Reviewerly/exHarmony huggingface dataset:
Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation

Objectives The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes. Design For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists. Results The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions. Conclusions The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.
Multiqubit time-varying quantum channels for NISQ-era superconducting quantum processors

Time-varying quantum channels (TVQCs) have been proposed as a model to include fluctuations of the relaxation (${T}_{1}$) and dephasing times (${T}_{2}$). In previous works, realizations of multiqubit TVQCs have been assumed to be equal for all the qubits of an error correction block, implying that the random variables that describe the fluctuations of ${T}_{1}$ and ${T}_{2}$ are block-to-block uncorrelated but qubit-wise perfectly correlated for the same block. In this article, we perform a correlation analysis of the fluctuations of the relaxation times of five multiqubit quantum processors. Our results show that it is reasonable to assume that the fluctuations of the relaxation and dephasing times of superconducting qubits are local to each of the qubits of the system. Based on these results, we discuss the multiqubit TVQCs when the fluctuations of the decoherence parameters for an error correction block are qubit-wise uncorrelated (as well as from block-to-block), a scenario we have named the fast time-varying quantum channel (FTVQC). Furthermore, we lower-bound the quantum capacity of general FTVQCs based on a quantity we refer to as the ergodic quantum capacity. Finally, we use numerical simulations to study the performance of quantum error correction codes when they operate over FTVQCs.
Fast Traversability Estimation for Wild Visual Navigation

Fig. 1: Wild Visual Navigation (WVN) learns to predict traversability from images via online self-supervised learning.Starting from a randomly initialized traversability estimation network without prior assumptions about the environment (a), a human operator drives the robot around areas that are traversable for the given platform (b).After a few minutes of operation, WVN learns to distinguish between traversable and untraversable areas (c), enabling the robot to navigate autonomously and safely within the environment (d).
Blockchain-based Security Framework for Metaverse: A Decentralized Approach

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse.
Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.
Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.
SKEAFN: Sentiment Knowledge Enhanced Attention Fusion Network for multimodal sentiment analysis

Multimodal sentiment analysis is an active research field that aims to recognize the user's sentiment information from multimodal data. The primary challenge in this field is to develop a high-quality fusion framework that effectively addresses the heterogeneity among different modalities. However, prior research has primarily concentrated on intermodal interactions while neglecting the semantic sentiment information conveyed by words in the text modality. In this paper, we propose the Sentiment Knowledge Enhanced Attention Fusion Network (SKEAFN), a novel end-to-end fusion network that enhances multimodal fusion by incorporating additional sentiment knowledge representations from an external knowledge base. Firstly, we construct an external knowledge enhancement module to acquire additional representations for the text modality. Then, we design a text-guided interaction module that facilitates the interaction between text and the visual/acoustic modality. Finally, we propose a feature-wised attention fusion module that achieves multimodal fusion by dynamically adjusting the weights of the additional and each modality's representations. We evaluate our method on three challenging multimodal sentiment analysis datasets: CMU-MOSI, CMU-MOSEI, and Twitter2019. The experiment results demonstrate that our model significantly outperforms the state-of-the-art models. The source code is publicly available at https://github.com/doubibobo/SKEAFN.
AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images

<h2>Summary</h2> Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.
Open-Domain Sign Language Translation Learned from Online Video

Existing work on sign language translationthat is, translation from sign language videos into sentences in a written language-has focused mainly on (1) data collected in a controlled environment or (2) data in a specific domain, which limits the applicability to realworld settings.In this paper, we introduce Ope-nASL, a large-scale American Sign Language (ASL) -English dataset collected from online video sites (e.g., YouTube).OpenASL contains 288 hours of ASL videos in multiple domains from over 200 signers and is the largest publicly available ASL translation dataset to date.To tackle the challenges of sign language translation in realistic settings and without glosses, we propose a set of techniques including sign search as a pretext task for pre-training and fusion of mouthing and handshape features.The proposed techniques produce consistent and large improvements in translation quality, over baseline models based on prior work. 1
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction

Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE).However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information.In this paper, we propose a bidirectional iterative prompt-tuning method for EAE, where the EAE task is treated as a clozestyle task to take full advantage of entity information and pre-trained language models (PLMs).Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction.Since template and verbalizer are two crucial components in a clozestyle prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kinds of templates for the EAE task.Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer stateof-the-art methods.Our code is available at https://github.com/HustMinsLab/BIP.
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey

We present the results of the NLP Community Metasurvey.Run from May to June 2022, it elicited opinions on controversial issues, including industry influence in the field, concerns about AGI, and ethics.Our results put concrete numbers to several controversies: For example, respondents are split in half on the importance of artificial general intelligence, whether language models understand language, and the necessity of linguistic structure and inductive bias for solving NLP problems.In addition, the survey posed meta-questions, asking respondents to predict the distribution of survey responses.This allows us to uncover false sociological beliefs where the community's predictions don't match reality.Among other results, we find that the community greatly overestimates its own belief in the usefulness of benchmarks and the potential for scaling to solve real-world problems, while underestimating its belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN

To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.Since not all the retrieved tagging examples benefit the model prediction, we construct a heterogeneous graph, and leverage graph neural networks (GNNs) to transfer information between the retrieved tagging examples and the input word sequence.The augmented node which aggregates information from neighbors is used to do prediction.This strategy enables the model to directly acquire similar tagging examples and improves the general quality of predictions.We conduct a variety of experiments on three typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant performance of our GNN-SL.Notably, GNN-SL achieves SOTA results of 96.9 (+0.2) on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the CWS task, and results comparable to SOTA performances on NER datasets, and POS datasets.
A Length-Extrapolatable Transformer

