Reviewerly/exHarmony huggingface dataset:
A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R3Det

The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R3Det algorithm is proposed in this paper. On the basis of R3Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R3Det and other models.
LAO-3D: A Symmetric Lightweight Block Cipher Based on 3D Permutation for Mobile Encryption Application

Data transmissions between smartphone users require security solutions to protect communications. Hence, encryption is an important tool that must be associated with smartphones to keep the user’s data safe. One proven solution to enhance the security of encryption algorithms is by using 3D designs on symmetric block ciphers. Although a 3D cipher design could improve the algorithms, the existing methods enlarge the block sizes that will also expand the key sizes and encryption rounds, thus decreasing their efficiency. Therefore, we propose the LAO-3D block cipher using a 3D permutation that offers security by providing confusion and diffusion characteristics. Five security analyses were conducted to assess the strengths of LAO-3D. The findings suggest that LAO-3D achieves better results compared to other existing lightweight block ciphers, with 98.2% non-linearity, 50% bit error rates for both plaintext and key modifications, surpasses 100% of the randomness test, and is immune to differential and linear cryptanalysis attacks. Moreover, the block cipher obtains competitive performance results in software applications. From the security analyses and performance tests, it is proven that LAO-3D can provide sufficient security at low costs in mobile encryption applications.
Debt-Financed Collateral and Stability Risks in the DeFi Ecosystem

The rise of Decentralized Finance (“DeFi”) on the Ethereum blockchain has enabled the creation of lending platforms, which serve as marketplaces to lend and borrow digital currencies. Initially, we categorize the activity of lending platforms within a standard regulatory framework. We then propose an Ethereum address grouping algorithm using activity over DeFi protocols and employ a novel classification algorithm to calculate the percentage of fund flows into DeFi lending platforms that can be attributed to debt created elsewhere in the system (“debt-financed collateral”). Based on our results, we conclude that the wide-spread use of stablecoins as debt-financed collateral increases financial stability risks in the DeFi ecosystem.
SiMaN: Sign-to-Magnitude Network Binarization

Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in handling discrete constraints while ensuring bit entropy maximization, which typically makes their weight optimization very difficult. Existing methods relax the learning using the sign function, which simply encodes positive weights into +1s, and -1s otherwise. Alternatively, we formulate an angle alignment objective to constrain the weight binarization to {0,+1} to solve the challenge. In this article, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise. Therefore, a high-quality discrete solution is established in a computationally efficient manner without the sign function. We prove that the learned weights of binarized networks roughly follow a Laplacian distribution that does not allow entropy maximization, and further demonstrate that it can be effectively solved by simply removing the l2 regularization during network training. Our method, dubbed sign-to-magnitude network binarization (SiMaN), is evaluated on CIFAR-10 and ImageNet, demonstrating its superiority over the sign-based state-of-the-arts. Our source code, experimental settings, training logs and binary models are available at https://github.com/lmbxmu/SiMaN.
Decentralized NFT-based Evolvable Games

The popularity of blockchain games continues to grow as Non-Fungible Tokens (NFTs) become the center of attention, contributing to the move towards Web3. We leverage the InterPlanetary File System (IPFS) and NFTs, backed by blockchains, to build a flexible, decentralized, and fair baseline system for trading games. Our solution creates a fully decentralized system, where new business models are enabled. In particular, we introduce and support the evolvability of ingame assets, enable their resale with dynamic pricing, depending on their rarity, and automatically provide a cut to the digital artists, without the need for a trusted (third) party. Our system guarantees that the in-game assets will remain online long-term, by orchestrating various decentralized services. Thus, users do not risk losing control over the artefacts or their value, even if the gaming company loses interest, or goes bankrupt. We considered and compared the Ethereum Name Service (ENS) and the InterPlanetary Name System (IPNS) as the naming component of the system and selected ENS for our solution, despite the fact it introduces monetary cost. Finally, we validate our claims and evaluate the feasibility and performance of the proposed system through a proof of concept implementation.
Cyberbullying Identification System Based Deep Learning Algorithms

