Reviewerly/exHarmony huggingface dataset:
“Hungarian” Image (Differencing) Descriptor

Cultural or natural heritage digitization has become an essential part of the knowledge economy in the EU. We propose a tool to improve the accessibility of hi-tech devices and the sustainability of cooperation in multidisciplinary teams in the first scanning session. The CRUSE scanning beginners may take into account a novel image descriptor of moderate size for practical applications, e.g. differencing similar CRUSE scans. Our approach combines a set of Harris corners with the Hungarian algorithm to achieve an informative visual representation in the form of a planar subgraph. We compare, on selected use-cases, the solution quality and/or disadvantages. The key practical contribution of our research is an original approach for an alternative way of image structure understanding, named "polygon shape descriptor". Our innovation is about transforming the image differencing using Harris corners and Hungarian edges for support and/or speed-up of visual comparison of very similar scans. Methodologically, we interrelate computer vision and computational geometry to enhance hi-tech accessibility in virtual reality classes and student projects.
Ancestral and Cultural Futuring: Speculative Design in an Indigenous ovaHimba context

This paper presents the first instance and experience of futuring in two indigenous ovaHimba communities in northwest Namibia. Over a series of sessions, we, as part of a broad green energy access project, explore futuring to stimulate and invoke alternative green energy use cases. These alternatives are premised on the opposition of the dominant needs-based and interventionist approach and imagination of unorthodox green energy utilisation that supersedes mainstream, rudimentary and obvious energy use. We reflect on the application of futuring, particularly speculative design, in an indigenous context, highlighting the communities' back-looking future perspective, and relevance and influence of ancestry and culture over the future. As well as accentuate the friction towards speculative design, arguing for its appropriation and alignment to a more grounded design approach. Moreover, we indicate the agency that it provides, allowing local participants to re-evaluate their values and practices and simultaneously determine the integration of technology into the future.
Dynamic updating self-training for semi-weakly supervised object detection

Semi-weakly supervised object detection (SWSOD) needs to label only a small portion of images in the training set to train an initial detector. This detector is then used to select some reliable unlabeled images and generate pseudo labels for them. The pseudo-labeled images are combined with the labeled images to re-train the detector. One potential problem of SWSOD is that some pseudo labels could be incorrect so that the detector cannot be improved progressively. To address this problem, we propose a dynamic updating self-training (DUST) mechanism, which divides unlabeled images into multiple folds ranging from simple to complex and dynamically updates pseudo labels. In each fold, we apply an intra-fold updating at each training iteration to iteratively select some images from the current fold and update their pseudo labels to retrain the detector. At the end of each fold, we apply an inter-fold updating to update the pseudo labels of all previous folds and transfer the remaining unlabeled images into the next fold for further pseudo labels mining. To reduce the influence of noisy labels during the intra-fold updating, we propose a label reliability sensitive (LRS) loss for the labeled images to weigh the cross entropy. Meanwhile, a sample difficulty sensitive (SDS) loss is proposed for unlabeled images to balance the contributions of samples with different confidences. Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate the effectiveness of our method. For instance, by using 20 positive images and 20 negative images in each category as the labeled images, our method outperforms the state-of-the-art method [46] by 4.1% and achieves 93% of the WSOD performance on the PASCAL VOC 2007 dataset.
Research on multi-UAVs route planning method based on improved bat optimization algorithm

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Bayesian Updating and Model Class Selection for Magnitude Estimation in Earthquake Early Warning: Application to Earthquake Sequences in Sichuan Region, China

ABSTRACT A key component of earthquake early warning system is the development of an accurate and robust predictive model relating the recorded waveform to seismicity and ground-shaking characteristics. This article presents a probabilistic Bayesian inference methodology to address relevant sources of uncertainty in the development of such predictive models. We investigate the calibration of models for the earthquake magnitude based on the maximum predominant period, considering the Sichuan region of Southwestern China as a case study. Established approaches for developing predictive models in this context adopt deterministic tools for some aspects of this development. They consider a linear regression calibrated typically through least squares optimization and frequently utilize the mean observations for each event averaging across the data available from different stations. The proposed Bayesian learning accommodates the following improvements: a model class selection is established, comparing across different candidate models to promote the most appropriate from accuracy and robustness perspectives; the full posterior distribution of the model parameters is identified, quantifying relevant uncertainties in their values; a heteroscedastic model is considered for the estimation error variance; and the observations are separately considered at the calibration stage. Each of these improvements ultimately addresses a different source of uncertainty impacting the predictive model development. We utilize transitional Markov chain Monte Carlo for obtaining samples from the posterior and for calculating the evidence to perform the model class selection. Different regression models are examined, and the Bayesian-based model identification is compared against the common least squares identification approach. Results show the value added by comparing across the different models and by considering a heteroskedastic variance model, offering insights into the advantages of Bayesian-based predictive models in earthquake early warning applications.
A general framework for personalising post hoc explanations through user knowledge integration

