Reviewerly/exHarmony huggingface dataset:
Reduce Over-smoothing in Label Non-Uniformity Learning Based on Graph Convolution Network

In the semi-supervised training of Graph Convolution Network (GCN), the output signals can be used to reveal the graph structure to some extent. By exploiting this property, the label non-uniformity training procedure attempts to extract high confidence nodes and drop the low important edges to increase the learning capacity of the base model. However, this approach leads to the increasing the over-smoothing issue in the learning process. Our works begin to examine the over-smoothing degrees in multi-step training with label non-uniformity weights through the mean average distance measure. Based on this observation, we propose the usage of residual connections to improve the base GCN. The resulting model can reduce the over-smoothing effect, and thus improve the learning capacity of the model. We evaluate our approach on 6 public datasets and the results show that our proposed model can improve the results of the GCN architecture and other baselines with a wide margin.
Learning Implicit Sentiment for Explainable Review-Based Recommendation

Users can publish reviews to express their detailed feelings about the items. Positive and negative sentiments about various aspects of an item co-existing in the same review may cause confusion in recommendations and generate inappropriate explanations. However, most current explainable recommendation methods fail to capture users’ implicit sentiment behind the reviews. In this paper, we propose a novel Implicit Sentiment learning model for Explainable review-based Recommendation, named ISER, which learns users’ implicit sentiments from reviews and explores them to generate recommendations with more fine-grained explanations. Specifically, we first propose a novel representation learning to model users/items based on the implicit sentiment behind the reviews. Then we propose two implicit sentiment fusion strategies for rating prediction and explanation generation respectively. Finally, we propose a multi-task learning framework to jointly optimize the rating prediction task and the explanation generation task, which improves the recommendation quality in a mutual promotion manner. The experiments demonstrate the effectiveness and efficiency of our proposed model compared to the baseline models.
Balanced and Explainable Social Media Analysis for Public Health with Large Language Models

As social media becomes increasingly popular, more and more public health activities emerge, which is worth noting for pandemic monitoring and government decision-making. Current techniques for public health analysis involve popular models such as BERT and large language models (LLMs). Although recent progress in LLMs has shown a strong ability to comprehend knowledge by being fine-tuned on specific domain datasets, the costs of training an in-domain LLM for every specific public health task are especially expensive. Furthermore, such kinds of in-domain datasets from social media are generally highly imbalanced, which will hinder the efficiency of LLMs tuning. To tackle these challenges, the data imbalance issue can be overcome by sophisticated data augmentation methods for social media datasets. In addition, the ability of the LLMs can be effectively utilised by prompting the model properly. In light of the above discussion, in this paper, a novel ALEX framework is proposed for social media analysis on public health. Specifically, an augmentation pipeline is developed to resolve the data imbalance issue. Furthermore, an LLMs explanation mechanism is proposed by prompting an LLM with the predicted results from BERT models. Extensive experiments conducted on three tasks at the Social Media Mining for Health 2023 (SMM4H) competition with the first ranking in two tasks demonstrate the superior performance of the proposed ALEX method. Our code has been released in https://github.com/YanJiangJerry/ALEX .
Privacy Preserving Machine Learning

This tutorial provides an introduction into the evolving topic of privacy preserving machine learning. It discusses how to run models on data from potentially multiple data providers without any data provider having to share any non-encrypted data with any other party. In particular, for use cases requiring large amounts of sensitive personal data, and in the context of strict regulations like GDPR in the European Union, this topic is highly relevant. We introduce the concept of (multiparty) homomorphic encryption and demonstrate the approach on two synthetic datasets of personal health information. Furthermore, this tutorial also provides an introduction into the most common (survival) models to predict health outcomes.
Visual Streams as Core Mechanisms

Previous articleNext article No AccessVisual Streams as Core MechanismsBenjamin HenkeBenjamin Henke Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The British Journal for the Philosophy of Science Just Accepted Society: The British Society for the Philosophy of Science Views: 8Total views on this site Views: 8Total views on this site Article DOIhttps://doi.org/10.1086/728262 Views: 8Total views on this site HistoryAccepted October 07, 2023 © 2023 The British Society for the Philosophy of Science. All Rights reserved.PDF download Crossref reports no articles citing this article.
Autonomous Multirobot Navigation and Cooperative Mapping in Partially Unknown Environments

