Reviewerly/exHarmony huggingface dataset:
UIEGAN: Adversarial Learning-Based Photorealistic Image Enhancement for Intelligent Underwater Environment Perception

Underwater image enhancement (UIE) is an essential task for intelligent environment perception in underwater remote visual sensing scenarios. However, the computing power of mobile platforms limits the usage of larger-scale models. In this paper, we propose a lightweight encoder-decoder architecture (UIENet) to enhance underwater images from visual sensors. We also involve the architecture into a generative adversarial model (UIEGAN) against a supervised discriminator to further perfect its corrective capabilities for the photo-realistic images with more global appearance and local details. The multi-resolution counterparts are embedded into the generator to diversify the feature representation of the original inputs. Further, UIEGAN guides the spatial attention module and the channel attention module to jointly enhance the global-local connection of the image. We evaluate the proposed method on benchmark datasets of UIEB and UFO-120 and report better performance than the state-of-the-art schemes, exceeding 11.15% and 12.85% on peak signal-to-noise ratio (PSNR) than the baselines of these datasets. Besides, by testing on the UIEB challenge, URPC and SQUID datasets without any reference images, our scheme outperforms the other methods on evaluation metrics to validate its generalization performance, and meanwhile uses a series of ablation study demonstrates the effectiveness of the functional modules.
Post-training Quantization for Neural Networks with Provable Guarantees

.While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To that end, we generalize a post-training neural network quantization method, GPFQ, that is based on a greedy path-following mechanism. Among other things, we propose modifications to promote sparsity of the weights, and rigorously analyze the associated error. Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights, i.e., level of overparametrization. Our result holds across a range of input distributions and for both fully connected and convolutional architectures thereby also extending previous results. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy compared to unquantized models. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.Keywordsdeep neural networksquantization of neural networksgreedy algorithmssparsityMSC codes68T0768W2562M4590C2568Q25
Context-Based and Adaptive Cybersecurity Risk Management Framework

Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing their common problems regarding cybersecurity culture, developing a cyber-risk management plan, and devising a more proactive and collaborative approach that is suitable according to their organization context. To this end, firstly various enterprise, Information Technology (IT), and cybersecurity risk management frameworks are thoroughly reviewed along with their advantages and limitations. Then, we propose a proactive cybersecurity risk management framework that is simple and dynamic, and that adapts according to the current threat and technology landscapes and organizational context. Finally, performance metrics to evaluate the framework are proposed.
Entanglement entropy production in Quantum Neural Networks

Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets for machine learning. However, quantitative studies of the QNN-generated entanglement have been investigated only for up to few qubits. Tensor network methods allow to emulate quantum circuits with a large number of qubits in a wide variety of scenarios. Here, we employ matrix product states to characterize recently studied QNN architectures with random parameters up to fifty qubits showing that their entanglement, measured in terms of entanglement entropy between qubits, tends to that of Haar distributed random states as the depth of the QNN is increased. We certify the randomness of the quantum states also by measuring the expressibility of the circuits, as well as using tools from random matrix theory. We show a universal behavior for the rate at which entanglement is created in any given QNN architecture, and consequently introduce a new measure to characterize the entanglement production in QNNs: the entangling speed. Our results characterise the entanglement properties of quantum neural networks, and provides new evidence of the rate at which these approximate random unitaries.
A warm-started trajectory planner for fixed-wing unmanned aerial vehicle formation

In this paper, an optimal control-based method with efficient warm-start is proposed to solve the trajectory planning problem for fixed-wing unmanned aerial vehicle formations. We first formulate the trajectory planning problem as an optimal control problem by analyzing the motion equations and various constraints. Secondly, an improved artificial potential field method is designed to meet the requirements of eliminating local oscillations, inter-formation coordination, and physical constraints with the objective of a high-quality homotopic warm-start. Last, a loose formation constraint strategy is employed to expand the solution space and thus facilitate the convergence process. Numerical simulations at different complexity levels show that the proposed planner is efficient and robust and that the designed warm-start strategy contributes significantly to the convergence of the method. Also, the proposed planner exhibits promising performance in solving the problem with multiple formations.
Size and Topology Optimization of Truss Structures Using Quantum-Based HS Algorithm

With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms combined with quantum computation are being developed. In this paper, the QbHS (Quantum based Harmony Search) algorithm was proposed by combining quantum computation and the conventional HS (Harmony Search) algorithms, and the size and topology optimization of the truss structure was performed. The QbHS algorithm has the same repetitive computational structure as the conventional HS algorithm. However, the QbHS algorithm constructed QHM (Quantum Harmony Memory) using the probability of Q-bit and proposed to perform pitch adjusting using the basic state of Q-bit. To perform weight optimization of truss structures using the proposed QbHS algorithm, 20 bar, 24 bar, and 72-bar truss structures were adopted as examples and compared with the results of the QE (Quantum Evolutionary) algorithm. As a result, it was confirmed that the QbHS algorithm had excellent convergence performance by finding a lower weight than the QE algorithm. In addition, by expressing the weight optimization results of the truss structure with an image coordinate system, the topology of the truss structure could be confirmed only by the picture. The results of this study are expected to play an important role in future computer information systems by combining quantum computation and conventional HS algorithms.
The Methods of Fall Detection: A Literature Review

Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development.
Performance Evaluation of YOLOv3, YOLOv4 and YOLOv5 for Real-Time Human Detection

The main concern of human detection using computer vision is to correctly identify people in an image and video. Human detection has been a topic of intensive study over the last decade. YOLO being single stage algorithms happen to offer better speed than two stage algorithms hence making them a better choice for real time object detection. This strategy has the benefit of offering a comprehensive study of contemporary human detection techniques as well as a manual for selecting the best ones for actual applications. In addition, Real-time human detection and occlusion issues are also looked at. In this paper, experimentation is done on real time image to verify the performance of different models of YOLO family i.e YOLOv3, YOLOv4 and YOLOv5. The experiment shows that YOLOv5 is best performer in terms of mAP with precision of 0.84 while YOLO v3 is the fastest but with a slightly less precision of 0.71. The mAP of the three algorithms were 0.86, 0.89 and 0.91 respectively.
Autonomous construction safety incentive mechanism using blockchain-enabled tokens and vision-based techniques

Current incentive mechanisms for construction safety highly rely on intermediaries and massive paperwork, which lay a foundation for opportunistic behavior and poor performance in the long run. As a solution, this study introduces an automatic incentive mechanism that rewards contractors with Fungible Tokens (FTs) and Non-Fungible Tokens (NFTs). Visual data of construction sites is processed through a computer-vision module, which reports safety performance. These reports are evaluated in a network of smart contracts, using a decentralized oracle network (DON), and token-based rewards are determined and distributed accordingly. Users can redeem their FT rewards or NFTs through an interactive front-end portal. The proposed method is successfully implemented through a synthetic case study to prove its feasibility. The proposed decentralized application (Dapp) provides a transparent, traceable, and immutable incentive mechanism that improves trust among stakeholders, and can be used as a template and expanded to fields beyond construction safety.
Bi-READ: Bi-Residual AutoEncoder based feature enhancement for video anomaly detection

Video anomaly detection (VAD) refers to identifying abnormal events in the surveillance video. Typically, reconstruction based video anomaly detection techniques employ convolutional autoencoders with a limited number of layers, which extracts insufficient features leading to improper network training. To address this challenge, an end-to-end unsupervised feature enhancement network, namely Bi-Residual Convolutional AutoEncoder (Bi-ResCAE) has been proposed that can learn normal events with low reconstruction error and detect anomalies with high reconstruction error. The proposed Bi-ResCAE network incorporates long–short residual connections to enhance feature reusability and training stabilization. In addition, we propose to formulate a novel VAD model that can extract appearance and motion features by fusing both the Bi-ResCAE network and optical flow network in the objective function to recognize the anomalous object in the video. Extensive experiments on three benchmark datasets validate the effectiveness of the model. The proposed model achieves an AUC (Area Under the ROC Curve) of 84.7% on Ped1, 97.7% on Ped2, and 86.71% on the Avenue dataset. The results show that the Bi-READ performs better than state-of-the-art techniques.
Pruning method for dendritic neuron model based on dendrite layer significance constraints

Abstract The dendritic neural model (DNM) mimics the non‐linearity of synapses in the human brain to simulate the information processing mechanisms and procedures of neurons. This enhances the understanding of biological nervous systems and the applicability of the model in various fields. However, the existing DNM suffers from high complexity and limited generalisation capability. To address these issues, a DNM pruning method with dendrite layer significance constraints is proposed. This method not only evaluates the significance of dendrite layers but also allocates the significance of a few dendrite layers in the trained model to a few dendrite layers, allowing the removal of low‐significance dendrite layers. The simulation experiments on six UCI datasets demonstrate that our method surpasses existing pruning methods in terms of network size and generalisation performance.
A federated learning approach to mixed fault diagnosis in rotating machinery

Rotating machinery is ubiquitous in modern industrial systems. Ensuring optimal operating conditions for rotating machinery is essential to satisfy stringent requirements on safety, efficiency, and reliability. State-of-the-art performance for fault detection, identification, and isolation for rotating machinery has been achieved using deep learning-based methods which generally require large quantities of high-quality supervised learning data. Collecting, labeling, and maintaining such data is resource-intensive and sometimes cost-prohibitive. Distributed data across multiple factories or organizations can be pooled together to learn a powerful collective model; however, this is not always possible due to the sensitive and proprietary nature of the data. To simultaneously alleviate these constraints of data availability and privacy, this paper develops a federated learning (FL) framework for the diagnosis of mixed faults from multiple factories. A duplet classifier is constructed to separate the mixed fault classification task into parallel networks where each network is responsible for one component. This classifier is trained under the FL framework and its performance is thoroughly examined for different data distributions across 30 participating factories. Experimental results show that the proposed methodology yields excellent mixed fault classification accuracy for all participating factories even under highly unbalanced and heterogeneous distribution of fault labels. Further studies highlight the data efficiency of the proposed method and its robustness to previously unseen fault types.
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community's interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model's superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.
Crafting transferable adversarial examples via contaminating the salient feature variance