The field of XAI aims at providing explanations about the behavior of AI methods to a user. In particular, local post-hoc interpretability approaches aim at generating explanations for a particular prediction of a trained machine learning model. It is generally recognized that such explanations should be adapted to each user: integrating user knowledge and taking into account the user specificity allows to provide personalized explanations and to improve the explanation understandability. Yet these elements appear to be rarely taken into account, and only in specific configurations. In this paper, we propose a general framework to allow this integration of user knowledge in post-hoc interpretability methods, relying on the addition of a compatibility term in the cost function. We instantiate the proposed formalization in two scenarios, varying in the explanation form they propose, in the case where the available user knowledge provides information about the data features. As a result, two new explainability methods are proposed, respectively named Knowledge Integration in Counterfactual Explanation (KICE) and Knowledge Integration in Surrogate Model (KISM). These methods are experimentally studied on several benchmark data sets to characterize the explanations they generate as compared to reference methods.
Exploring Graph-based Transformer Encoder for Low-Resource Neural Machine Translation

The Transformer is commonly used in Neural Machine Translation (NMT), but it faces issues with over-parameterization in low-resource settings. This means that simply increasing the model parameters significantly will not lead to improved performance. In this study, we propose a graph-based approach that slightly increases the parameters while significantly outperforming the scaled version of the Transformer. We accomplish this by utilizing Graph Neural Networks to encode Universal Conceptual Cognitive Annotation (UCCA), allowing the linguistic features of UCCA to be incorporated into the word embeddings. This improves the performance of the NMT system since the word embedding is now more capable and informative. Experimental results demonstrate that the proposed method outperforms the scaled Transformer model by +0.4, +0.41, and +0.33 BLEU, respectively, in English-Vietnamese/French/Czech datasets. Furthermore, this method reduces the number of parameters by 47% when compared to the scaled Transformer. A thorough analysis of error patterns reveals that the proposed method provides structural awareness to translation systems. Our code is available at: https://github.com/nqbinh17/UCCA_GNN.
Economic Risks Associated with Bit Coin: A Comprehensive Analysis

Bitcoin, the pioneering cryptocurrency, has revolutionized the financial landscape, but its use is not without economic risks. This paper provides a comprehensive analysis of the economic risks stemming from the use of Bitcoin. It explores the volatility of Bitcoin and the factors contributing to its price instability, including regulatory uncertainty, cybersecurity threats, and market manipulation. Additionally, it delves into the implications of illicit activities and money laundering associated with Bitcoin, highlighting the need for robust regulatory frameworks to mitigate these risks. The paper also examines the environmental impact of Bitcoin mining, emphasizing its energy-intensive nature and carbon footprint. The systemic risks posed by Bitcoin, such as market volatility spillover, interconnectedness with traditional financial institutions, counterparty risks, lack of regulation, financial crime, and cybersecurity vulnerabilities, are thoroughly analyzed. Through a comprehensive review of the literature and an in-depth understanding of the subject matter, this paper provides insights into the multifaceted economic risks associated with Bitcoin. It underscores the importance of proactive measures to address these risks, including enhanced regulations, transparency, cybersecurity measures, and sustainability practices. By mitigating these risks, the paper argues that Bitcoin can play a more secure and sustainable role in the future of finance.
Digital forensics analysis based on cybercrime and the study of the rule of law in space governance

Abstract With the rapid development of social informatization, the Internet has become an important channel for global information dissemination. The wireless network space and the real space are intertwined and have a significant impact on the political, economic, and cultural aspects of international society. For example, it can effectively solve the problem of information exchange between cities, regions, and countries. Wireless network crime and wireless network space security issues involve the development of information technology in various countries and, more importantly, involve all aspects of national security. In the age of science and technology, the current situation of cybercrime can be expressed by data as follows: the online crime rate is high. According to relevant statistics, nearly 5 million Internet users are involved in various cases every year due to the use of the Internet. This article starts with the digital forensics of wireless network crime and wireless network security management systems and discusses the composition, characteristics, existing problems, and future development directions of the current wireless network security management system. By comparing the wireless network security laws of China and the United States, using the latent Dirichlet allocation (LDA)-Gibbs model and the k -means algorithm to analyze the data, this article provides guidance for future research on wireless network space governance. The combination of digital forensics analysis based on cybercrime and space governance can improve the level of public security work. At present, the world has made some progress in cyberspace, but it still faces severe challenges. In the face of new situations and new measures, it is necessary to strengthen legislation, improve relevant systems, and strengthen supervision capacity. At the same time, it is necessary to establish perfect technical means and safeguards to ensure that this goal can be achieved.
Extended Mammogram Classification From Textural Features

