Reviewerly/exHarmony huggingface dataset:
Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey

With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and errors within knowledge graphs. To address this, reasoning becomes essential for supplementing and rectifying these shortcomings. Logical rule-based knowledge graph reasoning methods excel at performing inference by uncovering underlying logical rules, showcasing remarkable generalization ability and interpretability. Moreover, the flexibility of logical rules allows for seamless integration with diverse neural network models, thereby offering promising prospects for research and application. Despite the growing number of logical rule-based knowledge graph reasoning methods, a systematic classification and analysis of these approaches is lacking. In this review, we delve into the relevant research on logical rule-based knowledge graph reasoning, classifying them into four categories: methods based on inductive logic programming (ILP), methods that unify probabilistic graphical models and logical rules, methods that unify embedding techniques and logical rules, and methods that jointly use neural networks (NNs) and logical rules. We introduce and analyze the core concepts and key techniques, as well as the advantages and disadvantages associated with these methods, while also providing a comparative evaluation of their performance. Furthermore, we summarize the main problems and challenges, and offer insights into potential directions for future research.
Low-Resource Language Processing Using Improved Deep Learning with Hunter–Prey Optimization Algorithm

Low-resource language (LRL) processing refers to the development of natural language processing (NLP) techniques and tools for languages with limited linguistic resources and data. These languages often lack well-annotated datasets and pre-training methods, making traditional approaches less effective. Sentiment analysis (SA), which involves identifying the emotional tone or sentiment expressed in text, poses unique challenges for LRLs due to the scarcity of labelled sentiment data and linguistic intricacies. NLP tasks like SA, powered by machine learning (ML) techniques, can generalize effectively when trained on suitable datasets. Recent advancements in computational power and parallelized graphical processing units have significantly increased the popularity of deep learning (DL) approaches built on artificial neural network (ANN) architectures. With this in mind, this manuscript describes the design of an LRL Processing technique that makes use of Improved Deep Learning with Hunter–Prey Optimization (LRLP-IDLHPO). The LRLP-IDLHPO technique enables the detection and classification of different kinds of sentiments present in LRL data. To accomplish this, the presented LRLP-IDLHPO technique initially pre-processes these data to improve their usability. Subsequently, the LRLP-IDLHPO approach applies the SentiBERT approach for word embedding purposes. For the sentiment classification process, the Element-Wise–Attention GRU network (EWAG-GRU) algorithm is used, which is an enhanced version of the recurrent neural network. The EWAG-GRU model is capable of processing temporal features and includes an attention strategy. Finally, the performance of the EWAG-GRU model can be boosted by adding the HPO algorithm for use in the hyperparameter tuning process. A widespread simulation analysis was performed to validate the superior results derived from using the LRLP-IDLHPO approach. The extensive results indicate the significant superiority of the performance of the LRLP-IDLHPO technique compared to the state-of-the-art approaches described in the literature.
Normalized Linearly-Combined Chaotic System: Design, Analysis, Implementation, and Application

This work presents a general framework for developing a multi-parameter 1-D chaotic system for uniform and robust chaotic operation across the parameter space. This is important for diverse practical applications where parameter disturbance may cause degradation or even complete disappearance of chaotic properties. The wide uninterrupted chaotic range and improved chaotic properties are demonstrated with the aid of stability analysis, bifurcation diagram, Lyapunov exponent (LE), Kolmogorov entropy, Shannon entropy, and correlation coefficient. We also demonstrate the proposed system's amenability to cascading for further performance improvement. We introduce an efficient Field-Programmable Gate Array (FPGA)-based implementation and validate its chaotic properties using comparison between simulation and experimental results. Cascaded NLCS exhibits ALE (Average LE), CR (chaotic ratio), and CPS(chaotic parameter space) of 1.364, 100%, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.1\times 10^{12}$</tex-math></inline-formula> , respectively for 10-bit parameter values. We provide a thorough comparison of our system with prior works both in terms of performance and hardware cost. We also introduce a simple extension scheme to build 2-D robust, hyperchaotic NLCS maps. We present a novel reconfigurable multi-parameter Pseudo Random Number Generator (PRNG) and validate its randomness using two standard statistical tests, namely, NIST SP 800-22 and FIPS PUB 140-2. . Finally, we outline six potential applications where NLCS will be useful.
Deep Hashing Network With Hybrid Attention and Adaptive Weighting for Image Retrieval

