Reviewerly/exHarmony huggingface dataset:
Cascaded Zoom-in Detector for High Resolution Aerial Images

Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution (in terms of pixel size) images. Density cropping is a widely used method to improve this small object detection where the crowded small object regions are extracted and processed in high image-resolution. However, this is typically accomplished by adding other learnable components, thus complicating the training and inference over a standard detection process. In this paper, we propose an efficient Cascaded Zoom-in (CZ) detector that re-purposes the detector itself for density-guided training and inference. During training, density crops are located, labeled as a new class, and employed to augment the training dataset. During inference, the density crops are first detected along with the base class objects, and then input for a second stage of inference. This approach is easily integrated into any detector, and creates no significant change in the standard detection process, like the uniform cropping approach popular in aerial image detection. Experimental results on the aerial images of the challenging VisDrone and DOTA datasets verify the benefits of the proposed approach. The proposed CZ detector also provides state-of-the-art results over uniform cropping and other density cropping methods on the VisDrone dataset, increasing the detection mAP of small objects by more than 3 percentage points. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges

Abstract At present, the mainstream mode of machine learning algorithms is the data‐driven method, which mainly relies on the self‐learning ability of deep neural networks and continuously evolving models in data‐driven training. However, the pure data‐driven method has some critical problems, such as high data collection cost, poor interpretability and easy to be be disturbed by noise. Although the knowledge‐driven method has high stability, it lacks self‐learning and evolution ability in the face of comprehensive and complex problems. In recent years, the convergence of data and domain knowledge has combined the advantages of both learning paradigms. One typical way is to embed domain knowledge into the data‐driven model to improve the interpretability of the model, and then use the self‐learning ability of the data‐driven model to explore knowledge, and continuously iterate the domain knowledge to form a closed loop. The data‐knowledge dual‐driven methods have brought transformative innovations in machine learning. This review first introduced the advantages and necessity of the data‐knowledge dual‐driven model in the field of artificial intelligence. Then, the applications of the data‐knowledge dual‐driven model in the smart marine field were introduced. Finally, the challenges and trends of the data‐knowledge dual‐driven artificial intelligence are anticipated.
VDPVE: VQA Dataset for Perceptual Video Enhancement

Recently, many video enhancement methods have been proposed to improve video quality from different aspects such as color, brightness, contrast, and stability. Therefore, how to evaluate the quality of the enhanced video in a way consistent with human visual perception is an important research topic. However, most video quality assessment methods mainly calculate video quality by estimating the distortion degrees of videos from an overall perspective. Few researchers have specifically proposed a video quality assessment method for video enhancement, and there is also no comprehensive video quality assessment dataset available in public. Therefore, we construct a Video quality assessment dataset for Perceptual Video Enhancement (VDPVE) in this paper. The VDPVE has 1211 videos with different enhancements, which can be divided into three sub-datasets: the first sub-dataset has 600 videos with color, brightness, and contrast enhancements; the second sub-dataset has 310 videos with deblurring; and the third sub-dataset has 301 deshaked videos. We invited 21 subjects (20 valid subjects) to rate all enhanced videos in the VDPVE. After normalizing and averaging the subjective opinion scores, the mean opinion score of each video can be obtained. Furthermore, we split the VDPVE into a training set, a validation set, and a test set, and verify the performance of several state-of-the-art video quality assessment methods on the test set of the VDPVE.
Dilated Convolutional Transformer for High-Quality Image Deraining

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches.
Denoising Diffusion Models for Plug-and-Play Image Restoration

Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior. However, most existing methods focus on discriminative Gaussian denoisers. Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored. While several other attempts have been made to adopt diffusion models for image restoration, they either fail to achieve satisfactory results or typically require an unacceptable number of Neural Function Evaluations (NFEs) during inference. This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models. Experimental results on three representative IR tasks, including super-resolution, image deblurring, and inpainting, demonstrate that DiffPIR achieves state-of-the-art performance on both the FFHQ and ImageNet datasets in terms of reconstruction faithfulness and perceptual quality with no more than 100 NFEs. The source code is available at https://github.com/yuanzhi-zhu/DiffPIR
A Survey of Post-Quantum Cryptography: Start of a New Race