"The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated for two different mammogram databases (MIAS and DDSM) in order to assess its robustness. We discuss the changes required in the system, particularly at the level of the image preprocessing and feature extraction. Computational experiments are performed based on different methods for feature extraction, selection and classification. The results indicate an accuracy of 66.95% for the MIAS dataset and 54.1% for DDSM obtained using genetic algorithm based feature selection and Random Forest classification. Keywords: Breast cancer detection, Mammogram classification, GLRLM, Feature selection, Random Forests, MIAS, DDSM. "
Secure cloud data storage solution with better data accessibility and time efficiency

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Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology

Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.The original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset - MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin-stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique.To prioritize the performance of nuclei segmentation, we evaluated the model's outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 - 96.6), 81.8 (95% CI: 80.8 - 83.0), and 69.3 (95% CI: 68.2 - 70.0), respectively, on the internal test data set.Our proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis.
TEEDAG: A High-Throughput Distributed Ledger Based on TEE and Directed Acyclic Graph

With the arrival of the 5G era, the Internet of Things (IoT) has entered a new stage, and the amount of IoT data is growing rapidly. The traditional blockchain cannot handle massive amounts of data, which presents scalability challenges for blockchain technology. Existing blockchain improvement technologies such as off-chain payments, protocol improvements, and sharding techniques have performance bottlenecks and limitations in the data, which is rapidly growing. The blockchain is fundamentally a decentralized distributed ledger, and the traditional chain structure is inadequate for addressing concerns such as forks, double-spending attacks, and other factors in the current IoT landscape. In this paper, we propose a high-throughput distributed ledger based on Directed Acyclic Graph (DAG) named TEEDAG. We design a consensus algorithm based on self-referencing parallel chains combined with Trusted Execution Environment (TEE) to ensure the security of the consensus process. The experiment proves that TEEDAG demonstrates a significantly higher throughput compared to traditional blockchain solutions and offers improved security and efficiency compared to existing DAG-based distributed ledger solutions.
Self‐dual association schemes, fusions of Hamming schemes, and partial geometric designs

Abstract Partial geometric designs can be constructed from basic relations of association schemes. An infinite family of partial geometric designs were constructed from the fusion schemes of certain Hamming schemes in work by Nowak et al. (2016). A general method to create partial geometric designs from association schemes is given by Xu (2023). In this paper, we continue the research by Xu (2023). We will first study the properties and characterizations of self‐dual association schemes. Then using the characterizations of self‐dual association schemes and the representation theory (character tables) of commutative association schemes, we obtain characterizations and classifications of self‐dual (symmetric or nonsymmetric) association schemes of rank 4 that produce as many as possible nontrivial partial geometric designs or 2‐designs. In particular, for a primitive self‐dual symmetric association scheme of rank 4, if is a power of 3 and each of , , and induces a partial geometric design, then we will prove that is algebraically isomorphic to a fusion scheme of the Hamming scheme for some odd number .
Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system’s robustness and scalability to larger indoor environments with more complex safety hazards.
Multi-Angle Models and Lightweight Unbiased Decoding-Based Algorithm for Human Pose Estimation

When a top-down method is taken to the task of human pose estimation, the accuracy of joint point localization is often limited by the accuracy of human detection. In addition, conventional algorithms commonly encode the image to generate a heat map before processing, but the systematic error in decoding the heat map back to the original image has an impact on the positioning. Therefore, to address the two problems, we propose an algorithm that uses multiple angle models to generate the human boxes and then performs lightweight decoding to recover the image. The new boxes can better fit humans and the recovery error can be reduced. First, we split the backbone network into three sub-networks, the first sub-network is responsible for generating the original human box, the second sub-network is responsible for generating a coarse pose estimation in the boxes, and the third sub-network is responsible for a high-precision pose estimation. In order to make the human box fit the human body better, with only a small number of interfering pixels inside the box, models of the human boxes with multiple rotation angles are generated. The results from the second sub-network are used to select the best human box. Using this human box as input to the third sub-network can significantly improve the accuracy of the pose estimation. Then to reduce the errors arising from image decoding, we propose a lightweight unbiased decoding strategy that differs from traditional methods by combining multiple possible offsets to select the direction and size of the final offset. On the MPII dataset and the COCO dataset, we compare the proposed algorithm with 11 state-of-the-art algorithms. The experimental results show that the algorithm achieves a large improvement in accuracy for a wide range of image sizes and different metrics.