Position modeling plays a critical role in Transformers.In this paper, we focus on length extrapolation, i.e., training on short texts while evaluating longer sequences.We define attention resolution as an indicator of extrapolation.Then we propose two designs to improve the above metric of Transformers.Specifically, we introduce a relative position embedding to explicitly maximize attention resolution.Moreover, we use blockwise causal attention during inference for better efficiency.The proposed architecture is named Length-Extrapolatable (LEX) Transformer.We evaluate different Transformer variants on language modeling.Experimental results show that our model achieves better performance in both interpolation and extrapolation settings.The code will be available at https://aka.ms/LeX-Transformer.
PromptRank: Unsupervised Keyphrase Extraction Using Prompt

The keyphrase extraction task refers to the automatic selection of phrases from a given document to summarize its core content.Stateof-the-art (SOTA) performance has recently been achieved by embedding-based algorithms, which rank candidates according to how similar their embeddings are to document embeddings.However, such solutions either struggle with the document and candidate length discrepancies or fail to fully utilize the pretrained language model (PLM) without further fine-tuning.To this end, in this paper, we propose a simple yet effective unsupervised approach, PromptRank, based on the PLM with an encoder-decoder architecture.Specifically, PromptRank feeds the document into the encoder and calculates the probability of generating the candidate with a designed prompt by the decoder.We extensively evaluate the proposed PromptRank on six widely used benchmarks.PromptRank outperforms the SOTA approach MDERank, improving the F 1 score relatively by 34.18%, 24.87%, and 17.57% for 5, 10, and 15 returned results, respectively.This demonstrates the great potential of using prompt for unsupervised keyphrase extraction.We release our code at this url.
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task

Attribute Value Extraction (AVE) boosts many e-commerce platform services such as targeted recommendation, product retrieval and question answering.Most previous studies adopt an extractive framework such as named entity recognition (NER) to capture subtokens in the product descriptions as the corresponding values of target attributes.However, in the real world scenario, there also exist implicit attribute values that are not mentioned explicitly but embedded in the image information and implied text meaning of products, for which the power of extractive methods is severely constrained.To address the above issues, we exploit a unified multi-modal AVE framework named DEFLATE (a multi-modal unifieD framEwork For impLicit And expliciT AVE) to acquire implicit attribute values in addition to the explicit ones.DEFLATE consists of a QA-based generation model to produce candidate attribute values from the product information of different modalities, and a discriminative model to ensure the credibility of the generated answers.Meanwhile, to provide a testbed that close to the real world, we collect and annotate a multi-modal dataset with parts of implicit attribute values.Extensive experiments conducted on multiple datasets demonstrate that DEFLATE significantly outperforms previous methods on the extraction of implicit attribute values, while achieving comparable performances for the explicit ones.
Generating Better Items for Cognitive Assessments Using Large Language Models

Writing high-quality test questions (items) is critical to building educational measures but has traditionally also been a time-consuming process.One promising avenue for alleviating this is automated item generation, whereby methods from artificial intelligence (AI) are used to generate new items with minimal human intervention.Researchers have explored using large language models (LLMs) to generate new items with equivalent psychometric properties to human-written ones.But can LLMs generate items with improved psychometric properties, even when existing items have poor validity evidence?We investigate this using items from a natural language inference (NLI) dataset.We develop a novel prompting strategy based on selecting items with both the best and worst properties to use in the prompt and use GPT-3 to generate new NLI items.We find that the GPT-3 items show improved psychometric properties in many cases, whilst also possessing good content, convergent and discriminant validity evidence.Collectively, our results demonstrate the potential of employing LLMs to ease the item development process and suggest that the careful use of prompting may allow for iterative improvement of item quality.
LMentry: A Language Model Benchmark of Elementary Language Tasks

As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well.We present LMentry, a benchmark that avoids this "arms race" by focusing on a compact set of tasks that are trivial to humans, e.g.writing a sentence containing a specific word, identifying which words in a list belong to a specific category, or choosing which of two words is longer.LMentry is specifically designed to provide quick and interpretable insights into the capabilities and robustness of large language models.Our experiments reveal a wide variety of failure cases that, while immediately obvious to humans, pose a considerable challenge for large language models, including OpenAI's latest 175B-parameter instruction-tuned model, TextDavinci002.LMentry complements contemporary evaluation approaches of large language models, providing a quick, automatic, and easy-to-run "unit test", without resorting to large benchmark suites of complex tasks.Council of Europe.
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification

We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification.We formulate text classification as a neural text matching problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label matching, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning.PESCO achieves state-of-the-art performance on four benchmark text classification datasets.On DBpedia, we achieve 98.5% accuracy without any labeled data, which is close to the fully-supervised result.Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.
Impact of Adversarial Training on Robustness and Generalizability of Language Models

Adversarial training is widely acknowledged as the most effective defense against adversarial attacks.However, it is also well established that achieving both robustness and generalization in adversarially trained models involves a trade-off.The goal of this work is to provide an in depth comparison of different approaches for adversarial training in language models.Specifically, we study the effect of pretraining data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of transformer-based language models.Our findings suggest that better robustness can be achieved by pre-training data augmentation or by training with input space perturbation.However, training with embedding space perturbation significantly improves generalization.A linguistic correlation analysis of neurons of the learned models reveal that the improved generalization is due to 'more specialized' neurons.To the best of our knowledge, this is the first work to carry out a deep qualitative analysis of different methods of generating adversarial examples in adversarial training of language models.