Reviewerly/exHarmony huggingface dataset:
Cross-modality Multiple Relations Learning for Knowledge-based Visual Question Answering

Knowledge-based visual question answering not only needs to answer the questions based on images but also incorporates external knowledge to study reasoning in the joint space of vision and language. To bridge the gap between visual content and semantic cues, it is important to capture the question-related and semantics-rich vision-language connections. Most existing solutions model simple intra-modality relation or represent cross-modality relation using a single vector, which makes it difficult to effectively model complex connections between visual features and question features. Thus, we propose a cross-modality multiple relations learning model, aiming to better enrich cross-modality representations and construct advanced multi-modality knowledge triplets. First, we design a simple yet effective method to generate multiple relations that represent the rich cross-modality relations. The various cross-modality relations link the textual question to the related visual objects. These multi-modality triplets efficiently align the visual objects and corresponding textual answers. Second, to encourage multiple relations to better align with different semantic relations, we further formulate a novel global-local loss. The global loss enables the visual objects and corresponding textual answers close to each other through cross-modality relations in the vision-language space, and the local loss better preserves semantic diversity among multiple relations. Experimental results on the Outside Knowledge VQA and Knowledge-Routed Visual Question Reasoning datasets demonstrate that our model outperforms the state-of-the-art methods.
Contrastive Graph Prompt-tuning for Cross-domain Recommendation

Recommender systems commonly suffer from the long-standing data sparsity problem where insufficient user-item interaction data limits the systems’ ability to make accurate recommendations. This problem can be alleviated using cross-domain recommendation techniques. In particular, in a cross-domain setting, knowledge sharing between domains permits improved effectiveness on the target domain. While recent cross-domain recommendation techniques used a pre-training configuration, we argue that such techniques lead to a low fine-tuning efficiency, especially when using large neural models. In recent language models, prompts have been used for parameter-efficient and time-efficient tuning of the models on the downstream tasks—these prompts represent a tunable latent vector that permits to freeze the rest of the language model’s parameters. To address the cross-domain recommendation task in an efficient manner, we propose a novel Personalised Graph Prompt-based Recommendation (PGPRec) framework, which leverages the efficiency benefits from prompt-tuning. In such a framework, we develop personalised and item-wise graph prompts based on relevant items to those items the user has interacted with. In particular, we apply Contrastive Learning to generate the pre-trained embeddings, to allow an increased generalisability in the pre-training stage, and to ensure an effective prompt-tuning stage. To evaluate the effectiveness of our PGPRec framework in a cross-domain setting, we conduct an extensive evaluation with the top- k recommendation task and perform a cold-start analysis. The obtained empirical results on four Amazon Review datasets show that our proposed PGPRec framework can reduce up to 74% of the tuned parameters with a competitive performance and achieves an 11.41% improved performance compared to the strongest baseline in a cold-start scenario.
Unleashing the power of generative adversarial networks: A novel machine learning approach for vehicle detection and localisation in the dark

Abstract Machine vision in low‐light conditions is a critical requirement for object detection in road transportation, particularly for assisted and autonomous driving scenarios. Existing vision‐based techniques are limited to daylight traffic scenarios due to their reliance on adequate lighting and high frame rates. This paper presents a novel approach to tackle this problem by investigating Vehicle Detection and Localisation (VDL) in extremely low‐light conditions by using a new machine learning model. Specifically, the proposed model employs two customised generative adversarial networks, based on Pix2PixGAN and CycleGAN, to enhance dark images for input into a YOLOv4‐based VDL algorithm. The model's performance is thoroughly analysed and compared against the prominent models. Our findings validate that the proposed model detects and localises vehicles accurately in extremely dark images, with an additional run‐time of approximately 11 ms and an accuracy improvement of 10%–50% compared to the other models. Moreover, our model demonstrates a 4%–8% increase in Intersection over Union (IoU) at a mean frame rate of 9 fps , which underscores its potential for broader applications in ubiquitous road‐object detection. The results demonstrate the significance of the proposed model as an early step to overcoming the challenges of low‐light vision in road‐object detection and autonomous driving, paving the way for safer and more efficient transportation systems.
When Technology Meets Heritage

