Reviewerly/exHarmony huggingface dataset:
Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain.
Less is More? Investigating Meta-Learning’s Suitability in Sentence Compression for Low-Resource Data

The sentence compression task is essential in the text summarization process. Unfortunately, the lack of labeled data for specific domains restricts the training of deep learning models to address this problem effectively. In this paper, we present an approach using a meta-learning algorithm called MAML to tackle this issue and assess the viability of this technique for the given task, with particular emphasis on its comparison to a fine-tuned BERT model. Our experiments reveal that a simpler approach involving fine-tuning a language model, such as BERT, might indeed be more effective in low-resource scenarios, consistently outperforming the meta-learning techniques for this particular task.
Studying the Dependence of Embedding Representations on the Target of NLP Tasks

In many human languages, linguistic units represent text structure. Vector semantics is used in NLP to represent these units, known as embeddings. Evaluating the learned representations is crucial for identifying critical differences between the diverse existing embedding models in task-specific selection. However, the evaluation process is complex, with two approaches: intrinsic and extrinsic. While useful, aggregated evaluations often lack consistency due to result misalignment. This work investigates the dependencies and correlations between embeddings and NLP tasks. The goal is how to initially verify if the embeddings' dimensions (i.e., features) depend on the final task. The study then explores two research questions and presents findings from experiments.
Corrigendum: The Aspect Hypothesis and L2 Russian

Corrigendum: THE ASPECT HYPOTHESIS AND L2 RUSSIAN * Correspondence: wmmartelle@alaska.eduKeywords: same as original articleCorrigendum on: Whitehead Martelle W and Shirai Y (2023) The Aspect Hypothesis and L2 Russian. Front. Lang. Sci. 2:1119026. doi: 10.3389/flang.2023.1119026Incorrect Funding In the published article, there was an error in the Funding statement. The Funding statement was erroneously absent in the original published article. The correct Funding statement appears below.FUNDINGIn the published article, there was a mistake in the Funding statement. The Funding statement was missing in the original article. The correct statement should be as follows: “The authors acknowledge the support from the Case Western Reserve University Expanding Horizons Initiative in the College of Arts and Sciences through a Finish Line Fund award.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
COHESION OF PARAGRAPH IN EFL TEXTBOOK OF ESSAY WRITING CLASS OF UNIVERSITAS NEGERI MAKASSAR

This research aimed to provide descriptive accounts of the use of paragraph cohesion in the textbook in learning materials for an essay writing class at UNM Makassar. This research employed a qualitative approach with descriptive data exposure. Qualitative research is research that aims to describe research results. This research analyzes and reveals various qualitative information with a detailed description through cohesion in this textbook. The research design used is descriptive-analytical. This design is a research design that describes the research objectively. Researchers observe in-depth and conduct an in-depth analysis of the object of research. In the application of this research design, the first thing the researcher did was to collect data and references relevant to the object of research, sort and categorize the data found, process the data, and then present the data objectively following the results of interruptions to the object of research conducted. The findings showed. The data analysis presentation consists of (1) cohesion, which includes grammatical cohesion (reference, substitution, ellipsis, and conjunction), and lexical cohesion, which includes repetition, synonym, antonym, hyponym, and collocation. In this textbook, cohesion has an influential role in the learning activity. It is suggested that it is significant to In the preparation of textbooks, it is recommended to study the presentation of the learning material to make it easier for students to understand. To facilitate students' understanding of the material, each paragraph of the learning material in the textbooks of the essay writing course at UNM should pay more attention to cohesion.&#x0D; Keywords: Cohesion, Essay Writing, Qualitative Research.
Distributionally Robust Memory Evolution With Generalized Divergence for Continual Learning

