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Traditional Knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum Natural Language Processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j’s ability to analyze context-rich data, improving tasks like entity extraction nd knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, contextaware NLP applications. 

Machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize. 

Residue Number Systems (RNS) demonstrate the fascinating potential to serve integer addition/multiplication-intensive applications. The complexity of Artificial Intelligence (AI) models has grown enormously in recent years. From a computer system’s perspective, ensuring the training of these large-scale AI models within an adequate time and energy consumption has become a big concern. Matrix multiplication is a dominant subroutine in many prevailing AI models, with an addition/multiplication-intensive attribute. However, the data type of matrix multiplication within machine learning training typically requires real numbers, which indicates that RNS benefits for integer applications cannot be directly gained by AI training. The state-of-the-art RNS real number encodings, including floating-point and fixed-point, have defects and can be further enhanced. To transform default RNS benefits to the efficiency of large-scale AI training, we propose a low-cost and high-accuracy RNS fixed-point representation: Single RNS Logical Partition (S-RNS-Logic-P) representation with Scaling Down Postprocessing Multiplication (SD-Post-Mul). Moreover, we extend the implementation details of the other two RNS fixed-point methods: Double RNS Concatenation (D-RNS-Concat) and Single RNS Logical Partition (S-RNS-Logic-P) representation with Scaling Down Preprocessing Multiplication (SD-Pre-Mul). We also design the architectures of these three fixed-point multipliers. In empirical experiments, our S-RNS-Logic-P representation with SD-Post-Mul method achieves less latency and energy overhead while maintaining good accuracy. Furthermore, this method can easily extend to the Redundant Residue Number System (RRNS) to raise the efficiency of error-tolerant domains, such as improving the error correction efficiency of quantum computing. 

Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.