Reviewerly/exHarmony huggingface dataset:
Gaussian Process-Based Transfer Kernel Learning for Unsupervised Domain Adaptation

The discriminability and transferability of models are two important factors for the success of domain adaptation methods. Recently, some domain adaptation methods have improved models by adding a discriminant information extraction module. However, these methods need to carefully balance the discriminability and transferability of a model. To address this problem, we propose a new deep domain adaptation method, Gaussian Process-based Transfer Kernel Learning (GPTKL), which can perform domain knowledge transfer and improve the discrimination ability of the model simultaneously. GPTKL uses the kernel similarity between all samples in the source and target domains as a priori information to establish a cross-domain Gaussian process. By maximizing its likelihood function, GPTKL reduces the domain discrepancy between the source and target domains, thereby enhancing generalization across domains. At the same time, GPTKL introduces the deep kernel learning strategy into the cross-domain Gaussian process to learn a transfer kernel function based on deep features. Through transfer kernel learning, GPTKL learns a deep feature space with both discriminability and transferability. In addition, GPTKL uses cross-entropy and mutual information to learn a classification model shared by the source and target domains. Experiments on four benchmarks show that GPTKL achieves superior classification performance over state-of-the-art methods.
Rare Earth Elements in the Shok-Karagay Ore Fields (Syrymbet Ore District, Northern Kazakhstan) and Visualisation of the Deposits Using the Geography Information System

Rare earth elements deposited in ion-adsorption clay-type deposits in Northern Kazakhstan were recognised using mineralogical and geochemical methods. The diversity and mineralogical properties of the Shok-Karagay deposit and Syrymbet ore fields under investigation in this study are closely related to the process of the formation of the deposits as well as the deposits’ architecture. A combination of mineralogical research and digital technology (GIS) was used to characterise the deposits. Rare earth elements from the cerium series were found in the following quantities: La (in ppm), 43–200; Ce, 57–206; Sm, 100–300; Eu, 22–100. Yttrium-series elements were found in the following quantities: Y, 31–106; Gd, 100–200; Tb, 100–200; Dy, 0–300; Ho, 0–20; Er, 0–364; Tm, 0.28–0.85; Yb, 2.2–39; Lu, 0–200. The wireframe and block models indicated that the bodies’ forms were 1800 m wide, 3500 m long, and 20–40 m thick. The major REE group minerals in both bodies were monazite and xenotime, whereas the minor minerals included yttrium parisite, silicorabdophanite, thorite, and orangite; moreover, ilmenite and titanomagnetite were found. The 3D models that were constructed indicated that the mineralogy and geochemistry of the ore bodies played a determining role in the deposits’ architecture.
Towards Understanding the Geospatial Skills of ChatGPT

This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT's performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT's responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges of LLMs in spatially-oriented fields. We conduct our evaluation with two models, GPT-3.5 and GPT-4, to understand whether general improvements of an LLM translate to improvements in answering questions related to the spatial domain. We find that both GPT variants can pass a balanced, introductory GIS exam, scoring 63.3% (GPT-3.5) and 88.3% (GPT-4), which correspond to grades D and B+ respectively in standard US letter grading scale. In addition, we also identify specific questions and topics where the LLMs struggle to grasp spatial concepts, highlighting the challenges in teaching such topics to these models. Finally, we assess ChatGPT's performance in specific aspects of GIS, including spatial analysis, basic concepts of mapping, and data management. This granular analysis provides further insights into the strengths and weaknesses of ChatGPT's GIS literacy. This research contributes to the ongoing dialogue on the integration of AI models in education and can provide guidance for educators, researchers, and practitioners seeking to leverage LLMs in GIS. By focusing on specific questions or concepts that pose difficulties for the LLM, this study addresses the nuances of teaching spatial concepts to AI models and offers potential avenues for improvement in spatial literacy within future iterations of LLMs.
Smart Contract-Based Access Control Framework for Internet of Things Devices

The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance.
Asymmetric convolution Swin transformer for medical image super-resolution

Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic accuracy. Transformer-based methods, such as Image Restoration Using Swin Transformer (SwinIR) and Swin transformer for fast Magnetic Resonance Imaging (SwinMR), have shown prowess in this area but also exhibit limitations. Specifically, LayerNorm channel normalization diminishes high-frequency detail, while the Multilayer Perceptron prioritizes global information over local information. Moreover, low-resolution inputs contain substantial low-frequency information, treated uniformly in self-attention, hampering the effectiveness of shifted window-based self-attention. To address these challenges, this study proposes a novel Asymmetric convolution Swin Transformer Layer that leverages global and local information within adjacent windows or pixels. Furthermore, this study presents an innovative joint attention mechanism that integrates channel attention into the Swin Transformer architecture, allowing for the simultaneous capture of local and global information and enhancing the network's ability to adapt and represent information effectively. Based on these components, this study develops a joint Channel Attention and Swin Transformer Residual Network (CSRNet) for medical image super-resolution. To evaluate its performance, this study conducts comprehensive experiments on established datasets consisting of high-resolution medical images. Compared to the state-of-the-art medical image super-resolution methods, the proposed method outperforms in reconstructing medical images across qualitative and quantitative metrics.
An enhanced adaptive 3D path planning algorithm for mobile robots with obstacle buffering and improved Theta* using minimum snap trajectory smoothing

This study proposes an adaptive robot pathfinding algorithm (MS-W-Theta*) based on fused trajectory smoothing with 3D maps. Firstly, we introduce an obstacle buffer in the 3D map, which includes the height and volume of the AGV when carrying shelves. This enhancement improves the robot's path traversal in narrow spaces. Then, we propose an improved adaptive heuristic function and a cost function to optimize the total cost function of the algorithm, based on the obstacle distribution information on the 3D map. This step increases the purposefulness of the planned path. Finally, the original path is smoothed using a Minimum Snap polynomial algorithm to avoid the unstable speed or jitter phenomenon that may occur during the actual operation of the mobile robot and to improve the stability of the robot operation. The experimental results show that MS-W-Theta* exhibits a significant advantage in terms of running time, and the generated path curves better align with the requirements of real-world mobile robot scenarios. The practicality and high efficiency of the algorithm in path planning for workshop logistics robots are demonstrated through experiments, indicating a reduction in running time by 20% and an improvement in path alignment by 15% compared to the baseline algorithm.
Dynamic modeling and AI-based control of a cable-driven parallel robot

Controlling over-constrained cable-driven parallel robots (CDPRs) is a challenging task due to the complex dynamics of the system. Classical controllers require force distribution algorithms that involve an optimization problem, which is time consuming. In this paper, we propose an AI-based approach that learns a controller from simulated trajectories. A dynamic model of the CDPR is first validated experimentally on a real robot. Then, the controller is trained on the CDPR simulator with randomly generated trajectories using a deep deterministic policy gradient (DDPG). Finally, the trained controller is tested on different trajectories. Validation results show that the proposed approach is able to track unknown trajectories with a good accuracy.
Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes

Map generalization is a complex task that requires a high level of spatial cognition, and deep learning techniques have shown in numerous research fields that they could match or even outplay human cognition when knowledge is implicitly in the data. First experiments that apply deep learning techniques to map generalization tasks thereby adapt models from image processing, creating input data by rasterizing spatial vector data. Because image-based learning has major shortcomings for map generalization, this article investigates possibilities to learn directly from vector data, utilizing vector-based encoding schemes. First, we enhance preprocessing methods to match essential properties of deep learning models – namely regularity and feature description – and evaluate the performance of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Convolutional Neural Networks (GCNN) in combination with a feature-based encoding scheme. The results show that feature descriptors improve the accuracy of all three neural networks, and that the overall performances of the models are quite similar for both polygon and polyline shape classification tasks. In a second step, we implement an exemplary building generalization workflow based on shape classification and template matching, and discuss the generalization results based on a case study.
Technological Methods and Security of Information Resources in Dame Patience Goodluck Jonathan Automated Library, Ignatius Ajuru University of Education

