Abstract Non-Fungible Token (NFT) creators use digital signatures to ensure the ownership, authenticity, integrity, and nonrepudiation of their digital works. However, if the private key is compromised, an attacker can generate unauthorized NFTs by using the creator’s private key to issue valid signatures. These valid but unauthorized signatures will be accepted in the NFT market and cannot be revoked. Even if the NFT creators update their private-public key pairs, they cannot deny the NFTs generated by the attacker. To mitigate these risks, we propose revocable signature by introducing commitment mechanism and an Auxiliary Embedded Key ( AEK ) into the signature, while the regular verification process does not involve this AEK . If a valid but unauthorized signature is detected and needs to be revoked, AEK will be disclosed to perform the revocation operation. To illustrate the application of revocable signatures in NFT, we design and implement a revocable Elliptic Curve Digital Signature Algorithm (ECDSA) scheme with provable security. Experimental evaluations on the FIPS-recommended elliptic curves show that the performance of revocable ECDSA is comparable to the basic ECDSA, with additional 0.0303 s (P-256 curve) and 0.15 USD gas fee in Remix VM for revoking a signature.

Effective management of private keys is crucial to ensure the security and ownership of users’ data and digital assets in the Web3 environment. However, existing solutions often fail to adequately address private key management from the user’s perspective. Private key leakage and loss incidents occur frequently, resulting in significant losses of digital assets. Moreover, the conventional approach of revoking both the private and public keys after a leakage or loss accident is inconvenient in Web3, where the public key serves as the user’s wallet address or digital identity. To tackle the issue of user-side private key management in Web3, this paper presents KeyShield which is a leakage-and-loss-resilient private key protection scheme. KeyShield divides the user’s private key into three shares, securely stored across a primary device and a secondary device owned by the user, and a third storage module owned by the user or a semi-trusted service provider. For daily use of the private key, the user only needs to connect the primary and secondary devices. In the event of a leakage or loss, such as device theft or attack, an update process will be triggered to update the three shares, immediately invalidating the leaked or lost share while causing no changes to the public key. As a demonstration of KeyShield, we developed KeyShieldECC accessible on both Android and iOS platforms for managing Elliptic Curve Cryptography (ECC) private keys. The testing results show that for a 256-bit ECC private key, the daily use only needs 0.05 seconds and update needs 0.25 to 0.3 seconds on an ordinary smart phone.

Data trading has become a fundamental category of commerce in the current digital era, bringing along substantial opportunities for economic activities. The emergence of blockchain facilitates decentralized data trading, empowering users to maintain complete control over their own data and enabling direct peer-to-peer transactions. However, while users possess complete control over their data product and asset in a decentralized trading environment, they must take responsibility for their behaviors and face the consequences of dishonest behaviors either from themselves or counterparties. Therefore, behavior fairness problems arise, such as the seller refusing to deliver the correct data product after receiving the money and the buyer failing to pay the outstanding balance after receiving the data product. This article addresses these issues by proposing a decentralized fair data trading ecosystem leveraging the capability of cryptography and smart contracts. A novel verifiable proxy re-encryption (VPRE) scheme is designed, which introduces the verifiability of re-encryption keys into the original PRE schemes. The scheme is implemented using smart contracts, which ensures that trading can succeed if and only if the data seller provides a valid re-encryption key and the buyer pays the correct amount of money. Furthermore, experiments are conducted to evaluate the proposed ecosystem in terms of trading fairness, security, and cost. The results show that the proposed scheme can effectively terminate transactions involving fairness violations with affordable costs ranging from 39 276 Gwei to 296 983 Gwei. Without our solution, these unfair transactions will proceed and consume 77 301 Gwei to 754 447 Gwei gas fees.

