Reviewerly/exHarmony huggingface dataset:
Paraconsistent logic and query answering in inconsistent databases

Authors:
raw_author_name: C. A. Middelburg
raw_affiliation_string: Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

Abstract: This paper concerns the paraconsistent logic LPQ⊃,F and an application of it in the area of relational database theory. The notions of a relational database, a query applicable to a relational database, and a consistent answer to a query with respect to a possibly inconsistent relational database are considered from the perspective of this logic. This perspective enables among other things the definition of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. In an earlier paper, LPQ⊃ ,F is presented with a sequent-style natural deduction proof system. In this paper, a sequent calculus proof system is presented instead because such proof systems are generally considered more suitable as the basis of proof search procedures than natural deduction proof systems and proof search procedures can serve as the core of algorithms for computing consistent answers to queries.
Generative approaches for solving tangram puzzles

Authors:
raw_author_name: Fernanda Miyuki Yamada
raw_affiliation_string: Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofugaoka, Chofu, Tokyo, 182-8585, Japan
raw_author_name: Harlen Costa Batagelo
raw_affiliation_string: Center for Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, Santo André, São Paulo, 09210-580, Brazil
raw_author_name: João Paulo Gois
raw_affiliation_string: Center for Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, Santo André, São Paulo, 09210-580, Brazil
raw_author_name: Hiroki Takahashi
raw_affiliation_string: Artificial Intelligence Exploration Research Center, Meta-Networking Research Center, The University of Electro-Communications, Chofugaoka, Chofu, Tokyo, 182-8585, Japan

Abstract: Abstract The Tangram is a dissection puzzle composed of seven polygonal pieces that can form different patterns. Solving the Tangram is an irregular shape packing problem known to be NP-hard. This paper investigates the application of four deep-learning architectures—Convolutional Autoencoder, Variational Autoencoder, U-Net, and Generative Adversarial Network—specifically designed for solving Tangram puzzles. We explore the potential of these architectures in learning the complex spatial relationships inherent in Tangram configurations. Our experiments show that the Generative Adversarial Network competes well with other architectures and converges considerably faster. We further prove that traditional evaluation metrics based on pixel accuracy often fail in assessing the visual quality of the generated Tangram solutions. We introduce a loss function based on a Weighted Mean Absolute Error that prioritizes pixels representing inter-piece sections over those covered by individual pieces. Extending this loss function, we propose a novel evaluation metric as a more fitting measure for assessing Tangram solutions compared to traditional metrics. This investigation advances our understanding of the capabilities of artificial intelligence in complex geometrical problem domains.
A Hardware Realization Framework for Fuzzy Inference System Optimization

Authors:
raw_author_name: Saeid Gorgin
raw_affiliation_string: Department of Computer Engineering, Chosun University, Gwangju 61453, Republic of Korea
raw_author_name: Mohammad Sina Karvandi
raw_affiliation_string: Department of Computer Engineering, Chosun University, Gwangju 61453, Republic of Korea
raw_author_name: Somaye Moghari
raw_affiliation_string: Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood 3619995161, Iran
raw_author_name: Mohammad K. Fallah
raw_affiliation_string: Department of Computer Engineering, Chosun University, Gwangju 61453, Republic of Korea
raw_author_name: Jeong-A Lee
raw_affiliation_string: Department of Computer Engineering, Chosun University, Gwangju 61453, Republic of Korea

Abstract: Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.
Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation

Authors:
raw_author_name: Naihua Ji
raw_affiliation_string: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
raw_author_name: Rongyi Bao
raw_affiliation_string: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
raw_author_name: Zhao Chen
raw_affiliation_string: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
raw_author_name: Yiming Yu
raw_affiliation_string: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
raw_author_name: Hongyang Ma
raw_affiliation_string: School of Science, Qingdao University of Technology, Qingdao 266033, China

Abstract: In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments.
A Design Language for Prototyping and Storyboarding Data-Driven Stories

Authors:
raw_author_name: Morteza Asgari
raw_affiliation_string: Computer and Software Engineering Department, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada
raw_author_name: Thomas Hurtut
raw_affiliation_string: Computer and Software Engineering Department, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada

