Reviewerly/exHarmony huggingface dataset:
Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Authors:
raw_author_name: Ahmad Ihsan Ramdani
raw_affiliation_string: Energy Resources and Petroleum Engineering Department, King Abdullah University of Science and Technology, Saudi Arabia
raw_author_name: Volker Vahrenkamp
raw_affiliation_string: Energy Resources and Petroleum Engineering Department, King Abdullah University of Science and Technology, Saudi Arabia

Abstract: Abstract Geoscience datasets are fundamental for subsurface investigation. Paradoxically, they are sometimes exclusive and require subject-specific expertise to interpret and visualize. One such example is seismic interpretation. Geophysicists typically reconstruct ancient depositional settings by interpreting a myriad of seismic attributes and drawing analogs to the sedimentary process of the modern depositional environment (Posamentier et al. 2007; Vahrenkamp et al. 2019; Ramdani et al. 2021). Most of these interpretations will likely be reflection amplitude, frequency, impedance, or other geophysical attributes interpreted and "visualized" in the present-day geomorphology context (Posamentier et al. 2007; Warrlich et al. 2019; Ramdani et al. 2022b). The interpreter will then rely on verbal or written descriptions to convey their interpretation. Often, these descriptions are only well understood by fellow interpreters. Attempting to convey the same interpretation to a non-expert requires some degree of visual aid. Thus, a method to picture geophysical signals as a "depositional environment" is needed to bridge this gap. This study aims to leverage the application of generative AI as a tool for seismic interpretation. We propose a Conditional Generative Adversarial Network (CGAN)-based methodology capable of converting seismic attribute maps into photorealistic images of the modern satellite imagery analog as a visual aid for seismic interpretation.
Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Authors:
raw_author_name: Abdulazeez Abdulraheem
raw_affiliation_string: King Fahd University of Petroleum & Minerals
raw_author_name: Zeeshan Tariq
raw_affiliation_string: King Abdullah University of Science and Technology

Abstract: Abstract Lithology identification holds a pivotal role in the characterization of subsurface formations. In recent years, the advent of big data and the need for more precise lithology identification have spurred the growing adoption of machine learning algorithms. This paper's primary objective is to leverage unsupervised machine learning techniques for the identification and subsequent removal of anomalies inherent in complex datasets. The Isolation Forest model is the cornerstone of our approach for anomaly detection and elimination. To initiate this process, we employed the K-means algorithm to create clusters, followed by a evaluation using silhouette coefficients. Subsequently, we selected input data for each cluster and conducted exploratory data analysis both prior to and after the removal of outliers. The histograms portraying average anomaly scores for each cluster were thoughtfully presented. For real-time anomaly detection, we harnessed the power of the Isolation Forest model, which facilitated the creation of an Isolation Forest anomalies map by plotting neutron-porosity against bulk density. Our rigorous data analysis employed various statistical techniques, including data statistics, histograms, and cross plots between neutron-porosity and bulk density. This multifaceted approach successfully eradicated anomalies from the dataset, a fact vividly illustrated by the histograms, where anomalies were discernible through their negative scores. The Isolation Forest map conclusively demonstrated the effective removal of outliers from the dataset, underscoring the model's proficiency in identifying and mitigating these anomalies based on their negative scores. The Isolation Forest model has thus exhibited remarkable efficacy in the identification and elimination of data anomalies. Its versatility makes it a valuable asset for the detection and removal of outliers, deviations, or noise from datasets, rendering it particularly well-suited for anomaly detection and outlier mitigation in various analytical scenarios. Notably, the combination of the K-means and Isolation Forest algorithms emerges as a potent and advantageous approach, especially when dealing with extensive datasets and conducting comprehensive analyses.
An Optimizing Business Process: A Comprehensive Analysis of Amazon Inc.’s Information Architecture

Authors:
raw_author_name: Md Nasir Uddin Rana
raw_affiliation_string: Monroe College, USA
raw_author_name: Md Tanvir Islam
raw_affiliation_string: Monroe College, USA

Abstract: This analysis studies Amazon Inc.’s information system design in detail using context, use cases, test cases, class, and sequence diagrams. Our research aims to improve business processes by better understanding the system’s interactions, functionality, and data structure. The representation of data structure and entity relationships in class diagrams provides insights into the system’s component organization. Sequence diagrams also provide a clear picture of the sequential interactions between system elements, showing potential bottlenecks and optimization opportunities. It provides unique insights into Amazon’s approaches to keeping a competitive edge in the e-commerce industry, and it may be used as a resource for firms looking to improve their own information systems and procedures.
Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Authors:
raw_author_name: Guangya Yu
raw_affiliation_string: School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China; Zhejiang Laboratory, Hangzhou 311121, China
raw_author_name: Qi Ye
raw_affiliation_string: School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China
raw_author_name: Tong Ruan
raw_affiliation_string: School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China