This study investigates technological methods and security of information resources at Dame Patience Goodluck Jonathan Automated Library, Ignatius Ajuru University of Education. The study adopted a descriptive research survey design. The population of the study was 32 library staff comprising professionals and para-professionals in Dame Patience Goodluck Jonathan Automated Library, Ignatius Ajuru University of Education. A census sampling technique was used to select the entire population. The questionnaire was used for data collection and 27 copies of the questionnaires were found valid for data analysis. The study was analysed using simple percentages and mean and standard deviation were applied. The study revealed that the information resources available in Dame Patience Goodluck Jonathan Automated Library comprise of books, e-books, journals, e-journals, databases, encyclopaedias, the bible, and fiction. The security threats to information resources were mutilation, theft, microbes, humidity, and unauthorised access. The technological methods adopted to secure library resources were computers and burglary protection. Several challenges were found to militate against the security of information resources in the library such as lack of funds to procure security equipment, poor adoption of technological methods, lack of state-of-the-art infrastructure, and nonchalant attitudes of staff towards security measures to protect information resources. The study recommended the adoption of technologies such as CCTV, biometrics, and electronic doors in the security of library resources and training staff on their usage.
An Efficient and High-Quality Mesh Reconstruction Method with Adaptive Visibility and Dynamic Refinement

Image-based 3D reconstruction generates 3D mesh models from images and plays an important role in all walks of life. However, existing methods suffer from poor reconstruction quality and low reconstruction efficiency. To address this issue, we propose an improved optimization-based mesh reconstruction method with adaptive visibility reconstruction and dynamic photo-metric refinement. The adaptive visibility reconstruction adjusts soft visibility based on the observation and geometry structure of points to reconstruct details while suppressing noise in the rough mesh. The dynamic photo-metric refinement tunes the learning rate using historical gradients and stops to optimize converged triangles to speed up the mesh refinement. Experiments on BlendedMVS and real datasets showed that our method found a good balance between reconstruction quality and reconstruction efficiency. Compared with the state-of-the-art methods, OpenMVS and TDR, our method achieved higher reconstruction quality than OpenMVS and obtained competitive reconstruction quality with TDR, but required only one-third of the reconstruction time of OpenMVS and one-tenth of the reconstruction time of TDR. Our method balances reconstruction efficiency and reconstruction quality and can meet real-world application requirements.
ViT-DexiNet: a vision transformer-based edge detection operator for small object detection in SAR images

This paper introduces a novel edge detection operator called 'vision transformer-based Dexinet (ViT-DexiNet)' to address the challenges of detecting small objects in synthetic aperture radar (SAR) images. SAR images are typically impacted by strong multiplicative noise, making edge detection difficult. Existing traditional methods have limited spectral data preservation capabilities and often result in a loss of clarity and integrity of salient features in SAR images. The proposed ViT-DexiNet operator employs a series of interconnected layers to extract and refine salient edge features from SAR images. It utilizes a vision transformer self-attention layer to capture the pattern and structural details of image features crucial for determining edges. The extracted feature maps are then processed by the DexiNet architecture, which consists of a dense block, transfer block, and upsampling network. This architecture helps preserve edge information at different scales in deeper layers. The series of layered blocks generate edge maps, which are concatenated and averaged through smoothing to remove noise and enhance edge details in SAR images, resulting in a final high-quality edge map. To evaluate the proposed ViT-DexiNet method, both qualitative and quantitative analyses are conducted using standard edge detection operators such as Canny and Sobel. The empirical results demonstrate that the ViT-DexiNet surpasses baseline edge detection operators. The achieved values of proposed edge detection operator are 97.92%, 97.72%, 97.64% and 97.41%, respectively for the metrics accuracy, precision, recall and f1-score. The ViT-DexiNet offers high-quality edge maps, simplifying the interpretation of data for small object detection. Overall, the ViT-DexiNet method shows promise in overcoming the limitations of traditional approaches and improving the detection of edges in SAR images.
Applying virtual reality and augmented reality to the tourism experience: a comparative literature review

