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arXiv 2023年

作者： Held, Lena Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 hours of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://***/datasets/TrustHLT/LaCour. © 2023, CC BY-SA.

关键词： Laws and legislation

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Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

arXiv

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arXiv 2025年

作者： Yoo, Seong Jong Liu, Sisung Arshad, Muhammad Zeeshan Kim, Jinhyeok Kim, Young Min Aloimonos, Yiannis Fermüller, Cornelia Joo, Kyungdon Kim, Jinwook Hong, Je Hyeong Department of Computer Science University of Maryland College ParkMD United States Department of Artificial Intelligence Hanyang University Korea Republic of Faculty of Health Sciences University of Ottawa ON Canada Artificial Intelligence Graduate School UNIST Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Intelligence and Interaction Research Center Korea Institute of Science and Technology Korea Republic of Artificial Intelligence Graduate School The Department of Computer Science and Engineering UNIST Korea Republic of Department of Electronic Engineering Hanyang University Korea Republic of

Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous false positive matches and hinder large-scale puzzle solving. Existing global approaches, which optimize all potential fragment pairs simultaneously or data-driven models, are prone to local minima and face scalability issues when multiple pots are intermixed. Motivated by Structure-from-Motion (SfM) for 3D reconstruction from multiple images, we propose an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time, called Structure-from-Sherds++ (SfS++). Our method extends beyond simple replication of incremental SfM and leverages multi-graph beam search to explore multiple registration paths. This allows us to effectively filter out indistinguishable false matches and simultaneously reconstruct multiple pots without requiring prior information such as base or the number of mixed objects. Our approach achieves 87% reassembly accuracy on a dataset of 142 real fragments from 10 different pots, outperforming other methods in handling complex fracture patterns with mixed datasets and achieving state-of-the-art performance. Code and results can be found in our project page https://***/sfs/. Copyright © 2025, The Authors. All rights reserved.

关键词： Historic preservation

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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GPT4ESG: Streamlining Environment, Society, and Governance Analysis with Custom AI Models

GPT4ESG: Streamlining Environment, Society, and Governance A...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Lydia Hsiao-Mei Lin Fang-Kai Ting Ting-Jui Chang Jun-Wei Wu Richard Tzong-Han Tsai Department of Interaction Design National Taipei University of Technology Taipei City Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City Taiwan Center for GIS Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Many companies now prioritize Environment, Society, and Governance (ESG) and financial performance due to concerns about climate change. We created GPT4ESG, a quick method to analyze a company's ESG investment and influence. GPT4ESG is a model system architecture based on BERT and GPT with five parts:(1) Data cleaning: The data is cleaned and preprocessed to ensure it is in a suitable format for the model. (2) Tokenizer adaptation: A customized GPT assistant is used to assist in scoring, and results are given after expert review. (3) Model adjustment: A classification layer is added to the output. (4) Fine-tuning: The pre-trained model is fine-tuned according to the ESG field know-how. (5) Evaluation and Testing: The model was tested on a validation set during training and then on a testing set. We collected ESG reports of well-known listed companies in the technology industries in the United States as a data set. The experimental results showed that the customized GPT4ESG model was more effective than ESG-BERT in classifying ESG data. This model simplifies ESG reporting for stakeholders to make responsible decisions.

关键词： Training Adaptation models Accuracy Systems architecture Companies Data models Data mining

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Bridging Borders, Breaking Biases: Envisioning Technologies to Support North Korean Defectors in South Korea 25

Bridging Borders, Breaking Biases: Envisioning Technologies ...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hayoun Noh Hyunah Jo Ge Wang Max Van Kleek Younah Kang Department of Computer Science University of Oxford Oxford United Kingdom Yonsei University Seoul Republic of Korea Institute for Human-Centered Artificial Intelligence (HAI) Stanford University Stanford California USA Information and Interaction Design Yonsei University Seoul Republic of Korea

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Searching thousands of genomes to classify somatic and novel structural variants using STIX (vol 19, pg 445, 2022)

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NATURE METHODS 2022年第6期19卷 770-770页

作者： Chowdhury, Murad Pedersen, Brent S. Sedlazeck, Fritz J. Quinlan, Aaron R. Layer, Ryan M. BioFrontiers Institute University of Colorado Boulder CO USA University Medical Center Utrecht University Utrecht the Netherlands Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Department of Human Genetics University of Utah Salt Lake City UT USA Department of Biomedical Informatics University of Utah Salt Lake City UT USA Utah Center for Genetic Discovery University of Utah Salt Lake City UT USA Department of Computer Science University of Colorado Boulder CO USA

Structural variants are associated with cancers and developmental disorders, but challenges with estimating population frequency remain a barrier to prioritizing mutations over inherited variants. In particular, variability in variant calling heuristics and filtering limits the use of current structural variant catalogs. We present STIX, a method that, instead of relying on variant calls, indexes and searches the raw alignments from thousands of samples to enable more comprehensive allele frequency estimation.

关键词： Genome informatics Genomics Mutation Software Life sciences general Biological Techniques Biological Microscopy Biomedical Engineering/Biotechnology Bioinformatics Proteomics

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Neural P3M: A Long-Range interaction Modeling Enhancer for Geometric GNNs 38

Neural P3M: A Long-Range Interaction Modeling Enhancer for G...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science United States

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M. © 2024 Neural information processing systems foundation. All rights reserved.

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Somatic Mutations and Alzheimer's Disease

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JOURNAL OF ALZHEIMERS DISEASE 2022年第2期90卷 475-493页

作者： [Anonymous] [a] Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China [b] Department of Computer Science and Technology University of Cambridge UK [c] The Elton Laboratory for Molecular Neuroendocrinology Department of Human Molecular Genetics and Biochemistry Sackler Faculty of Medicine Adams Super Center for Brain Studies and Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-beta plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population.

关键词： Alzheimer's disease amyloid-beta peptide artificial intelligence big data late onset Alzheimer's disease somatic mutations tau

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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions

arXiv

引用

arXiv 2024年

作者： Xiao, Qing Zheng, Yuhang Fan, Xianzhe Zhang, Bingbing Lu, Zhicong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States University of Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa Iowa CityIA United States Department of Computer Science George Mason University FairfaxVA United States

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Vision Paper: Leveraging Industrial Big Data – Past, Present, and Future of the World Wide Lab

Vision Paper: Leveraging Industrial Big Data – Past, Presen...

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IEEE International Conference on Big Data

作者： Mohamed Behery Felix Glawe István Koren Martina Ziefle Gerhard Lakemeyer Philipp Brauner Knowledge-Based Systems Group RWTH Aachen University Aachen Germany Human Computer Interaction Center RWTH Aachen University Aachen Germany Chair of Process and Data Science RWTH Aachen University Aachen Germany

Many initiatives aim at answering the challenges of the fourth industrial revolution (Industry 4.0). A key aspect of these initiatives is the connectivity of the different industrial entities. Communication and information exchange between entities usually flows in two dimensions; a vertical one, that spans the different levels of abstraction within the company, and a horizontal one connecting the different entities in the supply chain. In contrast, the Internet of Production (IoP) presents the World Wide Lab (WWL) allowing an extra dimension of communication, which we call the cross-company dimension. Hence, the WWL integrates the different entities within and across potentially competing labs and companies. The WWL leverages and aggregates accessible production data fostering an exchange of information, that prevents redundant experimentation and increases quality of data-driven models by including data from diverse manufacturing settings. This paper discusses work done towards this vision and technical, economic, social, legal, and ethical challenges the WWL faces and provides a roadmap towards infrastructuring the WWL.

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