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User-Driven Counterfactual Generator: A Human Centered Exploration 1

User-Driven Counterfactual Generator: A Human Centered Explo...

Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Beretta, Isacco Cappuccio, Eleonora Manerba, Marta Marchiori Computer Science Department Università di Pisa Italy KDD Laboratory ISTI National Research Council Pisa Italy Computer Science Department Università Degli Studi di Bari Aldo Moro Italy

In this paper, we critically examine the limitations of the techno-solutionist approach to explanations in the context of counterfactual generation, reaffirming interactivity as a core value in the explanation interfa... 详细信息

关键词： Counterfactuals Human-Centered AI XAI XUI

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A Low-Cost Pipelined Architecture Based on a Hybrid Sorting Algorithm

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 717-730页

作者： Chen, You-Rong Ho, Chien-Chia Chen, Wei-Ting Chen, Pei-Yin The Digital Integrated Circuit Design Laboratory Department of Computer Science and Information Engineering National Cheng Kung University Tainan70101 Taiwan MediaTek Inc. Hsinchu30078 Taiwan

In this paper, a low-cost pipelined architecture based on a hybrid sorting algorithm is proposed. The proposed architecture is constructed with a bitonic sorter and several cascaded bidirectional insertion sorting units. The bidirectional insertion sorting unit uses the segmented sorted subsequence generated by the bitonic sorter as input, and records the maximum and minimum values of the subsequence. After all segmented subsequences are processed through the cascaded bidirectional insertion sorting units, a sorted sequence is obtained. The proposed architecture is implemented using the Verilog hardware description language (HDL) and synthesized using the Synopsys Design Compiler with a TSMC 90-nm cell library. The experimental results indicate that the proposed architecture can not only shorten sorting cycles but also reduce hardware area costs. Moreover, sorting cycles can be further shortened by increasing the parallelism of the proposed architecture. Under the configuration that 2048 32-bit data to be sorted and 16 data have to be processed simultaneously, the proposed architecture can improve the throughput-to-gate-count ratio by 16%, and throughput-to-power-consumption-ratio by 25% compared to the existing sorting design. The proposed architecture makes the most efficient use of hardware resources. © 2023 IEEE.

关键词： Costs

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Optimizing Feature Selection in Big Data: A Hybrid Spark and Fuzzy Approach

Optimizing Feature Selection in Big Data: A Hybrid Spark and...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Hada, Aman Singh Sahoo, Gyanaballav Samir Vamsi, Chinnapareddy Krishna Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Karnataka Mangalore575025 India

ISBN: (纸本)9798331517892

The exponential growth of big data presents both immense opportunities and significant challenges. While vast datasets hold the key to unlocking groundbreaking insights, efficiently extracting value requires sophisticated feature selection techniques. Traditional methods often struggle with the sheer volume and complexity of big data. This paper addresses this challenge by proposing a novel hybrid feature selection algorithm by leveraging Apache PySpark's distributed computing power. Combining a robust feature selection technique with a novel weighting scheme, our method outperforms existing hypercuboid and fuzzy Rough Set methods. The hybrid approach achieves superior accuracy of 72.1% with a reduced feature set, demonstrating its effectiveness in identifying salient features for big data analysis. © 2024 IEEE.

关键词： Data accuracy

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Triadic Rules for Analysis of Productive and Well-Being Social in Activity-Based Working Environments

Triadic Rules for Analysis of Productive and Well-Being Soci...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Oliveira, Thiago H. C. Song, Mark A. J. Zárate, Luis E. Applied Computational Intelligence Laboratory – LICAP Computer Science Department Pontifical Catholic University of Minas Gerais Brazil

A longitudinal database records data and its variations over a period of time. The objective of this article is to use this resource, together with the Triadic Concept Analysis theory, to analyze and characterize how employees adapted and felt before, during and after the implementation of an activity-based work environment which is defined as a flexible work setting where employees have the autonomy to choose where they perform their tasks, seeking locations that offer optimal solutions in terms of social interaction, communication, and collaboration. The results seek to support the implementation of this concept, verifying how, and under what conditions, key points of employee experiences vary over time. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Data mining

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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models 39

Expensive Multi-Objective Bayesian Optimization Based on Dif...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Bingdong Di, Zixiang Lu, Yongfan Qian, Hong Wang, Feng Yang, Peng Tang, Ke Zhou, Aimin Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Computer Science Wuhan University Wuhan430072 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)157735897X