Purpose The purpose of this paper is to investigate how previous literature has analyzed the role of augmented reality and virtual reality in the field of tourism, distinguishing between studies focused on one technology or the other as both have characteristics that profoundly differentiate them. Design/methodology/approach This study carries out a critical review to assess and synthesize the literature on augmented reality and virtual reality in tourism. Literature searches are conducted using various keywords, resulting in a selection of 84 articles (19 on augmented reality and 65 on virtual reality) from 39 indexed journals. Findings The research findings demonstrate an increasing scholarly focus on exploring the application of virtual reality and augmented reality within the realm of tourism. These results highlight a noticeable progression in recent years with respect to different matters, such as methodologies, used theories or considered variables, among others. Based on these results, it is proposed a future research agenda that seeks to establish a cohesive framework and drive the development of both augmented reality and virtual reality research in tourism. Originality/value By conducting an individual and comparative review of the literature on the application of augmented reality and virtual reality in tourism, this research helps elucidate the specific lines of research for each technology while providing a better understanding of how each technology can be used to generate effective tourist experiences.
Revealing People’s Sentiment in Natural Italian Language Sentences

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.
Always Provide Context: The Effects of Code Context on Programming Error Message Enhancement

Programming error messages (PEMs) are notoriously difficult for novice programmers to utilise. Many efforts have been made to enhance PEMs such that they are reworded to explain problems in terms that novices can understand. However, the effectiveness of these efforts to enhance PEMs has been weak or inconclusive. This work seeks to determine the role that code context has on programming error message enhancement. Erroneous Java code written by novices was sampled from the Blackbox Mini dataset. The erroneous code was presented to expert raters with four different PEM variants: javac (control), Decaf -- an error message enhancing IDE -- and two variants generated using GPT-4: one that enhanced just the javac error message alone, and one that incorporates the code context in the prompt. We find that providing code context to LLMs increases the likelihood of correct explanations for underlying errors, produces more specific fixes for erroneous programs, and produces fixes that are more likely to be correct. In large language models, the community now has a resource that is capable of taking code context into account, to the benefit of novice programmers.
Jailbreaker in Jail: Moving Target Defense for Large Language Models

Large language models (LLMs), known for their capability in understanding and following instructions, are vulnerable to adversarial attacks. Researchers have found that current commercial LLMs either fail to be "harmless" by presenting unethical answers, or fail to be "helpful" by refusing to offer meaningful answers when faced with adversarial queries. To strike a balance between being helpful and harmless, we design a moving target defense (MTD) enhanced LLM system. The system aims to deliver non-toxic answers that align with outputs from multiple model candidates, making them more robust against adversarial attacks. We design a query and output analysis model to filter out unsafe or non-responsive answers. %to achieve the two objectives of randomly selecting outputs from different LLMs. We evaluate over 8 most recent chatbot models with state-of-the-art adversarial queries. Our MTD-enhanced LLM system reduces the attack success rate from 37.5% to 0%. Meanwhile, it decreases the response refusal rate from 50% to 0%.
Photonic Meta‐Neurons

Abstract Artificial intelligence (AI) has witnessed a growing integration into numerous domains of production and daily life. Such powerful AI has emerged from electronic simulations of human neurons. In the post‐Moore era, Von‐Neumann architecture and high energy consumption have posed challenges for the advancement of electronic AI. In light of these limitations, a new concept of photonic meta‐neurons is proposed, which holds the potential to address these issues by optically emulating biological neurons through the utilization of metasurfaces. Meta‐neurons could enable flexible modulation of photonic signals through hierarchical and high‐dimensional manipulation of light fields. The flat and thin design of the meta‐neuron reduces the spatial constraints. In comparison to electronic AI, meta‐neurons offer a range of desirable attributes, including the potential for achieving light‐speed processing, parallel computing, clean energy utilization, high energy efficiency, and an in‐memory computing architecture. The meta‐neuron concept can serve as a promising starting point for photonic AI. With its advantages of high‐speed computing and environmental friendliness, meta‐neurons will have broad applications in various fields, particularly for latency‐critical tasks.