Article
Computer Science, Theory & Methods
Florian Bruse, Maurice Herwig, Martin Lange
Summary: This paper presents a weight measure for formal languages based on the summands of a geometric series discounted by the fraction of words of certain length in the language. It shows that this weight measure is computable for regular languages. As an application, a distance metric between languages is derived as the weight of their symmetric difference, which can be used in automatic grading of standard exercises in formal language theory classes.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xianrun Chen, Sai Ji, Chenchen Wu, Yicheng Xu, Yang Yang
Summary: This paper introduces the diversity-aware fair k-supplier problem and presents an efficient 5-approximation algorithm to address the fairness and over-representation issues in facility selection.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Theory & Methods
S. O. Mashchenko
Summary: This paper investigates a fuzzy matrix game with fuzzy sets of player strategies and proposes a method to construct a game value using Zadeh's extension principle and the approach to fuzzy matrix games. It is proved that the fuzzy sets of players strategies in a fuzzy matrix game generate a game value in the form of a type-2 fuzzy set on the real line.
FUZZY SETS AND SYSTEMS
(2024)
Article
Computer Science, Theory & Methods
Marzieh Shamsizadeh, Mohammad Mehdi Zahedi, Mohamad Javad Agheli Goki
Summary: In this paper, we study a new generalization for the notion of fuzzy automata, called hesitant L-fuzzy automaton (HLFA). The mathematics framework for the theory of HLFA is presented. Moreover, the concepts of hesitant L-fuzzy behavior and inverse hesitant L-fuzzy behavior recognized by a type of HLFA are introduced. Additionally, a minimal complete accessible deterministic hesitant L-fuzzy automaton is presented for recognizing any hesitant L-fuzzy language, and an algorithm is proposed to determine the states of the minimal hesitant L-fuzzy automaton along with its time complexity.
FUZZY SETS AND SYSTEMS
(2024)
Article
Computer Science, Theory & Methods
Masaaki Kanzaki, Yota Otachi, Giovanni Viglietta, Ryuhei Uehara
Summary: Sliding block puzzles play a crucial role in computational complexity, with their complexity varying depending on the rules and set of pieces. In this study, we explore the computational complexities of jumping block puzzles, a newer concept in the puzzle community. We analyze different variants of these puzzles based on real puzzles and a natural model, and determine their complexities. Our findings show that these puzzles are generally PSPACE-complete, with additional cases being NP-complete or solvable in polynomial-time.
THEORETICAL COMPUTER SCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari
Summary: This paper proposes a novel approach called Differentiable rendering-based multi-view Image-Language Fusion (DILF) for zero-shot 3D shape understanding. The approach leverages large-scale language models to generate textual prompts enriched with 3D semantics and uses a differentiable renderer with learnable rendering parameters to produce representative multi-view images. Experimental results demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Chen, Yuzhu Hu, Qifeng Lai, Wei Wang, Junxin Chen, Han Liu, Gautam Srivastava, Ali Kashif Bashir, Xiping Hu
Summary: Depression is a common mental illness, and research on multimodal data-based depression detection is crucial. This study proposes an intra-modal and inter-modal fusion framework (IIFDD) for corpus-based depression detection. The intra-modal fusion module integrates low-dimensional pre-designed features and high-dimensional deep representation to better capture semantic information. The inter-modal fusion module combines features from different modalities with attention mechanisms, achieving state-of-the-art performance in depression detection.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Dalibor Cimr, Hamido Fujita, Damian Busovsky, Richard Cimler
Summary: Automated computer-aided diagnosis (CAD) is an effective method for early detection of health issues, and this study proposes a CAD system for seizure detection with optimized complexity. The results demonstrate the effectiveness of the proposed model in providing decision support in both clinical and home environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jose A. Barbero-Aparicio, Alicia Olivares-Gil, Juan J. Rodriguez, Cesar Garcia-Osorio, Jose F. Diez-Pastor
