications, users also need to know the confidence (reliability) of the prediction results for more appropriate processing. For example, giving a healthy patient the possibility of suffering from some disease can assist physicians in an optimal therapy. However, existing work has not provided an effective measure to indicate how accurate the classification is. Therefore, in this paper, we propose an effective confidence-based early classification of time series. Firstly, based on a set of base time series classifiers trained at different timestamps, we propose a dynamic decision fusion method to measure the confidence of a predicted result by fusing the results of multiple base classifiers. Secondly, by analyzing the distribution of confidence values, we develop an adaptive learning method for the confidence threshold to simultaneously optimize the two conflicting objectives: accuracy and earliness. Finally, the experimental results conducted on 45 equal-length datasets and 8 variable-length datasets clearly show that our proposed approach can achieve the superior in early classification compared to state-of-the-art approaches.

In most existing trust evaluation studies, a single value is computed based on the ratings given to a service provider to indicate the current trust level. This is useful but may not represent the trust features well under certain circumstances. Alternatively, a complete set of trust ratings can be transferred to a service client for local trust calculation. But this obviously creates a big overhead in communication as the data set usually has a large size covering a long service history. In this paper, we present a novel two-dimensional aggregation approach consisting of both vertical and horizontal aggregations of trust ratings. The vertical aggregation calculates the aggregated rating representing the trust level for the services delivered in a small time period. The horizontal aggregation applies our proposed optimal algorithm to determine the minimal number of time intervals, within each of which a trust vector with three values can be calculated to represent all the ratings in that time interval and retain the trust features well. Hence, a small set of trust vectors can represent a large set of trust ratings. This is significant for large-scale trust rating transmission and trust evaluation. Experiments have been conducted to illustrate the properties of our proposed approach.

Local causal structure learning aims to discover and distinguish the direct causes and direct effects of a target variable. However, the state-of-the-art local causal structure learning algorithms need to perform an exhaustive subset search within the currently selected variables for PC (i.e., parents and children) discovery. In this article, we propose an efficient local causal structure learning algorithm around a target variable, called LCS-FS ( L ocal C ausal S tructure learning by F eature S election). First, to construct the local causal skeleton of the target, we employ feature selection for finding PC without searching for conditioning sets to speed up PC discovery, leading to improve the skeleton construction efficiency. Second, to orient edges in this local causal skeleton, we propose an efficient method to find separating sets from the subsets of PC for identifying V-structures. With the integration of feature selection and the new way of finding separating sets, LCS-FS recursively finds the spouses of Markov blankets in local causal skeleton for edge orientations, until the direct causes and direct effects of the target are distinguished. The experiments on five benchmark Bayesian networks with the number of variables from 35 to 801 validate that our algorithm achieves higher efficiency and better accuracy than the state-of-the-art local causal structure learning algorithms.