Reviewerly/exHarmony huggingface dataset:
GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction

Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks, and it has achieved promising results in Relation Extraction (RE) tasks in recent years. Although the existing prompt-based RE methods have outperformed the methods based on the fine-tuning paradigm, these methods require domain experts to design prompt templates, making them hard to generalize. In this paper, we proposed a Generative context-Aware Prompt-tuning method (GAP) to address these limitations. Our method consists of three crucial modules: (1) a pretrained prompt generator module that extracts or generates the relation triggers from the context and embeds them into the prompt tokens, (2) an in-domain adaptive pretraining module that further trains the Pretrained Language Models (PLMs) to promote the adaptability of the model, and (3) a joint contrastive loss that prevents PLMs from generating results unrelated to the relation labels while optimizing our model more effectively. We observed that the context-enhanced prompt tokens generated by GAP can better guide PLMs to make accurate relationship predictions. And the in-domain pretraining can effectively inject domain knowledge to enhance the robustness of the model. We conduct experiments on four public RE datasets under the supervised and few-shot settings. The experimental results have demonstrated the superiority of GAP over existing benchmark methods and GAP shows remarkable improvements in few-shot settings, with average F1 score enhancements of 3.5%, 2.7%, and 3.4% on the TACRED, TACREV, and Re-TACRED datasets, respectively. Furthermore, GAP still achieved state-of-the-art (SOTA) performance in supervised settings.
MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.
An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images

Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.
Identifying Brexit voting patterns in the British house of commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects

Brexit and its implications are an ongoing topic of interest since the Brexit referendum in 2016. In 2019 the House of commons held a number of "indicative" and "meaningful" votes as part of the Brexit approval process. The voting behaviour of members of the parliament in these votes is investigated to gain insight into the Brexit approval process. In particular, a mixture model with concomitant covariates is developed to identify groups of members of parliament who share similar voting behaviour while also considering characteristics of the members of parliament. The novelty of the method lies in the flexible structure used to model the effect of concomitant covariates on the component weights of the mixture, with the (potentially nonlinear) terms represented as a smooth function of the covariates. Results show this approach allows to quantify the effect of the age of members of parliament, as well as preferences and competitiveness in the constituencies they represent, on their position towards Brexit. This helps grouping the aforementioned politicians into homogeous clusters, whose composition departs sensibly from that of the parties.
Bitcoin forks: What drives the branches?

Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.
Examining perceptions of utilizing augmented reality in hybrid learning environment: instructors and students perspectives

Purpose This study aims to explore the perceptions of language learners and instructors on using augmented reality (AR) in a hybrid learning environment. Design/methodology/approach Mixed-method research design was used to elicit information from 62 participants on the study’s objective. Data were collected and analyzed to examine the participants’ views on using AR in language teaching in a hybrid environment. Findings This study unveils that while most studies acknowledge AR as an effective instructional delivery, students and instructors perceive some setbacks. Other novel insights provided by this study reveal necessities to consider before implementing AR in classroom settings. Research limitations/implications This study provides insights into the widely reported effectiveness of AR in the English language-teaching domain. This study suggests that considering the dispositions of learners and instructors toward digitally enhanced learning, using AR without good teaching practices and approaches may not yield expected learning outcomes. Originality/value This study advances scientific knowledge on the use of AR in hybrid learning models by providing empirical evidence to show the perceived effectiveness of AR. It further provides a robust understanding of the pedagogical implications of using AR in classroom settings drawing from diverse lenses. This helps ensure that educational settings that integrate AR consider the novel findings of this study before such implementation.
Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Abstract Microarray technology, as applied to the fields of bioinformatics, biotechnology, and bioengineering, has made remarkable progress in both the treatment and prediction of many biological problems. However, this technology presents a critical challenge due to the size of the numerous genes present in the high-dimensional biological datasets associated with an experiment, which leads to a curse of dimensionality on biological data. Such high dimensionality of real biological data sets not only increases memory requirements and training costs, but also reduces the ability of learning algorithms to generalise. Consequently, multiple feature selection (FS) methods have been proposed by researchers to choose the most significant and precise subset of classified genes from gene expression datasets while maintaining high classification accuracy. In this research work, a novel binary method called i BABC-CGO based on the island model of the artificial bee colony algorithm, combined with the chaos game optimization algorithm and SVM classifier, is suggested for FS problems using gene expression data. Due to the binary nature of FS problems, two distinct transfer functions are employed for converting the continuous search space into a binary one, thus improving the efficiency of the exploration and exploitation phases. The suggested strategy is tested on a variety of biological datasets with different scales and compared to popular metaheuristic-based, filter-based, and hybrid FS methods. Experimental results supplemented with the statistical measures, box plots, Wilcoxon tests, Friedman tests, and radar plots demonstrate that compared to prior methods, the proposed i BABC-CGO exhibit competitive performance in terms of classification accuracy, selection of the most relevant subset of genes, data variability, and convergence rate. The suggested method is also proven to identify unique sets of informative, relevant genes successfully with the highest overall average accuracy in 15 tested biological datasets. Additionally, the biological interpretations of the selected genes by the proposed method are also provided in our research work.
A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud

In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.
GASx: Explainable Artificial Intelligence For Detecting GPS Spoofing Attacks

