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Found 34 papers, 1 papers with code
Tracing Traditions: Automatic Extraction of Isnads from Classical Arabic Texts
We present our work on automatically detecting isnads, the chains of authorities for a re-port that serve as citations in hadith and other classical Arabic texts.
Privacy at a Price: Exploring its Dual Impact on AI Fairness
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining.
Citations as Queries: Source Attribution Using Language Models as Rerankers
This paper explores new methods for locating the sources used to write a text, by fine-tuning a variety of language models to rerank candidate sources.
Learn to Unlearn: A Survey on Machine Unlearning
This inspired recent research on removing the influence of specific data samples from a trained ML model.
Look how they have grown: Non-destructive Leaf Detection and Size Estimation of Tomato Plants for 3D Growth Monitoring
The overall measurement system (leaf detection and size estimation algorithms combine) delivers an RMSE value of 8. 13mm and an R^2 value of 0. 899.
Seeing the Fruit for the Leaves: Towards Automated Apple Fruitlet Thinning
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards.
Tradeoffs in Resampling and Filtering for Imbalanced Classification
We examine the tradeoffs in model performance involved in choices of training sample and filter training and test data in heavily imbalanced token classification task and examine the relationship between the magnitude of these tradeoffs and the base rate of the phenomenon of interest.
The Fellowship of the Authors: Disambiguating Names from Social Network Context
Unlike prior work, therefore, we seek to leverage the information that can be gained from looking at association networks of individuals derived from textual evidence in order to disambiguate names.
Content-based Models of Quotation
We explore the task of quotability identification, in which, given a document, we aim to identify which of its passages are the most quotable, i. e. the most likely to be directly quoted by later derived documents.
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification
Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.
Realistic Differentially-Private Transmission Power Flow Data Release
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.
Detecting de minimis Code-Switching in Historical German Books
Code-switching has long interested linguists, with computational work in particular focusing on speech and social media data (Sitaram et al., 2019).
Optical Character Recognition
Optical Character Recognition (OCR)
Model Elicitation through Direct Questioning
We show how to generate questions to refine the robot's understanding of the teammate's model.
Designing Environments Conducive to Interpretable Robot Behavior
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
Finite State Machine Pattern-Root Arabic Morphological Generator, Analyzer and Diacritizer
Accuracy results are: root computed from a word (92{\%}), word generation from a root (100{\%}), non-root properties of a word (97{\%}), and diacritization (84{\%}).
The Cost of Privacy in Asynchronous Differentially-Private Machine Learning
The experiments illustrate that collaboration among more than 10 data owners with at least 10, 000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy.
Report on the First HIPstIR Workshop on the Future of Information Retrieval
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision.
Towards Explainable AI Planning as a Service
Explainable AI is an important area of research within which Explainable Planning is an emerging topic.
The Value of Collaboration in Convex Machine Learning with Differential Privacy
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets.
Noisy Neural Language Modeling for Typing Prediction in BCI Communication
Language models have broad adoption in predictive typing tasks.
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice
Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans.
Multi-Input Attention for Unsupervised OCR Correction
We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding.
A Multi-Context Character Prediction Model for a Brain-Computer Interface
Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods.
Lifted Region-Based Belief Propagation
Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation.
