National Pingtung University

We investigate the control of a two-link robot manipulator through the application of sliding mode control (SMC), proportional-integral-derivative (PID) control, and their hybrid control strategy.Firstly, a mathematical model incorporating nonlinear coupling effects is derived based on the Lagrangian method.Then, SMC, PID, and hybrid controllers are compared based on disturbance rejection, stability, and time-domain responses.In addition, a genetic algorithm (GA) is employed for PID parameter optimization, improving system performance and efficiency.Overall, the PID-SMC controller achieves an effective balance between stability and response tracking accuracy.The results of this study provide a reference for control strategy development in robotic systems, aligning with smart manufacturing applications.

In 5G core networks, data storage commonly adopts a centralized architecture. Users frequently encounter issues such as insufficient security and integrity, as well as heightened privacy exposure during registration and cross-regional roaming processes, creating security vulnerabilities. To address these challenges, this research proposes integrating blockchain into the 5GC architecture and designs an innovative Blockchain Function (BCF) as an extended functional module for the core network. Leveraging blockchain's immutability and verifiability, combined with encrypted storage mechanisms, BCF prevents sensitive user data from being directly exposed on-chain, ensuring the security and traceability of critical user data. Experimental results demonstrate that this architecture not only avoids single points of failure but also provides a more robust trust scenario in local environments. The proposed model demonstrates significant advantages in security, availability, and system reliability. This proves the feasibility and scalability of integrating blockchain into 5GC, laying the groundwork for future cross-domain collaboration and 6G development.

Abstract Authoritarian regimes rely heavily on informants to infiltrate and surveil dissident organizations. While the role of informants in state repression is well-documented, existing research often overlooks how secret police use the intelligence gathered from informants without compromising their identities. Here, we examine the conditions under which secret police agencies seek to protect informants from exposure. We argue that agencies face a tradeoff between the risks of exposing informants and the costs of implementing protection, often prioritizing protection for informants who are deeply embedded in dissident networks and provide rare, high-value intelligence. Using declassified surveillance reports from 1970s Taiwan, we find that protection is most likely when informants are close to well-connected dissidents, interact with behind-the-scenes activists, or provide sensitive information about internal conflicts. Deeply embedded informants offer high-value intelligence but face greater risks of exposure, making them especially worth protecting. These findings contribute to research on surveillance and repression by showing how secret police manage the risks of informant exposure while leveraging human intelligence to counter dissent.

This paper considers minimizing the age of information (AoI) of large-scale networks with periodic traffic and packet deadlines under an age-aware random access protocol, accounting for the spatial distributions of transmitter-receiver pairs and the mutual interference under age-aware behaviors. We develop a nested Markov chain model to characterize such behaviors, which manages the jumps between consecutive beginnings of periods and the evolution during an arbitrary period. Based on this model, we derive the network average AoI using tools from stochastic geometry. Numerical results verify our analytical findings and demonstrate the benefit of the age awareness.

As Moore’s Law slows, alternative computing paradigms are essential for tackling complex combinatorial optimization problems. This paper introduces DigiQUBO, a high-performance digital Quadratic Unconstrained Binary Optimization (QUBO) solver designed for quantum-inspired optimization. DigiQUBO leverages a advanced Digital Annealing (DA) approach to efficiently solve large-scale QUBO problems, offering a competitive alternative to traditional annealing methods. To evaluate its effectiveness, DigiQUBO is applied to the Traveling Salesman Problem (TSP), a well-known NP-hard problem with applications in logistics, network design, and computational biology. In solving a 40-city Traveling Salesman Problem (TSP), DigiQUBO achieved an average route distance improvement of 13% compared to Simulated Annealing (SA), and a reduction of 37% compared to D-Wave’s LeapHybridSampler, while maintaining comparable runtime to Fujitsu DA3. DigiQUBO significantly enhances solution quality and computational efficiency by integrating two key advanced techniques: two-way one-hot group encoding for improved constraint satisfaction and Bayesian Optimization for automated annealing parameter tuning. Performance comparisons against Simulated Annealing (SA), Fujitsu DA3, and D-Wave’s LeapHybridSampler demonstrate superior results in balancing accuracy and execution time. The results highlight DigiQUBO as a promising digital annealing technology that bridges the gap between classical and quantum optimization, offering a practical and efficient solution in the post-Moore’s Law era.