Due to the low computational cost of Hamming distance, hashing-based image retrieval has been universally acknowledged. Therefore, it is becoming increasingly important to quickly generate high-precision hash codes (also hash features) from images. However, the existing deep hashing methods are vulnerable to image content variations; that is, it is difficult to generate stable and consistent hash codes for similar images. In addition, generating hash codes of different lengths requires retraining the model, which is expensive in training time. To address these problems, this paper proposes a deep hashing network (DHN) with a hybrid attention mechanism and adaptive weighting (HAAW) learning. It mainly consists of a feature extraction module, feature refinement module, classification layer, hash layer and an adaptive weight layer. In particular, the hybrid attention mechanism combines bottom-up pixel saliency and top-down semantic constraints, in which the former is achieved through channel and spatial attention (CSA) and the latter is supervised by classification labels. In this way, it encourages the network to focus on dominant semantic features without being disturbed by irrelevant objects so that semantically similar images can be mapped to approximate hash codes. We further propose an adaptive weighting learning algorithm to generate weights for each bit of the hash code generated by the deep network. Then, we directly generate shorter hash codes from the available long hash code according to the importance of bits represented by the weights. This avoids retraining the network for learning hash codes of different lengths. Extensive experiments on public CIFAR-10, NUS_WIDE and ImageNet datasets show that our method has achieved substantial improvements over the counterparts in terms of precision and speed.
Automating Quality Control of Irradiance Data with a Comprehensive Analysis for Southern Africa

A review of quality control for large irradiance datasets is applied as a case study for the Southern African Universities Radiometric Network (SAURAN) database. The quality control procedure is automated and applied to 24 stations from the database with a total of 848,189 hourly datapoints. From this, the individual station’s data quality is also analysed. The assessment validates the automated methodology without the need for a user-based review of the data. The SAURAN database can play a significant role in advancing solar and wind energy; however, the number of offline stations hinders this process. Data scarcity remains an obstacle to these goals, and therefore, recommendations are provided to address this. Recommendations regarding each site’s usability in time-series and discrete applications are made, which provides an overall indication of the SAURAN database’s irradiance measurement quality. Of the 24 measuring stations assessed, eight are recommended, 11 are recommended with cautious use, and five are recommended with extremely cautious use. These recommendations are based on multiple factors, such as whether a dataset has more than one full year of data or is missing minimal datapoints. Further, a study of the irradiance correlation between the stations was conducted. The results indicated groupings of different stations that showed highly correlated irradiance measurements and similar weather patterns. This is useful if a proposed renewable energy power plant, such as PV, falls within a cluster where the data from the SAURAN database can be used as a substitute if no data is available. SAURAN presents an opportunity for Southern Africa to increase its research outputs in solar and wind energy and lessen its dependency on fossil fuel-based energy production.
Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice

The vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron, WaveRNN, and GE2E neural networks. The results of attacks using voice cloning were analyzed and discussed in the context of a subjective assessment of cloned voice fidelity. Subjective test results and attempts to authenticate speakers proved that the tested biometric identity verification system might resist voice cloning attacks even if humans cannot distinguish cloned samples from original ones.
FLRAM: Robust Aggregation Technique for Defense against Byzantine Poisoning Attacks in Federated Learning

In response to the susceptibility of federated learning, which is based on a distributed training structure, to byzantine poisoning attacks from malicious clients, resulting in issues such as slowed or disrupted model convergence and reduced model accuracy, we propose a robust aggregation technique for defending against byzantine poisoning attacks in federated learning, known as FLRAM. First, we employ isolation forest and an improved density-based clustering algorithm to detect anomalies in the amplitudes and symbols of client local gradients, effectively filtering out gradients with large magnitude and angular deviation variations. Subsequently, we construct a credibility matrix based on the filtered subset of gradients to evaluate the trustworthiness of each local gradient. Using this credibility score, we further select gradients with higher trustworthiness. Finally, we aggregate the filtered gradients to obtain the global gradient, which is then used to update the global model. The experimental findings show that our proposed approach achieves strong defense performance without compromising FedAvg accuracy. Furthermore, it exhibits superior robustness compared to existing solutions.
Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multiple scenes and multiple sizes, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.
Self-Supervised Non-Uniform Low-Light Image Enhancement Combining Image Inversion and Exposure Fusion