Continual learning (CL) aims to learn a non-stationary data distribution and not forget previous knowledge. The effectiveness of existing approaches that rely on memory replay can decrease over time as the model tends to overfit the stored examples. As a result, the model's ability to generalize well is significantly constrained. Additionally, these methods often overlook the inherent uncertainty in the memory data distribution, which differs significantly from the distribution of all previous data examples. To overcome these issues, we propose a principled memory evolution framework that dynamically adjusts the memory data distribution. This evolution is achieved by employing distributionally robust optimization (DRO) to make the memory buffer increasingly difficult to memorize. We consider two types of constraints in DRO: <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> -divergence and Wasserstein ball constraints. For <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> -divergence constraint, we derive a family of methods to evolve the memory buffer data in the continuous probability measure space with Wasserstein gradient flow (WGF). For Wasserstein ball constraint, we directly solve it in the euclidean space. Extensive experiments on existing benchmarks demonstrate the effectiveness of the proposed methods for alleviating forgetting. As a by-product of the proposed framework, our method is more robust to adversarial examples than compared CL methods.
Using Semi-automatic Annotation Platform to Create Corpus for Argumentative Zoning

Argumentative Zoning (AZ) is a tool to extract salient information from scientific texts for further Natural Language Processing (NLP) tasks, e.g. scientific articles summarisation. AZ defines the main rhetorical structure in scientific articles. The lack of large AZ annotated benchmark datasets along with the manual annotation complexity of scientific texts form a bottle neck in utilizing AZ for scientific NLP tasks. Aiming to solve this problem, in previous work, we presented an AZ-annotation platform that defines and uses four categories, or zones (Claim, Method, Result, Conclusion) that are used to label sentences in scientific articles. The platform helps to create benchmark datasets to be used with the AZ tool. In this work we look at the usability of the said platform to create/expand datasets for AZ. We present a annotation experiment, composed of two annotation rounds, selected scientific articles from the ACL anthology corpus are annotated using the platform. We compare the user annotations with a ground truth annotation and compute the inter annotation agreement. The annotations obtained in this way are used as training data for various BERT-based models to predict the zone of a given sentence from a scientific article. We compare the trained models with a model trained on a baseline AZ corpus.
Evaluation of MetaHumans as Digital Tutors in Augmented Reality in Medical Training

Abstract The digitizing of medical education has been winning more support in recent years. One of the options is the implementation of Augmented Reality to complement or replace conventional methods in medical training and to create a more efficient learning environment for medical students and prospective staff in healthcare. In this work a Digital Tutor in Augmented Reality in medical training is evaluated with the aim of finding out whether it offers an additional value compared to conventional learning and teaching methods. Therefore, an application for medical training was arranged and a user survey with (n = 24) students from the Faculty of Life Sciences at the University of ANONYMIZED was carried out. The utilized application had two versions. One version included the Digital Tutor, who verbally delivered the instructions, and the other version included a text box with written instructions. The students evaluated these two applications through a questionnaire. Further, to validate this work an expert interview was conducted with a person in charge of designing concepts for the digitizing of education for the Faculty of Life Sciences at the University of ANONYMIZED. Through the survey and interview it was concluded that the Digital Tutor has an additional value compared to the text box. In the future the interactivity of the Digital Tutor with the students should be improved and a larger study should be done to further validate this outcome.
SkillsLab+ - Digitization of the Otoscopy Station through State of the Art Methods of Augmented Reality

Abstract The paper discusses SkillsLab+, a medical augmented reality application developed to digitize the practical course SkillsLab. This application provides medical students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station, one of the stations in SkillsLab+, allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides several advantages over traditional teaching methods. The implementation of the application with the Unreal Engine on the Magic Leap One headset is discussed. The application is modular, allowing the addition of new stations with ease. Anatomically correct models and 3D models of diagnostic tools provide the necessary realism and precision for the application. Learning and exam modes have been included in the application to enhance the learning experience. The evaluation of the application was conducted using a questionnaire completed by 79 bachelor and master students from the fields of medical informatics and digital biomedicine and health sciences. On average, 54.25% of students had a positive response to the digital otoscopy station in the SkillsLab+ application, while 22.71% had a negative response. The questionnaire results indicated that the digitization of the otoscopy station through augmented reality in the SkillsLab+ application was well-received by the students. The SkillsLab+ application digitizes the practical course SkillsLab and offers several advantages over traditional teaching methods. It provides students with a digital tool to learn and apply practical hand movements, initial treatment, and diagnostic techniques. The otoscopy station in SkillsLab+ allows students to learn the correct use of otoscope inserts and perform case discrimination. The digitization of this station provides students with a digital tool that can be used anywhere, any number of times. The evaluation of the application using a questionnaire showed that the application was well received by the students.
Mask R-CNN Outperforms U-Net in Instance Segmentation for Overlapping Cells

