Reviewerly/exHarmony huggingface dataset:
Self Adaptive Global-Local Feature Enhancement for Radiology Report Generation

Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas. Most existing works pay attention to the holistic impression of medical images, failing to utilize important anatomy information. However, in actual clinical practice, radiologists usually locate important anatomical structures, and then look for signs of abnormalities in certain structures and reason the underlying disease. In this paper, we propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report. Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR). Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information. Finally, the captioning generator generates the radiology reports through multi-granularity features. Experiment results illustrate that our model achieved the state-of-the-art performance on two benchmark datasets including the IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to leverage the multi-grained information from radiology images and texts so as to help generate more accurate reports.
Multi-Object Tracking as Attention Mechanism

We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does not require any attached modules, such as the Kalman filter, Hungarian algorithm, transformer blocks, or graph networks. Conventional MOT models are built upon the multi-step modules listed above, and thus the computational cost is high. Our proposed end-toend MOT model, TicrossNet, is composed of a base detector and a cross-attention module only. As a result, the overhead of tracking does not increase significantly even when the number of instances (N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> ) increases. We show that TicrossNet runs in real-time; specifically, it achieves 32.6 FPS on MOT17 and 31.0 FPS on MOT20 (Tesla V100), which includes as many as >100 instances per frame. We also demonstrate that TicrossNet is robust to N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> ; thus, it does not have to change the size of the base detector, depending on N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , as is often done by other models for real-time processing.
Improving Video Colorization by Test-Time Tuning

With the advancements in deep learning, video colorization by propagating color information from a colorized reference frame to a monochrome video sequence has been well explored. However, the existing approaches often suffer from overfitting the training dataset and sequentially lead to suboptimal performance on colorizing testing samples. To address this issue, we propose an effective method, which aims to enhance video colorization through test-time tuning. By exploiting the reference to construct additional training samples during testing, our approach achieves a performance boost of 1 ∼ 3 dB in PSNR on average compared to the baseline. Code is available at: https://github.com/IndigoPurple/T3.
Densely Connected Swin-UNet for Multiscale Information Aggregation in Medical Image Segmentation

Image semantic segmentation is a dense prediction task in computer vision that is dominated by deep learning techniques in recent years. UNet, which is a symmetric encoder-decoder end-to-end Convolutional Neural Network (CNN) with skip connections, has shown promising performance. Aiming to process the multiscale feature information efficiently, we propose a new Densely Connected Swin-UNet (DCS-UNet) with multiscale information aggregation for medical image segmentation. Firstly, inspired by Swin-Transformer to model long-range dependencies via shift-window-based self-attention, this work proposes the use of fully ViT-based network blocks with a shift-window approach, resulting in a purely self-attention-based U-shape segmentation network. The relevant layers including feature sampling and image tokenization are re-designed to align with the ViT fashion. Secondly, a full-scale deep supervision scheme is developed to process the aggregated feature map with various resolutions generated by different levels of decoders. Thirdly, dense skip connections are proposed that allow the semantic feature information to be thoroughly transferred from different levels of encoders to lower level decoders. Our proposed method is validated on a public benchmark MRI Cardiac segmentation data set with comprehensive validation metrics showing competitive performance against other variant encoder-decoder networks. The code is available at https://github.com/ziyangwang007/VIT4UNet.
Transformer-Based Variable-Rate Image Compression with Region-of-Interest Control

This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-interest (ROI) functionality. Inspired by prompt tuning, we introduce prompt generation networks to condition the transformer-based autoencoder of compression. Our prompt generation networks generate content-adaptive tokens according to the input image, an ROI mask, and a rate parameter. The separation of the ROI mask and the rate parameter allows an intuitive way to achieve variable-rate and ROI coding simultaneously. Extensive experiments validate the effectiveness of our proposed method and confirm its superiority over the other competing methods.
High-Accuracy Gesture Recognition using Mm-Wave Radar Based on Convolutional Block Attention Module

