Reviewerly/exHarmony huggingface dataset:
Investment decisions determinants in the GCC cryptocurrency market: a behavioural finance perspective

Purpose This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC). Design/methodology/approach The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions. Findings The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F -statistic indicates the general model's acceptability in the results. Practical implications The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market. Originality/value This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.
Larsemann Hills: Geomorphometric modeling and mapping

Geomorphometric modeling is widely used in geosciences. However, geomorphometric modeling and mapping of Antarctic oases has not been performed so far. This article presents the first results of our work on geomorphometric modeling and mapping of the Larsemann Hills obtained in the frameworks of the 68th Russian Antarctic Expedition in January–April 2023. As input data, we used a fragment of the Reference Elevation Model of Antarctica (REMA). From the extracted and edited digital elevation model, we derived digital models and maps of the following 17 morphometric variables: slope, aspect, horizontal curvature, vertical curvature, mean curvature, Gaussian curvature, minimal curvature, maximal curvature, unsphericity curvature, difference curvature, vertical excess curvature, horizontal excess curvature, ring curvature, accumulation curvature, catchment area, topographic index, and stream power index. We also conducted a field geomorphometric interpretation work to provide correct physical geographic, geological, and geomorphological interpretations of morphometric maps. In the fieldwork, we carried out 54 foot routes with the total length of about 422 km. During the routes, we collected 150 rock samples for further petrological and mineralogical analyses as well as three-dimensional modeling of the samples. Derived morphometric maps can be useful for structural geological and process-oriented hydrological studies. The ultimate goal of the ongoing work is to create a large-scale geomorphometric atlas of Antarctic oases and other ice-free Antarctic territories.
Applying Blockchain Technology and the Internet of Things to Improve the Data Reliability for Livestock Insurance

Animal husbandry is a vital sector in China's agriculture sector, contributing to over one-third of its agricultural output, and more than 40% of farmers' income. However, this industry is vulnerable to risks arising from production and operation, such as disease outbreaks, natural disasters, and market fluctuations. Livestock insurance can help mitigate these risks, but the lack of reliable data on shed environments has hindered its effectiveness. The objective of this study is to propose a livestock shed environmental regulatory platform that utilizes blockchain and the Internet of Things to ensure data authenticity, real-time monitoring, and transparency in the regulatory process. The platform also automates the insurance process, reducing costs and improving efficiency. The proposed platform employs blockchain to ensure data authenticity and devices to monitor and collect real-time environmental data. It also utilizes smart contracts to automate the insurance process, from negotiating and signing contracts to making insurance claims. The system's design rationale, architecture, and implementation are detailed. The proposed platform has been implemented and currently manages over 300,000 livestock animals with more than 350,000 insurance contracts signed. The use of blockchain and the Internet of Things has ensured data authenticity, real-time monitoring, and transparency in the regulatory process, while the automation of the insurance process has reduced costs and improved efficiency. The proposed livestock shed environmental regulatory platform has the potential to improve the effectiveness of livestock insurance in China by addressing the critical issue of data reliability. The use of blockchain and the Internet of Things has enabled real-time monitoring, data authenticity, and transparency in the regulatory process, while the automation of the insurance process has improved efficiency and reduced costs. This platform could serve as a model for other countries looking to improve the effectiveness of their livestock insurance programs.
Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.
Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.
Mitigating quantum errors via truncated Neumann series

Quantum gates and measurements on quantum hardware are inevitably subject to hardware imperfections that lead to quantum errors. Mitigating such unavoidable errors is crucial to explore the power of quantum hardware better. In this paper, we propose a unified framework that can mitigate quantum gate and measurement errors in computing quantum expectation values utilizing the truncated Neumann series. The essential idea is to cancel the effect of quantum error by approximating its inverse via linearly combining quantum errors of different orders produced by sequential applications of the quantum devices with carefully chosen coefficients. Remarkably, the estimation error decays exponentially in the truncated order, and the incurred error mitigation overhead is independent of the system size, as long as the noise resistance of the quantum device is moderate. We numerically test this framework for different quantum errors and find that the computation accuracy is substantially improved. Our framework possesses several vital advantages: it mitigates quantum gate and measurement errors in a unified manner, it neither assumes any error structure nor requires the tomography procedure to completely characterize the quantum errors, and most importantly, it is scalable. These advantages empower our quantum error mitigation framework to be efficient and practical and extend the ability of near-term quantum devices to deliver quantum applications.
An efficient deep learning model using network pruning for fake banknote recognition