Museum digitalization is not a new idea, but digital materials are actively introduced into museums worldwide. Information Computer Technologies are supporting the digitalization of museums. The digitalization of museums is connected with the preservation and presentation of objects of cultural heritage. The use of ICT affects positively the interaction between visitors and collections. This is in alliance with the primary outcomes of digital curation. Recent studies confirm that the digitalization of exhibits in museums improves the visitors’ experience. Among the digital tools used in museums are QR codes. QR codes are a new way to upgrade visitors’ experience in museums. It is an easy and fast-paced solution to inform visitors about the museum’s objects. QR codes are attached to the exhibits of the museum and visitors receive information about them. Additionally, QR codes can be used in museums to help visitors learn the history of a country interactively and act as tools for e-learning. This study aims to present the development and application of QR codes in a museum of contemporary art in the city of Ioannina to help visitors understand better the history of the exhibits in the museum. We developed the QR codes for the exhibits of this museum, according to its needs. For the application of QR codes in the museum we used the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. According to our findings, digital material offers an excellent alternative for visitors to understand the local history with the aim of QR codes.
Construction and application of a multilevel geohazard domain ontology: A case study of landslide geohazards

The occurrence of geohazards entails sudden, unpredictable, and cascading effects, with numerous conceptual frameworks and intricate spatiotemporal relationships existing between hazard events. Presently, the absence of a unified mechanism for describing and expressing geohazard knowledge poses substantial challenges in terms of sharing and reusing domain-specific knowledge pertaining to geohazards. Therefore, it is imperative to address the issue of constructing a cohesive descriptive model that facilitates the sharing and reuse of geohazard knowledge. In this study, we propose a multilayered ontology construction method tailored specifically for the domain of landslide geological hazards. By comparing existing methods, we establish a hierarchical structure and expression framework for the geological hazard ontology. Notably, our approach seamlessly integrates the conceptual and semantic layers in the relationship description at each level, enabling association representation of hazard data across multiple tiers. We define essential concepts and attributes related to landslide geological hazards, along with their respective interrelationships. To achieve effective knowledge sharing and reuse, we model the ontology of the landslide geological disaster domain using the Web Ontology Language (OWL). This modeling approach serves as a powerful tool that facilitates the sharing and reuse of disaster-related knowledge. Finally, we verify the method's validity and reliability by employing illustrative case studies. The results demonstrate that the proposed approach imposes an affordable workload on human resources. Additionally, the foundational domain ontology significantly enhances information retrieval performance, thereby yielding satisfactory outcomes.
Is bitcoin ESG‐compliant? A sober look

Abstract Much of the media focus surrounding Bitcoin (BTC) has been on the ‘E’ (environmental) element of the ESG investing approach. Given the amount of electricity consumed by BTC mining, and the resulting large carbon emissions, BTC has faced substantial criticism of its overly negative environmental impact, which is critically reviewed in this article. This one‐sided discussion, however, ignores the ‘S’ (social) and ‘G’ (governance) elements entirely. To remedy that, we explore BTC's positive impact on the ‘S’ (user satisfaction, data protection and privacy, human rights, and criminal activity), and ‘G’ (accounting integrity and transparency, compensation, and principles of good governance) components.
UACNet: A universal automatic classification network for microseismic signals regardless of waveform size and sampling rate

In microseismic monitoring, various types of vibration events are often collected. Realizing the automatic identification of microseismic events in many suspected events is the basis of monitoring timeliness. However, due to the different sampling methods of microseismic data provided by different products, the data often contains different waveform sizes and sampling frequencies. This makes it difficult for existing approaches to be widely used in different projects without data preprocessing. In this paper, we propose the Universal Automatic Classification Network (UACNet), a deep learning approach that automatically identifies microseismic data in engineering without preprocessing. The UACNet model includes multiple convolution layers, adaptive average pooling layers, fully connected layers, and UAC blocks. UAC block is a residual structure with multiple convolutional layers and reset and update gates. The adaptive average pooling layer unifies the input size, and the UAC block functions as a feature extraction network to mine sufficient features from data. We test the proposed UACNet on engineering data and compare it with existing common and advanced methods. As a result, UACNet passed the ablation study, and the classification accuracy of UACNet is 95.62%, which is higher than 89.14% of CNN, 91.24% of ResNet, 91.04% of CapsNet, and 86.16% of RTFN, respectively. Moreover, the influence of waveform size, sampling rate, signal-to-noise ratios, and amplitude on the accuracy of UACNet is analyzed. The results show that UACNet can overcome the influence of these factors and truly realize automatic real-time classification of microseismic signals without preprocessing.
Exploring the potential of automated speech recognition for scoring the Korean Elicited Imitation Test