Low-light image enhancement is a challenging task in non-uniform low-light conditions, often resulting in local overexposure, noise amplification, and color distortion. To obtain satisfactory enhancement results, most models must resort to carefully selected paired or multi-exposure data sets. In this paper, we propose a self-supervised framework for non-uniform low-light image enhancement to address these issues, only requiring low-light images on their own for training. We first design a robust Retinex model-based image exposure enhancement network (EENet) to obtain global brightness enhancement and noise removal of images by carefully designing the loss function of each decomposition map. Then, to correct overexposed areas in the enhanced image, we incorporate the inverse image of the low-light image for enhancement using EENet. Furthermore, a three-branch asymmetric exposure fusion network (TAFNet) is designed. The two enhanced images and the original image are used as the TAFNet inputs to obtain a globally well-exposed and detail-rich image. Experimental results demonstrate that our framework outperforms some state-of-the-art methods in visual and quantitative comparisons.
Government Fund Distribution and Tracking System using Blockchain Technology

Governments must address a wide range of governmental responsibilities. State government operations require many transactions related to various functions that must be carried out throughout the state. This comprises new projects, repairs and maintenance, contract awards, government employee salaries, farmer programmes, and so on. The low-level corruption that is sometimes impossible to trace is a key impediment to the state's success. Because of the existing system, tracking it is a challenging effort.We propose in this paper a smart system for tracking money granted to the state government as they move through the government process at each stage.We employ blockchain technology to safeguard transactions at every level while retaining transparency and sealing every transaction with proofs as the funds go forward. Blockchain, or block chain, is a growing set of documents called blocks that are linked together via encryption. Each block contains the previous block's cryptographic hash, a timestamp, and transaction data.
A Taxonomy of SETA Methods and Linkage to Delivery Preferences

Cybersecurity threats targeting users are common in today's information systems. Threat actors exploit human behavior to gain unauthorized access to systems and data. The common suggestion for addressing this problem is to train users to behave better using SETA programs. The notion of training users is old, and several SETA methods are described in scientific literature. Yet, incidents stemming from insecure user behavior continue to happen and are reported as one of the most common types of incidents. Researchers argue that empirically proven SETA programs are needed and point out focus on knowledge rather than behavior, along with poor user adoption, as problems with existing programs. The present study aims to research user preferences regarding SETA methods, with the motivation that a user is more likely to adopt a program perceived positively. A qualitative approach is used to identify existing SETA methods, and a quantitative approach is used to measure user preferences regarding SETA delivery. We show that users prefer SETA methods to be effortless and flexible and outline how existing methods meet that preference. The results outline how SETA methods respond to user preferences and how different SETA methods can be implemented to maximize user perception, thereby supporting user adoption.
Evaluating the Efficacy of a Desktop Virtual Reality Field Trip for Public Outreach

This evaluation study examines the implementation of a desktop virtual reality field trip (dVFT) for public outreach. After experiencing the dVFT, 139 participants completed a survey to examine their immersion, presence, engagement, perceived learning about the local environment, VR design features, and affective learning. The majority of participants reported favorable outcomes regarding all the variables above. No significant differences were found between male and female learners for each of the six constructs. No significant differences were found between adults’ and youths’ perceptions of immersion, presence, and attitudes. Adults ages 19 and older had statistically significant higher mean engagement levels, mean perceptions of learning about the local environment, and VR design features than youth. Our findings support that learning about one’s local environment with a dVFT can have a positive impact on engagement and learning, particularly in public outreach settings.
Work, Trade, Learn: Developing an Immersive Serious Game for History Education