In recent years, pruning methods have been proposed to reduce the size of image classification models based on CNNs and to shorten their inference times. However, most of them are based on setting the less important parameters of the model to zero, but not on reshaping the network. Therefore, in this paper we propose a pruning methodology for sequential CNNs that modifies the shape of the network (both convolutional and fully connected layers) and was applied to four sequential networks (a custom, AlexNet, VGG11 and VGG16) for the "Original and counterfeit Colombian peso banknotes" dataset. The pruned models were evaluated quantitatively and qualitatively. First, in terms of accuracy against FLOPs and parameter reduction. Second, using HiResCAM to explain the patterns on which the model is based for decision making. Quantitative results show a reduction in parameters and FLOPs of about 75% for a reduction in accuracy of up to 0.5% across all models in this study. With larger reductions of about 95%, the AlexNet-pruned and VGG16-pruned models significantly reduced their accuracy (38.1% and 21.9%, respectively), while the custom-pruned model and the VGG11-pruned model reduced their accuracy by only 0.3% and 0.9%, respectively. Furthermore, using HiResCAM, it was observed that the custom-pruned model and the VGG11-pruned model better preserved the original model activations, even at high pruning percentages, and achieved lower accuracy reductions than their competitors. With the proposed methodology, any sequential model can be pruned and reshaped while largely retaining the accuracy of the unpruned model, or even improving it for low pruning percentages.
Estimation of fused-filament-fabrication structural vibro-acoustic performance by modal impact sound

Accurately simulating and designing the vibro-acoustic performance of 3D printed structures is a challenging task due to the lack of material elastic properties, particularly when designing without specialized measurement instruments. To tackle this issue, we propose a numerical-experimental estimation method based on modal impact sound, which only requires a microphone for measurement. Specifically, we established a modal impact sound simulation pipeline that considers the material anisotropy and estimated the material elastic property by minimizing the residuals between the simulated and recorded modal impact sound features. Our method outperforms the state-of-the-art modal-sound approach in terms of convergence speed and accuracy, and achieves an average modal-frequency residual of 0.66% across two materials and six infills. We also designed and manufactured a xylophone using the estimated material elasticity for further demonstration and verification.
Automated and Enhanced Leucocyte Detection and Classification for Leukemia Detection Using Multi-Class SVM Classifier

In this day and age, surrounded by innumerable forms of technology, the use of various autonomous systems to recognize various ailments has tremendously benefited the medical industry.An important medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears.Invaluable details regarding the patient's health may be revealed, such as the discovery of acute lymphatic leukaemia or other serious disorders.This study provides a paradigm for detecting acute lymphoblastic leukemia from a microscopic vision of white blood cells.Microscopic images must go through a thorough pre-processing phase before being classified.In this study, WBCs are separated from blood smear images using morphological techniques, and the segmented region is then searched for a set of textural, geometrical, and statistical properties.Four different machine learning techniques are used to examine the performance of these algorithms: random forest (RF), support vector machine (SVM), naive Bayes classifier (NB), and K nearest neighbor (KNN).The SVM is effective in classifying and identifying the acute lymphoblastic cell that produces leukemia malignancy, as can be observed after careful comparison.A single classifier is virtually completely useless given the variety of blood smear pictures.As a result, we considered using EMC-SVM to classify leukocytes.The suggested method successfully distinguishes white blood cells from sample blood smear images, and accurately categorizes each segmented cell into the relevant group.
Cross Modal Adaptive Few-Shot Learning Based on Task Dependence

