Recently a divergence-sensitive branching bisimilarity has been proposed and studied for the randomized CCS model. In this article, we give an equivalent inductive characterization for the bisimilarity, which is a probabilistic extension of the previous work on the non-probabilistic model. Based on the new characterization, a novel polynomial-time verification algorithm for the divergence-sensitive branching bisimilarity is proposed.

Abstract We revisit the communication primitive in ambient calculi. Previously, such communication was confined to first-order (FO) mode (e.g., merely names or capabilities of ambients can be sent), local mode (e.g., the communication only occurs inside an ambient), or particular cross-hierarchy mode (e.g., parent-child communication). In this work, we explore further higher-order (HO) communication in ambient calculi. Specifically, such a communication mechanism allows sending a whole piece of a program across the borders of ambients and is the only form of communication that can happen exactly between ambients. Since ambients are basically of HO nature (i.e., those being moved may be ambients themselves), in a sense, it appears more natural to have HO communication than FO communication. We stipulate that communications merely occur between equally positioned ambients in a peer-to-peer fashion (e.g., between sibling ambients). Following this line, we drop the local or other forms of communication that violate this criterion. As the workbench, we work on a variant of Fair Ambients extended with HO communication, FAHO. This variant also strengthens the original version in that entirely real-identity interaction is guaranteed. We study the semantics, bisimulation, and expressiveness of FAHO. Particularly, we provide the operational semantics using a labeled transition system. Over the semantics, we define the bisimulation in line with the standard notion of bisimulation for ambients and prove that the bisimulation equivalence (i.e., bisimilarity) is a congruence. In addition, we demonstrate that bisimilarity coincides with observational congruence (i.e., barbed congruence). Moreover, we show that FAHO can encode a minimal Turing-complete HO calculus and thus is computationally complete.

Mixing services provide unlinkability for blockchains by breaking the link between sender/receiver identities and are highly appreciated for their compatibility with the underlying blockchains. Many efforts have been made to provide mixing services for either non-confidential or confidential payments. For confidential payments, all the known mixing protocols are designed for confidential blockchains using homomorphic commitment. There is, however, no satisfactory solution for confidential blockchains using public key encryption (PKE), such as PGC and Zether.

Payment channel hubs (PCHs) serve as a promising solution to achieving quick off-chain payments between pairs of users. They work by using an untrusted tumbler to relay the payments between the payer and payee and enjoy the advantages of low cost and high scalability. However, the most recent privacy-preserving payment channel hub solution that supports variable payment amounts suffers from limited unlinkability, e.g., being vulnerable to the abort attack. Moreover, this solution utilizes zero-knowledge proofs, which bring huge costs on both computation time and communication overhead. Therefore, how to design PCHs that support variable amount payments and unlinkability, but reduce the use of huge-cost cryptographic tools as much as possible, is significant for the large-scale practical applications of off-chain payments.

Software defect prediction is an active topic in the field of software engineering. Cross-project defect prediction (CPDP) adopts the defect data set of the source project to predict the defects of the target project. However, the metrics of the source project and those of the target project are often different, and the traditional CPDP has certain limitations at this time. To address the inconsistency of source and target metrics, researchers propose heterogeneous cross-project defect prediction (HCPDP). To improve the performance of the HCPDP, we propose new Two-stage Cost-sensitive Local Models (TCLM). TCLM aims to improve on the problem of feature selection, linear inseparability of heterogeneous data, class imbalance and data adoption problems in HCPDP. Firstly, in the feature selection stage, we add cost information to improve the feature selection algorithm. Then, KCCA (Kernel Canonical Correlation Analysis) is used to project and map the heterogeneous data into a common feature space so as to mitigate the problem of inconsistent feature sets of the source and the target projects. Secondly, in the model training stage, we adopt local models to improve the performance, and introduce cost information to deal with the class imbalance problem. To verify the effectiveness of the TCLM method, we conduct large-scale empirical study on 24 projects in the AEEEM, PROMISE, NASA, and Relink datasets. Experimental results show that TCLM indeed outperforms the previous work. Therefore, we recommend using the TCLM method to build an HCPDP model.

Abstract This paper investigates the interactive power of the higher-order pi-calculus extended with parameterization. We study two kinds of parameterization: name parameterization and process parameterization. We show that each of these kinds of parameterization results in an interactively complete model, in the sense that they can express the elementary interactive model (named C ) with built-in recursive functions.

Meta search engine is an effective tool for searching information online. In comparison with independent search engine like Google, Bing, and etc., meta search engine has a wider coverage and can meet the requirement of information retrieval in a better manner. In particular, when a query is received from the user, the meta search engine sends it to some proper candidate member engines, collects results from them, and then replies to the user. An important issue here is how to better select the underlying member search engines. In this paper, we focus on the engine selection in meta search engine. We propose a selection design based on the combination of weighted round robin algorithm and artificial neural network. The experimental results show that our design can indeed improve the relevancy between the query and member search engine, and thus the effectivity of member selection.

In this paper, we integrate the traditional finite-state automata (words, or string based) into the membrane computing paradigm, as previous work prevalently concentrated on multiset based automata. We apply P systems with string objects (worms) to implement finite automata, that is, simulating their running, showing that P systems with string objects can properly hold the computability of finite automata. We give the concept of P system with string objects and finite automata, describe the implementation details, and finally make some future work expectation