To perform collaborative exploration tasks in outdoor environments, multi-robot systems require effective task planning and high-precision co-localization. However, there are many challenges in real-world environments, such as unavailable navigation maps and unpredictable obstacles. In this paper, we present a system architecture for autonomous multi-robot navigation and cooperative Simultaneous Localization and Mapping (SLAM). To enable accurate and efficient multi-agent navigation, this work proposes a hierarchical multi-robot path planning pipeline involving two tasks, i.e., global multi-robot planning and local navigation. The first task is formulated into a min-max k-Chinese postman problem and solved by a genetic algorithm. The second task is transformed into the autonomous collision-free movement of each robot and solved by a reinforcement learning method. To improve the flexibility of cooperative mapping in unknown outdoor environments, this work proposes an online multi-robot light detection and ranging (LiDAR) SLAM system, which can flexibly select heterogeneous robots equipped with different sensor combinations. Under the condition of no prior navigation map, this work realizes multi-robot cooperative environmental exploration and reconstruction. The proposed system architecture is tested and validated via real-world experiments and some public datasets. Experimental results exhibit the superior performance of the proposed method concerning accuracy, stability, and data efficiency.
C-NARS/P: An Open-Source Tool for Classification of Narratives in Survey Data Using Pre-Trained Language Models

This report presents an open-source tool (C-NARS/P) created to classify narratives in survey data using pre-trained language models. Unlike conventional machine learning methods, pre-trained language models such as BERT can utilize the context of words in a text to make accurate predictions. In this report, technical details of functions and parameter setups for implementing pre-trained language models for classification are provided with screenshots of the code file to guide users in the validation and improvement of the implementation. C-NARS/P can be modified for a wide range of text classification tasks with ease.
Enhancing Privacy Preservation in Verifiable Computation through Random Permutation Masking to Prevent Leakage

Outsourcing computation has become increasingly popular due to its cost-effectiveness, enabling users with limited resources to conduct large-scale computations on potentially untrusted cloud platforms. In order to safeguard privacy, verifiable computing (VC) has emerged as a secure approach, ensuring that the cloud cannot discern users’ input and output. Random permutation masking (RPM) is a widely adopted technique in VC protocols to provide robust privacy protection. This work presents a precise definition of the privacy-preserving property of RPM by employing indistinguishability experiments. Moreover, an innovative attack exploiting the greatest common divisor and the least common multiple of each row and column in the encrypted matrices is introduced against RPM. Unlike previous density-based attacks, this novel approach offers a significant advantage by allowing the reconstruction of matrix values from the ciphertext based on RPM. A comprehensive demonstration was provided to illustrate the failure of protocols based on RPM in maintaining the privacy-preserving property under this proposed attack. Furthermore, an extensive series of experiments is conducted to thoroughly validate the effectiveness and advantages of the attack against RPM. The findings of this research highlight vulnerabilities in RPM-based VC protocols and underline the pressing need for further enhancements and alternative privacy-preserving mechanisms in outsourcing computation.
Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow

In the rapidly advancing field of large language model (LLM) research, platforms like Stack Overflow offer invaluable insights into the developer community’s perceptions, challenges, and interactions. This research aims to analyze LLM research and development trends within the professional community. Through the rigorous analysis of Stack Overflow, employing a comprehensive dataset spanning several years, the study identifies the prevailing technologies and frameworks underlining the dominance of models and platforms such as Transformer and Hugging Face. Furthermore, a thematic exploration using Latent Dirichlet Allocation unravels a spectrum of LLM discussion topics. As a result of the analysis, twenty keywords were derived, and a total of five key dimensions, “OpenAI Ecosystem and Challenges”, “LLM Training with Frameworks”, “APIs, File Handling and App Development”, “Programming Constructs and LLM Integration”, and “Data Processing and LLM Functionalities”, were identified through intertopic distance mapping. This research underscores the notable prevalence of specific Tags and technologies within the LLM discourse, particularly highlighting the influential roles of Transformer models and frameworks like Hugging Face. This dominance not only reflects the preferences and inclinations of the developer community but also illuminates the primary tools and technologies they leverage in the continually evolving field of LLMs.
Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces

The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties.This connection creates a paradigm for statistical inference.However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature.It is imperative to develop the concept of the distribution function in a more general space to meet emerging needs.Note that the linearity allows us to use hypercubes to define the distribution function in a Euclidean space.Still, without the linearity in a metric space, we must work with the metric to investigate the probability measure.We introduce a class of metric distribution functions through the metric only.We overcome this challenging step by proving the correspondence theorem and the Glivenko-Cantelli theorem for metric distribution functions in metric spaces, laying the foundation for conducting rational statistical inference for metric space-valued data.Then, we develop a homogeneity test and a mutual independence test for non-Euclidean random objects and present comprehensive empirical evidence to support the performance of our proposed methods.
Application of Artificial Intelligence in the Mammographic Detection of Breast Cancer in Saudi Arabian Women

Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography results. The aim of this study was to examine the performance and accuracy of an AI system in breast cancer screening among Saudi women. Materials and Methods: This is a retrospective cross-sectional study that included 378 mammograms collected from 2017 to 2021 from government hospitals in Jeddah, Saudi Arabia. The patients’ demographic and clinical information were collected from files and electronic medical records. The radiologists’ assessments of the mammograms were based on Breast Imaging Reporting and Data System (BIRADS) scores. Follow-up or biopsy reports verified the radiologists’ findings. The MammoScreen system was the AI tool used in this study. Data were analyzed using SPSS Version 25. Results: The patients’ mean age was 50.31 years. Most patients had breast density B (42.3%) followed by A (27.2%) and C (25.9%). Most malignant cases were invasive ductal carcinomas (37.3%). Of the 181 cancer cases, 36.9% were BIRADS category V. The area under the curve for the AI detection (0.923; 95% confidence interval [CI], 0.893–0.954) was greater than that for the radiologists’ interpretation (0.838; 95% CI, 0.796–0.881). The AI detection agreed with the histopathological result in 167 positive (91.3%) and 182 negative cases (93.3%). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. The radiologist’s interpretation agreed with the pathology report in 180 positive (73.8%) and 134 negative cases (100%). Its sensitivity, specificity, PPV, NPV, and accuracy were 100%, 67.7%, 73.8%, 100%, and 83.1%, respectively. Conclusions: The AI system tested in this study had better accuracy and diagnostic performance than the radiologists and thus could be used as a support diagnostic tool for breast cancer detection in clinical practice and to reduce false-positive recalls.
The Where of Mineral Names: Windmountainite: Wind Mountain, Otero County, New Mexico

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSDerek D. V. Leung enjoyed the opportunity to work on a new mineral during his undergraduate studies. This opened new research opportunities to him, including those related to his current PhD studies. Dr. John Rakovan and Ms. Paige E. dePolo are thanked for providing helpful comments that improved the manuscript. The location index map was prepared by Mr. William W. Besse.Additional informationNotes on contributorsDerek D. V. LeungDerek D. V. Leung is a PhD candidate in geology at Laurentian University, researching the relationship between Cr- and V-bearing green micas and gold deposits; one of his research themes focuses on the crystal chemistry of modular rock-forming minerals.Andrew M. McDonaldDr. Andrew M. McDonald, after whom andymcdonaldite is named, is a professor of mineralogy at Laurentian University. He has worked for more than three decades on the mineralogy of Mont Saint-Hilaire, Quebec, Canada, describing many new minerals from here, along with branching off into the worlds of magmatic sulfides, platinum-group minerals, and applied mineralogy (indicator minerals).
Who’s Who in Mineral Names: John C. Ebner Jr. (b. 1931)