Feature ordering is a significant data preprocessing method in incremental attribute learning (IAL), where features are gradually trained according to a given order. Previous research showed feature ordering is crucial to the IAL performance. It is relevant to each feature's discrimination ability, which can be calculated by single discriminability (SD). However, when feature dimensions increase, feature discrimination ability should also be calculated incrementally, because discrimination ability in lower dimensional spaces is different from that in higher spaces. Thus based on SD, accumulative discriminability (AD), a new statistical metric for incremental feature discrimination ability estimation, is designed. Moreover, a criterion that summarizes all the produced values of AD is employed to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. In addition, in order to reduce the time consumption, an effective feature ordering approach is developed. Compared with the feature ordering obtained by other approaches, the method outlined in this paper obtained good final classification results, which indicates that, firstly, feature discrimination ability should be incrementally estimated in IAL; and secondly, feature ordering derived by AD and its corresponding approaches are applicable with IAL.

Based on XML, RDF(S) has been recommended as the basic layer of the Semantic Web, providing a foundation for the higher layers of ontology, logic and trust. Although RDF(S) enables specification and identification of data and relationships between da

We propose a novel Minimum Distance guided Genetic Algorithm (MDGA) for Multi-User Detection (MUD) in a synchronous Multi-Carrier Code Division Multiple Access (MC-CDMA) broadband wireless system. In contrast to conventional GAs, our MDGA exploits adequately the output from a bank of Matched Filters as guidance. It starts with a balanced ratio of exploration and exploitation which is maintained throughout the process. A novel replacement strategy is proposed which increases dramatically the convergence rate as compared to the conventional GAs. This allows us to use the simplest form of genetic operators to gain significant reduction in computational complexity as well as near-optimum results. The simulation results demonstrate that our scheme achieves 99.54% and 50+% reduction in computational complexity as compared to the MUD schemes using exhaustive search and conventional GA respectively.

Researchers have proposed intelligent product-brokering applications to help facilitate the m-commerce shopping process. However, most algorithms require explicit, user-provided feedback to learn about user preference. In practical applications, users may not be motivated to provide unrewarded and time-consuming feedback. By adopting a cognitive approach, this paper investigates the possibility of replacing user feedback with user behavioral data analysis during product browsing. By means of evolutionary algorithms, the system is able to derive corresponding models that simulate the user's shopping behavior. User group profiling is also implemented to help identify the user's shopping patterns. Upon simulations of trial cases with consistent and rational shopping patterns, our experimental results confirm this approach being promising. The system shows high accuracy in detecting the preferences of the user. The algorithms are also portable and effective across different products.

The idea of using a team of weak learners to learn a dataset is a successful one in literature. In this paper, we explore a recursive incremental approach to ensemble learning. In this paper, patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore formed, which is solved using evolutionary algorithms. The evolutionary algorithms identify the "easy" and the "difficult" clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. Incrementally, neural networks are added to the ensemble to focus on solving successively difficult examples. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. In this paper, we show that the generalization accuracy of the proposed algorithm is always better than that of the underlying weak learner. Empirical studies show generally good performance when compared to other state-of- the-art methods.

In this article, an evolutionary algorithm for multiobjective optimization problems in a dynamic environment is studied. In particular, we focus on decremental multiobjective optimization problems, where some objectives may be deleted during evolution—for such a process we call it objective decrement. It is shown that the Pareto-optimal set after objective decrement is actually a subset of the Pareto-optimal set before objective decrement. Based on this observation, the inheritance strategy is suggested. When objective decrement takes place, this strategy selects good chromosomes according to the decremented objective set from the solutions found before objective decrement, and then continues to optimize them via evolution for the decremented objective set. The experimental results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the strategy, where the evolution is restarted when objective decrement occurs. More solutions with better quality are found during the same time span. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 847–866, 2007.

This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data.

Task decomposition with pattern distributor (PD) is a new task decomposition method for multilayered feedforward neural networks (NNs). Pattern distributor network is proposed that implements this new task decomposition method. We propose a theoretical model to analyze the performance of pattern distributor network. A method named reduced pattern training (RPT) is also introduced, aiming to improve the performance of pattern distribution. Our analysis and the experimental results show that RPT improves the performance of pattern distributor network significantly. The distributor module's classification accuracy dominates the whole network's performance. Two combination methods, namely, crosstalk-based combination and genetic-algorithm (GA)-based combination, are presented to find suitable grouping for the distributor module. Experimental results show that this new method can reduce training time and improve network generalization accuracy when compared to a conventional method such as constructive backpropagation or a task decomposition method such as output parallelism (OP).

Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a minimum squared error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a constructive backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost.

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically MLRT performs considerably well as compared with RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than that of the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3, are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system.

Object Composition Petri Nets, Priority Petri Nets, Dynamic OCPN, and Enhanced P-Nets have extended the original Petri Net to achieve the modeling of media synchronization and asynchronous user interactions during multimedia playback. The dynamic Petri Net (DPN) has been conceptualized to tackle existing problems in these two areas of modeling distributed multimedia systems. DPN features dynamic modeling elements which allows iteration and hence is able to reduce graph sizes of synchronous playback models while allowing greater details to be shown. DPN also introduces asynchronous event handling techniques that are powerful and effective. DPN was used in the design and modeling of a multimedia orchestration tool which is a typical representation of an application that works in a distributed multimedia system.

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.

Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. In this paper, we explore the use of feature selection in modular GA-based classification. We propose a new feature selection technique, Relative Importance Factor (RIF), to find irrelevant features in the feature space of each module. By removing these features, we aim to improve classification accuracy and reduce the dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that RIF can be used to determine irrelevant features and help achieve higher classification accuracy with the feature space dimension reduced. The complexity of the resulting rule sets is also reduced which means the modular classifiers with irrelevant features removed will be able to classify data with a higher throughput.

Incremental training has been used for genetic algorithm (GA)-based classifiers in a dynamic environment where training samples or new attributes/classes become available over time. In this article, ordered incremental genetic algorithms (OIGAs) are proposed to address the incremental training of input attributes for classifiers. Rather than learning input attributes in batch as with normal GAs, OIGAs learn input attributes one after another. The resulting classification rule sets are also evolved incrementally to accommodate the new attributes. Furthermore, attributes are arranged in different orders by evaluating their individual discriminating ability. By experimenting with different attribute orders, different approaches of OIGAs are evaluated using four benchmark classification data sets. Their performance is also compared with normal GAs. The simulation results show that OIGAs can achieve generally better performance than normal GAs. The order of attributes does have an effect on the final classifier performance where OIGA training with a descending order of attributes performs the best. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1239–1256, 2004.

We propose a new class of cellular automata, self-programming cellular automata (SPCA), with specific application to pseudorandom number generation. By changing a cell's state transition rules in relation to factors such as its neighboring cell's states, behavioral complexity can be increased and utilized. Interplay between the state transition neighborhood and rule selection neighborhood leads to a new composite neighborhood and state transition rule that is the linear combination of two different mappings with different temporal dependencies. It is proved that when the transitional matrices for both the state transition and rule selection neighborhood are nonsingular, SPCA will not exhibit nongroup behavior. Good performance can be obtained using simple neighborhoods with certain CA length, transition rules, etc. Certain configurations of SPCA pass all DIEHARD and ENT tests with an implementation cost lower than current reported work. Output sampling methods are also suggested to improve output efficiency by sampling the outputs of the new rule selection neighborhoods.

This paper proposes a variation of two-dimensional (2-D) cellular automata (CA) by adopting a simpler structure than the normal 2-D CA and a unique neighborship characteristic-asymmetric neighborship. The randomness of 2-D CA based on asymmetric neighborship is discussed and compared with one-dimensional (1-D) and 2-D CA. The results show that they are better than 1-D CA and could compete with conventional 2-D CA under certain array setting, output method, and transition rule. Furthermore, the structures of 2-D CA based on asymmetric neighborship were evolved using some multiobjective genetic algorithm. The evolved 2-D CA could pass DIEHARD tests with only 50 cells, which is less than the minimal number of cells (i.e., 55 cells) needed for neighbor-changing 1-D CA to pass DIEHARD. In addition, a refinement procedure to reduce the cost of 2-D CA based on asymmetric neighborship is discussed. The minimal number of cells found is 48 cells for it to pass DIEHARD. The structure of this 48-cell 2-D CA is identical to that of the evolved 10 * 5 2-D CA, except that 2 horizontal cells in the evolved 10 * 5 2-D CA are removed.