Abstract U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN outperforms U-Net in segmenting overlapping cells and achieves comparable performance if they do not intersect. Our study provides valuable decision support to practitioners in selecting an appropriate method when solving instance segmentation tasks using deep learning, as well as important insights into enhancing the accuracy of such approaches in biomedical image analysis.
Fake Review Detection via Heterogeneous Graph Attention Network

An approach based on a combination of semantic and non-semantic features of reviews is recognized as the most effective method for detecting fake reviews. However, existing deep learning-based fake review detection models have two main limitations. The first one is the extraction of word embedding, they only consider acquiring semantic features of text, but ignore that a good word embedding should ensure uniformity and alignment. Secondly, the non-semantic features are not effectively utilized. To solve these problems, this paper proposes a deep learning fake review detection model MFBH (Multi-feature Fusion using BERT-whitening and Heterogeneous graph attention network). It uses the BERT-whitening model in the text feature extraction part, i.e., the vectors extracted by BERT are linearly transformed into isotropic vectors conforming to the standard Gaussian distribution. In addition eight non-semantic features are extracted as entity links of meta-paths.Finally, the obtained text features are used as the node features of the graph and the adjacency matrix composed of meta-paths as the structural features of the graph, and the features are fused through a heterogeneous graph attention network. Experiments were conducted on the publicly available dataset of Yelp website, and the accuracy reached 93.81% on the restaurant dataset and 90.25% on the hotel dataset, which proved the effectiveness and generalization of the MFBH model.
Find Important Training Dataset by Observing the Training Sequence Similarity

It is imperative to eliminate training data that has minimal impact on model accuracy. In addition to eliminating training data that share similar features, we propose a novel concept called training sequence, which signifies the trajectory of each training data in terms of correct or incorrect prediction during each training epoch. We eliminate training data that exhibit similar training trajectories. We complement this approach with the identification of hard-to-forget training data that consistently demonstrate accurate prediction. We conducted extensive experiments on various classical classification tasks and compared our approach with forgetting-score method. Our experimental findings demonstrate that our approach outperforms the forgetting-score approach by up to 13.2% and is particularly effective at low training data retention ratios, implying that our method can choose important training datasets with satisfactory performance. Our open-source code is available at the following link: https://github.com/sheldonlll/angle_method .
Dynamic Memory-Based Continual Learning with Generating and Screening

Deep neural networks suffer from catastrophic forgetting when continually learning new tasks. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real-world applications where access to past data is limited. Therefore, We propose a two-stage framework that dynamically reproduces data features of previous tasks to reduce catastrophic forgetting. Specifically, at each task step, we use a new memory module to learn the data distribution of the new task and reproduce pseudo-data from previous memory modules to learn together. This enables us to integrate new visual concepts with retaining learned knowledge to achieve a better stability-malleability balance. We introduce an N-step model fusion strategy to accelerate the memorization process of the memory module and a screening strategy to control the quantity and quality of generated data, reducing distribution differences. We experimented on CIFAR-100, MNIST, and SVHN datasets to demonstrate the effectiveness of our method.
Text Encryption Using ECC and Chaotic Map

With the advancement of technology in the modern society, there is also a need for an advance security system. The public-key encryption known as elliptic curve cryptography (ECC) is considered to be more secure than traditional methods such as Rivest, Shamir, Adleman (RSA) for the same key size. In ECC, a key pair is generated by selecting a point on an elliptic curve and a secret integer, called the private key. In this research, we proposed an ECC-based chaotic map text encryption scheme. A chaotic sequence is generated where BitXoR is performed with the input ASCII characters. The XoRed data is grouped, and ECC is performed to each group to generate the cipher text. The chaotic system provides better randomness while ECC provides efficient security. Experimental analysis shows that the proposed method can efficiently encrypt and decrypt the input text data.
Towards Robustness of Large Language Models on Text-to-SQL Task: An Adversarial and Cross-Domain Investigation