Abstract: The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.
MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

Authors:
raw_author_name: Mahmut Kaya
raw_affiliation_string: Department of Artificial Intelligence and Data Engineering, Firat University, Elazig, Turkey
raw_author_name: Ali Şenol
raw_affiliation_string: Department of Computer Engineering, Tarsus University, Mersin, Turkey

Abstract: Abstract Stream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.
AI Based Unstructured Document Geotagging Solution

Authors:
raw_author_name: Anik Pal
raw_affiliation_string: SLB
raw_author_name: Mihir Gandhi
raw_affiliation_string: SLB

Abstract: Abstract In the oil and gas industry, extracting relevant information for new projects often involves scrolling through a large unorganized or semi-organized unstructured electronic repository of unstructured documents inside an enterprise. However, the digital availability of these unstructured documents and their proper characterization with respect to E&amp;P domain objects remain a significant challenge. To address this issue, we developed an - "AI-based unstructured document geotagging solution" that utilizes text mining, optical character recognition (OCR), natural language processing (NLP), and named entity recognition (NER) to extract entity relations for spatial E&amp;P objects and document category (for example well completion, log data print, daily drilling report etc.) from documents content autonomously. We employed popular language models spaCy, Roberta-base, GPT2 and GPT3.5 and compared the results. The extracted information was then linked to a structured database that contained geo-locations. The solution was developed using the company specific data sciences platform but is capable of being deployed for an operator on-premises or in the cloud. The end-to-end solution can be integrated with any GIS-based dashboard solution, allowing user to select objects from the map and the model can predict related documents to the objects along with document category. This solution will allow user to obtain an organized list of related documents out of electronic repository with unorganized collection of uncharacterized documents, which will allow the user to prioritize and deploy only content of interest to be ingested to the insight and analytics system often hosted in cloud environment.
CCDA: A Novel Method to Explore the Cross-Correlation in Dual-Attention for Multimodal Sentiment Analysis

Authors:
raw_author_name: Peicheng Wang
raw_affiliation_string: School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Shuxian Liu
raw_affiliation_string: School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Jinyan Chen
raw_affiliation_string: School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China

Abstract: With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: (1) inefficiency when modeling the intramodality and intermodality dynamics and (2) inability to effectively fuse multimodal features. In this paper, we propose the CCDA (cross-correlation in dual-attention) model, a novel method to explore dynamics between different modalities and fuse multimodal features efficiently. We capture dynamics at intra- and intermodal levels by using two types of attention mechanisms simultaneously. Meanwhile, the cross-correlation loss is introduced to capture the correlation between attention mechanisms. Moreover, the relevant coefficient is proposed to integrate multimodal features effectively. Extensive experiments were conducted on three publicly available datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The experimental results fully confirm the effectiveness of our proposed method, and, compared with the current optimal method (SOTA), our model shows obvious advantages in most of the key metrics, proving its better performance in multimodal sentiment analysis.
Efficient removal of 2,4,6-trinitrotoluene (TNT) from industrial/military wastewater using anodic oxidation on boron-doped diamond electrodes

Authors:
raw_author_name: Jakub Nawała
raw_affiliation_string: Military University of Technology, S. Kaliskiego 2, 00-908, Warsaw, Poland

Abstract: Abstract With growing public concern about water quality particular focus should be placed on organic micropollutants, which are harmful to the environment and people. Hence, the objective of this research is to enhance the security and resilience of water resources by developing an efficient system for reclaiming industrial/military wastewater and protecting recipients from the toxic and cancerogenic explosive compound—2,4,6-trinitrotoluene (TNT), which has been widely distributed in the environment. This research used an anodic oxidation (AO) process on a boron-doped diamond (BDD) electrode for the TNT removal from artificial and real-life matrices: marine water and treated wastewater. During experiments, TNT concentrations were significantly decreased, reaching the anodic degradation efficiency of above 92% within two hours and &gt; 99.9% after six hours of environmental sample treatment. The presented results show the great potential of AO performed on BDD anodes for full-scale application in the industry and military sectors for TNT removal.
Inherently privacy-preserving vision for trustworthy autonomous systems: Needs and solutions