Non-contact gesture recognition is a new type of human-computer interaction with broad prospects in many applications. Motivated by the need for more precise micro-motion gesture recognition using mm-wave radar in recent years, a novel micro-motion gesture recognition network based on the Convolutional Block Attention Module (CBAM) is proposed here. The MMWCAS radar from TI is used to collect gesture echoes. During data pre-processing, the Range-time Map, Doppler-time Map, Azimuth-time Map and Elevation-time Map of the gestures are extracted and employed to characterize the motion features. A DenseNet and CBAM-based gesture recognition network is designed to identify the 12 types of micro-motion gestures using the mixed Feature-time Map as input. According to the experimental results, the accuracy rate reaches 99.03%, achieving high-accuracy gesture recognition. It has been discovered that the network focuses on the first half of the gesture movement, which improves recognition accuracy.
Explainable Deep Reinforcement Learning for Knowledge Graph Reasoning

Artificial intelligence faces a considerable challenge in automated reasoning, particularly in inferring missing data from existing observations. Knowledge graph (KG) reasoning can significantly enhance the performance of context-aware AI systems such as GPT. Deep reinforcement learning (DRL), an influential framework for sequential decision-making, exhibits strength in managing uncertain and dynamic environments. Definitions of state space, action space, and reward function in DRL directly dictate the performances. This chapter provides an overview of the pipeline and advantages of leveraging DRL for knowledge graph reasoning. It delves deep into the challenges of KG reasoning and features of existing studies. This chapter offers a comparative study of widely used state spaces, action spaces, reward functions, and neural networks. Furthermore, it evaluates the pros and cons of DRL-based methodologies and compares the performances of nine benchmark models across six unique datasets and four evaluation metrics.
Offensive Artificial Intelligence in Cybersecurity

Offensive artificial intelligence's (AI) growing role in cybersecurity raises concerns about ethics, defense, and human implications. AI is integrated into national capabilities, fueling algorithmic warfare while enabling AI's malicious use by cybercriminals and state hackers. Offensive AI intensifies cyberattacks, leading to unpredictable and stealthy assaults on unprepared organizations. Defensive measures must evolve to counter these threats effectively. Human factors are critical in developing AI-driven offensive measures. As AI reshapes offensive cybersecurity, ethical questions emerge. Policymakers, researchers, and practitioners must collaborate to ensure responsible AI development, mitigate algorithmic warfare risks, and address challenges in integrating AI into national security. This chapter aims to foster discourse on offensive AI and cybersecurity, emphasizing the cascading effects, such as defensive AI and algorithmic warfare.
Impact of Conversational and Generative AI Systems on Libraries: A Use Case Large Language Model (LLM)

ABSTRACTThe study aims to examine how artificial intelligence (AI) could potentially affect specific services provided by academic libraries in the near future. To achieve this, the study uses three different Generative AI systems: ChatGPT, Perplexity, and iAsk.Ai. By analyzing the potential impact of AI on academic library services, the study aims to provide insights into how libraries can adapt to these changes to better serve their patrons. The three AI systems selected for this study represent different AI approaches that can be used in academic libraries. ChatGPT, for example, is a conversational AI system that can provide quick answers to patrons’ queries, while Perplexity is a language model that can assist with tasks such as cataloging and content classification. iAsk.Ai is a natural language processing (NLP) system that can assist with research and reference inquiries. By assessing the potential impact of these AI systems on academic library services, the study can provide insights into the future of library services and how AI could be used to enhance them. Ultimately, this could help academic libraries prepare for and adapt to the changing technological landscape, ensuring that they continue to meet the needs of their patrons in the years to come.KEYWORDS: ChatGPTconversational AI systemgenerative AI systemiAsk.Ailibrariesperplexity Disclosure statementNo potential conflict of interest was reported by the author(s).
Remote Sensing Image Target Detection and Recognition Based on YOLOv5