This paper presents a new genetic algorithm approach to multiobjective optimization problems--incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

This paper proposes a class decomposition approach to improve the performance of GA-based classifier agents. This approach partitions a classification problem into several class modules in the output domain, and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently, and results obtained from them are integrated to form the final solution by resolving conflicts. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that class decomposition can help achieve higher classification rate with training time reduced.

A number of soft computing approaches such as neural networks, evolutionary algorithms, and fuzzy logic have been widely used for classifier agents to adaptively evolve solutions on classification problems. However, most work in the literature focuses on the learning ability of the individual classifier agent. This article explores incremental, collaborative learning in a multiagent environment. We use the genetic algorithm (GA) and incremental GA (IGA) as the main techniques to evolve the rule set for classification and apply new class acquisition as a typical example to illustrate the incremental, collaborative learning capability of classifier agents. Benchmark data sets are used to evaluate proposed approaches. The results show that GA and IGA can be used successfully for collaborative learning among classifier agents. © 2003 Wiley Periodicals, Inc.

A self-modifiable color Petri net (SMCPN) which has multimedia synchronization capability and the ability to model user manipulation and network event (i.e., network congestion, etc.) handling is proposed in this paper. In SMCPN, there are two types of tokens: resource tokens representing resources to be presented and color tokens with two subtypes: one associated with some commands to modify the net mechanism in operation, another associated with a number to decide iteration times. Also introduced is a new type of resource token, named reverse token, that moves in the opposite direction of arcs. When user manipulation/network event occurs, color tokens associated with the corresponding interrupt handling commands will be injected into places that contain resource tokens. These commands are then executed to handle the user manipulation/network event. SMCPN has the desired general programmability in the following sense: 1) it allows handling of user manipulations or prespecified events at any time while keeping the Petri net design simple and easy. 2) It allows the user to customize event handling beforehand. This means the system being modeled can handle not only commonly seen user interrupts (e.g., skip, reverse, freeze), the user is free to define new operations, including network event handling. 3) It has the power to simulate self-modifying protocols. A simulator has been built to demonstrate the feasibility of SMCPN.

Cellular automata (CA) has been used in pseudorandom number generation for over a decade. Recent studies show that two-dimensional (2-D) CA pseudorandom number generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-D) CA PRNGs, but they are more complex to implement in hardware than 1-D CA PRNGs. In this paper, we propose a new class of 1-D CA - controllable cellular automata (CCA)-without much deviation from the structural simplicity of conventional 1-D CA. We first give a general definition of CCA and then introduce two types of CCA: CCA0 and CCA2. Our initial study shows that these two CCA PRNGs have better randomness quality than conventional 1-D CA PRNGs, but that their randomness is affected by their structures. To find good CCA0/CCA2 structures for pseudorandom number generation, we evolve them using evolutionary multiobjective optimization techniques. Three different algorithms are presented. One makes use of an aggregation function; the other two are based on the vector-evaluated genetic algorithm. Evolution results show that these three algorithms all perform well. Applying a set of randomness tests on the evolved CCA PRNGs, we demonstrate that their randomness is better than that of 1-D CA PRNGs and can be comparable to that of 2-D CA PRNGs.

A self-modifiable color Petri net model (SMCPN) that has multimedia synchronization capability is proposed in this paper. By introducing color tokens associated with basic commands, this model can handle user interaction flexibility. By combining numbers with the color tokens, SMCPN allows iteration to be modeled and hence is able to reduce the graph sizes of synchronous playback models while allowing greater details to be shown. SMCPN can also be used in the design and modeling of a multimedia tool with Memex functions like forward, backward, and trail history functions in a Web browser.