Adversarial attacks play a vital role in the development of deep learning techniques, which can evaluate the robustness of deep neural networks (DNNs) as well as explore their decision mechanism. Recently, feature-level attacks have been proposed to contaminate the internal feature maps of the source model at each iteration, providing a new method to produce transferable adversarial examples. In this paper, we uncover two neglected problems behind current feature-level attacks and propose an ingenious Salient Feature Variance Attack (SFVA). Concretely, we first apply a Combined Feature Enhancement Transformation (CFET) on the copies of clean images to estimate the optimal feature weight. Then we construct an efficient objective based on the variance of salient features and adopt a classical attack MI-FGSM (MI) to add adversarial noises to the clean image along the direction of gradients. Moreover, we also make it possible to combine the ensemble strategy with feature-level attacks. Abundant experiments on the ImageNet dataset forcefully confirm the superiority of SFVA, which has become a state-of-the-art feature-level attack. Furthermore, we also evaluate the robustness of the practical online model with SFVA, where the 90% attack success rate reveals a worrying fact that the real-world deployed models are subject to serious security threats.
Can AI Outperform Humans on Cognitive-demanding Tasks in Science?

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E-LifeSeeker: An Interactive Lifelog Search Engine for LSC’23

Lifelogging is referred to as the process of automatically capturing the everyday activities of an individual to ultimately create a digital diary for further sharing, which can be challenging to manage and retrieve due to its multimodal nature. Lifelog retrieval systems not only have the potential to transform the way people interact and understand their lives, but also provide insights into their behaviour, habits, and preferences. The Lifelog Search Challenge (LSC) is a live benchmarking challenge to evaluate the performance of lifelog search tools in a real-time. This paper describes the modifications made to the E-LifeSeeker retrieval system, which participates in the 6th LSC challenge. This year, we enhance not only the underlying core engine with the latest pre-trained embedding models but also the user interface to be more intuitive for novice users. Moreover, Differential Networks are implemented to address the new question-answering task this year. These new modalities are designed to provide users with a more intuitive and efficient search experience, easing the process of locating information needed from the huge collection of egocentric lifelog images.
LifeInsight: An Interactive Lifelog Retrieval System with Comprehensive Spatial Insights and Query Assistance

In this paper, we introduce LifeInsight – an interactive lifelog retrieval system developed for the sixth annual Lifelog Search Challenge (LSC’23). LifeInsight incorporates semantic search mechanisms from state-of-the-art lifelog retrieval systems while focusing on providing insights into the lifelogger’s routine using spatial information to support question-answering tasks. The system employs the Bootstrapping Language-Image Pre-training (BLIP) model for zero-shot image-text retrieval, which has been shown to achieve higher recall scores than the CLIP model on the Flickr30K dataset. In addition, the Elastic Search filtering mechanism is utilized to remove irrelevant images. Apart from semantic search mechanisms, the system also supports visual similarity search by comparing the inner product distance between the vectors in the lifelog image corpus and the query image. Furthermore, the system includes an explicit relevance feedback function, AI-based query description rewriting, and visual-example-generating features to re-phrase the query to describe it better and support end-users envisioning the targeted image for retrieval.
Multi-image Feature Map-Based Watermarking Techniques Using Transformer

Nowadays, protecting multimedia data is a significant challenge because of the advancement of technology and software. The embedding process heavily relies on watermarking to accomplish multimedia security in terms of content authentication, proof of ownership, and tamper detection. Our objective is to develop an invariant watermark that can survive different signal-processing attacks. We presented a unique hybrid technique (DWT-QR-SWT) and multi-image invariant features generated as a watermark using a Transformer encoder-decoder model. The encoded image features are subsampled using PCA in order to decrease the dimensionality of the watermark image. The first two images are used as watermark1 and the next two images as watermark2 to produce multi-watermark feature maps. To embed the watermark, a hybrid DWT-QR decomposition has been applied to the original image1. On the primary watermarked image, two Level Stationary Wavelet Transform (SWT) were applied to embed the secondary watermark2. At the extraction phase, the tampered image is recovered by passing the extracted watermark image as input to the transformer decoder. A multi-image watermark increases data embedding capabilities and also achieves two-level content authentication, tamper detection, localization, and recovery. With a PSNR of 59.05 dB, the testing result demonstrates great resilience and improved imperceptibility.