Reviewerly/exHarmony huggingface dataset:
White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.
Smart Contracts and the Law

Smart contracts raise a number of interesting legal issues. An ongoing legal debate concerns a number of potential legal pitfalls related to smart contracts, including the existence of a valid, binding legal contract, the ability to reconcile the immutability of blockchain records with contractual deficiencies, governing law and applicable jurisdiction, consumer protection, anti-money laundering/combating the financing of terrorism (AML/CFT) requirements, implications of automated, unstoppable execution, as well as privacy and confidentiality of smart contracts deployed on borderless decentralized blockchain networks. This chapter provides an overview of the most commonly invoked advantages of smart contracts and highlights the fervor of the scholarly legal debate surrounding smart contracts. It also presents a range of legislative initiatives taken so far in response to the smart contracts phenomenon.
Investigating the effect of noise channels on the quality of unitary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math> -designs

Unitary $t$-designs have a wide variety of applications in quantum information theory, such as quantum data encryption and randomized benchmarking. However, experimental realizations of $t$-designs are subject to noise. Here we investigate the effect of noise channels on the quality of single-qubit $t$-designs. The noise channels we study are bit flips, phase flips, bit and phase flips, phase damping, amplitude damping, and depolarizing noise. We consider two noise models: The first has noise applied before the $t$-design unitary operations, while the second has noise applied after the unitary operations. We show that the single-qubit 1-design is affected only by amplitude damping, while numeric results obtained for the 2-, 3-, 4-, and 5-designs suggest that a $2t$-design is significantly more sensitive to noise than a $(2t\ensuremath{-}1)$-design and that, with the exception of amplitude damping, a $(2t+1)$-design is as sensitive to noise as a $2t$-design. Numeric results also reveal substantial variations in sensitivity to noise throughout the Bloch sphere. In particular, $t$-designs appear to be most sensitive to noise when acting on pure states and least sensitive to noise for the maximally mixed state. For depolarizing noise, we show that our two noise models are equivalent, and for the other noise channels, numeric results obtained for the model where noise is applied after the unitaries reflect the transformation of the noise channel into a depolarizing channel, an effect exploited in randomized benchmarking with 2-designs.
A method for identifying Chinese place names in typhoon events based on lattice-BiLSTM-CRF

The types of Chinese place names in typhoon events are diverse and the place names are long, which increases the difficulty of place name recognition. At the same time, how to use two different granularity semantic units of Chinese characters and words to assist neural network models to recognize place names is also a problem. In order to improve the accuracy of Chinese place name recognition in typhoon events, a dictionary of place names in typhoon events was constructed. The dictionary of place names and HowNet dictionary are combined for learning features of place names in typhoon events. Based on the Lattice-BiLSTM-CRF model, the two granular semantics of Chinese are integrated to realize the recognition of Chinese place names. It is verified by an experiment that this method can improve the accuracy of Chinese place name recognition in typhoon events.
Study on the spatial patter of small towns based on computer image processing and image recognition: taking Guanhaiwei Town of Cixi as an example

This paper uses GIS and Fragstats4.2 software to quantitatively analyze the landscape space of Guanhaiwei Town, and visualize the data combined with image processing technology to enhance its spatial expression, analyze the hydrological characteristics of river corridors in Guanhaiwei Town, and the accessibility of traffic corridors, etc., and summarize the current problems. It is found that the construction land patches are concentrated on the traffic arteries and there are construction land disturbances within the woodland patches; the construction land is widely distributed, with scattered scattered points and irregular shapes. The woodland is mainly distributed in the south of the town, and the green patches in the town are less and the layout of green space is not reasonable enough, and the articulation with the surrounding landscape is weak. In the traffic land, the patches are highly fragmented and the landscape area is small. The patches of agricultural land are the landscape matrix, but their edges are highly fragmented, with small patches distributed sporadically and irregular in shape. The water patches were highly fragmented and no isolation zone was established. The study used GIS for image processing and spatial analysis, which fully reflects the importance and application of computer technology in geographic research.
Cross spatio-temporal attention network for change detection

Detecting building changes based on high-resolution remote sensing images constitutes one of the most important tools for urban management, methods based on deep learning are substantially more efficient than traditional methods of detecting changes. Nevertheless, there are several problems with most models, such as incorrect edge detection results and missing detection results. Due to continuous down sampling, there is a loss of detailed information in the image as well as an insufficient ability to characterize features. Our paper proposes a multi-scale feature dependency network to improve the performance of change detection models. By combining the multi-scale embedding module with the spatiotemporal attention module, we can extract the original image features, preserve the rich context with multi-scale convolution, and establish long-short distance feature dependencies across different scale patches using long-short distance attention. Furthermore, we introduce a dynamic position deviation calculation method to accurately determine the relative position relationship between features during the attentional action phase. In this manner, the accuracy of model detection will be improved. Multi-level feature fusion helps improve the model's ability to perceive detail by fusing features at different scales, capturing semantic information from these features, and enhancing the feature representation capability. The publicly available building dataset LEVIR-CD was used to conduct comparative experiments with mainstream methods. In both visual detection and quantitative evaluation metrics, our network outperforms the comparison method.
The DLT Pilot Regime and DeFi

