Reviewerly/exHarmony huggingface dataset:
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling

Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, and electronic health records. The conventional approaches for biomedical NER mainly use traditional machine learning techniques, such as Conditional Random Fields and Support Vector Machines or deep learning-based models like Recurrent Neural Networks and Convolutional Neural Networks. Recently, Transformer-based models, including BERT, have been used in the domain of biomedical NER and have demonstrated remarkable results. However, these models are often based on word-level embeddings, limiting their ability to capture character-level information, which is effective in biomedical NER due to the high variability and complexity of biomedical texts. To address these limitations, this paper proposes a hybrid approach that integrates the strengths of multiple models. In this paper, we proposed an approach that leverages fine-tuned BERT to provide contextualized word embeddings, a pre-trained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. In addition, also we propose an enhanced labelling method as part of pre-processing to enhance the identification of the entity's beginning word and thus improve the identification of multi-word entities, a common challenge in biomedical NER. By integrating these models and the pre-processing method, our proposed model effectively captures both contextual information and detailed character-level information. We evaluated our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11. These results illustrate the proficiency of our proposed model in performing biomedical Named Entity Recognition.
Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision

The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm's feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates ($<0.01$ bpp), offering new insights for human-machine collaborative compression.
Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval

Cross-modal medical image-report retrieval task plays a significant role in clinical diagnosis and various medical generative tasks. Eliminating heterogeneity between different modalities to enhance semantic consistency is the key challenge of this task. The current Vision-Language Pretraining (VLP) models, with cross-modal contrastive learning and masked reconstruction as joint training tasks, can effectively enhance the performance of cross-modal retrieval. This framework typically employs dual-stream inputs, using unmasked data for cross-modal contrastive learning and masked data for reconstruction. However, due to task competition and information interference caused by significant differences between the inputs of the two proxy tasks, the effectiveness of representation learning for intra-modal and cross-modal features is limited. In this paper, we propose an efficient VLP framework named Masked Contrastive and Reconstruction (MCR), which takes masked data as the sole input for both tasks. This enhances task connections, reducing information interference and competition between them, while also substantially decreasing the required GPU memory and training time. Moreover, we introduce a new modality alignment strategy named Mapping before Aggregation (MbA). Unlike previous methods, MbA maps different modalities to a common feature space before conducting local feature aggregation, thereby reducing the loss of fine-grained semantic information necessary for improved modality alignment. Qualitative and quantitative experiments conducted on the MIMIC-CXR dataset validate the effectiveness of our approach, demonstrating state-of-the-art performance in medical cross-modal retrieval tasks.
An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification

This article presents a new polynomial parameterized sigmoid called SIGTRON, which is an extended asymmetric sigmoid with Perceptron, and its companion convex model called SIGTRON-imbalanced classification (SIC) model that employs a virtual SIGTRON-induced convex loss function. In contrast to the conventional $\pi$-weighted cost-sensitive learning model, the SIC model does not have an external $\pi$-weight on the loss function but has internal parameters in the virtual SIGTRON-induced loss function. As a consequence, when the given training dataset is close to the well-balanced condition, we show that the proposed SIC model is more adaptive to variations of the dataset, such as the inconsistency of the scale-class-imbalance ratio between the training and test datasets. This adaptation is achieved by creating a skewed hyperplane equation. Additionally, we present a quasi-Newton optimization(L-BFGS) framework for the virtual convex loss by developing an interval-based bisection line search. Empirically, we have observed that the proposed approach outperforms $\pi$-weighted convex focal loss and balanced classifier LIBLINEAR(logistic regression, SVM, and L2SVM) in terms of test classification accuracy with $51$ two-class and $67$ multi-class datasets. In binary classification problems, where the scale-class-imbalance ratio of the training dataset is not significant but the inconsistency exists, a group of SIC models with the best test accuracy for each dataset (TOP$1$) outperforms LIBSVM(C-SVC with RBF kernel), a well-known kernel-based classifier.
Disentangling Representation of Variational Autoencoders Based on Cloud Models

&lt;p&gt;Variational autoencoder (VAE) has the problem of uninterpretable data generation process, because the features contained in the VAE latent space are coupled with each other and no mapping from the latent space to the semantic space is established. However, most existing algorithms cannot understand the data distribution features in the latent space semantically. In this paper, we propose a cloud model-based method for disentangling semantic features in VAE latent space by adding support vector machines (SVM) to feature transformations of latent variables, and we propose to use the cloud model to measure the degree of disentangling of semantic features in the latent space. The experimental results on the CelebA dataset show that the method obtains a good disentangling effect of semantic features in the latent space, which proves the effectiveness of the method from both qualitative and quantitative aspects.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard at the time of submission (Aug 24th, 2022).
Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Berdasarkan Sumber Belajarnya