History education often struggles with a lack of interest from students. Serious games can help make learning about history more engaging. Students can directly experience situations of the past as well as interact and communicate with agents representing people of the respective era. This allows for situated learning. Besides using computer screens, the gameplay can also be experienced using immersive Virtual Reality (VR). VR adds an additional spatial level and can further increase the engagement as well as vividness. To investigate the benefits of using VR for serious games targeting the learning of history, we developed a serious game for desktop-3D and VR. Our serious game puts a player into the role of a medieval miller’s apprentice. Following a situated learning approach, the learner operates a mill and interacts with several other characters. These agents discuss relevant facts of the medieval life, thus enabling the construction of knowledge about the life in medieval towns. An evaluation showed that the game in general was successful in increasing the user’s knowledge about the covered topics as well as their topic interest in history. Whether the immersive VR or the simple desktop version of the application was used did not have any influence on these results. Additional feedback was gathered to improve the game further in the future.
A Portable Multi-user Cross-Platform Virtual Reality Platform for School Teaching in Malawi

This paper discusses and evaluates a self-contained portable multi-user cross-platform Virtual Reality (VR) setup that was devised and configured using off the shelf technologies and devices. This paper exemplifies how some fundamental challenges like those faced in Malawi in relation to technology use, can be addressed, to allow for the use of VR technology as a potential solution to improving the quality of secondary school education in situations where the challenges in question are faced. This paper explains how the proposed VR setup was evaluated, where the results of that evaluation indicate that the proposed portable multi-user cross-platform VR setup is viable and can potentially be used for secondary school teaching. This is a follow-up to previous work that outlined the design and implementation of a VR software application to showcase the capabilities and functionality of this “Synchronous Multi-User Cross-Platform Virtual Reality for School Teachers”, which consisted of using questionnaire data collected from school educators in England and it was part of a larger study. Whilst the challenges addressed are those that are faced in Malawi, the platform has more general applicability to a range of teaching contexts.
A Platform for Analyzing Students’ Behavior in Virtual Spaces on Mozilla Hubs

Social Virtual Reality (VR) or “Metaverse” platforms provide teachers with the opportunity to use educational virtual spaces for both distance learning and face-to-face teaching, to take advantage of its unique learning affordances. However, designing virtual spaces and incorporating them into teaching presents a challenge. Since teachers are typically not around when students use virtual spaces, it is difficult for them to ascertain how the spaces are being used during the classes. This article outlines a project to develop a data-logging and visualization tool for teachers making use of Mozilla Hubs. The tool tracks student behavior and presents the data in 3D as raw data points or heatmaps and only requires a standardized set-up with no need for client or server-side programming, thus allowing teachers with limited programming experience to use it. This framework will be further developed to include more visualization and analytical tools, such as trajectories and clustering, and will be made publicly available upon completion of the development.
An Immersive Laboratory Environment for a Customized Learning Experience

Science, technology, engineering, and mathematics (STEM) are important drivers of innovation, yet students are often unmotivated and do not understand why they need to learn these subjects. Virtual reality (VR) and virtual environments are useful tools for conceptual understanding with a high degree of immersion. They enable the creation of engaging and inspiring learning experiences. Additionally, personalization and customization can promote motivated usage and high user acceptability. A customized experience that takes into account the unique characteristics of the player can thus be an essential factor in learning. In this paper, we explore the potential of a customized learning environment to increase students’ motivation in learning physics. We present an AB study with 95 students to evaluate their motivation and experience during the learning process. The results indicate that the learning experience slightly increases when the learning process takes place in a customizable experimental environment.
Research Agenda 2030: The Great Questions of Immersive Learning Research

The research areas of the Immersive Learning community cover many different interests and perspectives on teaching and learning with immersive technologies. Based on existing efforts to map the field of research, we gathered 35 participants at the iLRN 2022 conference during an open hybrid workshop. These volunteers formed expert groups focusing on five possible perspectives on Immersive Learning. The expert groups gathered and summarized possible research questions with regards to an “Agenda 2030”, meaning the most intriguing questions that should be addressed during the years to come. We let all participants vote on these research endeavors regarding their academic value and importance for the community. As a results, we gathered a total of 23 ranked questions. These questions were subsumed into ten topics forming a Research Agenda for Immersive Learning 2030 (RAIL.2030).