Reviewerly/exHarmony huggingface dataset:
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets.
Evidence Reconciled Neural Network for Out-of-Distribution Detection in Medical Images

Near Out-of-Distribution (OOD) detection is a crucial issue in medical applications, as misdiagnosis caused by the presence of rare diseases inevitablely poses a significant risk. Recently, several deep learning-based methods for OOD detection with uncertainty estimation, such as the Evidential Deep Learning (EDL) and its variants, have shown remarkable performance in identifying outliers that significantly differ from training samples. Nevertheless, few studies focus on the great challenge of near OOD detection problem, which involves detecting outliers that are close to the training distribution, as commonly encountered in medical image application. To address this limitation and reduce the risk of misdiagnosis, we propose an Evidence Reconciled Neural Network (ERNN). Concretely, we reform the evidence representation obtained from the evidential head with the proposed Evidential Reconcile Block (ERB), which restricts the decision boundary of the model and further improves the performance in near OOD detection. Compared with the state-of-the-art uncertainty-based methods for OOD detection, our method reduces the evidential error and enhances the capability of near OOD detection in medical applications. The experiments on both the ISIC2019 dataset and an in-house pancreas tumor dataset validate the robustness and effectiveness of our approach. Code for ERNN has been released at https://github.com/KellaDoe/ERNN .
Fast Non-Markovian Diffusion Model for Weakly Supervised Anomaly Detection in Brain MR Images

In medical image analysis, anomaly detection in weakly supervised settings has gained significant interest due to the high cost associated with expert-annotated pixel-wise labeling. Current methods primarily rely on auto-encoders and flow-based healthy image reconstruction to detect anomalies. However, these methods have limitations in terms of high-fidelity generation and suffer from complicated training processes and low-quality reconstructions. Recent studies have shown promising results with diffusion models in image generation. However, their practical value in medical scenarios is restricted due to their weak detail-retaining ability and low inference speed. To address these limitations, we propose a fast non-Markovian diffusion model (FNDM) with hybrid-condition guidance to detect high-precision anomalies in the brain MR images. A non-Markovian diffusion process is designed to enable the efficient transfer of anatomical information across diffusion steps. Additionally, we introduce new hybrid pixel-wise conditions as more substantial guidance on hidden states, which enables the model to concentrate more efficiently on the anomaly regions. Furthermore, to reduce computational burden during clinical applications, we have accelerated the encoding and sampling procedures in our FNDM using multi-step ODE solvers. As a result, our proposed FNDM method outperforms the previous state-of-the-art diffusion model, achieving a 9.56% and 19.98% improvement in Dice scores on the BRATS 2020 and ISLES datasets, respectively, while requiring only six times less computational cost.
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis

Multiple instance learning is a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of whole slide images, the number of individual slides is often rather small, leading to a small number of labeled samples. To improve training, we propose and investigate novel data augmentation strategies for multiple instance learning based on the idea of linear and multilinear interpolation of feature vectors within and between individual whole slide images. Based on state-of-the-art multiple instance learning architectures and two thyroid cancer data sets, an exhaustive study was conducted considering a range of common data augmentation strategies. Whereas a strategy based on to the original MixUp approach showed decreases in accuracy, a novel multilinear intra-slide interpolation method led to consistent increases in accuracy.
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

Early and accurate disease detection is crucial for patient management and successful treatment outcomes. However, the automatic identification of anomalies in medical images can be challenging. Conventional methods rely on large labeled datasets which are difficult to obtain. To overcome these limitations, we introduce a novel unsupervised approach, called PHANES (Pseudo Healthy generative networks for ANomaly Segmentation). Our method has the capability of reversing anomalies, i.e., preserving healthy tissue and replacing anomalous regions with pseudo-healthy (PH) reconstructions. Unlike recent diffusion models, our method does not rely on a learned noise distribution nor does it introduce random alterations to the entire image. Instead, we use latent generative networks to create masks around possible anomalies, which are refined using inpainting generative networks. We demonstrate the effectiveness of PHANES in detecting stroke lesions in T1w brain MRI datasets and show significant improvements over state-of-the-art (SOTA) methods. We believe that our proposed framework will open new avenues for interpretable, fast, and accurate anomaly segmentation with the potential to support various clinical-oriented downstream tasks. Code: https://github.com/ci-ber/PHANES
Maximum Entropy on Erroneous Predictions: Improving Model Calibration for Medical Image Segmentation

Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly calibrated and unreliable models. In this work we introduce Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for segmentation networks which selectively penalizes overconfident predictions, focusing only on misclassified pixels. Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. We benchmark the proposed strategy in two challenging segmentation tasks: white matter hyperintensity lesions in magnetic resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI. The experimental results demonstrate that coupling MEEP with standard segmentation losses leads to improvements not only in terms of model calibration, but also in segmentation quality.
Efficient Federated Learning for Modern NLP

Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, our measurements show that FedNLP is prohibitively slow due to the large model sizes and the resultant high network/computation cost. Towards practical FedNLP, we identify as the key building blocks adapters, small bottleneck modules inserted at a variety of model layers. A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive. No silver-bullet configuration exists: the optimal choice varies across downstream NLP tasks, desired model accuracy, and mobile resources. To automate adapter configuration, we propose AdaFL1, a framework that enhances the existing FedNLP with two novel designs. First, AdaFL progressively upgrades the adapter configuration throughout a training session; the principle is to quickly learn shallow knowledge by only training fewer and smaller adapters at the model's top layers, and incrementally learn deep knowledge by incorporating deeper and larger adapters. Second, AdaFL continuously profiles future adapter configurations by allocating participant devices to trial groups. Extensive experiments show that AdaFL can reduce FedNLP's model convergence delay to no more than several hours, which is up to 155.5× faster compared to vanilla FedNLP and 48× faster compared to strong baselines.
SignQuery: A Natural User Interface and Search Engine for Sign Languages with Wearable Sensors

Search Engines such as Google, Baidu, and Bing have revolutionized the way we interact with the cyber world with a number of applications in recommendations, learning, advertisements, healthcare, entertainment, etc. In this paper, we design search engines for sign languages such as American Sign Language (ASL). Sign languages use hand and body motion for communication with rich grammar, complexity, and vocabulary that is comparable to spoken languages. This is the primary language for the Deaf community with a global population of ≈ 500 million. However, search engines that support sign language queries in native form do not exist currently. While translating a sign language to a spoken language and using existing search engines might be one possibility, this can miss critical information because existing translation systems are either limited in vocabulary or constrained to a specific domain. In contrast, this paper presents a holistic approach where ASL queries in native form as well as ASL videos and textual information available online are converted into a common representation space. Such a joint representation space provides a common framework for precisely representing different sources of information and accurately matching a query with relevant information that is available online. Our system uses low-intrusive wearable sensors for capturing the sign query. To minimize the training overhead, we obtain synthetic training data from a large corpus of online ASL videos across diverse topics. Evaluated over a set of Deaf users with native ASL fluency, the accuracy is comparable with state-of-the-art recommendation systems for Amazon, Netflix, Yelp, etc., suggesting the usability of the system in the real world. For example, the re-call@10 of our system is 64.3%, i.e., among the top ten search results, six of them are relevant to the search query. Moreover, the system is robust to variations in signing patterns, dialects, sensor positions, etc.
Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data

Multivariate time series (MTS) data collected from multiple sensors provide the potential for accurate abnormal activity detection in smart healthcare scenarios. However, anomalies exhibit diverse patterns and become unnoticeable in MTS data. Consequently, achieving accurate anomaly detection is challenging since we have to capture both temporal dependencies of time series and inter-relationships among variables. To address this problem, we propose a Residual-based Anomaly Detection approach, Rs-AD, for effective representation learning and abnormal activity detection. We evaluate our scheme on a real-world gait dataset and the experimental results demonstrate an F1 score of 0.839.
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup

On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower inference by table lookup, to reduce inference cost. LUT-NN learns the typical features for each operator, named centroid, and precompute the results for these centroids to save in lookup tables. During inference, the results of the closest centroids with the inputs can be read directly from the table, as the approximated outputs without computations.
Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR Glasses

Sign-to-911 offers a compact mobile system solution to fast and runtime American Sign Language (ASL) and English translations. It is designated as 911 call services for ASL users with hearing disabilities upon emergencies. It enables bidirectional translations of ASL-to-English and English-to-ASL. The signer wears the AR glasses, runs Sign-to-911 on his/her smartphone and glasses, and interacts with a 911 operator. The design of Sign-to-911 departs from the popular deep learning based solution paradigm, and adopts simpler traditional AI/machine learning (ML) models. The key is to exploit ASL linguistic features to simplify the model structures and improve accuracy and speed. It further leverages recent component solutions from graphics, vision, natural language processing, and AI/ML. Our evaluation with six ASL signers and 911 call records has confirmed its viability.
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.
Facial Mask Completion Using StyleGAN2 Preserving Features of the Person

Due to the global outbreak of coronaviruses, people are increasingly wearing masks even when photographed. As a result, photos uploaded to web pages and social networking services with the lower half of the face hidden are less likely to convey the attractiveness of the photographed persons. In this study, we propose a method to complete facial mask regions using StyleGAN2, a type of Generative Adversarial Networks (GAN). In the proposed method, a reference image of the same person without a mask is prepared separately from a target image of the person wearing a mask. After the mask region in the target image is temporarily inpainted, the face orientation and contour of the person in the reference image are changed to match those of the target image using StyleGAN2. The changed image is then composited into the mask region while correcting the color tone to produce a mask-free image while preserving the person's features.
Data augmentation for sentiment classification with semantic preservation and diversity