Cyberbullying is characterized by deliberate and sustained peer aggression, as well as a power differential between the victim and the perpetrators or abusers. Cyberbullying can have a variety of consequences for victims, including mental health problems, poor academic performance, a tendency to drop out of work, and even suicidal thoughts. The main objective of this study was to develop a cyberbullying detection system (CDS) to uncover hateful and abusive behaviour on social media platforms. Two experiments were carried out to train and test the proposed system with binary and multiclass cyberbullying classification datasets. Hybrid deep learning architecture consisting of convolutional neural networks integrated with bidirectional long short-term memory networks (CNN-BiLSTM) and single BiLSTM models were compared in terms of their ability to classify social media posts into several bullying types related to gender, religion, ethnicity, age, aggression, and non-cyberbullying. Both classifiers showed promising performance in the binary classification dataset (aggressive or non-aggressive bullying), with a detection accuracy of 94%. For the multiclass dataset, BiLSTM outperformed the combined CNN-BiLSTM classifier, achieving an accuracy of 99%. A comparison of our method to the existing method on the multiclass classification dataset revealed that our method performed better in detecting online bullying.
Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano

Taupō volcano, located within the Taupō Volcanic Zone (TVZ) in the central North Island of Aotearoa-New Zealand, is one of the world’s most active silicic caldera systems. Silicic calderas such as Taupō are capable of a broad and complex range of volcanological activity, ranging from minor unrest episodes to large destructive supereruptions. A critical tool for volcanic risk management is eruption forecasting. The Bayesian Event Tree for Eruption Forecasting (BET_EF) is one probabilistic eruption forecasting tool that can be used to produce short-term eruption forecasts for any volcano worldwide. A BET_EF model is developed for Taupō volcano, informed by geologic and historic data. Monitoring parameters for the model were obtained through a structured expert elicitation workshop with 30 of Aotearoa-New Zealand’s volcanologists and volcano monitoring scientists. The eruption probabilities output by the BET_EF model for Taupō volcano’s 17 recorded unrest episodes (between 1877 and 2019) were examined. We found time-inhomogeneity in the probabilities stemming from both the changes over time in the monitoring network around Taupō volcano and increasing level of past data (number of non-eruptive unrest episodes). We examine the former issue through the lens of the latest episodes, and the latter by re-running the episodes assuming knowledge of all 16 other episodes (calibration to 2021 data). The time variable monitoring network around Taupō volcano and parameter weights had a substantial impact on the estimated probabilities of magmatic unrest and eruption. We also note the need for improved monitoring and data processing at Taupō volcano, the existence of which would prompt updates and therefore refinements in the BET_EF model.
Pedestrian detection based on YOLOv3 multimodal data fusion

Multi-sensor fusion has essential applications in the field of target detection. Considering the current actual demand for miniaturization of on-board computers for driverless vehicles, this paper uses the multimodal data YOLOv3 (MDY) algorithm for pedestrian detection on embedded devices. The MDY algorithm uses YOLOv3 as the basic framework to improve pedestrian detection accuracy by optimizing anchor frames and adding small target detection branches. Then the algorithm is accelerated by using TensorRT technology to improve the real-time performance in embedded devices. Finally, a hybrid fusion framework is used to fuse the LIDAR point cloud data with the improved YOLOv3 algorithm to compensate for the shortcomings of a single sensor and improve the detection accuracy while ensuring speed. The improved YOLOv3 improves AP by 6.4% and speed by 11.3 FPS over the original algorithm. The MDY algorithm achieves better performance on the KITTI dataset. To further verify the feasibility of the MDY algorithm, an actual test was conducted on an unmanned vehicle with Jetson TX2 embedded device as the on-board computer within the campus scenario, and the results showed that the MDY algorithm achieves 90.8% accuracy under real-time operation and can achieve adequate detection accuracy and real-time performance on the embedded device.
Implementation of a Novel Fully Convolutional Network Approach to Detect and Classify Cyber-Attacks on IoT Devices in Smart Manufacturing Systems

