The widespread application of robotic technology across social contexts has underscored the potential of anthropomorphic features in enhancing social attributes and user acceptance, drawing increasing academic attention. Despite its emergence as a prominent research topic in human-robot interaction (HRI), existing studies lack systematic bibliometric reviews. To address this gap, we conducted a comprehensive review of 1,082 academic publications from the Web of Science Core Collection. By constructing knowledge maps that include collaboration networks, co-citation clustering, and keyword co-occurrence analysis, we identified the developmental trajectory and key themes in anthropomorphism research within HRI. The analysis revealed three core components: application scenarios in HRI, anthropomorphism theory and psychological effects, social behavior and emotional interaction. Keyword clustering further identified four major research hotspots: system design, emotional interaction, user adoption and experience, and assistive interaction techniques. This study provides a comprehensive depiction of the knowledge landscape of anthropomorphic research in HRI, offering valuable references for further research direction.

How to balance lighting and texture details to achieve the desired visual effect remains the bottleneck of existing low-light image enhancement methods. In this paper, we propose a novel Unpaired Textual-attention Generative Adversarial N network (UT-GAN) for low-light text image enhancement task. UT-GAN first uses the Zero-DCE net for initial illumination recovery and our TAM module is proposed to translate text information into a textual attention mechanism for the overall network, emphasizing attention to the details of text regions. Moreover, the method constructs an AGM-Net module to mitigate noise effects and fine-tune the illumination. Experiments show that UT-GAN outperforms existing methods in qualitative and quantitative evaluation on the widely used the low-light datasets LOL and SID.

Soft error rate has been increasing due to the shrinking size of transistors, leading to an elevated risk of catastrophic failures in modern computer systems. Error detection by duplicating instructions (EDDI) is a software-based technique to mitigate soft errors with a low runtime performance overhead and has been widely adopted in many safety- and mission-critical real-time systems such as space applications. However, these systems are commonly sensitive to runtime performance overheads the protection techniques incur. Few studies have investigated the performance of EDDI across various system designs and operational parameters, hence lacking a complete understanding in the literature. In this paper, we conduct comprehensive experiments to study the variation of EDDI runtime performance overhead and characterize the root causes. We find that there exist significant variations in performance overheads of EDDI, due to a few architectural and program-level factors. Based on the findings, we propose two practical techniques FuzzyB and Celer: FuzzyB uses an input searching technique to bound EDDI runtime performance overhead across different inputs for a given program; while Celer reduces EDDI run-time performance overheads using compiler transformation (by 25.08% reduction).

Soft errors are prevalent in modern High-Performance Computing (HPC) systems, resulting in silent data corruptions (SDCs), compromising system reliability. Instruction duplication is a widely used software-based protection technique against SDCs. Existing instruction duplication techniques are mostly implemented at LLVM level and may suffer from low SDC coverage at assembly level. In this paper, we evaluate instruction duplication at both LLVM and assembly levels. Our study shows that existing instruction duplication techniques have protection deficiency at assembly level and are usually over-optimistic in the protection. We investigate the root-causes of the protection deficiency and propose a mitigation technique, Flowery, to solve the problem. Our evaluation shows that Flowery can effectively protect programs from SDCs evaluated at assembly level.

The Physical Unclonable Function (PUF) circuit generates a random, unclonable key by extracting random deviations of the manufacturing processes. In this paper, a delay ternary PUF (DT-PUF) circuit scheme based on Carbon Nanotube Field Effect Transistor (CNFET) is proposed. In this scheme, the two inputs and two outputs (2I2O) multi-value delay circuit and the multi-valued arbiter circuit is designed by the threshold controllable CNFET. Then, the ternary signals logic 0, logic 1 and logic 2 transmission delay path is realized by cascading multi-bit 2I2O multi-value delay circuits. Secondly, the delay deviation of the two identical transmission paths is set as the random source. With the help of ternary arbiter, the random delay competes to produce the unclonable ternary PUF output data. Finally, the proposed DT-PUF circuit is designed under 32nm CNFET standard model library, and the circuit is simulated and analyzed by HSPICE. Experiment results show that DT-PUF circuit has the correct logic function, and the distribution is 30.3%@logic 0, 36%@logic 1, 33.7%@logic 2, near ideal about 33.3%.