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Emotion Classification using Generative Pre-trained Embedding and Machine Learning

Emotion detection from user-generated text has drawn tremendous attention in the recent era for multiple reasons. Hence, there remain significant prospects in the urge for a state-of-the-art approach for emotion classification. In this research article, we explore the realm of GPT (Generative Pre-trained Transformer) models and their embedding, the extent of application of GPT text embedding for emotion analysis is still limited. In this paper, we explore the advantages of GPT embedding and focus on its integration. In this paper, we synthesized key findings and insights on different embedding techniques adopted for emotion prediction. Our research aims to contribute to the ongoing evolution of emotion classification techniques. We proposed an innovative approach to leverage GPT embedding acquisition step for fine-grained emotion classification from user-generated text using machine learning algorithms. Our results suggest that GPT embeddings can be effectively used to improve the accuracy and effectiveness of machine learning models for emotion detection and sentiment analysis tasks. This is likely due to the semantic strength of GPT embeddings and their ability to encode rich linguistic data. In this article, we discuss the potential breakthroughs offered by this novel approach for improved performance in emotion classification using machine learning algorithms.
Multi-Level Node Authorization using Recurrent Neural Networks for Secure Health Monitoring System

Rapid technological progress has allowed for the creation of novel healthcare solutions that aims to improve patient care and monitoring. Data security and privacy are given high priority in the proposed system, which is a health monitoring platform that leverages the capabilities of the Internet of Things (IOT) and deep learning methods like Transformers in RNN to gather and analyse health data with high precision. For reliable anomaly detection, early risk prediction, and individualized healthcare recommendations, deep learning algorithms are used to sift through the data. The main objective of this research is to design an efficient model for strong authentication to improve the security levels. Given the delicate nature of individual health information, security and privacy are of the utmost importance in health monitoring systems. The SHMS addresses these issues by incorporating strong authentication and encryption technologies to protect information at rest and in transit. An individual's right to decide how their personal health data is used is also carefully protected by these systems. This study describes a major advancement in healthcare technology: a system for secure, accurate, and real-time health monitoring that uses the Internet of Things and a Transformer-based RNN in deep learning. This technology not only improves the ability of healthcare providers to provide timely and effective care, but also protects the privacy and security of patient data. This research presents a Multi-Level Node Authorization and Access Control Model with Dual Key Authentication using Recurrent Neural Networks (MLNA-ACM-DKA-RNN) for secure health monitoring system. The proposed model when compared with the traditional models exhibit better performance.
Image Processing and Legendre Polynomials

In this paper, we consider a basis of square integrable functions on a rectangle, contained in R^2, constructed with Legendre polynomials, suitable for the analogical description of images on the plane. After extending the Legendre polynomials to any arbitrary interval of the form [a,b], from its original form on [-1,1], we generalize the basis of Legendre polynomials to two dimensions. The extension to color images can be realized with the obvious procedure of considering togheter three monocromatic images and the extension to three (and more) dimensions is straighforward. We present some mathematical constructions such as Gelfand triples appropriate on this context. We compare the digital and analogical descriptions of the model.
Jatmo: Prompt Injection Defense by Task-Specific Finetuning

Large Language Models (LLMs) are attracting significant research attention due to their instruction-following abilities, allowing users and developers to leverage LLMs for a variety of tasks. However, LLMs are vulnerable to prompt-injection attacks: a class of attacks that hijack the model's instruction-following abilities, changing responses to prompts to undesired, possibly malicious ones. In this work, we introduce Jatmo, a method for generating task-specific models resilient to prompt-injection attacks. Jatmo leverages the fact that LLMs can only follow instructions once they have undergone instruction tuning. It harnesses a teacher instruction-tuned model to generate a task-specific dataset, which is then used to fine-tune a base model (i.e., a non-instruction-tuned model). Jatmo only needs a task prompt and a dataset of inputs for the task: it uses the teacher model to generate outputs. For situations with no pre-existing datasets, Jatmo can use a single example, or in some cases none at all, to produce a fully synthetic dataset. Our experiments on seven tasks show that Jatmo models provide similar quality of outputs on their specific task as standard LLMs, while being resilient to prompt injections. The best attacks succeeded in less than 0.5% of cases against our models, versus 87% success rate against GPT-3.5-Turbo. We release Jatmo at https://github.com/wagner-group/prompt-injection-defense.
Smart Tool for Earlier Prediction of Breast Cancer Using AI

