This paper focuses on simulating text dialogues in which impressions between speakers improve during speed dating. This simulation involves selecting an utterance from multiple candidates generated by a text generation model that replicates a specific speaker's utterances, aiming to improve the impression from their dialogue partner. Accurately selecting an utterance that improves the impression is crucial for the simulation. We believe that whether an utterance improves the speaker's impression from their partner may depend on the personalities of both parties. However, recent methods for utterance selection do not consider the impression per utterance or the personalities. To address this issue, we propose a method that predicts whether an utterance improves the speaker's impression from their partner, considering the personalities. The evaluation results showed that personalities are useful in predicting impression changes per utterance. Furthermore, we conducted a human evaluation of simulated dialogues using our method. The results showed that it could simulate dialogues more favorably received than those selected without considering personalities.

We prove two results about transforming any convex polyhedron, modeled as a linkage L of its edges. First, if we subdivide each edge of L in half, then L can be continuously flattened into a plane. Second, if L is equilateral and we again subdivide each edge in half, then L can be reversed, i.e., turned inside-out. A linear number of subdivisions is optimal up to constant factors, as we show (nonequilateral) examples that require a linear number of subdivisions. For nonequilateral linkages, we show that more subdivisions can be required: even a tetrahedron can require an arbitrary number of subdivisions to reverse. For nonequilateral tetrahedra, we provide an algorithm that matches this lower bound up to constant factors: logarithmic in the aspect ratio.

Currently, active research on learning analytics is being conducted at research institutions worldwide. However, relying solely on objective data derived from the accumulation and analysis of learning data may not discern internal factors influencing learners' decision-making in their academic pursuits. Therefore, a combination of subjective data collection and analysis is essential for learning and educational support. This study sought to examine how the provision of learning-analytics-based feedback can stimulate a change in learners' consciousness and action to improve their learning in discrete mathematics class. Objective and subjective data were collected simultaneously from e-learning and motivation tests, respectively, and analyzed using machine learning. Thereafter, the results were provided as relative evaluation feedback to the learners. To confirm its effectiveness, we provided feedback in the class for three years. To investigate the effects of feedback on student motivation, we compared the results for the motivation scales of the Motivated Strategies for Learning Questionnaire (MSLQ) survey conducted before and after the feedback. The results confirmed that for the factors of MSLQ survey that had significant effects on learning outcomes, the differences in the mean subscale scores before and after feedback were positive for “value of learning, ” “self-efficacy, ” “external goal-orientation, ” “possession of skills, ” and “internal goal-orientation” and only negative for “test anxiety”. Teachers can utilize this feedback to stimulate a change in their consciousness and action to improve their learning, and, subsequently, their learning outcomes in discrete mathematics.

Active Directory (AD) is widely used as an authentication server in many organizations. AD centrally manages users and computers and their authentications. Thus, it is very useful but also tends to be leveraged by attackers. Attacks leveraging ADs such as a Silver Ticket have been observed, but they are challenging to detect since they abuse specifications of Kerberos authentication, not vulnerabilities.

Various knowledge graphs are now available, and useful knowledge can be extracted from them. Measuring the similarity between two nodes in a knowledge graph is one of the fundamental steps required for extracting knowledge from it. In this paper, we propose a novel measure for representing the similarity between two nodes: LRoleSim. We define LRoleSim as an extension of RoleSim in a deductive manner, and we do not use inductive methods, such as embedding graphs into vector spaces. This means that LRoleSim inherits the axiomatic properties of RoleSim and functions as an admissible role similarity measure. The RoleSim measure was proposed for measuring the similarity between two nodes in homogeneous information networks, where nodes and edges are being treated either as of the same type or as of un-typed nodes or edges. In contrast, every node in a knowledge graph has its own type, and every edge has a direction and type. LRoleSim is designed so that it captures all of these types and directions as well as the topological structure of each node's neighbor. Experiments on real-world knowledge graph datasets verified that LRoleSim captures the semantic meaning of node similarity. Moreover, our experiments show that LRoleSim outperforms RoleSim in terms of computational efficiency.

Due to the recent popularity of sports cycling, many people are interested in cycling tourism. Notifying novices of irregular cycling forms is helpful to avoid muscle fatigue. However, the conventional technologies for detecting irregular cycling forms by inertial measurement unit (IMU) alone face significant limitations in accurately capturing cycling movements. Moreover, simple threshold-based methods fail to account for individual differences among cyclists and fatigue-induced variations. Hence, we propose a novel AI architecture for detecting irregular cycling forms. The proposed architecture combines data from IMUs and 2D Light Detection and Ranging for more accurate detection of irregular cycling forms. Our proposed architecture achieved 0.900 in accuracy, 0.892 in precision, and 0.841 in recall for an F1-score of 0.862, demonstrating the effectiveness of our approaches. Compared to models using only IMUs data, our proposed architecture achieved a significant improvement in precision, increasing by 10.0 points.

The manual creation of a ceiling plan consumes many human resources to confirm the current state of existing buildings for renovation. Identifying the positions and the types of existing fixtures is crucial for drawing a ceiling plan. Therefore, to assist in drawing a ceiling plan, an efficient method that generates a reliable photorealistic whole ceiling surface image is essential. We present a method of fixture-aware panoramic ceiling image synthesis using an omnidirectional camera. A fixture-aware panoramic ceiling image is a photorealistic image that depicts the whole ceiling surface and no seams on ceiling fixtures for its reliability regarding assistance in drawing a ceiling plan. The proposed method is remarkably effortless because it requires the users to work only 2 procedures for synthesizing the image, 1) shooting an omnidirectional video underneath the ceiling and 2) fixture measurement in the image. In the experiment, we evaluated the resulting image for the positional and shape accuracy of the ceiling fixtures and the time efficiency of the measurement. Our proposed method demonstrated a positional accuracy of 2.25 times higher, a shape accuracy of 5.87 times higher, and a time efficiency of the measurement 11.09 times higher than the baseline.

General-purpose computing on graphics processing units (GPGPUs) has become increasingly prevalent, with hybrid CPU-GPU systems at the forefront of parallel computing. Dynamic load balancing is highly effective in maximizing CPU and GPU utilization in such environments. Backtracking-based load balancing, utilizing work-stealing, offers a promising strategy for task parallelism. However, Tascell, a task-parallel language that implements this mechanism, currently lacks GPU support, limiting its potential for hybrid CPU-GPU parallelism and constraining its application in computationally intensive tasks. In this paper, we demonstrate an experimental study on enabling Tascell to fully utilize the computational power of CPU-GPU hybrid environments by writing both CPU-oriented and GPU-oriented code for workers, allowing each worker to selectively execute either implementation based on the task size and GPU availability. Using this technique, we implemented hybrid CPU-GPU programs for three applications using Tascell: recursive block matrix multiplication, 2D stencil computations and Mandelbrot set calculations. The GPU-oriented code was implemented using OpenACC or the NVBLAS library. We conducted performance evaluations on both high-performance and workstation-grade CPU-GPU hybrid computing environments. The results demonstrated that in the workstation-grade environment, the hybrid approach outperformed both CPU-only and GPU-only configurations. Notably, hybrid CPU-GPU executions achieved performance improvements of up to 12.9% and 25.2% in 2D stencil applications compared to GPU-only and CPU-only executions, respectively. These findings provide valuable insight into the effective use of hybrid CPU-GPU systems within a backtracking-based load balancing framework.