Data augmentation is a commonly-used technique to avoid over-fitting in deep learning. However, the mechanism behind effective data augmentation methods is unclear. To address this issue, we explore and identify two critical factors: semantic preservation and diversity to assess the quality of data augmentation in natural language processing. Our study focus on text sentiment classification and examines these two factors on two commonly-used data augmentation methods: synonym replacement and random deletion. Based on the discovery, we propose two new augmentation methods: TF-IDF word dropout and adaptive synonym replacement. Experimental results demonstrate that these two new data augmentation methods are effective. Moreover, with further experiments, we summarize three strategies for improving data augmentation methods in sentiment classification task. These strategies are employing online augmentation, introducing word importance into word sampling process, and filtering augmented data based on the current model state. We hope that our study will inspire some new perspectives on the underlying principles of data augmentation’s effectiveness and contribute to a systematic study of data augmentation methods in future.
The psychological and functional factors driving metaverse resistance

Purpose While the metaverse is promised to be the next big step for the Internet, this new technology may also bear negative impacts on individuals and society. Drawing on innovation resistance literature, this article explores the reasons for metaverse resistance. Design/methodology/approach The study is based on 66 semi-structured interviews, and the subsequent data were analysed thematically. Findings The findings revealed 11 reasons for metaverse resistance: lack of understanding, lack of regulation, addiction avoidance, claustrophobia, loss of social ties, disconnection from reality, privacy concerns, extreme consumer society, unseen benefits, infeasibility and nausea. Practical implications By understanding the various reasons for metaverse resistance managers and policymakers can make better decisions to overcome the challenges facing this innovation, rather than adopting a “one-size-fits-all” approach. Originality/value While the literature has mainly adopted a positive perspective on the metaverse, this research offers a more nuanced view by identifying the reasons why consumers may resist the metaverse. Furthermore, this study introduces for the first-time “addiction-driven-innovation-resistance (ADIR)” as a potential reason for metaverse resistance, which may also apply to other cases of innovation resistance, when new innovations are perceived as being “too good” and therefore potentially addictive.
Elite‐of‐the‐Elites Driven Five‐Layered Gravitational Search Algorithm for Optimization

The gravitational search algorithm (GSA) is a popular optimization method inspired by celestial interactions. And the MLGSA is an outstanding variant of GSA. To improve its performance, we propose the elite‐of‐the‐elites driven five‐layered gravitational search algorithm (EFGSA). EFGSA introduces a new population layer comprising elite individuals, improving the balance between exploration and exploitation. Experimental results on the 2017 IEEE Conference on Evolutionary Computation (CEC) benchmark functions demonstrate EFGSA's superiority over the MLGSA algorithm in terms of solution quality and convergence speed. The obtained results underscore the efficacy of EFGSA in addressing optimization problems. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Now you have to pay! A deeper look at publishing practices of predatory journals

Abstract In this study, by using Beall's (Scholarly open‐access, 2014; Beall's list of predatory journals and publishers, 2018) predatory journal lists as well as direct e‐mail solicitations from journals, we intentionally submitted a poorly written manuscript to 58 open‐access journals using counterfeit names and affiliations. Although there have been several studies examining the practices of questionable journals, there is a lack of research investigating the interactive processes in detail. Our analysis, then, was to provide a more comprehensive view of the underlying reasoning for the acceptance or rejection of a manuscript. Of the 31 journals acknowledging receipt of our manuscript, 21 accepted it either unexpurgated or asked only for cosmetic revisions. Regarding ‘positive responses’, we point to five common flaws associated with such journals, namely that (1) they lack any interest in the researchers who are submitting manuscripts; (2) they do not judge academic writing in accordance with expected conventions; (3) they appear to be indifferent to scholarship including research design, plagiarism issues, and citation quality; (4) their review process is opaque and overly hasty, and (5) the tone they use in correspondence e‐mail messages is highly inappropriate. Based upon the investigation, it is clear that such journals' primary aim is in securing the article processing fee. Our findings paint a more comprehensive picture of questionable journal practices with the hope of disseminating such information to the broader scholarly community.
Question answering over knowledge graphs using BERT based relation mapping

Abstract A knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error‐prone. Here we propose a question‐answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT‐based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end‐to‐end system with a subset of QALD‐4, LC‐QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1‐score.
<scp>SLChain</scp>: A secure and low‐storage pressure sharding blockchain

Abstract As a distributed ledger technology, blockchain's low throughput is challenging to support large‐scale applications. Sharding is one way to solve this problem, but the security of existing sharded blockchains is not enough. In addition, with the increase of time, the node storage pressure in the shard will also increase. Therefore, SLChain was proposed as a secure and low storage pressure sharding blockchain. SLChain works together between shards for block validation to avoid inconsistencies caused by a single shard being controlled by adversaries. As well as by relying on a reputation‐established confirmation committee that allows blocks to be confirmed quickly and without forking. In addition, a secure ledger pruning mechanism is designed to make the space required for nodes grow very slowly over time, reducing the storage pressure on nodes. The results show that SLChain can improve security and throughput, and the node storage pressure is small.