Reviewerly/exHarmony huggingface dataset:
Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks

U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated learning (FL) and split learning (SL) have been proposed. However, achieving a balance between the local computational cost, model privacy, and parallel training remains a challenge. In this articler, we propose a novel hybrid learning paradigm called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Corrected</b> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> plit <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ederated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> earning ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DC-SFL</b> ) for U-shaped medical image networks. To preserve data privacy, including the input, model parameters, label and output simultaneously, we propose to split the network into three parts hosted by different parties. We propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> eight <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> orrection <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> trategy ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DWCS</b> ) to stabilize the training process and avoid the model drift problem due to data heterogeneity. To further enhance privacy protection and establish a trustworthy distributed learning paradigm, we propose to introduce additively homomorphic encryption into the aggregation process of client-side model, which helps prevent potential collusion between parties and provides a better privacy guarantee for our proposed method. The proposed DC-SFL is evaluated on various medical image tasks, and the experimental results demonstrate its effectiveness. In comparison with state-of-the-art distributed learning methods, our method achieves competitive performance.
Pre-processing Pipelines for Effective Segmentation of Lymphatic Filariasis Parasite Images

Lymphatic Filariasis commonly known as ‘Elephantiasis’ is a vector congenital disease that creates disfiguring as well as disabling in the forbearer. This gruesome disease is acquired in the childhood phase, but its clinical manifestation arises subsequently in life. It may cause short-term or perpetual disability. The significance of developing robust and efficient techniques to diagnose the Lymphatic Filariasis parasites within the peripheral blood smear has been accentuated by W.H.O. as well as the government counterpart. So, automated screening of the peripheral blood smear to detect the presence of microfilariae (mf) has to be initiated. The acquired images may contain various noises due to different illumination environments as well as the restricted image-acquiring resolution capabilities. Therefore, this research aims to eradicate the flaws of noise with the pipeline of various pre-processing techniques as follows: (i) color space transformation, (ii) image filtration using median filter, (iii) morphological operators of dilation and erosion, (iv) binary segmentation, and (v) smoothing and discretization. The use of various pre-processing techniques has produced resultant images which can be visually and numerically verified by 11 morphological image quality indexes. After observing these diversified image quality indexes, it may be concluded that the aforementioned pre-processing pipeline embraces image enhancement for better-segmented results.
Large Language Models for Cultural Heritage

This research explores the potential applications of Generative Pre-trained Transformer (GPT) by OpenAI, a Large Language Model (LLM), in the realm of cultural heritage. It investigates GPT’s role as a digital storytelling machine that can be trained and guided to act as a museum guide and a recommender system for cultural spaces. LLM’s advanced language understanding capabilities make it an interactive guide, providing personalized and information to visitors about artworks and historical contexts. As a recommender system, GPT can offer tailored suggestions based on user preferences and past interactions, enhancing the visitor experience and encouraging exploration. It is a system extremely capable in handling language and with that power, it can act as a digital storytelling machine, creating immersive narratives that bring exhibits to life by weaving historical information with imaginative elements. The paper presents experiment results and evaluations, highlighting GPT’s potential to revolutionize visitor engagement in cultural spaces. However, ethical considerations and challenges associated with large language models in cultural contexts are also addressed, emphasizing the need for thoughtful implementation and ongoing evaluation to ensure inclusivity and accuracy while preserving cultural integrity.
GitHub Actions: The Impact on the Pull Request Process

Abstract Software projects frequently use automation tools to perform repetitive activities in the distributed software development process. Recently, GitHub introduced GitHub Actions , a feature providing automated workflows for software projects. Understanding and anticipating the effects of adopting such technology is important for planning and management. Our research investigates how projects use GitHub Actions , what the developers discuss about them, and how project activity indicators change after their adoption. Our results indicate that 1,489 out of 5,000 most popular repositories (almost 30% of our sample) adopt GitHub Actions and that developers frequently ask for help implementing them. Our findings also suggest that the adoption of GitHub Actions leads to more rejections of pull requests (PRs), more communication in accepted PRs and less communication in rejected PRs, fewer commits in accepted PRs and more commits in rejected PRs, and more time to accept a PR. We found similar results when segmenting our results by categories of GitHub Actions . We suggest practitioners consider these effects when adopting GitHub Actions on their projects.
Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive Learning