Elicited Imitation Tests (EITs) are often used as a practical tool for measuring L2 proficiency. In this study, we explore the potential of applying three commercial automated speech recognition (ASR) tools—Amazon, Google, and Naver—in combination with several transcription scoring metrics to automate Korean EIT scoring. Two hundred and four participants' previously human-scored EITs (Isbell & Lee, 2022) were compared with automated EIT scores based on ASR transcriptions. Nearly all combinations of ASR platforms and transcription metrics correlated with original human EIT total scores above .90, with the Naver and Google transcriptions yielding the strongest correlations (r = .94-.96). Moreover, examining the item-level relationships between transcription metrics and original EIT scores yielded moderate-to-large correlations near .80 for the Google and Naver transcriptions, thereby providing additional support for the validity of automated EIT scoring. We conclude with implications for prospective users and developers of machine-scored EITs.
GAPs for Shallow Implementation of Quantum Finite Automata

Quantum fingerprinting is a technique that maps classical input word to a quantum state.The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography.One of the examples of quantum fingerprinting is quantum automata algorithms for MOD p = {a i•p | i ≥ 0} languages, where p is a prime number.However, implementing such an automaton on the current quantum hardware is not efficient.Quantum fingerprinting maps a word x ∈ {0, 1} n of length n to a state |ψ(x) of O(log n) qubits, and uses O(n) unitary operations.Computing quantum fingerprint using all available qubits of the current quantum computers is infeasible due to a large number of quantum operations.To make quantum fingerprinting practical, we should optimize the circuit for depth instead of width in contrast to the previous works.We propose explicit methods of quantum fingerprinting based on tools from additive combinatorics, such as generalized arithmetic progressions (GAPs), and prove that these methods provide circuit depth comparable to a probabilistic method.We also compare our method to prior work on explicit quantum fingerprinting methods.
MultiResEdge: A deep learning-based edge detection approach

Edge detection is a fundamental technique in image processing and computer vision that plays a crucial role in various applications including object recognition, shape analysis, segmentation, feature extraction, image enhancement, image understanding, compression, and preprocessing. It involves identifying the boundaries or edges between different objects or regions which enables us to extract valuable visual information, analyze image structure, and facilitate subsequent computer vision tasks. In this study, we highlighted two primary issues that are faced in edge detection: edge connectivity and edge thickness. Choosing the right threshold for reliable edge detection has long been one of the most difficult problems in traditional edge detection techniques. To deal with these issues, the present research is motivated to execute a new deep learning-based edge detection model named MultiResEdge which is a tweaked variant of the original UNet framework. Instead of directly integrating encoder and decoder features, the suggested model makes use of a MutiRes block to modify spatial information from different scales, and a residual path with a set of convolutions to convey the features from the encoder to the decoder. To reduce the gap between the encoder and decoder features, we advocate using a combination of convolutional and skip connections. We also incorporated semantic edge information with semantic curves to improve the border. In addition, we have integrated a multi-step preprocessing technique that mitigates the false positives and false negatives and hence improves the accuracy, precision, recall, and F1 score. The extensive experiment is performed using images from two benchmark datasets to justify the predicted edge comparing it with the corresponding ground truth in terms of various objective metrics (such as entropy, MSE, PSNR, SSIM, and FSIM). Overall, our proposed edge detection model has obtained an accuracy of 99% along with an F1 score of 98% which is better than the related state-of-the-art methodologies.
TDMO: Dynamic multi-dimensional oversampling for exploring data distribution based on extreme gradient boosting learning