Summary: This paper presents a comprehensive analysis of deep transfer learning, supervised, and semi-supervised methods in the context of protein fitness prediction. The study focuses on small datasets and explores the combination of different data sources to enhance the model's performance. The findings suggest that deep transfer learning shows promising performance in small dataset scenarios and highlights its robustness and versatility in protein fitness prediction tasks.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jinghua Tan, Muhammet Deveci, Juan Li, Kaiyang Zhong
Summary: This study proposes a deep learning framework with visual clues to unveil the factors and their function on stock movements. By encoding unstructured textual media information and fusing it with other structured market factors, as well as utilizing a pre-estimating method, the readability and predictive accuracy of investment strategies are improved.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Qilei Li, Mingliang Gao, Guisheng Zhang, Wenzhe Zhai, Jinyong Chen, Gwanggil Jeon
Summary: With the rapid progress in multimodal learning and the emergence of vision-language foundation models, disinformation created by artificial neural networks has become widespread, causing significant negative impact on society. In this paper, a novel framework called Vision-language Knowledge Interaction (ViKI) is proposed to detect and identify manipulated multimodal disinformation. By exploring semantic correlation and cross-modality knowledge interaction, ViKI produces accurate predictions for detecting and grounding disinformation.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Lucas Ruchel, Edson Tavares de Camargo, Luiz Antonio Rodrigues, Rogerio C. Turchetti, Luciana Arantes, Elias Procopio Duarte Jr.
Summary: LHABcast is a leaderless hierarchical atomic broadcast algorithm that improves scalability by being fully decentralized and hierarchical. It uses local sequence numbers and timestamps to order messages and achieves significantly lower message count compared to an all-to-all strategy, both in fault-free and faulty scenarios.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoding Wang, Jianmin Liu, Hui Lin, Sahil Garg, Mubarak Alrashoud
Summary: This paper proposes a new method for predicting multi-modal trajectories by integrating multi-source visual feature. A spatial-temporal cross attention fusion module is developed to capture the spatiotemporal interactions among vehicles, while leveraging the road's geographic structure to improve prediction accuracy. Experimental results demonstrate the superiority of our method over other methods in both unimodal and multimodal prediction.
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Lucas Perotin, Sandhya Kandaswamy, Hongyang Sun, Padma Raghavan
Summary: Resource scheduling is crucial in High-Performance Computing systems, and previous research has mainly focused on a single type of resource. With advancements in hardware and the rise of data-intensive applications, considering multiple resources simultaneously is necessary to improve overall application performance. This study presents a Multi-Resource Scheduling Algorithm (MRSA) that minimizes the makespan of computational workflows by efficiently allocating resources and optimizing scheduling order. Simulation results demonstrate that MRSA outperforms baseline methods in various scenarios.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2024)
Article
Computer Science, Software Engineering
Edgar Sarmiento-Calisaya, Julio Cesar Sampaio do Prado Leite
Summary: This research introduces an automated requirements analysis approach that combines natural language processing, Petri-nets, and visualization techniques to improve the quality of scenario-based specifications, identify defects, and anticipate inconsistencies.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Article
Computer Science, Software Engineering
Jessica Diaz, Jorge Perez, Isaque Alves, Fabio Kon, Leonardo Leite, Paulo Meirelles, Carla Rocha
Summary: This paper presents empirical research on the structure of DevOps teams in software-producing organizations to better understand the organizational structure and characteristics of teams adopting DevOps. A theory of DevOps taxonomies is built through analysis, and its consistency with other taxonomies is tested.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
Review
Computer Science, Software Engineering
Orvila Sarker, Asangi Jayatilaka, Sherif Haggag, Chelsea Liu, M. Ali Babar
Summary: This study provides a comprehensive view of the challenges and critical success factors in the design, implementation, and evaluation stages of phishing education, training, and awareness (PETA). The findings highlight the need to address human-centric issues, bridge users' knowledge gaps, and adopt personalized approaches to enhance defense against phishing attacks.
JOURNAL OF SYSTEMS AND SOFTWARE
(2024)