Authors:
raw_author_name: Niko Suenderhauf
raw_affiliation_string: Queensland University of Technology (QUT), Centre for Robotics, Brisbane, 4001, QLD, Australia
raw_author_name: Peter Corke
raw_affiliation_string: Queensland University of Technology (QUT), Centre for Robotics, Brisbane, 4001, QLD, Australia
raw_author_name: Donald G. Dansereau
raw_affiliation_string: Australian Centre For Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, 2006, NSW, Australia

Abstract: Vision is an effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, identity, and activity of humans within that scene. This raises important questions about the reach, lifespan, and misuse of this information. This paper is a call to action to consider privacy in robotic vision. We propose a specific form of inherent privacy preservation in which no images are captured or could be reconstructed by an attacker, even with full remote access. We present a set of principles by which such systems could be designed, employing data-destroying operations and obfuscation in the optical and analogue domains. These cameras never see a full scene. Our localisation case study demonstrates in simulation four implementations that all fulfil this task. The design space of such systems is vast despite the constraints of optical-analogue processing. We hope to inspire future works that expand the range of applications open to sighted robotic systems.
Transformer-based text similarity and second language proficiency: A case of written production by learners of Korean

Authors:
raw_author_name: Gyu-Ho Shin
raw_affiliation_string: Department of Linguistics, University of Illinois Chicago, 601 S Morgan St, Chicago, IL 60607, USA
raw_author_name: Boo Kyung Jung
raw_affiliation_string: Department of East Asian Languages & Literatures, University of Pittsburgh, 2714 Cathedral of Learning, Pittsburgh, PA 15260, USA
raw_author_name: Seongmin Mun
raw_affiliation_string: Humanities Research Institute, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, South Korea

Abstract: The present study applies two transformer models (BERT; GPT-2) to analyse argumentative essays produced by two first-language groups (Czech; English) of second-language learners of Korean and investigates how informative similarity scores of learner writing obtained by these models explain general language proficiency in Korean. Results show three major aspects on model performance. First, the relationships between the similarity scores and the proficiency scores differ from the tendencies between human rating scores and the proficiency scores. Second, the degree to which the similarity scores obtained by each model explain the proficiency scores is asymmetric and idiosyncratic. Third, the performance of the two models is affected by learners’ native language and essay topic. These findings invite the need for researchers and educators to pay attention to how computational algorithms operate, together with learner language characteristics and language-specific properties of the target language, in utilising Natural Language Processing methods and techniques for their research or instructional purposes.
A Good View for Graph Contrastive Learning

Authors:
raw_author_name: Xueyuan Chen
raw_affiliation_string: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China

Abstract: Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a “view” dictates the information captured by the representation, thereby influencing the model’s performance. However, assessing the quality of information in these views poses challenges, and determining what constitutes a good view remains unclear. This paper addresses this issue by establishing the definition of a good view through the application of graph information bottleneck and structural entropy theories. Based on theoretical insights, we introduce CtrlGCL, a novel method for achieving a beneficial view in graph contrastive learning through coding tree representation learning. Extensive experiments were conducted to ascertain the effectiveness of the proposed view in unsupervised and semi-supervised learning. In particular, our approach, via CtrlGCL-H, yields an average accuracy enhancement of 1.06% under unsupervised learning when compared to GCL. This improvement underscores the efficacy of our proposed method.
Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

Authors:
raw_author_name: Chao Qu
raw_affiliation_string: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Xiaoyu Chen
raw_affiliation_string: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Qihan Xu
raw_affiliation_string: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Jing Han
raw_affiliation_string: Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: The supervised super-resolution (SR) methods based on simple degradation assumptions (e.g., bicubic downsampling) have unsatisfactory generalization ability on real-world thermal images. To enhance the SR effect of real-world sceneries, we introduce an unsupervised SR framework for thermal images, incorporating degradation modeling and corresponding SR. Inspired by the physical prior that high frequency affects details and low frequency affects thermal contrast, we propose a frequency-aware degradation model, named TFADGAN. The model achieves image quality migration between thermal detectors of different resolutions by degrading different frequency components of the image from high-resolution (HR) to low-resolution (LR). Specifically, by adversarial learning with unpaired LR thermal images, the complex degradation processes of HR thermal images at low and high frequencies are modeled separately. Benefiting from the thermal characteristics mined from real-world images, the degraded images generated by TFADGAN are similar to LR thermal ones in terms of detail and contrast. Then, the SR model is trained based on the pseudo-paired data consisting of degraded images and HR images. Extensive experimental results demonstrate that the degraded images generated by TFADGAN provide reliable alternatives to real-world LR thermal images. In real-world thermal image experiments, the proposed SR framework can improve the peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM) by 1.28 dB and 0.02, respectively.