The synthetic minority oversampling technique (SMOTE) is the most general and popular solution for imbalanced data. Although SMOTE is effective in solving the class imbalance problem in most cases, it insufficiently exploits the data prior distribution. Additionally, most existing SMOTE variants randomly produce new instances between a minority sample and its nearest neighbors, which carries the risk of noise propagation. To address this, in this paper, local distribution trust estimation based on extreme gradient boosting (XGBoost) and dynamic multi-dimensional oversampling (TDMO) is proposed as a novel approach to exploring data distributions. First, undersampling and XGBoost techniques are introduced to train multiple balanced subsets to identify the internal structure of the original data and obtain the classification prediction accuracy of each instance, called the confidence level (CL). Then, instances with low CL (i.e., noise) are filtered out, and the densities of the two classes in the neighborhood of the non-noise instances are evaluated to create candidate samples to expand the diversity of the minority class. Finally, the minority class is enhanced by combining multiple samples in a multi-dimensional feature space. Extensive experimental results demonstrate that TDMO outperformed the comparative oversampling methods clearly and obtained the optimal classification results.
Mathematical expression recognition using a new deep neural model

In this paper, we propose a novel deep neural model for Mathematical Expression Recognition (MER). The proposed model uses encoder-decoder transformer architecture that is supported by additional pre/post-processing modules, to recognize the image of mathematical formula and convert it to a well-formed language. A novel pre-processing module based on domain prior knowledge is proposed to generate random pads around the formula's image to create more efficient feature maps and keeps all the encoder neurons active during the training process. Also, a new post-processing module is developed which uses a sliding window to extract additional position-based information from the feature map, that is proved to be useful in the recognition process. The recurrent decoder module uses the combination of feature maps and the additional position-based information, which takes advantage of a soft attention mechanism, to extract the formula context into the LaTeX well-formed language. Finally, a novel Reinforcement Learning (RL) module processes the decoder output and tunes its results by sending proper feedbacks to the previous steps. The experimental results on im2latex-100k benchmark dataset indicate that each devised pre/post-processing as well as the RL refinement module has a positive effect on the performance of the proposed model. The results also demonstrate the higher accuracy of the proposed model compared to the state-of-the-art methods.
A novel approach to assessing natural resource injury with Bayesian networks

Abstract Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream‐dwelling bird species. We compared the BN‐generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field‐based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2023;00:1–12. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology &amp; Chemistry (SETAC).
Review for Augmented Reality Shopping Application for Mobile Systems

The evolution of e-commerce has, in recent times, made a significant advance largely due to the forced digital transformation and to Sars-cov-2. Thus, augmented reality (AR) technology is increasingly being adopted by companies either to increase brand value or to improve the shopping experience for their consumers. This work aims to present a comparative study between some shopping applications using AR, augmented reality shopping applications (ARSAs), which are carried out in stores or online. The applications were studied in articles in Open Access format and the focus was to understand what their conclusions were as well as the problems and trends of future research toward this type of technologies.
Astrodynamics-Informed Kinodynamic Sampling-Based Motion Planning for Relative Spacecraft Motion

This paper presents a sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state, subject to kinodynamic (simultaneous kinematic and dynamics) constraints. We leverage theoretical advancement in the field of sampling-based motion planning in robotics and extend their application to achieve motion planning in astrodynamics. We present an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that takes advantage of a spacecraft’s natural motion to compute a safe, fuel-efficient motion plan for spacecraft reconfiguration in a cluttered environment. This algorithm is developed in the relative orbital element space of the deputy spacecraft around a chief spacecraft that provides very useful geometric insight to relative spacecraft motion, as well as helps in generating an overall smooth final transfer trajectory without demanding additional postprocessing of the transfer solution. Through several example scenarios, we demonstrate that the AIKMP algorithm is regularly able to compute safe and fuel-efficient solutions to relative transfer problems. We also show that this algorithm iteratively improves on its computed solutions and thus holds the capability of finding near-optimal transfer solutions given a sufficient number of algorithm iterations—without requiring an initial guess of the solution.