Few-shot learning (FSL) is a new machine learning method that applies the prior knowledge from some different domains tasks.The existing FSL models of metric-based learning have some drawbacks, such as the extracted features cannot reflect the true data distribution and the generalization ability is weak.In order to solve the problem in the present, we developed a model named cross modal adaptive few-shot learning based on task dependence (COOPERATE for short).A feature extraction and task representation method based on task condition network and auxiliary co-training is proposed.Semantic representation is added to each task by combining both visual and textual features.The measurement scale is adjusted to change the property of parameter update of the algorithm.The experimental results show that the COOPERATE has the better performance comparing with all approaches of the monomode and modal alignment FSL.
A Verifiable Multi-Secret Sharing Scheme Based on Short Integer Solution

With the possible birth of the quantum computer, traditional secret sharing schemes have been unable to meet security requirements.We proposed a new verifiable multi-secret sharing scheme based on the short integer solution problem.By utilizing a symmetric binary polynomial, secrets and secret shares can be generated, and then we convert the secret shares into binary string on , which can be identified by one-way anti-collision hash function on the lattice, so that multiple secrets can be reconstructed safely.The advantages mainly focus on verifiability without interaction in the distribution phase and less memory requirement.In a secret sharing scheme, verifiability prevents the dealer to share the wrong shares and forces the participants to submit their shares correctly.Meanwhile, the interaction can be reduced, which means the security is improved.In a multi-secret sharing scheme, releasing the public values is inevitable, this paper has less public values and less size of shares per secret size to reduce the pressure of memory consumption in the proper parameters.In the end, it can also effectively resist the quantum attack.
REAF: Remembering Enhancement and Entropy-Based Asymptotic Forgetting for Filter Pruning

Neurologically, filter pruning is a procedure of forgetting and remembering recovering. Prevailing methods directly forget less important information from an unrobust baseline at first and expect to minimize the performance sacrifice. However, unsaturated base remembering imposes a ceiling on the slimmed model leading to suboptimal performance. And significantly forgetting at first would cause unrecoverable information loss. Here, we design a novel filter pruning paradigm termed Remembering Enhancement and Entropy-based Asymptotic Forgetting (REAF). Inspired by robustness theory, we first enhance remembering by over-parameterizing baseline with fusible compensatory convolutions which liberates pruned model from the bondage of baseline at no inference cost. Then the collateral implication between original and compensatory filters necessitates a bilateral-collaborated pruning criterion. Specifically, only when the filter has the largest intra-branch distance and its compensatory counterpart has the strongest remembering enhancement power, they are preserved. Further, Ebbinghaus curve-based asymptotic forgetting is proposed to protect the pruned model from unstable learning. The number of pruned filters is increasing asymptotically in the training procedure, which enables the remembering of pretrained weights gradually to be concentrated in the remaining filters. Extensive experiments demonstrate the superiority of REAF over many state-of-the-art (SOTA) methods. For example, REAF removes 47.55% FLOPs and 42.98% parameters of ResNet-50 only with 0.98% TOP-1 accuracy loss on ImageNet. The code is available at https://github.com/zhangxin-xd/REAF.
Quantum Attacks on Type-3 Generalized Feistel Scheme and Unbalanced Feistel Scheme with Expanding Functions

Quantum algorithms are raising concerns in the field of cryptography all over the world. A growing number of symmetric cryptography algorithms have been attacked in the quantum setting. Type-3 generalized Feistel scheme (GFS) and unbalanced Feistel scheme with expanding functions (UFS-E) are common symmetric cryptography schemes, which are often used in cryptographic analysis and design. We propose quantum distinguishing attacks on Type-3 GFS and UFS-E in the quantum chosen plaintext attack setting. The results of key recovery are better than those based on exhaustive search in the quantum setting.
Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

Shui manuscripts are part of the national intangible cultural heritage of China.Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate.To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported.Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters.The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments.Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies.The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.
A Fine-Grained Object Detection Model for Aerial Images Based on YOLOv5 Deep Neural Network

Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects.This seriously limits the application of these advanced detection algorithms in remote sensing object detection.How to apply horizontal detection in remote sensing images has important research significance.The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning.Circular smooth label (CSL) solved this problem well by transforming the regression of angle into a classification form.YOLOv5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small objects.We use YOLOv5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets, and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images.Our improved model achieves an average category accuracy of 39.2% on the FAIR1M dataset.Although our method does not achieve satisfactory results, this approach is very efficient and simple, reducing the hardware requirements of the model.