Reviewerly/exHarmony huggingface dataset:
OEGR-DETR: A Novel Detection Transformer Based on Orientation Enhancement and Group Relations for SAR Object Detection

Object detection in SAR images has always been a topic of great interest in the field of deep learning. Early works commonly focus on improving performance on convolutional neural network frameworks. More recent works continue this path and introduce the attention mechanisms of Transformers for better semantic interpretation. However, these methods fail to treat the Transformer itself as a detection framework and, therefore, lack the development of various details that contribute to the state-of-the-art performance of Transformers. In this work, we first base our work on a fully multi-scale Transformer-based detection framework, DETR (DEtection TRansformer) to utilize its superior detection performance. Secondly, to acquire rotation-related attributes for better representation of SAR objects, an Orientation Enhancement Module (OEM) is proposed to facilitate the enhancement of rotation characteristics. Then, to enable learning of more effective and discriminative representations of foreground objects and background noises, a contrastive-loss-based GRC Loss is proposed to preserve patterns of both categories. Moreover, to not restrict comparisons exclusively to maritime objects, we have also developed an open-source labeled vehicle dataset. Finally, we evaluate both detection performance and generalization ability on two well-known ship datasets and our vehicle dataset. We demonstrated our method’s superior performance and generalization ability on both datasets.
An Investigation of Applying Large Language Models to Spoken Language Learning

People have long desired intelligent conversational systems that can provide assistance in practical scenarios. The latest advancements in large language models (LLMs) are making significant strides toward turning this aspiration into a tangible reality. LLMs are believed to hold the most potential and value in education, especially in the creation of AI-driven virtual teachers that facilitate language learning. This study focuses on assessing the effectiveness of LLMs within the educational domain, specifically in the areas of spoken language learning, which encompass phonetics, phonology, and second language acquisition. To this end, we first introduced a new multiple-choice question dataset to evaluate the effectiveness of LLMs in the aforementioned scenarios, including the understanding and application of spoken language knowledge. Moreover, we investigated the influence of various prompting techniques such as zero- and few-shot methods (prepending the question with question-answer exemplars), chain-of-thought (CoT) prompting, in-domain exemplars, and external tools. We conducted a comprehensive evaluation of popular LLMs (20 distinct models) using these methods. The experimental results showed that the task of extracting conceptual knowledge posed few challenges for these LLMs, whereas the task of application questions was relatively difficult. In addition, some widely proven effective prompting methods combined with domain-specific examples resulted in significant performance improvements compared to the zero-shot baselines. Additionally, some other preliminary experiments also demonstrated the strengths and weaknesses of different LLMs. The findings of this study can shed light on the application of LLMs to spoken language learning.
Implementation of Fault-Tolerance Mechanism in Quorum-Based Blockchain Provisioning in Cloud Infrastructure Using Replication and Monitoring Protocols

Decentralized and distributed blockchain technology has superior potential. Quorum, as a permissioned blockchain platform, provides enhanced privacy and performance that are suitable for enterprise needs. Cloud, as the infrastructure for Quorum-based blockchain, is vulnerable to failures. Failures originating from errors need to be tolerated through improved reliability and fault-tolerance procedures. Fault-tolerance and reliability are still challenging to implement in cloud infrastructure for Quorum-based blockchain networks. This research focuses on implementing fault-tolerance mechanisms in the provisioning of Quorum-based blockchain in a cloud environment. The research explores the use of hybrid fault tolerance by adopting a combination of reactive protocol, namely replication, and proactive protocol, namely monitoring. The replication protocol creates data redundancy with reactive treatment performed in case of failures, ensuring data availability even in failure conditions. The monitoring protocol provides real-time monitoring of the blockchain network's condition and performance, enabling proactive detection and mitigation of system failures. Overall, this research utilizes automation in its operational processes. It employs tools such as Amazon Web Services, Quorum, Docker, Terraform, Ansible, and Truffle. The research design and implementation are divided into cloud infrastructure, blockchain network, and interface. The research testing results demonstrate that the system infrastructure is capable of achieving fault tolerance using replication and monitoring protocols, as well as making transactions within the blockchain network.
Generative-Model-Based Autonomous Intelligent Unmanned Systems