"Who’s Who in Mineral Names: John C. Ebner Jr. (b. 1931)." Rocks & Minerals, 98(6), pp. 558–559 ACKNOWLEDGMENTSThanks are extended to Dr. Hexiong Yang, Joan Beyer, and Dr. Anthony R. Kampf for their careful reviews of and helpful suggestions for this article.Additional informationNotes on contributorsRon GibbsRon Gibbs, a retired mining engineer, is a research associate in the Department of Geosciences at the University of Arizona in Tucson and a past contributor to Rocks & Minerals.
Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today’s digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application.
LA4OX - A Live Authentication for Online Exam

Online learning systems, especially remote learner assessment systems, are experiencing difficulties with verifying the identity of the learners. The username-password approach faces two main issues when applied to online examination systems: (1) It is difficult to remember strong passwords (2) It is difficult to verify the student who is assessing. Live authentication utilizing biometrics can be used to solve these two issues. However, it is challenging for a single biometric system to maintain stability and prevent spoofing. Many studies have shown that multibiometrics systems outperform unibiometric systems, but they have typically been implemented using simple fusion methods. In this work, we propose a feature-level and score-level fusion method utilizing multi-head attention module. Separately, facial features and voice features are extracted, then they are fused by the multi-head attention module. Moreover, two protocols for live authentication are presented in this paper. The proposed model is tested on a popular dataset and a real-world dataset. On VoxCeleb 1 dataset, the proposed method outperforms individual biometrics in terms of validation results, demonstrating accuracy rates of 98.66% for voice-only, 95.72% for face-only, and 99.75% for our model.
Continuous Learning for Automatic Identification of OC EO Antique Glass Jewelry

In terms of the Oc Eo culture, with the diversity found in existing artifacts, we propose using an artificial intelligence system to automatically and thoroughly identify Oc Eo glass jewelry through SEM gemological analysis. We hope that this approach, which has only emerged in archaeology worldwide in the past five years, can improve the quality of identification compared to the classic methods used in Vietnamese archaeology. Our research is driven by the special conditions of the archaeology field, where we aim to utilize evolving continuous learning on archaeological databases, by comparing and selecting the most suitable model that meets the acceptable performance of the dataset. This Recognition Automatic System for Oc Eo Glasses (RAS-OEG), which is capable of distinguishing between Oc Eo and non-Oc Eo glass ornaments, is freely distributed to experts and archaeologists.
The Improved Pedestrian Target Detection and Re-recognition Algorithm

Image processing technology can be applied to detect and recognize the specific pedestrian target. To well detect moving pedestrian targets in complex occlusion scenes, the improved pedestrian detection algorithm based on YOLOv5 and re-recognition algorithm based on foreground mask estimation are proposed in this paper. In the improved pedestrian detection algorithm, aiming at interclass occlusion, a new two-branch fusion detection head is designed, the loss function and sample matching strategy are modified, meanwhile the non-maximum suppression algorithm is optimized. Aiming at in-class occlusion, SoftNMS algorithm is introduced to improve the detection capability in occlusion scene. In the modified pedestrian re-recognition algorithm, to enhance the pedestrian re-recognition rate under intricate backgrounds, HRNetV2 network replaces ResNet50 as the backbone network and three loss functions are optimized. Experimental results show that modified algorithms improves the performance of pedestrian target detection and re-recognition in complex occlusion scenes.
Accumulating Word Representations in Multi-level Context Integration for ERC Task

Emotion Recognition in Conversations (ERC) has attracted augmented interest recently because of its pronounced adaptability, which is to forecast the sentiment label for each utterance given a conversation as context. In order to identify the emotion of a focal sentence, it is crucial to model its meaning fused with contextual information. Many recent studies have focused on capturing different types of context as supporting information and integrated it in various ways: local and global contexts or at the speaker level through intra-speaker and inter-speaker integration. However, the importance of word representations after context integration has not been investigated completely, while word information is also essential to reflect the speaker's emotions in the conversation. Therefore, in this work, we endeavor to investigate the impact of accumulating word vector representations on sentence modeling fused with multi-level contextual integration. To this end, we propose an effective method for sentence modeling in ERC tasks and achieve competitive state-of-the-art results across four widely recognized bench-mark datasets: Iemocap, MELD, EmoryNLP, and DailyDialog. Our source code can be accessed via the following link: github.com/yingjie7/per_erc/tree/AccumWR.