This study strengthens the validation of learner speech assessment in the Common European Framework of Reference (CEFR) by analyzing the quantitative variables related to fluency and accuracy across four CEFR levels (A2, B1, B2, and C1). Drawing on a learner corpus approach, we examine 500,000 tokens from the Louvain International Database of Spoken English Interlanguage (LINDSEI) and its extensions, supplemented by post hoc rater evaluations. Three task types-a semi-monologic topic discussion, a dialogic interaction, and a monologic picture description-are used to elicit variation in speech production. The analysis focuses on speech rates, the frequency of filled and unfilled pauses, and error rates to unveil developmental trends in learner speech. The results reveal strong correlations between these fluency and accuracy metrics and CEFR levels, with speech rate emerging as the most reliable indicator of proficiency. The frequency of unfilled pauses decreases as proficiency increases, while filled pauses, although less critical to fluency assessment, offer insights into speech planning mechanisms. Error rates similarly decline with higher proficiency, reflecting greater accuracy in speech production. Exemplary instances for each CEFR level are presented, offering practical metrics for teaching, assessment, and rater training. While the study's limitations include an overrepresentation of Mandarin Chinese learners and the exclusion of pronunciation errors, these gaps highlight avenues for future research. This study provides empirical, task-sensitive evidence to enrich CEFR can-do descriptors, enhance rater training, and refine speaking assessments, contributing to more effective language teaching, learning, and assessment practices.

Abstract In recent years, quantum computing has gradually extended its influence beyond the realm of physics research into the fields of electrical engineering and computer science. Most researchers and programmers remain more familiar with traditional algorithmic techniques based on conventional computer architectures. To address this gap, this study proposes a quantum algorithmic circuit design framework with Grover’s search speedup technique and provides theoretical proofs for some design techniques, aiming to facilitate knowledge transfer and ease the learning curve for designers entering the field of quantum algorithm development. Since combinatorial optimization problems in graph theory serve as the foundation for many practical applications, this study adopts the well-known Connected Dominating Set (CDS) problem as a design example to illustrate the practical applicability of the proposed quantum algorithmic design framework and presents a quantum circuit as a potential solution for addressing realworld challenges in network optimization and related applications. In addition, the circuit proposed in this paper can serve as a quantum oracle to identify connected dominating sets (CDSs) of a graph. When the oracle is applied to a superposition of vertex subsets, Grover’s search algorithm achieves a quadratic speedup. We designed a method for adjusting the initial amplitudes so that the search can be biased toward smaller CDSs, which maximizes the probability of finding the minimum CDS.

The increasing prevalence of sexual harassment cases within educational settings underscored the urgency of integrating social issues into the learning process. This study examined the effect of implementing a Problem-Based Learning (PBL) model based on sexual harassment case data on students’ understanding of statistical concepts and data processing skills. A pre-experimental research design with a one-group pretest–posttest model was employed. The sample consisted of 100 fifth-grade students from Muhammadiyah 18 Elementary School in Surabaya, selected through purposive sampling. The research instrument was a statistical concept comprehension test, and the data were analyzed using a paired sample t-test. The analysis results showed a correlation coefficient of 0.662 with a significant value of p < 0.001, indicating that the implementation of the PBL model significantly improved students’ conceptual understanding. The effect size (Cohen’s d = 0.97) indicated a large practical impact of the intervention. These findings suggest that embedding real social issues into mathematics and statistics instruction can simultaneously strengthen cognitive skills and foster social awareness among students. However, as this study employed a one-group pretest–posttest design, further research with control groups is recommended to strengthen generalisability

Deep learning delivers strong results but with rapidly rising compute demands; analyses from MIT and the MIT IBM Watson AI Lab indicate this path is economically and environmentally unsustainable. Post hoc explanations do not make black box models transparent; work in Nature Machine Intelligence recommends inherently interpretable models for high stakes decisions. Interpretable Generalization, reported in IEEE Transactions on Information Forensics and Security, is accurate and self explanatory but faces a CPU bottleneck from cubic intersect and subset search and large intermediate bitsets. We present IG GPU, a PyTorch re-architecture that offloads intersections, subset tests, and evidence accumulation to a single GPU with bit packed 64 bit tensors and dynamic batching. On the full 148,517 record NSL KDD dataset, IG GPU finishes in 18 minutes on an RTX 4070 Ti with recall 0.957, precision 0.973, and AUC 0.961; across NSL KDD, UNSW NB15, and UKM IDS20, it provides average 125 fold and up to 430 fold acceleration, indicating that the observed speed-ups arise from kernel-level parallelism rather than device-specific artifacts, while preserving byte level evidence, reconciling intrinsic interpretability with real-time throughput for dependable and secure computing.