The main task of remote sensing image target detection is to locate and classify the targets of interest in remote sensing images, which plays an important role in intelligence investigation, disaster relief, industrial application, and other fields. However, the targets in the remote sensing image scene have special problems such as special perspective, scale diversity, multi-direction, small targets, and high background complexity. In this paper, the YOLOv5 target detection algorithm is improved according to the above characteristics. Aiming at the problem of large target size span in remote sensing images, this paper uses K-Means++ clustering algorithm to eliminate the problem in which the original clustering algorithm is sensitive to initial position, noise, and outliers, and optimizes the instability caused by K-Means clustering to obtain preset anchor frames. Aiming at the redundancy of background information around the location of remote sensing image targets, the large number of small targets, and the denseness of targets, a double IoU-aware decoupling head (DDH) is introduced at the output end to replace the coupled yolo head, which eliminates the interference caused by different task sharing parameters. At the same time, the correlation between positioning accuracy and classification accuracy is improved by the IoU-aware method. The attention mechanism is introduced into the backbone network to optimize the detection and small target detection in complex backgrounds. The mAP of the improved YOLOv5 algorithm is improved by 9%, and the detection effect of small targets and dense targets is significantly improved. At the same time, the paper has achieved good results through the verification of the DIOR remote sensing data set and has also achieved good performance advantages in comparison with other models.
Delegated Proof of Stake Consensus Mechanism Based on Community Discovery and Credit Incentive

Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as "one ballot, one vote", a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving "one ballot, multiple votes", and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.
Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability.
GameFi: The perfect symbiosis of blockchain, tokens, DeFi, and NFTs?

GameFi is a portmanteau of “game” and “finance.” The concept involves blockchain games that offer economic incentives to play, otherwise known as play-to-earn (P2E) games. We explain in detail how GameFi differs from traditional games, and carve out its unique value proposition. We also explore how the mechanics of blockchain games influence the social facet of P2E games, where guilds and clans essentially function as profit-sharing organizations. GameFi leverages disparate elements of the crypto space: tokens, DeFi, and NFTs. Lastly, we discuss in detail some of the challenges of GameFi.
A novel heuristic algorithm for solving engineering optimization and real-world problems: People identity attributes-based information-learning search optimization

With the scale and dimension of engineering optimization and real-world problems increasing, it will be difficult to find the optimum solutions. This paper proposes a novel people identity attributes-based heuristic technology, named the People Identity Attributes-based Information-learning Search Optimization (ISO), inspired by people's psychological assessment and learning behaviors for information resources based on the social status-based identity attribute and the self-survival demands-based identity attribute. For the former, a four-level information delivery mechanism is constructed through leader, manager, executor, and freelancer, emphasizing global optimization. For the latter, the staged transforming model based on random accumulation and directed induction behaviors are constructed according to self-survival demands, emphasizing global optimization and exploration. The dual identity attributes-based search strategy emphasizes psychological assessment and selection for the information loss, including the learning behavior for two kinds of information resources with different identity attributes, and the recovering behavior for the information loss by constructing the information resilience equation, which emphasizes local optimization and exploitation. This paper qualitatively analyzes the swarm behavior, search history, and the exploration and exploitation capabilities of ISO. The optimization performances are quantitatively analyzed for ISO and 9 competitive algorithms on 39 benchmark tests, including the convergence, solution accuracy, robustness, sensitivity, significance, statistical investigation-based Wilcoxon test and Friedman test. The scalability of ISO is investigated on CEC2017 (30Dim, 50Dim, 100Dim) and the latest CEC2022 (10Dim, 20Dim) suites. The results reveal that compared to other competitive algorithms, ISO possesses best computing performance with ranking the first in all competitors. In addition, the proposed ISO and 12 competitors consider 10 constraint engineering optimization problems and the real application of path planning with multiple obstacles, suggesting that ISO possesses significant optimization performance.
Decentralized Autonomous Organizations (DAOs): Catalysts for enhanced market efficiency