Prompt tuning has achieved great success in various sentence-level classification tasks by using elaborated label word mappings and prompt templates. However, for solving token-level classification tasks, e.g., named entity recognition (NER), previous research, which utilizes N-gram traversal for prompting all spans with all possible entity types, is time-consuming. To this end, we propose a novel prompt-based contrastive learning method for few-shot NER without template construction and label word mappings. First, we leverage external knowledge to initialize semantic anchors for each entity type. These anchors are simply appended with input sentence embeddings as template-free prompts (TFPs). Then, the prompts and sentence embeddings are in-context optimized with our proposed semantic-enhanced contrastive loss. Our proposed loss function enables contrastive learning in few-shot scenarios without requiring a significant number of negative samples. Moreover, it effectively addresses the issue of conventional contrastive learning, where negative instances with similar semantics are erroneously pushed apart in natural language processing (NLP)-related tasks. We examine our method in label extension (LE), domain-adaption (DA), and low-resource generalization evaluation tasks with six public datasets and different settings, achieving state-of-the-art (SOTA) results in most cases.
A Privacy Enforcing Framework for Data Streams on the Edge

Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep Neural Networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.
The Persuasive Power of Emoticons in Electronic Word-of-Mouth Communication on Social Networking Services

Emotional expressions are ubiquitous in electronic word-of-mouth (eWOM) communication, but their effect on eWOM persuasiveness and the underlying mechanisms in the context of social networking services (SNS) have been underexplored. This research focuses on an extensively used nonverbal emotional cue in computer-mediated communication—the emoticon. Drawing on the emotion as social information model (EASI), we propose a conceptual framework to understand whether, how, and when emoticons influence the persuasiveness of eWOM on SNS. Results from a field experiment and a series of online experiments show that emoticons can increase eWOM persuasiveness through the mediating effects of enhanced recipient empathy and trust toward the sender and that these effects vary across situations. Specifically, the persuasive effect of emoticons occurs for both positive and negative eWOM when recipients and senders are close to each other. However, this effect occurs only for negative eWOM when recipients and senders have distant relationships. We discuss the theoretical and practical implications of these findings and identify several opportunities for future research.
Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an uncertainty navigator is constructed for better using mutual features and generating segmentation results. Besides, an uncertainty instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models.
Fully Bayesian VIB-DeepSSM

Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-DeepSSM) is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. However, VIB is only half-Bayesian and lacks epistemic uncertainty inference. We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble. Additionally, we introduce a novel combination of the two that further enhances uncertainty calibration via multimodal marginalization. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy. $$^*$$ (Source code is publicly available: https://github.com/jadie1/BVIB-DeepSSM )
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery

Advances in GPT-based large language models (LLMs) are revolutionizing natural language processing, exponentially increasing its use across various domains. Incorporating uni-directional attention, these autoregressive LLMs can generate long and coherent paragraphs. However, for visual question answering (VQA) tasks that require both vision and language processing, models with bi-directional attention or models employing fusion techniques are often employed to capture the context of multiple modalities all at once. As GPT does not natively process vision tokens, to exploit the advancements in GPT models for VQA in robotic surgery, we design an end-to-end trainable Language-Vision GPT (LV-GPT) model that expands the GPT2 model to include vision input (image). The proposed LV-GPT incorporates a feature extractor (vision tokenizer) and vision token embedding (token type and pose). Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image. Quantitatively, we prove that the LV-GPT model outperforms other state-of-the-art VQA models on two publically available surgical-VQA datasets (based on endoscopic vision challenge robotic scene segmentation 2018 and CholecTriplet2021) and on our newly annotated dataset (based on the holistic surgical scene dataset). We further annotate all three datasets to include question-type annotations to allow sub-type analysis. Furthermore, we extensively study and present the effects of token sequencing, token type and pose embedding for vision tokens in the LV-GPT model.
Medical federated learning with joint graph purification for noisy label learning

In terms of increasing privacy issues, Federated Learning (FL) has received extensive attention in medical imaging. Through collaborative training, FL can produce superior diagnostic models with global knowledge, while preserving private data locally. In practice, medical diagnosis suffers from intra-/inter-observer variability, thus label noise is inevitable in dataset preparation. Different from existing studies on centralized datasets, the label noise problem in FL scenarios confronts more challenges, due to data inaccessibility and even noise heterogeneity. In this work, we propose a federated framework with joint Graph Purification (FedGP) to address the label noise in FL through server and clients collaboration. Specifically, to overcome the impact of label noise on local training, we first devise a noisy graph purification on the client side to generate reliable pseudo labels by progressively expanding the purified graph with topological knowledge. Then, we further propose a graph-guided negative ensemble loss to exploit the topology of the client-side purified graph with robust complementary supervision against label noise. Moreover, to address the FL label noise with data silos, we propose a global centroid aggregation on the server side to produce a robust classifier with global knowledge, which can be optimized collaboratively in the FL framework. Extensive experiments are conducted on endoscopic and pathological images with the comparison under the homogeneous, heterogeneous, and real-world label noise for medical FL. Among these diverse noisy FL settings, our FedGP framework significantly outperforms denoising and noisy FL state-of-the-arts by a large margin. The source code is available at https://github.com/CUHK-AIM-Group/FedGP.
Generating Bell states and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>N</mml:mi></mml:math> -partite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>W</mml:mi></mml:math> states of long-distance qubits in superconducting waveguide QED

