Designed as a distributed data storage system, blockchain employs a consensus mechanism to validate the addition of new data blocks while saving the need for mediation by a centralized server. However, when the blockchain technology is applied in decision-making applications, the consensus mechanism requires the decision to be deterministic, which will ignore different recommendations made by multiple agents and may put the computing system at risk. In this paper, we propose and develop SMARPchain, a paradigm of reputational probabilistic blockchain which is based on smart markers. The distinguishing characteristic of our SMARPchain approach is that it enables multiway branching which allows the result produced by each agent to be kept on the blockchain as a branchchain. Moreover, SMARPchain computes a probabilistic score on each branchchain based on the associated agent's reputation to help the decision maker reach an informed decision. SMARPchain satisfies three desirable requirements of recognizability, compatibility, and authenticability, and evaluation results show that SMARPchain will enhance the security and robustness of multi-agent decision making systems with acceptable overhead.

Advancements in blockchain technology and network technology are bringing in a new era in electronic voting systems. These systems are characterized by enhanced security, efficiency, and accessibility. In this paper, we compose a comparative analysis of blockchain-based electronic voting (e-voting) systems using blockchain technology, cryptographic techniques, counting methods, and security requirements. The core of the analysis involves a detailed examination of blockchain-based electronic voting systems, focusing on the variations in architecture, cryptographic techniques, vote counting methods, and security. We also introduce a novel blockchain-based e-voting system, which integrates advanced methodologies, including the Borda count and Condorcet method, into e-voting systems for improved accuracy and representation in vote tallying. The system’s design features a flexible and amendable blockchain structure, ensuring robustness and security. Practical implementation on a Raspberry Pi 3 Model B+ demonstrates the system’s feasibility and adaptability in diverse environments. Our study of the evolution of e-voting systems and the incorporation of blockchain technology contributes to the development of secure, transparent, and efficient solutions for modern democratic governance.

NLP-inspired deep learning for binary code analysis demonstrates notable performance. Considering the diverse Instruction Set Architectures (ISAs) on the market, it is important to be able to analyze code of various ISAs. However, training a deep learning model usually requires a large amount of data, which poses a challenge for certain ISAs such as PowerPC that suffer from the "data scarcity" issue. For instance, acquiring a large dataset of PowerPC malware proves to be challenging. Moreover, given a binary analysis task and multiple ISAs, it takes much time and effort (e.g., for data collection, labeling and cleaning, and parameter tuning) to train one model per ISA. We propose a new direction, retargeted-architecture binary code analysis, to handle the data scarcity issue and alleviate the per-ISA effort. Our idea is to transfer knowledge from one ISA to others-that is, a model, trained with rich data and much time and effort for one ISA, can perform prediction for others without any modification. We showcase the idea through two important tasks: malware detection and function similarity detection. An extensive evaluation involving four ISAs (x86, ARM, MIPS, and PowerPC) demonstrates the effectiveness of the approach and the high performance is interpreted.