The crypto-asset market has shown variations in efficiency across assets and time, but limited research has explored the driving factors beyond liquidity. Exploiting a dataset of 122 crypto-assets, with imbalanced data, this study investigates the impact of market conditions and inherent asset characteristics on return predictability. Our findings reveal that both factors significantly influence the efficiency of crypto-assets. Notably, Decentralized Autonomous Organizations (DAOs) exhibit higher efficiency compared to non-DAO projects. This suggests that transparent decentralized decision-making models can reduce information asymmetry, leading to a more efficient market pricing. Furthermore, we show a positive relationship between market efficiency and increased liquidity and age. These insights shed light on the role of DAOs as catalysts for enhancing market efficiency and have important implications for investors and market participants in the crypto-asset market.
A Systematic Review of Existing Early Warning Systems’ Challenges and Opportunities in Cloud Computing Early Warning Systems

This paper assessed existing EWS challenges and opportunities in cloud computing through the PSALSAR framework for systematic literature review and meta-analysis. The research used extant literature from Scopus and Web of Science, where a total of 2516 pieces of literature were extracted between 2004 and 2022, and through inclusion and exclusion criteria, the total was reduced to 98 for this systematic review. This review highlights the challenges and opportunities in transferring in-house early warning systems (that is, non-cloud) to the cloud computing infrastructure. The different techniques or approaches used in different kinds of EWSs to facilitate climate-related data processing and analytics were also highlighted. The findings indicate that very few EWSs (for example, flood, drought, etc.) utilize the cloud computing infrastructure. Many EWSs are not leveraging the capability of cloud computing but instead using online application systems that are not cloud-based. Secondly, a few EWSs have harnessed the computational techniques and tools available on a single platform for data processing. Thirdly, EWSs combine more than one fundamental tenet of the EWS framework to provide a holistic warning system. The findings suggest that reaching a global usage of climate-related EWS may be challenged if EWSs are not redesigned to fit the cloud computing service infrastructure.
Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV

The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers’ concerns about data privacy, while Decentralized Federated Learning (DFL) provides new possibilities to address this issue. However, DFL still faces challenges regarding the non-IID data of passing vehicles. To tackle this challenge, a novel DFL framework, Hierarchical Decentralized Federated Learning (H-DFL), is proposed to achieve qualified distributed training among vehicles by considering data complementarity. We include vehicles, base stations, and data center servers in this framework. Firstly, a novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity. In this way, vehicles train their models based on local data, exchange model parameters in each group, and achieve a qualified local model without the interference of imbalanced data. On a higher level, a local model trained by each group is submitted to the data center to obtain a model covering global features. Base stations maintain the local models of different groups and judge whether the local models need to be updated according to the global model. The experimental results based on real-world data demonstrate that H-DFL dose not only reduces communication latency with different participants but also addresses the challenges of non-IID data in vehicles.
Access control in Internet of Things: A survey

The speedy boom of Internet of Things (IoT) devices has revolutionized many fields including smart cities, healthcare, smart homes, etc. Most of these devices exchange sensitive and private data. Due to their exposure to the Internet, the IoT devices can be easily victims of hacking and tampering attacks. Also, attacks that seek to alter their settings, change their behavior and exhaust their resources are very common. Access control solutions help considerably IoT systems to efficiently cope with these attacks and ensure the required security level. In this paper, we survey the most relevant IoT access control solutions starting from the basic centralized ones to the more modern and distributed solutions that rely on emerging technologies such as blockchain and fog computing. Additionally, our work offers a spotlight on the IoT inter-organizational access control solutions. We demystify the challenges, problems and benefits of each surveyed approach. Furthermore, a novel taxonomy of the access control approaches according to their architecture, underlying layers and organizational aspect is proposed. Based on our deep study, we present the future directions of access control in the age of IoT.