The mobile agent paradigm is a promising technology for smart e-commerce of the next generation. However, security-related issues must be satisfactorily solved before they come into full commercial use. We propose a secure approach for mobile agent migration control based on passport and visa (P/V). P/V as up-to-date digital credentials not only serve the agent-host authentication purpose, but also can be used by hosts to trace the agent route and by communities to control the migration of mobile agents. The binding scheme further provides a way to protect these digital credentials and eliminate the misuse of them on illegal agents. We discuss the design issues in detail and evaluate the implementation of the proposed system.

In order to find an appropriate architecture for a large-scale real-world application automatically and efficiently, a natural method is to divide the original problem into a set of subproblems. In this paper, we propose a simple neural-network task decomposition method based on output parallelism. By using this method, a problem can be divided flexibly into several subproblems as chosen, each of which is composed of the whole input vector and a fraction of the output vector. Each module (for one subproblem) is responsible for producing a fraction of the output vector of the original problem. The hidden structure for the original problem's output units are decoupled. These modules can be grown and trained in parallel on parallel processing elements. Incorporated with a constructive learning algorithm, our method does not require excessive computation and any prior knowledge concerning decomposition. The feasibility of output parallelism is analyzed and proved. Some benchmarks are implemented to test the validity of this method. Their results show that this method can reduce computational time, increase learning speed and improve generalization accuracy for both classification and regression problems.

With the development of Internet computing and software agent technologies, agent-based electronic commerce (e-commerce) is emerging. Software agents have demonstrated tremendous potential in conducting various tasks in e-commerce. However, when agents are initially created, they have little knowledge and experience with relatively lower capability. They should also strive to adapt themselves to the changing environment. It is advantageous if they have the ability to learn and evolve. This paper addresses evolution of software agents in e-commerce. Agent fitness and life cycle are proposed as evolution mechanisms, and modularized agent structure is introduced to facilitate the evolution process. Genetic Programming (GP) operators are employed to restructure agents in the proposed multi-agent evolution cycle.

In this paper, we propose a new method for task decomposition based on output parallelism, in order to find the appropriate architectures for large-scale real-world problems automatically and efficiently. By using this method, a problem can be divided flexibly into several sub-problems as chosen, each of which is composed of the whole input vector and a fraction of the output vector. Each module (for each subproblem) is responsible for producing a fraction of the output vector of the original problem. This way, the hidden structure for the original problem's output units is decoupled. These modules can be grown and trained in sequence or in parallel. Incorporated with the constructive learning algorithm, our method does not require excessive computation and any prior knowledge concerning decomposition. The feasibility of output parallelism is analyzed and proved. Several benchmarks are implemented to test the validity of this method. Their results show that this method can reduce computation time, increase learning speed, and improve generalization accuracy for both classification and regression problems.

Agent-based electronic commerce (e-commerce) has attracted more and more efforts in academic and industrial fields. However, little work has been devoted to exploring the learning and evolving capability of software agents. This paper addresses issues of evolving software agents in e-commerce. Agent structure and organization are introduced to facilitate the evolution process, and agent fitness and life cycle are employed as evolution mechanisms. Two approaches are proposed to equip agents with evolution features. One approach is to use Genetic Programming (GP) operators to restructure agents. Another one is to enhance the knowledge of agents by exchanging with other agents.

The achievement of media synchronization has been dealt with in the Object Composition Petri Net (OCPN) model and the extended OCPN (XOCPN) model. Yet these two models are not enough for synchronization of computers in a distributed environment. This paper proposes a new Petri Net model-Prioritized Petri Net (P-net). The modeling power and properties of P-nets are analyzed. We apply the P-net model to distributed multimedia synchronization, using our version of Distributed Object Composition Petri Net (DOCPN) model. Using the DOCPN model, we can coordinate operations among distributed computer sites. The scenario is like a conductor conducting an orchestra to perform a symphony.

Generalized minimum distance decoding schemes are found for majority logic decodable codes. These decoding schemes are parallel to the classical majority logic decoding schemes and thus can be easily implemented.