Unmanned Aerial Systems heavily depend on the Global Positioning System (GPS) for navigation. However, the GPS signals are subject to different types of threats including GPS spoofing attacks. While many machine learning methods have been successfully applied to detect spoofing attacks in these unmanned systems, the focus has mainly been on developing accurate prediction models, without delving into the reasons behind the predictions. We believe that understanding the underlying factors leading to a signal being classified as spoofed is crucial for gaining insights and effectively mitigating the effects of spoofing. In this paper, we propose a machine learning approach that incorporates explainable artificial intelligence techniques, specifically Shapley Additive Explanations (SHAP), to analyze why a signal is classified as a spoofed signal. Our approach utilizes a tree-based ensemble model, achieving a high F1 score of 0.956 for three different types of spoofing attacks. By leveraging SHAP, our analysis uncovers distinctive characteristics associated with each type of spoofing, providing valuable insights into the factors contributing to a signal being classified as spoofed.
Hexagon | NovAtel’s Jamming and Spoofing Detection and Classification Performance During the Norwegian JammerTest 2023

Global Navigation Satellite Systems (GNSS) are widely used in critical infrastructure and safety-of-life applications. With such widespread use, open signal descriptions, and a crowded RF spectrum, jamming and spoofing are well-known threats to GNSS. Various detection and mitigation methods have been developed to deal with these threats. GNSS Resilience and Integrity Technology (GRIT) is a firmware suite developed for NovAtel’s OEM7 receivers to expand situational awareness and interference detection and mitigation tools across applications and environments to protect against GNSS threats including jamming and spoofing attacks. GRIT includes the Interference Toolkit (ITK) and Spoofing Detection Toolkit (SK) to identify when a GNSS signal is under threat. GRIT relies on different countermeasures ranging from jamming detection and characterization to spoofing detection and mitigation to provide solution integrity and reliability. The Galileo Open Service Navigation Message Authentication (OSNMA) provides message authentication as an additional layer of GNSS protection. JammerTest 2023 was conducted September 18-22, 2023 in Bliek, Norway. Various attacks were generated to interfere with and manipulate over-the-air signals to mislead the participant’s equipment. This paper shows the jamming and spoofing detection performance of NovAtel’s OEM7 receivers with actual data collected during the JammerTest 2023. Spectrum monitoring and jamming characterization on all GNSS bands supports jamming detection. The effectiveness of GRIT anti-jam and anti-spoofing technology is demonstrated using representative complex spoofing and jamming test cases during this event. OSNMA results for various scenarios are provided and discussed to demonstrate it as a complementary layer of protection to be accompanied by other security features and monitoring techniques for full protection.
Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.
Unraveling the crypto market: A journey into decentralized finance transaction network

The explosive growth of decentralized finance (DeFi) has revolutionized the accessibility and functionality of financial services. To gain valuable insights into this evolving ecosystem, analyzing transaction data using network analysis methodology proves to be highly effective. Network analysis allows us to uncover intricate relationships, patterns, and communities within the DeFi market by examining participant transactions. We model nodes group representing actors or wallet addresses, and edges represent transactions between wallet addresses. This study focuses on three prominent DeFi token-based Ethereum protocols: DAI, UNI, and WBTC. We analyzed 5,802,742 transaction data spanning from January 2022 to January 2023 of those three tokens. Using network topology metrics, we uncover market size, average transaction of each wallet address, market density, and reachability (diameter). We also detect the presence of transaction clusters and grouping quality using the modularity metric. At last, we employ centrality calculation to identify the most important wallet address and their role in the market. We gain insight by comparing those three tokens and reveal valuable insight into patterns and relationships, network dynamics, decentralized nature, and the presence of intermediaries in the token economy market. Our objective is to uncover hidden dynamics and trace asset flows, which can provide valuable information to market participants, regulators, and innovators seeking to optimize the DeFi infrastructure, ensure stability, and mitigate risks. Leveraging the power of network analysis offers the potential to shape the future of DeFi by enhancing efficiency, security, and accessibility, thereby fostering financial inclusion and empowering individuals and businesses globally.
Slide-block: End-to-end amplified security to improve DevOps resilience through pattern-based authentication

DevOps represents the fusion of cultural philosophies, tools, and practices that rapidly enhance an organization's capacity to deploy services and applications. Cloud-based tools, a subset of DevOps services, facilitate collaboration between development and operations teams within an organization. However, persistent challenges such as inadequate security management, substantial leakage of sensitive data, and system/service unavailability pose significant threats to sustainability. We propose an end-to-end enhanced security framework to fortify DevOps resilience by implementing authentication and vulnerability management through the Slide-Block methodology. Our approach comprises four sequential processes: pattern-based authentication, tri-level access control, privacy-focused data storage, and vulnerability management and correction. Initially, we establish candidate legitimacy through pattern-based authentication using the Magnificent Chacha-Poly 1305 algorithm. Subsequently, we devise effective access policies using the Enhanced Deep Deterministic Policy Gradient (EDDPG) algorithm, employing tri-level access control based on trust value, attributes, and roles for optimal user and developer selection via the African Vulture Optimization Algorithm (AVOA). Moreover, we encrypt data in transit and at rest using Mcha-Poly 1305, considering sensitivity, and store it in a blockchain to enhance data privacy. Our approach incorporates a sliding window blockchain for secure data transmission and storage. Finally, we identify and address attack and application-based issues using the Tweak Naive Bayes (Tweak-NB) algorithm and Intruder Vulnerability Scanner (IVS). Our Slide-Block framework demonstrates superior performance in detection rate, authentication time, packet loss, security strengthening, communication overhead, and latency compared to existing models.
Enhancing machine learning-based sentiment analysis through feature extraction techniques

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model's performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.