In order to improve the autonomous decision-making ability and dynamic environment adaptability of unmanned systems, a universal framework of generative intelligent unmanned systems (GIUS) is proposed and designed. The GIUS framework consists of a human-machine interaction module, an environment perceiving module, a task generation and autonomous decision-making module, and a motion planning module. By obtaining the overall task description through human-machine interaction, GIUS can autonomously perceive and predict environment task information through generative models. Based on the understanding of the environment, the unmanned system can achieve task generation and decision-making independently, and realize path generation and motion generation. Under the framework of GIUS, unmanned systems can perform various tasks in complex environments with minimal or no human intervention. Compared to existing unmanned systems, generative intelligent unmanned systems have significant improvements in autonomy, intelligence, and efficiency.
Smart Contract Security

This chapter offers a comprehensive overview of smart contract security, emphasizing the importance of identifying and addressing challenges and vulnerabilities. The chapter presents a detailed smart contract security checklist for developers and auditors, followed by an in-depth analysis of the top security vulnerabilities. It underscores the importance of smart contract audits, highlighting the need for rigorous auditing processes to identify and remediate potential security risks. The chapter concludes with reflections on the future of smart contract security, emphasizing the need for continuous improvement and adaptation to stay ahead of emerging threats. It highlights the importance of robust security practices for ensuring the safety and long-term success of Web3 platforms. In summary, this chapter serves as a thorough guide to smart contract security, focusing on risk mitigation, proactive security measures, and rigorous auditing. It provides valuable insights and practical advice for navigating the complex and rapidly evolving world of decentralized platforms, ensuring a safer and more robust Web3 ecosystem.
Web-Based Accounting Information System Analysis and Design for Inventory and Sales in PT SIP based on Artificial Intelligence Technology

The rapid development of technologies is changing how retail businesses operate, specifically in recording their sales and inventory information into Accounting Information Systems (AIS). With the trending implementation of Artificial Intelligence (AI), it can enhance the usage of the information system by providing better information and strategies. However, to maximize the usage of technology, businesses must have good internal control in order to prevent frauds that may occur in their business. PT Sentral Indo Persada is one of the retail companies that currently is still recording all their transactions manually. Hence, it caused several problems in tracking their sales and inventory, which could be fixed by computerizing all their operational activities. This study aims to provide solutions using a Web-Based Accounting Information System with the utilization of AI technology that able to give internal control recommendations. The data and information will be collected in a qualitative method using field study, interview, and examining documents. The result of this research is in the form of a document or artifact design, such as; Activity Diagram, Multi-Layer Sequence Diagram, Updated Class Diagram, AI Algorithm, and Recommendations for PT Sentral Indo Persada's internal control.
RoCS: Knowledge Graph Embedding Based on Joint Cosine Similarity

Knowledge graphs usually have many missing links, and predicting the relationships between entities has become a hot research topic in recent years. Knowledge graph embedding research maps entities and relations to a low-dimensional continuous space representation to predict links between entities. The present research shows that the key to the knowledge graph embedding approach is the design of scoring functions. According to the scoring function, knowledge graph embedding methods can be classified into dot product models and distance models. We find that the triple scores obtained using the dot product model or the distance model were unbounded, which leads to large variance. In this paper, we propose RotatE Cosine Similarity (RoCS), a method to compute the joint cosine similarity of complex vectors as a scoring function to make the triple scores bounded. Our approach combines the rotational properties of the complex vector embedding model RotatE to model complex relational patterns. The experimental results demonstrate that the newly introduced RoCS yields substantial enhancements compared to RotatE across various knowledge graph benchmarks, improving up to 4.0% in hits at 1 (Hits@1) on WN18RR and improving up to 3.3% in Hits@1 on FB15K-237. Meanwhile, our method achieves some new state-of-the-art (SOTA), including Hits@3 of 95.6%, Hits@10 of 96.4% on WN18, and mean reciprocal rank (MRR) of 48.9% and Hits@1 of 44.5% on WN18RR.
Smart contract applications in the built environment: How prepared are Nigerian construction stakeholders?