We show how to generate Bell states and $N$-partite $W$ states of long-distance superconducting (SC) qubits in a SC waveguide QED system, where SC qubits are coupled to an open microwave transmission line. In the two-qubit case, the Bell state of two long-distance qubits can be a dark state of the system by choosing appropriate system parameters. If one proper microwave pulse drives one of two qubits, the two qubits will evolve from their ground states to a Bell state. Further, we extend this scheme to the multiqubit case. We show that $W$ states of $N$ long-distance qubits can also be generated. Because both the Bell and $W$ states are decoupled from the waveguide (i.e., dark states of the system), they are steady and have very long lifetimes in the ideal case without decoherence of qubits. In contrast to the ideal case, the presence of decoherence of qubits limits the lifetimes of the Bell and $W$ states. Our study provides an alternative scheme for generating Bell states and $N$-partite $W$ states in SC waveguide QED, which can be used to entangle long-distance nodes in waveguide quantum networks.
Visual Exploration of Literature Using Connected Papers: A Practical Approach

The paper aims to examine the visual exploration tool "Connected Papers" (www.connectedpapers.com), which identifies relevant literature based on content similarities and displays the result in the form of visual clusters. Connected Papers searches through Semantic Scholar literature corpus and discovers the most relevant related research papers using a specialized algorithm. The researcher will have to identify the most relevant paper as an "origin paper" among the retrieved papers. The origin paper acts as a base for the formation of a literature graph based on similarities. The unique features (i.e. "Prior" and "Derivate") help a researcher to identify the most relevant literature. In this paper, the topic "Use of drones in Agriculture" is selected to demonstrate the process of literature exploration using Connected Papers. A set of relevant papers is retrieved, out of which the researcher will have to select one of the most appropriate relevant papers. That paper is termed as the origin paper to create the visualization of literature. Connected Papers suggests the most relevant papers based on the search keywords, but the graph is solely based on a researcher's judgement when selecting an origin paper. As explained in the paper, the researcher of other domains may adopt the process to understand the literature mapping phenomena for their own discipline.
Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system

This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items? cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.
Stitched Wide Field of View Light Field Image Quality Assessment: Benchmark Database and Objective Metric

Due to the limitation of commercial light field camera hardware devices, the imaging field of view is quite narrow. Numerous Light Field Image (LFI) stitching algorithms have been developed to expand the field of view. However, it is highly challenging to compare the performance of LFI stitching algorithms in a fair manner. Currently, due to the absence of a comprehensive benchmark database for subjective rating and a reliable objective quality metric, it is fairly difficult to comprehensively and accurately compare the actual performance of existing LFI stitching algorithms. In this study, we dedicate our efforts to the development of quality metrics for stitched Wide field of view LFI (WLFI) from subjective and objective assessment aspects. Specifically, we build the first stitched WLFI database, which provides the stitched WLFIs generated by eight representative LFI stitching algorithms, along with their corresponding subjective rating scores. Secondly, an effective blind stitched WLFI quality metric is developed to accurately assess the visual quality degradation. Extensive experiments conducted over our established WLFI database demonstrate that the proposed metric achieves higher consistency with subjective ratings than the competing quality metrics.
The Aggregation of ROAD Data in the ARIADNE Pipeline: pitfalls and successes

In this article we describe an online database about human evolution, called the ROCEEH Out of Africa Database (ROAD), and discuss our experience in aggregating Palaeolithic data from ROAD in the ARIADNE data processing pipeline. As of April 2023, ROAD contains more than 2400 localities in Africa and Eurasia dating between three million and 20,000 years ago. The database is transdisciplinary by nature and includes cultural artefacts, human and animal fossils, and plant remains. These finds are stored in a relational database, which is part of a structured, web-based, geographic information system. The process of preparing ROAD data for integration with ARIADNE taught us lessons about our own dataset, which we share here.