As software development increasingly relies on external collaboration, organizations face new risks of intellectual property leakage beyond traditional concerns about deployed software. Even when the source code is protected, adversaries may infer sensitive internal program specifications by observing the program behavior during the development and testing phases.This paper addresses the problem of specification leakage through behavioral observation in collaborative software development. We propose a novel software development method that centers on specially crafted test doubles referred to as secure mocks. Secure mocks serve as drop-in replacements for original components during development and testing while preventing the exposure of sensitive internal specifications through observable behavior. We formalize the correctness conditions for secure mocks and define the secure mock construction problem as a constraint satisfaction problem parameterized by the program to protect, the development specification, and a security policy. Our approach enables secure test-driven development (TDD) with external collaborators, bridging the gap between traditional TDD styles. We discuss the implications for secure collaboration with external developers and outline future research directions for automating secure mock generation and integrating this paradigm into real-world development pipelines.

Third-party libraries are widely used and constantly evolving. When migrating client code to a new API, a major challenge is editing client code to adapt to incompatible changes of the API. Some tools provide automated migration that synthesizes a generic patch from migration samples in a pair of beforeand after-migration snippets. However, they still have limited applicability due to the difficulty in retrieving an adequate pair in which before- and after-migration snippets invoke the sourceand destination-API of the migration respectively. In this paper, we present AUTOMIG, which addresses the problem by a patch synthesis from a single API usage example that invokes the destination-API. Due to the absence of a beforemigration version of the API usage example, it is nontrivial to find out the invariant context of API invocation through the migration. To synthesize a patch that preserves the context in the client code, AUTOMIG synthesizes patches in two-stage patch synthesis procedure. The first stage synthesizes a generic patch to describe a replacement of API. The second stage modmes the generic patch to preserve the context of the API invocation in the client code. Our experiment using a dataset that provides real-world Java codes shows that AUTOMIG synthesized a correct patch for 92.2% of the code on average. Our manual investigation on synthesized patches shows that AUTOMIG can synthesize highquality patches that preserve the context of API invocation of each client code.

The rubber hand illusion (RHI) is a bodily illusion that a fake rubber hand is felt as if it was one's own hand when both the hands are synchronously stimulated. Also, it is well-known that people can experience the RHI when the movements of the visible fake and invisible real hands are synchronized. However, underlying mechanism of the RHI and relationship between action and body ownership are still open questions. Based on the RHI paradigm, the present paper discusses how the agency of a body part affects the sense of body ownership at the other body part. In this study, we examined if a simple up-and-down action of right index finger induces the RHI to left hand which was kept still by using virtual reality and robotics technologies. Our experimental results indicated that the action of the right index finger allowed the embodiment of the virtual hands at both the right and left hands. This implies that only a simple action of a body part increases the sense of body ownership at the other body part.

Reliability prediction has been widely studied in many research fields to improve product and system reliability in manufacturing systems. Traditionally, to establish the prediction model, modelers would use all training data without preference. However, the prediction model based only on the most recent data may have better performance. In this paper, to realize an accurate prediction with the most recent data sets, we use the grey model to establish the reliability model. Then, the cubic spline function is integrated into the grey model to enhance the prediction capability of GM(1, 1), a single variable first order grey model. The newly generated model is defined as 3spGM(1, 1). To further improve the prediction accuracy, the particle swarm optimization (PSO) algorithm is applied to 3spGM(1, 1). We call the improved version P-3spGM(1, 1). Finally, we validated the effectiveness of the proposed model using failure data sets of electric product manufacturing systems.

The need for intelligent systems has grown in the past decade because of the increasing demand on humans and machines to perform better. The researchers of artificial intelligence (AI) have responded to these needs with the development of intelligent hybrid systems. This paper describes the modeling language for interacting hybrid systems in which we will build a new hybrid model of cellular automata and multiagent technology. Simulations with complex behavior will be model social dynamics where the focus is on the emergence of properties of local interactions. Therefore, in our approach, cellular automata form a useful framework for the multiagent simulation model and the model will be used for traffic system which lies in coordinating the local behavior of individual agent to provide an appropriate system-level behavior in grid of interacting organisms.

The prediction problem of first typhoon has been studied in the meteorological field. The reason is that accurate prediction helps in early stage prevention and decreasing economic loss. But the appeared dates of first typhoon are uncertain every year. Grey system theory is one of the methods that are used to study uncertainty, it is superior in mathematical analysis of systems with uncertain information. The change of first typhoon can be view as a grey process, therefore grey system theory can be used to solve the prediction problem. Since the change of first typhoon is affected by variant random factors, it is difficult to obtain high predicted accuracy by single grey model (GM). Therefore, we presented statistical methods to improve the predicted accuracy of GM. In this paper, we proposed a hybrid grey-based model to predict the first typhoon over the South China Sea. The work procedure: First, GM(1,1) is applied to predict the appeared dates of first typhoon. Second, second order polynomial regression is integrated into GM(1,1) to improve predicted accuracy. This new generated model is defined as RGM(1,1). Finally, Markov-chain is integrated into RGM(1,1) to enhance the predicted accuracy further. This new generated model is defined as MRGM(1,1). The experimental results show that the proposed hybrid MRGM(1,1) model has proved an effective tool in first typhoon prediction problem.

A lot of clustering algorithms based on grey system theory, especially based on the grey relational matrix, have been already reported, which finds out a centroid of each class by moving given objects as vectors. We developed new clustering procedure called grey K-means, which is able to handle the number of required clusters such as the hard K-means or the fuzzy c-means. Assume that the number of found clusters by the proposal is between 1 and the number of classified instances, a required threshold value is exist in [0,1]. We defined a value range of the threshold as the interval grey number, and the range is specified automatically until obtaining the required clusters. In addition a new clustering method which analyzes the grey relational matrix closely instead of moving vectors is suggested. Several well-known data sets in the classification problem are applied, and we discuss their performances and the optimal threshold value.

This paper proposes a decision rule of extraction and reduction that is based on grey lattice classification. This proposal method comes from joining between rough set theory and grey theory as an approximation algorithm. Grey lattice operations are defined by combining interval grey number in grey theory with interval lattice operations in interval algebra. By defining the equivalents in interval grey number, given data space is correspondent to equivalents of rough set. This proposal method classifies each data set into 3-patterns from given training samples, as existing possibility class, newly made possibility class and existing necessity class. As given examples which require only necessity class, decision rule is simplified by a reduction procedure.