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multiobjective optimization

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An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems

An Incremental Multi-Level, Multi-Scale Approach to Assessme...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Shilpika, Shilpika Lusch, Bethany Vishwanath, Venkatram Papka, Michael E. Argonne National Laboratory Argonne Leadership Computing Facility United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798350355543

With the growing complexity in architecture and the size of large-scale computing systems, monitoring and analyzing system behavior and events has become daunting. Monitoring data amounting to terabytes per day are collected by sensors housed in these massive systems at multiple fidelity levels and varying temporal resolutions. In this work, we develop an incremental version of multiresolution dynamic mode decomposition (mrDMD), which converts high-dimensional data to spatial-temporal patterns at varied frequency ranges. Our incremental implementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable information in the massive environment log dataset, which is then visually aligned with the processed hardware and job log datasets through our generalizable rack visualization using D3 visualization integrated into the Jupyter Notebook interface. We demonstrate the efficacy of our approach with two use scenarios on a real-world dataset from a Cray XC40 supercomputer, Theta. © 2024 IEEE.

关键词： Dynamic mode decomposition high performance computing incremental analysis mrDMD online analysis scalability visualization

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Recognition Performance Variation Across Demographic Groups Through the Eyes of Explainable Face Recognition

Recognition Performance Variation Across Demographic Groups ...

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International Conference on Automatic Face and Gesture Recognition

作者： Marco Huber Anh Thi Luu Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Face recognition systems are susceptible to differences in performance across demographic or non-demographic groups. However, the understanding of the behavior of face recognition models given such biases is still very limited and based mainly on observing model performance indicators when training/testing data is varied. On the other hand, very recently, face recognition explainability has gained increasing attention enabling the spatial explanation of face matching processes between two face images. This overcame the inapplicability of existing visual explainability methods to explain face matching decisions as they are designed for pure classification tasks. In this paper, and for the first time, we investigate the inner behavior of face recognition models with respect to bias using face recognition explainability tools. Using two state-of-the-art explainability tools, five models with different bias patterns, and a set of visualization tools, our investigation led to a set of interesting observations. This included noticing the tendency of more biased models to have more distributed attention on the facial image in comparison to focusing on the main facial features for the less biased models, all when considering the most discriminated demographic group.

关键词： Visualization Face recognition Focusing Gesture recognition Data models Task analysis Facial features

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Spike Sorting and Event Detection in Neuromorphic Computing 10

Spike Sorting and Event Detection in Neuromorphic Computing

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vaishnavi, Vakada G. Sai Sree Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350384369

Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main objective of neuromorphic computing is to create computational systems that imitate the parallel processing, learning, and energy efficiency observed in biological neural networks. Two essential tasks in the analysis of brain data from extracellular recordings are spike sorting and event detection. Spike sorting involves identifying and categorizing discrete neural spikes, while event detection focuses on recognizing specific patterns or occurrences within the spiking activity of neuromorphic networks. Typically, each neuron generates spikes with a characteristic shape, and these clusters correspond to the activity of different neurons. Here, the outcome of spike sorting is the identification of which spike corresponds to which neuron. In-depth analysis of the spike sorting and event detection which are two critical processes in interpreting the intricate neuronal activity that underpins brain function are provided. © 2024 IEEE.

关键词： Neurons

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Extracting sequential frequent itemsets from probabilistic sequences database

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International Journal of Information Technology (Singapore) 2023年第5期15卷 2509-2515页

作者： Seddiki, Imane Nouioua, Farid Barkat, Abdelbasset Computer Science Department Mouhamed El Bachir El Ibrahimi University Bordj Bou Arreridj El Anceur 34000 Algeria Computer Science Department Laboratory of Informatics and its Applications (LIAM) University of M’sila M’sila 28000 Algeria

computers now handle large amounts of data, leading to the emergence of data mining as a science to extract useful information from this data. Frequent itemset mining is a popular technique used to discover relationships between items in big data. However, in real-life scenarios, data may be incomplete or uncertain, posing a challenge for frequent itemset mining. This paper proposes a novel approach for mining sequential frequent itemsets from uncertain sequence databases. The approach comprises two main parts: extracting a set of probabilistic frequent itemsets, and filtering this set using the sequential information in the data. At the end of the paper, we provide a comparison with an existing method to further demonstrate the value of our approach. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Data mining Frequent Itemset Probabilistic database Sequences database Uncertain

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