Recent advances in large language models (LLMs) like ChatGPT have led to impressive results on various natural language processing (NLP) challenges including text-to-SQL task, which aims to automatically generate SQL queries from natural language questions. However, these language models are still subject to vulnerabilities such as adversarial attacks, domain shift and lack of robustness, which can greatly affect their performance and reliability. In this paper, we conduct a comprehensive evaluation of large language models, such as ChatGPT, on their robustness in text-to-SQL tasks. We assess the impact of adversarial and domain generalization perturbations on LLMs using seven datasets, five of which are popular robustness evaluation benchmarks for text-to-SQL tasks and two are synthetic adversarial datasets generated by ChatGPT. Our experiments show that while LLMs exhibit promise as zero-shot text-to-SQL parsers, their performances degrade under adversarial and domain generalization perturbations, with varying degrees of robustness depending on the type and level of perturbations applied. We also explore the impact of usage-related factors such as prompt design on the performance and robustness of LLMs. Our study provides insights into the limitations and potential directions for future research to enhance the performance and robustness of LLMs on text-to-SQL and other NLP tasks.
Explaining, Evaluating and Enhancing Neural Networks’ Learned Representations

Most efforts in interpretability in deep learning have focused on (1) extracting explanations of a specific downstream task in relation to the input features and (2) imposing constraints on the model, often at the expense of predictive performance. New advances in (unsupervised) representation learning and transfer learning, however, raise the need for an explanatory framework for networks that are trained without a specific downstream task. We address these challenges by showing how explainability can be an aid, rather than an obstacle, towards better and more efficient representations. Specifically, we propose a natural aggregation method generalizing attribution maps between any two (convolutional) layers of a neural network. Additionally, we employ such attributions to define two novel scores for evaluating the informativeness and the disentanglement of latent embeddings. Extensive experiments show that the proposed scores do correlate with the desired properties. We also confirm and extend previously known results concerning the independence of some common saliency strategies from the model parameters. Finally, we show that adopting our proposed scores as constraints during the training of a representation learning task improves the downstream performance of the model.
Multi-source Open-Set Image Classification Based on Deep Adversarial Domain Adaptation

Existing unsupervised domain adaptation methods are often limited to specific settings, such as single-source open-set or multi-source closed-set scenarios. These limitations hinder the application of deep models in various domains. To address this issue, this study proposes a novel unsupervised adaptive learning method specifically designed for multi-source open-set scenarios to enhance the generalization performance of deep models. In particular, we incorporate a regularization term into the model to reduce the disparity in feature representations across different source domains, thus obtaining domain-invariant features. Additionally, an adversarial loss function is introduced to learn discriminative feature representations between known and unknown classes, and a weighted learning strategy is employed to dynamically adjust the distribution of known and unknown class samples in the feature space. Furthermore, to address the inadequate handling of unknown classes in existing methods, we further partition the unknown class samples in the target domain. The proposed model is evaluated on three datasets, and consistently outperforms baseline methods and benchmark single-source open-set unsupervised domain adaptation methods.
Analysis of the advantages of applying VR to education

In 2021, the concept of Metaverse had become popular globally. VR, as the carrier and business form of the development of the Metaverse, entered the public's perspective; it has been widely applied in high-end technological fields such as new media, aerospace, and military so far. In the future, it will be widely applied as an advanced technological means in the field of education, promoting the digitization of teaching processes. Therefore, this paper analyzes the advantages of applying VR to education in order to enrich teaching methods and create a new educational model.
Conception of a Native Connection of Data Gloves with Haptic Feedback to an Augmented Reality-Headset with a planned evaluation in a medical use case

Abstract The integration of data gloves into Augmented Reality (AR) applications for medical training increases immersion and realism. By using devices with haptic feedback, better and more intuitive control can be achieved. This has implications for learning and exam preparation, but also enables new home-based learning and independence from physical models and presence. Smooth integration and connection of devices is essential for this purpose. This work aims to demonstrate native integration between an augmented reality headset, and a pair of data gloves. In the future, the use of AR and data gloves in medicine will continue to grow and technological advances will create even more realistic and immersive learning opportunities. The study will evaluate if the SkillsLab+ AR application and haptic feedback provide value to medical training and if they can be used effectively in medical education. The implementation shows promise, and the technology may have a future in other areas as well.