Information security is a fundamental and urgent issue in the digital transformation era. Cryptographic techniques and digital signatures have been applied to protect and authenticate relevant information. However, with the advent of quantum computers and quantum algorithms, classical cryptographic techniques have been in danger of collapsing because quantum computers can solve complex problems in polynomial time. Stemming from that risk, researchers worldwide have stepped up research on post-quantum algorithms to resist attack by quantum computers. In this review paper, we survey studies in recent years on post-quantum cryptography (PQC) and provide statistics on the number and content of publications, including a literature overview, detailed explanations of the most common methods so far, current implementation status, implementation comparisons, and discussion on future work. These studies focused on essential public cryptography techniques and digital signature schemes, and the US National Institute of Standards and Technology (NIST) launched a competition to select the best candidate for the expected standard. Recent studies have practically implemented the public key encryption/key encapsulation mechanism (PKE/KEM) and digital signature schemes on different hardware platforms and applied various optimization measures based on other criteria. Along with the increasing number of scientific publications, the recent trend of PQC research is increasingly evident and is the general trend in the cryptography industry. The movement opens up a promising avenue for researchers in public key cryptography and digital signatures, especially on algorithms selected by NIST.
Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement.
Exploring the Ethical Dimensions of Using ChatGPT in Language Learning and Beyond

The emergence of ChatGPT in the field of education has opened up new opportunities for language learning, but it has also brought about significant ethical considerations that must be carefully considered and addressed to ensure that this technology is used responsibly. With the field of artificial intelligence (AI) advancing at an unprecedented rate, it is imperative for educators and administrators to remain vigilant in monitoring the ethical implications of integrating ChatGPT into language education and beyond. This paper will explore several ethical dimensions concerning the use of ChatGPT, a sophisticated language model developed by OpenAI, in language education. It will discuss privacy, bias, reliability, accessibility, authenticity, and academic integrity as significant ethical implications to consider while integrating ChatGPT into the language classroom. By gaining an initial understanding of the ethical implications involved in utilizing ChatGPT in language education, students, teachers, and administrators will be able to make informed decisions about the appropriate use of the technology, ensuring that it is employed in an ethical and responsible manner.
RoSteALS: Robust Steganography using Autoencoder Latent Space

Data hiding such as steganography and invisible watermarking has important applications in copyright protection, privacy-preserved communication and content provenance. Existing works often fall short in either preserving image quality, or robustness against perturbations or are too complex to train. We propose RoSteALS, a practical steganography technique leveraging frozen pretrained autoencoders to free the payload embedding from learning the distribution of cover images. RoSteALS has a lightweight secret encoder of just 300k parameters, is easy to train, has perfect secret recovery performance and comparable image quality on three benchmarks. Additionally, RoSteALS can be adapted for novel cover-less steganography applications in which the cover image can be sampled from noise or conditioned on text prompts via a denoising diffusion process. Our model and code are available at https://github.com/TuBui/RoSteALS.
ConvMLP: Hierarchical Convolutional MLPs for Vision

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods on image classification. However, most methods adopt spatial MLPs which take fixed-dimension inputs, therefore making it difficult to apply them as backbones to downstream tasks such as object detection and semantic segmentation, which require inputs with arbitrary dimension. Moreover, single-stage designs further limit the performance in other computer vision tasks and fully-connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a Hierarchical Convolutional MLP for visual recognition, which is a lightweight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4 GMACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters. Our code and pre-trained models are open-sourced at https://github.com/SHI-Labs/Convolutional-MLPs.
Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect itself.This paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark.
A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

As a crucial data preprocessing method in data mining, feature selection (FS) can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features. Evolutionary computing (EC) is promising for FS owing to its powerful search capability. However, in traditional EC-based methods, feature subsets are represented via a length-fixed individual encoding. It is ineffective for high-dimensional data, because it results in a huge search space and prohibitive training time. This work proposes a length-adaptive non-dominated sorting genetic algorithm (LA-NSGA) with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective high-dimensional FS. In LA-NSGA, an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths, and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively. Moreover, a dominance-based local search method is employed for further improvement. The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.
Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.
Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution

The rapid advancement of deep learning has significantly accelerated progress in target detection. However, the detection of small targets remains challenging due to their susceptibility to size variations. In this paper, we address these challenges by leveraging the latest version of the You Only Look Once (YOLOv7) model. Our approach enhances the YOLOv7 model to improve feature preservation and minimize feature loss during network processing. We introduced the Spatial Pyramid Pooling and Cross-Stage Partial Channel (SPPCSPC) module, which combines the feature separation and merging ideas. To mitigate missed detections in small target scenarios and reduce noise impact, we incorporated the Coordinate Attention for Efficient Mobile Network Design (CA) module strategically. Additionally, we introduced a dynamic convolutional module to address misdetection and leakage issues stemming from significant target size variations, enhancing network robustness. An experimental validation was conducted on the FloW-Img sub-dataset provided by Okahublot. The results demonstrated that our enhanced YOLOv7 model outperforms the original network, exhibiting significant improvement in leakage reduction, with a mean Average Precision (mAP) of 81.1%. This represents a 5.2 percentage point enhancement over the baseline YOLOv7 model. In addition, the new model also has some advantages over the latest small-target-detection algorithms such as FCOS and VFNet in some respects.
Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction

Since social media platforms are widely used and popular, they have given us more opportunities than we can even imagine. Despite all of the known benefits, some users may abuse these opportunities to humiliate, insult, bully, and harass other people. This issue explains why there is a need to reduce such negative activities and create a safe cyberspace for innocent people by detecting cyberbullying activity. This study provides a comparative analysis of deep learning methods used to test and evaluate their effectiveness regarding a well-known global Twitter dataset. To recognize abusive tweets and overcome existing challenges, attention-based deep learning methods are introduced. The word2vec with CBOW concatenated formed the weights included in the embedding layer and was used to extract the features. The feature vector was input into a convolution and pooling mechanism, reducing the feature dimensionality while learning the position-invariant of the offensive words. A SoftMax function predicts feature classification. Using benchmark experimental datasets and well-known evaluation measures, the convolutional neural network model with attention-based long- and short-term memory was found to outperform other DL methods. The proposed cyberbullying detection methods were evaluated using benchmark experimental datasets and well-known evaluation measures. Finally, the results demonstrated the superiority of the attention-based 1D convolutional long short-term memory (Conv1DLSTM) classifier over the other implemented methods.
A competitive and collaborative-based multilevel hierarchical artificial electric field algorithm for global optimization

Competitive and collaborative strategies and topologies are among the most essential concepts and greatly influence the optimization ability of population-based optimization algorithms. To update individuals' information, this article proposes a multilevel hierarchical artificial electric field algorithm with competitive and collaborative strategies (PAEFA). The proposed algorithm constructs a multilevel structure and places them in specific layers. The whole population is divided into two groups of winners and losers by pairwise comparison of their fitness in the same layer. Losers collaborate with their respective winners, whereas winners collaborate with individuals who are on the upper layers. In the proposed algorithm, each individual has their own learning mechanism, which can learn from more than one exemplar, rather than only from the global best. With the knowledge of this structure, the diversity of the population increases, which strengthens the performance of the scheme. To verify the adaptability of the proposed algorithm, extensive experiments are performed on the CEC 2017 test suite at 30, 50, and 100 dimensions. We have studied the diversity factor of PAEFA using all three dimensions. These experiments suggest that PAEFA outperforms over thirty state-of-the-art algorithms in terms of accuracy, statistical results, and convergence speed while achieving comparable computational time in most cases and showing the validity of results. The PAEFA algorithm achieves superior performance compared to other state-of-the-art algorithms on 87.60% and 80.05% of problems in terms of accuracy and statistical significance across all three dimensions, respectively.
Generation Z's psychology and new‐age technologies: Implications for future research

Abstract Generation Z (Gen Z), also known as digital natives, constitutes one‐third of the world' population. Despite notable contributions and progress explaining Gen Z's behavior, significant gaps remain in our understanding of digital natives, their interactions with new‐age technologies and how these technologies can be utilized to provide a better quality of life for this group of consumers. In this review article, we provide a background on Gen Z's consumer psychology and interactions with new‐age technologies, followed by an overview of the articles published in our special section. Finally, we suggest new areas for future research on Gen Z's consumer psychology and interactions with new‐age technologies. Specifically, we propose four main themes for future research in this area: (1) Gen Z and new‐age technologies, specifically generative artificial intelligence (AI), quantum computing, the metaverse, virtual influencers, and live streaming; (2) Gen Z consumers and global issues, specifically economic and social issues, sustainability and environmental issues, and health and wellbeing; (3) combining theories, concepts and disciplines to understand Gen Z and new‐age technologies; and (4) new methods and forms of collaboration.