Abstract In recent years, Internet of things (IoT) devices have been widely implemented and industrially improved in manufacturing settings to monitor, collect, analyze, and deliver data. Nevertheless, this evolution has increased the risk of cyberattacks, significantly. Consequently, developing effective intrusion detection systems based on deep learning algorithms has proven to become a dependable intelligence tool to protect Industrial IoT devices against cyber-attacks. In the current study, for the first time, two different classifications and detection long short-term memory (LSTM) architectures were fine-tuned and implemented to investigate cyber-security enhancement on a benchmark Industrial IoT dataset (BoT-IoT) which takes advantage of several deep learning algorithms. Furthermore, the combinations of LSTM with FCN and CNN demonstrated how these two models can be used to accurately detect cyber security threats. A detailed analysis of the performance of the proposed models is provided. Augmenting the LSTM with FCN achieves state-of-the-art performance in detecting cybersecurity threats.
A Systematic Literature Review of Handheld Augmented Reality Solutions for People with Disabilities

Mobile applications on smartphones and tablets have become part of our everyday lives. The number of augmented reality (AR) technology applications is also increasing. Augmented reality has proven to be effective in various areas of human life, from education, marketing, and training to navigation. All people have the right to access information and use available technologies, but not everyone has the same opportunities. To contribute to the digital inclusion of people who are often disadvantaged in this regard, we should think about the accessibility of digital technologies, including mobile augmented reality (MAR). The specificity of MAR is a new way of human-computer interaction compared to traditional mobile solutions. The objective of this review paper is to analyze the handheld AR solutions developed for people with different disabilities to identify accessibility challenges related to interaction when performing different tasks in AR. It also explores and presents accessibility features and other accessibility best practices, as well as potential future research directions related to the personalization and customization of such solutions for individuals. The results of this literature review can contribute to the creation of accessibility guidelines in the field of handheld AR and encourage the development of accessible AR solutions that can benefit not only people with disabilities but also people without disabilities.
Model Fusion from Unauthorized Clients in Federated Learning

A key feature of federated learning (FL) is that not all clients participate in every communication epoch of each global model update. The rationality for such partial client selection is largely to reduce the communication overhead. However, in many cases, the unselected clients are still able to compute their local model updates, but are not “authorized” to upload the updates in this round, which is a waste of computation capacity. In this work, we propose an algorithm FedUmf—Federated Learning with Unauthorized Model Fusion that utilizes the model updates from the unselected clients. More specifically, a client computes the stochastic gradient descent (SGD) even if it is not selected to upload in the current communication epoch. Then, if this client is selected in the next round, it non-trivially merges the outdated SGD stored in the previous round with the current global model before it starts to compute the new local model. A rigorous convergence analysis is established for FedUmf, which shows a faster convergence rate than the vanilla FedAvg. Comprehensive numerical experiments on several standard classification tasks demonstrate its advantages, which corroborate the theoretical results.
Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

With the increasingly important role of image segmentation in the field of computed tomography (CT) image segmentation, the requirements for image segmentation technology in related industries are constantly improving. When the hardware resources can fully meet the needs of the fast and high-precision image segmentation program system, the main means of how to improve the image segmentation effect is to improve the related algorithms. Therefore, this study has proposed a combination of genetic algorithm (GA) and Great Law (OTSU) algorithm to form an image segmentation algorithm-immune genetic algorithm (IGA) algorithm. The algorithm has improved the segmentation accuracy and efficiency of the original algorithm, which is beneficial to the more accurate results of CT image segmentation. The experimental results in this study have shown that the operating efficiency of the OTSU segmentation algorithm is up to 75%. The operating efficiency of the GA algorithm is up to 78%. The operating efficiency of the IGA algorithm is up to 92%. In terms of operating efficiency, the OTSU segmentation algorithm has more advantages. In terms of segmentation accuracy, the highest accuracy rate of OTSU segmentation algorithm is 45%. The accuracy of the GA algorithm is 80%. The highest accuracy of the IGA algorithm is 97%. The IGA algorithm is more powerful in terms of operating efficiency and accuracy. Therefore, the application of the IGA algorithm to CT image segmentation is beneficial to doctors to better judge the lesions and improve the diagnosis rate.
Impact of instructors’ online teaching readiness on satisfaction in the emergency online teaching context