Abstract: Data-driven stories (DDS) are digital forms of storytelling that arrange data and visualizations to communicate a narrative of information to an audience. They have been growing rapidly over the past decades. As a result, a great degree of versatility appears in the forms of published DDS. The recent structures of DDS are more complex, respecting their arrangement, composition, features, and inner parts. In the current academic research, neither storytelling techniques nor any taxonomies suggest visual mechanisms to distinguish between different layouts, compositions, and arrangements. The lack of an expressive visual solution that integrates different parts of DDS under one structure prevents the authors from trying more alternative design paths in the story design process. In this proposed work, we unify all the constructing parts of DDS to define the narrative structure as a visually structured representation of the DDS narrative, which is formed and designed by their constructing elements. This solution proposes a design language consisting of a set of design rules that integrate the visual elements to represent the DDS narrative structure. Our evaluation of the audit process out of 100 DDS examples confirms that the design language is comprehensive, expressive, and versatile. Additionally, we developed DataStoryDesign, a system that incorporates this visual solution to facilitate prototyping and storyboarding DDS for a team of DDS authors. The preliminary result of the exploratory evaluation indicates that such a solution is effective in prototyping and storyboarding DDS. In addition, our findings confirmed that the existence of our design language improves the visual communication between different personas in the DDS production workflow.
Automated Identification of Sensitive Financial Data Based on the Topic Analysis

Authors:
raw_author_name: Jiqiang Liu
raw_affiliation_string: School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
raw_author_name: Yeping Yang
raw_affiliation_string: School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China

Abstract: Data governance is an extremely important protection and management measure throughout the entire life cycle of data. However, there are still data governance issues, such as data security risks, data privacy breaches, and difficulties in data management and access control. These problems lead to a risk of data breaches and abuse. Therefore, the security classification and grading of data has become an important task to accurately identify sensitive data and adopt appropriate maintenance and management measures with different sensitivity levels. This work started from the problems existing in the current data security classification and grading work, such as inconsistent classification and grading standards, difficult data acquisition and sorting, and weak semantic information of data fields, to find the limitations of the current methods and the direction for improvement. The automatic identification method of sensitive financial data proposed in this paper is based on topic analysis and was constructed by incorporating Jieba word segmentation, word frequency statistics, the skip-gram model, K-means clustering, and other technologies. Expert assistance was sought to select appropriate keywords for enhanced accuracy. This work used the descriptive text library and real business data of a Chinese financial institution for training and testing to further demonstrate its effectiveness and usefulness. The evaluation indicators illustrated the effectiveness of this method in the classification of data security. The proposed method addressed the challenge of sensitivity level division in texts with limited semantic information, which overcame the limitations on model expansion across different domains and provided an optimized application model. All of the above pointed out the direction for the real-time updating of the method.
Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection

Authors:
raw_author_name: Jinwei Zhang
raw_affiliation_string: School of Mechanical Engineering, North University of China, Taiyuan 030051, China
raw_author_name: Xijing Zhu
raw_affiliation_string: School of Mechanical Engineering, North University of China, Taiyuan 030051, China
raw_author_name: Jing Li
raw_affiliation_string: School of Mechanical Engineering, North University of China, Taiyuan 030051, China

Abstract: Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.
Semi-Supervised Implicit Augmentation for Data-Scarce VQA

Authors:
raw_author_name: Bhargav Dodla
raw_affiliation_string: Indian Institute of Technology, Madras 600036, India; Microsoft India (R&D) Private Limited, Bengaluru 560103, India
raw_author_name: Kartik Hegde
raw_affiliation_string: Indian Institute of Technology, Madras 600036, India
raw_author_name: A. N. Rajagopalan
raw_affiliation_string: Indian Institute of Technology, Madras 600036, India

Abstract: Vision-language models (VLMs) have demonstrated increasing potency in solving complex vision-language tasks in the recent past. Visual question answering (VQA) is one of the primary downstream tasks for assessing the capability of VLMs, as it helps in gauging the multimodal understanding of a VLM in answering open-ended questions. The vast contextual information learned during the pretraining stage in VLMs can be utilised effectively to finetune the VQA model for specific datasets. In particular, special types of VQA datasets, such as OK-VQA, A-OKVQA (outside knowledge-based), and ArtVQA (domain-specific), have a relatively smaller number of images and corresponding question-answer annotations in the training set. Such datasets can be categorised as data-scarce. This hinders the effective learning of VLMs due to the low information availability. We introduce SemIAug (Semi-Supervised Implicit Augmentation), a model and dataset agnostic strategy specially designed to address the challenges faced by limited data availability in the domain-specific VQA datasets. SemIAug uses the annotated image-question data present within the chosen dataset and augments it with meaningful new image-question associations. We show that SemIAug improves the VQA performance on data-scarce datasets without the need for additional data or labels.
Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies

Authors:
raw_author_name: Xiao-Dong Wang
raw_affiliation_string: School of Computer Science and Technology, Xidian University, Xi’an 710126, China
raw_author_name: Bo Yan
raw_affiliation_string: School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
raw_author_name: Qiang Gao
raw_affiliation_string: Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China; Institute of Biomedical Sciences, Fudan University, Shanghai 200030, China

Abstract: In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a "black box" nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the "black box" of AI’s decision-making process.
Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT

Authors:
raw_author_name: Jiarong Ye
raw_affiliation_string: Roche Diagnostics Solutions, Santa Clara, CA, USA
raw_author_name: Shivam Kalra
raw_affiliation_string: Roche Diagnostics Solutions, Santa Clara, CA, USA
raw_author_name: Mohammad Saleh Miri
raw_affiliation_string: Roche Diagnostics Solutions, Santa Clara, CA, USA

Abstract: Abstract Developing a clinical AI model necessitates a significant amount of highly curated and carefully annotated dataset by multiple medical experts, which results in increased development time and costs. Self-supervised learning (SSL) is a method that enables AI models to leverage unlabelled data to acquire domain-specific background knowledge that can enhance their performance on various downstream tasks. In this work, we introduce CypherViT , a cluster-based histo-pathology phenotype representation learning by self-supervised multi-class-token hierarchical Vision Transformer (ViT). CypherViT is a novel backbone that can be integrated into a SSL pipeline, accommodating both coarse and fine-grained feature learning for histopathological images via a hierarchical feature agglomerative attention module with multiple classification (cls) tokens in ViT. Our qualitative analysis showcases that our approach successfully learns semantically meaningful regions of interest that align with morphological phenotypes. To validate the model, we utilize the DINO self-supervised learning (SSL) framework to train CypherViT on a substantial dataset of unlabeled breast cancer histopathological images. This trained model proves to be a generalizable and robust feature extractor for colorectal cancer images. Notably, our model demonstrates promising performance in patch-level tissue phenotyping tasks across four public datasets. The results from our quantitative experiments highlight significant advantages over existing state-of-the-art SSL models and traditional transfer learning methods, such as those relying on ImageNet pre-training.
A dynamic approach for visualizing and exploring concept hierarchies from textbooks

Authors:
raw_author_name: Sabine Wehnert
raw_affiliation_string: Faculty of Computer Science, Human-Centred Artificial Intelligence, Otto von Guericke University Magdeburg, Magdeburg, Saxony-Anhalt, Germany; Human-Centred Technologies for Educational Media, Leibniz Institute for Educational Media, Georg Eckert Institute, Brunswick, Germany
raw_author_name: Ernesto William De Luca
raw_affiliation_string: Faculty of Computer Science, Human-Centred Artificial Intelligence, Otto von Guericke University Magdeburg, Magdeburg, Saxony-Anhalt, Germany; Human-Centred Technologies for Educational Media, Leibniz Institute for Educational Media, Georg Eckert Institute, Brunswick, Germany

Abstract: In this study, we propose a visualization technique to explore and visualize concept hierarchies generated from a textbook in the legal domain. Through a human-centered design process, we developed a tool that allows users to effectively navigate through and explore complex hierarchical concepts in three kinds of traversal techniques: top-down, middle-out, and bottom-up. Our concept hierarchies offer an overview over a given domain, with increasing level of detail toward the bottom of the hierarchy which is consisting of entities. In the legal use case we considered, the concepts were adapted from section headings in a legal textbook, whereas references to law or legal cases inside the textbook became entities. The design of this tool is refined following various steps such as gathering user needs, pain points of an existing visualization, prototyping, testing, and refining. The resulting interface offers users several key features such as dynamic search and filter, explorable concept nodes, and a preview of leaf nodes at every stage. A high-fidelity prototype was created to test our theory and design. To test our concept, we used the System Usability Scale as a way to measure the prototype's usability, a task-based survey to asses the tool's ability in assisting users in gathering information and interacting with the prototype, and finally mouse tracking to understand user interaction patterns. Along with this, we gathered audio and video footage of users when participating in the study. This footage also helped us in getting feedback when the survey responses required further information. The data collected provided valuable insights to set the directions for extending this study. As a result, we have accounted for varying hierarchy depths, longer text spans than only one to two words in the elements of the hierarchy, searchability, and exploration of the hierarchies. At the same time, we aimed for minimizing visual clutter and cognitive overload. We show that existing approaches are not suitable to visualize the type of data which we support with our visualization.