Breast carcinoma is a commonly occurring medical condition, and early detection is essential for successful treatment. In terms of forecasting the danger of breast cancer, machine learning algorithms have produced encouraging results. In this study, we investigate how well the effective supervised algorithms CatBoost and XGBoost perform at detecting the existence of breast cancer. We took information from a publicly accessible dataset about the tumor size, texture, area, perimeter, and so on. We used a range of measures, including precision, recall, precision, accuracy, and F1-score, to gauge how well the models performed. Our results show that both XGBoost and CatBoost, having XGBoost scoring 93.5% and CatBoost scoring 92.3% respectively, are quite accurate in detecting the presence of breast cancer. Our work’s high degree of accuracy indicates these models can be a valuable tool for finding breast cancer early on. It also demonstrates the capability of employing efficient supervised algorithms for breast cancer prediction.
Document-Level Event Extraction via Aggregate Argument Information Model

Document-level event extraction (DEE) is extremely important for the events described throughout a long document. Although the existing sentence-level event extraction (SEE) methods are extensively studied, they are not effective due to the challenge of the DEE task: the association between event arguments is not considered in models. This is also one of the hot topics for current DEE research. To address the abovementioned challenge, we propose a method called the Aggregate Argument Information Model (AAIM), which fuses the information of short and long texts and takes advantage of position information of event arguments in sentences and hence captures the interaction semantics among the event arguments to some extent. The experimental results on the large-scale dataset ChFinAnn show that AAIM outperforms the comparison method by 1.0 in F1.
Hate Speech Detection Model on Web 3.0 Based Platform using Blockchain and NLP

With the increase in usage of social media applications like Facebook, Twitter, or Instagram, hate speech is also on the rise. Hate speech can be defined as the ill talk toward any race, caste, religion, and ethnicity. Now with the new development of web 3.0 which is decentralized, it is difficult to control elements like hate speech because there is no central body that can control it. In this research paper, we have proposed an NLP-based hate speech detection model for Web 3.0 that is able to detect hate speech with the accuracy of 89% before it gets to Blockchain. The NLP-based algorithms, IPFS, and smart contracts are used.
ProbVLM: Probabilistic Adapter for Frozen Vison-Language Models

Large-scale vision-language models (VLMs) like CLIP successfully find correspondences between images and text. Through the standard deterministic mapping process, an image or a text sample is mapped to a single vector in the embedding space. This is problematic: as multiple samples (images or text) can abstract the same concept in the physical world, deterministic embeddings do not reflect the inherent ambiguity in the embedding space. We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment in a post-hoc manner without needing large-scale datasets or computing. On four challenging datasets, i.e., COCO, Flickr, CUB, and Oxford-flowers, we estimate the multi-modal embedding uncertainties for two VLMs, i.e., CLIP and BLIP, quantify the calibration of embedding uncertainties in retrieval tasks and show that ProbVLM outperforms other methods. Furthermore, we propose active learning and model selection as two real-world downstream tasks for VLMs and show that the estimated uncertainty aids both tasks. Lastly, we present a novel technique for visualizing the embedding distributions using a large-scale pre-trained latent diffusion model. Code is available at https://github.com/ExplainableML/ProbVLM
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition

For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such image cropping methods are limited in the range of image views. Some methods have been suggested to extrapolate the images and predict cropping boxes from the extrapolated image. Nonetheless, the synthesized extrapolated regions may be included in the cropped image, making the image composition result not real and potentially with degraded image quality. In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i.e., UNIC). In this way, the cropped image is a sub-image of the image acquired by the predicted camera view, and thus can be guaranteed to be real and consistent in image quality. Specifically, our framework takes the current camera preview frame as input and provides a recommendation for view adjustment, which contains operations unlimited by the image borders, such as zooming in or out and camera movement. To improve the prediction accuracy of view adjustment prediction, we further extend the field of view by feature extrapolation. After one or several times of view adjustments, our method converges and results in both a camera view and a bounding box showing the image composition recommendation. Extensive experiments are conducted on the datasets constructed upon existing image cropping datasets, showing the effectiveness of our UNIC in unbounded recommendation of camera view and image composition. The source code, dataset, and pre-trained models is available at https://github.com/liuxiaoyu1104/UNIC.
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds

Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of off-board 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage in leveraging the long-term temporal context representation. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero. Concretely, an offline tracker coupled with a multi-frame detector is proposed to focus on the completeness of generated object tracks. An attention-mechanism refining module is proposed to strengthen contextual information interaction across long-term sequential point clouds for object refining with decomposed regression methods. Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods. Notably, DetZero ranks 1st place on Waymo 3D object detection leaderboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with 85.15 mAPH (L2) detection performance. Further experiments validate the application of taking the place of human labels with such high-quality results. Our empirical study leads to rethinking conventions and interesting findings that can guide future research on offboard 3D object detection.