This study aims at investigating the predictive effects of online teaching readiness factors on instructors' satisfaction and confidence in the emergency online teaching context. The target population is instructors who experienced online teaching during the COVID-19 pandemic, particularly in South Korea. Two hundred and thirteen instructors from 15 universities in South Korea responded to a survey asking about online teaching readiness (technological, pedagogical, online communication, time and environment management, and institutional support readiness), satisfaction, and confidence. Hierarchical regression analysis showed that pedagogical and online communication readiness have significant positive effects on both satisfaction and confidence, while showing lower means than other online teaching readiness sub-categories. This research concludes that specific strategies and skills for pedagogical and online communication readiness have to be stressed in professional development programs for online teaching.
GoMIC: Multi-view image clustering via self-supervised contrastive heterogeneous graph co-learning

Abstract Graph learning is being increasingly applied to image clustering to reveal intra-class and inter-class relationships in data. However, existing graph learning-based image clustering focuses on grouping images under a single view, which under-utilises the information provided by the data. To address that, we propose a self-supervised multi-view image clustering technique under contrastive heterogeneous graph learning. Our method computes a heterogeneous affinity graph for multi-view image data. It conducts Local Feature Propagation (LFP) for reasoning over the local neighbourhood of each node and executes an Influence-aware Feature Propagation (IFP) from each node to its influential node for learning the clustering intention. The proposed framework pioneeringly employs two contrastive objectives. The first targets to contrast and fuse multiple views for the overall LFP embedding, and the second maximises the mutual information between LFP and IFP representations. We conduct extensive experiments on the benchmark datasets for the problem, i.e. COIL-20, Caltech7 and CASIA-WebFace. Our evaluation shows that our method outperforms the state-of-the-art methods, including the popular techniques MVGL, MCGC and HeCo.
An Abnormal Traffic Detection Method for IoT Devices Based on Federated Learning and Depthwise Separable Convolutional Neural Networks

As a bridge for information interaction between people and things, and things and things, IoT devices bring security issues and data privacy protection issues that have always been the main challenges in the IoT environment. In terms of abnormal traffic detection of IoT devices, data sharing between device data is usually not possible. This makes the deep learning method for model training based on a large amount of data unable to fully exert its strength due to the lack of IoT device attack instances, resulting in the problem of low detection accuracy. To this end, we propose an abnormal traffic detection model for IoT devices, FL-DSCNN (Federated Learning and Depthwise separable convolutional neural networks). First, the mayfly optimization algorithm is used to select the traffic features, and the model training time is reduced by reducing the feature dimension. Then, by introducing the FL framework, the depthwise separable convolutional neural network is used as a local model for collaborative training without sharing private data, avoiding the problem of lack of labeled data due to the “data silos” phenomenon while protecting data privacy. In addition, we experimentally verify the proposed method on the existing public dataset Aposemat IoT-23 dataset and compare and evaluate it with existing methods. The experimental results show that the method can achieve two-class and multi-class detection respectively. The detection accuracy rates of 98.52% and 97.73% prove the progress and superiority of the proposed FL-DSCNN model in the detection of abnormal traffic of IoT devices.
Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as <i>necessary-but-not-sufficient</i> precondition of the downstream in a new notion of Space Economy 4.0 - Part 2: Software developments

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System's Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.