39 Result(s)
within
Artificial Intelligence
Theory of Computation
**ya Sato
Chapter
Conference Paper
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Chapter and Conference Paper
Can Machines and Humans Use Negation When Describing Images?
Can negation be depicted? It has been claimed in various areas, including philosophy, cognitive science, and AI, that depicting negation through visual expressions such images and pictures is challenging. Rece...
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Chapter and Conference Paper
Enhancing the Parallel UC2B Framework: Approach Validation and Scalability Study
Anomaly detection is a critical aspect of uncovering unusual patterns in data analysis. This involves distinguishing between normal patterns and abnormal ones, which inherently involves uncertainty. This paper...
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Chapter and Conference Paper
A Partitioned Memory Architecture with Prefetching for Efficient Video Encoders
A hardware video encoder based on recent video coding standards such as HEVC and VVC needs to efficiently handle a massive number of memory accesses to search motion vectors. To this end, first, this paper pre...
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Chapter and Conference Paper
Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning
Deep learning is a vital technology in our lives today. Both the size of training datasets and neural networks are growing to tackle more challenging problems with deep learning. Distributed deep neural networ...
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Chapter and Conference Paper
Multi-objectivization Relaxes Multi-funnel Structures in Single-objective NK-landscapes
This paper investigated the impacts of multi-objectivization on solving combinatorial single-objective NK-landscape problems with multiple funnel structures. Multi-objectivization re-formulates a single-objective...
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Chapter and Conference Paper
Argument Reduction of Constrained Horn Clauses Using Equality Constraints
Constrained Horn Clauses (CHCs) have recently been studied extensively as a common, uniform foundation for automated program verification. Various program verification problems have been shown to be reducible ...
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Chapter and Conference Paper
Visually Analyzing Universal Quantifiers in Photograph Captions
Universal quantifiers have been the subject of much work in symbolic and diagrammatic logic. However, little attention is paid to the question of how they can be visually grounded, that is, depicted in real im...
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Chapter and Conference Paper
Scaling the PageRank Algorithm for Very Large Graphs on the Fugaku Supercomputer
The PageRank algorithm is a widely used linear algebra method with many applications. As graphs with billions or more of nodes become increasingly common, being able to scale this algorithm on modern HPC arch...
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Chapter and Conference Paper
Interactive Process Drift Detection Framework
This paper presents a novel tool for detecting drifts in process models. The tool targets the challenge of defining the better parameter configuration for detecting drifts by providing an interactive user inte...
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Chapter and Conference Paper
Pareto Front Estimation Using Unit Hyperplane
This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vect...
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Chapter and Conference Paper
On Parametric Border Bases
We study several properties of border bases of parametric polynomial ideals and introduce a notion of a minimal parametric border basis. It is especially important for improving the quantifier elimination alg...
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Chapter and Conference Paper
Reducing Partner’s Cognitive Load by Estimating the Level of Understanding in the Cooperative Game Hanabi
Hanabi is a cooperative game for ordering cards through information exchange, and has been studied from various cooperation aspects, such as self-estimation, psychology, and communication theory. Cooperation i...
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Chapter and Conference Paper
ArtPDGAN: Creating Artistic Pencil Drawing with Key Map Using Generative Adversarial Networks
A lot of researches focus on image transfer using deep learning, especially with generative adversarial networks (GANs). However, no existing methods can produce high quality artistic pencil drawings. First, a...
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Chapter and Conference Paper
Logical Inference as Cost Minimization in Vector Spaces
We propose a differentiable framework for logic program inference as a step toward realizing flexible and scalable logical inference. The basic idea is to replace symbolic search appearing in logical inference...
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Chapter and Conference Paper
A Distribution Control of Weight Vector Set for Multi-objective Evolutionary Algorithms
For solving multi-objective optimization problems with evolutionary algorithms, the decomposing t...
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Chapter and Conference Paper
Performance Evaluation of Tsunami Inundation Simulation on SX-Aurora TSUBASA
As tsunamis may cause damage in wide area, it is difficult to imme...
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Chapter and Conference Paper
HoIce: An ICE-Based Non-linear Horn Clause Solver
The ICE framework is a machine-learning-based technique originally introduced for inductive invariant inference over transition systems, and building on the supervised learning paradigm. Recently, we adapted t...
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Chapter and Conference Paper
Automated Generation of New Concepts from General Game Playing
In this paper, we propose algorithms to extract explicit concepts from general games and these concepts are useful to understand semantics of games using General Game Playing as a research domain. General Game...
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Chapter and Conference Paper
Detecting Demand-Supply Situations of Hotel Opportunities: An Empirical Analysis of Japanese Room Opportunities Data
This study analyzes the availability of room opportunity types collected from a Japanese hotel booking site. The status of opportunity type is empirically analyzed from a comprehensive point of view. We charac...
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Chapter and Conference Paper
Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization
Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user-defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective opti...