Abstract Studies have demonstrated that advanced technology, such as smart contract applications, can enhance both pre- and post-contract administration within the built environment sector. Smart contract technology, exemplifying blockchain technologies, has the potential to improve transparency, trust, and the security of data transactions within this sector. However, there is a dearth of academic literature concerning smart contract applications within the construction industries of developing countries, with a specific focus on Nigeria. Consequently, this study seeks to explore the relevance of smart contract technology and address the challenges impeding its adoption, offering strategies to mitigate the obstacles faced by smart contract applications. To investigate the stakeholders, this research conducted 14 virtual interview sessions to achieve data saturation. The interviewees encompassed project management practitioners, senior management personnel from construction companies, experts in construction dispute resolution, professionals in construction software, and representatives from government construction agencies. The data obtained from these interviews underwent thorough analysis employing a thematic approach. The study duly recognizes the significance of smart contract applications within the sector. Among the 12 identified barriers, issues such as identity theft and data leakage, communication and synchronization challenges, high computational expenses, lack of driving impetus, excessive electricity consumption, intricate implementation processes, absence of a universally applicable legal framework, and the lack of a localized legal framework were recurrent impediments affecting the adoption of smart contract applications within the sector. The study also delves into comprehensive measures to mitigate these barriers. In conclusion, this study critically evaluates the relevance of smart contract applications within the built environment, with a specific focus on promoting their usage. It may serve as a pioneering effort, especially within the context of Nigeria.
Model-Reference Reinforcement Learning for Safe Aerial Recovery of Unmanned Aerial Vehicles

In this study, we propose an aerial rendezvous method to facilitate the recovery of unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for the future use of UAVs. The main contribution of this study is the development of a promising method for online generation of feasible rendezvous trajectories for UAVs. First, the wake vortex of a carrier aircraft is analyzed using the finite element method, and a method for establishing a safety constraint model is proposed. Subsequently, a model-reference reinforcementearning algorithm is proposed based on the potential function method, which can ensure the convergence and stability of training. A combined reward function is designed to solve the UAV trajectory generation problem under non-convex constraints. The simulation results show that, compared with the traditional artificial potential field method under different working conditions, the success rate of this method under non-convex constraints is close to 100%, with high accuracy, convergence, and stability, and has greater application potential in the aerial recovery scenario, providing a solution to the trajectory generation problem of UAVs under non-convex constraints.
Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment

Abstract Underwater images are often influenced by color casts, low contrast, and blurred details. We observe that images taken in natural settings typically have similar histograms across color channels, while underwater images do not. To improve the natural appearance of an underwater image, it is critical to improve the histogram similarity across its color channels. To address this problem, we develop a histogram similarity-oriented color compensation method that corrects color casts by improving the histogram similarity across color channels in the underwater image. In addition, we apply the multiple attribute adjustment method, including max-min intensity stretching, luminance map-guided weighting, and high-frequency edge mask fusion, to enhance contrast, saturation, and sharpness, effectively addressing problems of low contrast and blurred details and eventually enhancing the overall appearance of underwater images. Particularly, the method proposed in this work is not based on deep learning, but it effectively enhances a single underwater image. Comprehensive empirical assessments demonstrated that this method exceeds state-of-the-art underwater image enhancement techniques. To facilitate public assessment, we made our reproducible code available at https://gitee.com/wanghaoupc/UIE_HS2CM2A.
FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis

Sentence-level sentiment analysis is a natural language processing model growing rapidly and strongly due to its role in artificial intelligence systems. There are many approaches to developing and improving the performance of sentence-level sentiment analysis with satisfactory performance, among which deep neural network methods are notable. However, sentence-level sentiment analysis methods based on deep neural networks often have two limitations: (i) The system architecture is not deep enough; (ii) Sentences containing unclear sentiments cannot be processed. To solve the above two challenges, in this paper, we propose a new sentence-level sentiment analysis method called Fuzzy-enhanced Deep Neural Networks (FeDN2) by deepening deep neural networks by adding fuzzy and defuzzy classes. FeDN2 includes the following main layers: (i) BERT layer to convert sentences into vectors. (ii) Fuzzification layer to blur sentence vectors. (iii) Deep convolutional layers to extract high-level features. (iv) Defuzzification layer to convert high-level feature vectors into clear values. (v) Fully connected layer to learn non-linear combinations of these high-level features. (vi) Classification layer to identify the sentiment polarity of sentences. FeDN2 was tested on two benchmark datasets. The results demonstrated that it can improve the performance of previous sentence-level sentiment analysis methods based on deep neural networks.
Classification of Multiple H&amp;E Images via an Ensemble Computational Scheme

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.
An Intelligent Control Method for Servo Motor Based on Reinforcement Learning

Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adjust its control strategy online to reduce the servo error caused by a change in operation conditions. We verify its performance on three different servo motors and control tasks. The experimental results show that the proposed method can achieve smaller servo errors than others in most cases.
Lightweight 2D Human Pose Estimation Based on Joint Channel Coordinate Attention Mechanism

Traditional human pose estimation methods typically rely on complex models and algorithms. Lite-HRNet can achieve an excellent performance while reducing model complexity. However, its feature extraction scale is relatively single, which can lead to lower keypoints’ localization accuracy in crowded and complex scenes. To address this issue, we propose a lightweight human pose estimation model based on a joint channel coordinate attention mechanism. This model provides a powerful information interaction channel, enabling features of different resolutions to interact more effectively. This interaction can solve the problem of human pose estimation in complex scenes and improve the robustness and accuracy of the pose estimation model. The introduction of the joint channel coordinate attention mechanism enables the model to more effectively retain key information, thereby enhancing keypoints’ localization accuracy. We also redesign the lightweight basic module using the shuffle module and the joint channel coordinate attention mechanism to replace the spatial weight calculation module in the original Lite-HRNet model. By introducing this new module, we not only improve the network calculation speed and reduce the number of parameters of the entire model, but also ensure the accuracy of the model, thereby achieving a balance between performance and efficiency. We compare this model with current mainstream methods on the COCO and MPII dataset. The experimental results show that this model can effectively reduce the number of parameters and computational complexity while ensuring high model accuracy.
Deep Dive into the Augmented Reality Customer Experience and Adoption Research: A Bibliometric Study

The study aims to systematically understand Augmented Reality (AR) technology user experience and adoption studies in recent years. AR technology has garnered significant attention due to its technological advancements, diverse application development, and validated use cases. This research utilises citation and structural knowledge analysis through Biblioshiny to uncover current trends, impacts, and critical, well-developed themes from 186 journal papers, conference papers, and book chapters retrieved from the Scopus Database. Research interest in the AR topic has grown, although the citation rate has remained relatively stable, indicating that technology is still evolving. The study has identified seven important and well-developed research themes, which include user experience and various AR adoptions in cultural heritage, healthcare (especially in rehabilitation and cognitive assessment), the teaching and learning process, gamification, automated vehicles, and the pedagogical and educational domains. Additionally, the study has identified several challenges that may hinder future progression, such as the intangible characteristics of the AR experience, research preparation (including training, participant diversity, and engagement), and the maturity of technology and use-case scenarios. Despite the increasing volume of research on AR, limited research has focused on AR user experience and acceptance, a gap addressed by this study.
Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies

This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOLO algorithm. Through image capture and processing, UAVs can rapidly identify congested areas and transmit this information to ground-based traffic lights, facilitating dynamic traffic control adjustments. Moreover, VLC establishes a communication link between UAVs and traffic lights that complements existing RF-based solutions, underscoring visible light’s potential as a reliable and energy-efficient communication medium. In addition to integrating UAVs and VLC, we propose a new communication protocol and messaging system for this framework, enhancing its adaptability to varying traffic flows. This research represents a significant stride toward developing more efficient, sustainable, and resilient urban transportation systems.