This chapter discusses the new DLT (Distributed Ledger Technology) Pilot Regime issued by the EU, considering its use and applicability regarding DeFi (Decentralized Finance). As set out in the ESMA (European Securities and Markets Authority) Report on crypto-assets, the authorities are having difficulties interpreting the existing legal requirements, discussing their application to crypto-assets and DLT structures. The DLT Pilot Regime is intended to operate in a way akin to a regulatory sandbox, permitting and framing the operation of market infrastructures that process trades in crypto-assets qualified as financial instruments and that otherwise would not be allowed to operate or would be subject to excessive and burdensome requirements. The regime aims to create legal certainty in view of the rules currently defined for secondary markets in financial instruments, particularly if these rules are fully suited to DLT and crypto-assets while also ensuring the protection of investors, market integrity and financial stability.
A study of metallogenic potential index of ores from the Bayan Obo Nb-Fe-REE deposit and its indication of geometallurgy and remote sensing exploration

Rare earth resources are of great economic and strategic importance. The Bayan Obo, as the largest Nb-Fe-REE deposit in the world, have attracted much attention in mining field. This paper provides two index parameters of ore that can be used to quickly distinguish the ore types with exploitation value or industrial significance, which is helpful for geometallurgy and remote sensing prospecting. Seven ore types from the main and eastern orebodies of Bayan Obo deposits were collected for this study. The composition of mineral and element of each sample was acquired by TESCAN Integrated Mineral Analyzer (TIMA). Method of principal component analysis (PCA) was employed to analyze the compositional data. Metallogenic potential index of minerals and elements (MPIM and MPIE) were defined to make quantitative appraisal on the resource potentiality of Bayan Obo deposit. The ore types with a higher index are thought to be rich in REE. The results show that MPIM and MPIE are helpful to judge the metallogenic potential and mineral exploration in Bayan Obo deposit. The research is expected to be applied to the fields of geometallurgy and remote sensing exploration.
Hydrocarbon microseepage information extracting and oil-gas prospective area prediction based on landsat-8 remote sensing images

The phenomenon of hydrocarbon micro leakage in oil and gas reservoirs is common, and at least 85% of oil and gas fields in the world currently have hydrocarbon micro leakage. Remote sensing technology can quickly, efficiently and economically detect and extract surface mineral anomaly information caused by hydrocarbon leakage, so as to provide favorable exploration targets in the initial stage of oil and gas exploration, which has important social and economic significance for reducing exploration risks and accelerating exploration process. Based on the hydrocarbon microleakage theory, this study extracted clay mineralization, carbonation and red layer fading and alteration information in the study area using Landsat8 OLI image data, and used GIS tools for overlay analysis. Combined with the comprehensive analysis of geological structure and other factors, the oil and gas prospect areas in northeast Sichuan are predicted, designate 5 oil and gas prediction areas.
Graph neural network-based similarity relationship construction model for geospatial services

During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.
Formal Modelling and Verification of Probabilistic Resource Bounded Agents

Abstract Many problems in Multi-Agent Systems (MASs) research are formulated in terms of the abilities of a coalition of agents. Existing approaches to reasoning about coalitional ability are usually focused on games or transition systems, which are described in terms of states and actions. Such approaches however often neglect a key feature of multi-agent systems, namely that the actions of the agents require resources. In this paper, we describe a logic for reasoning about coalitional ability under resource constraints in the probabilistic setting. We extend Resource-bounded Alternating-time Temporal Logic (RB-ATL) with probabilistic reasoning and provide a standard algorithm for the model-checking problem of the resulting logic Probabilistic resource-bounded ATL (pRB-ATL). We implement model-checking algorithms and present experimental results using simple multi-agent model-checking problems of increasing complexity.
Mechanical behavior of carbon and aramid fiber reinforced composites with various layups under tension

The present paper deals with investigation of mechanical properties of aramid and carbon fiber reinforced polymer specimens with different layups. Experimental results were obtained for tensile testing of ±45° laminate and tensile testing of orthotropic and quasi-isotropic layups. Digital image correlation was utilized to process the images of the specimen's surfaces captured throughout mechanical testing and to obtain the strain data. The localization of deformations in composites based on aramid and carbon fibers occurs in different ways. It depends on the layup, fabric or fiber has been used and inherent properties of fibers used. The pliability of aramid fibers, their high potential for deformation, as well as the woven of fibers into a fabric, leads to localization of deformations in the area of bundles due to the combined action of all these factors. Carbon fibers are also prone to strain localizations at low applied loads due to misalignments and the absence of preloading.
Ticketless Entry in Heritage Museums

Being one of the largest networks of ticketing systems operating over 1,015,000 km and transporting over 22 million visitors daily, e-ticketing systems spend over CR 94,000 to operate efficiently [1]. From the recent proposal for smart cities, ticketing systems are projecting to museums and cultural monuments with the current trend towards digitization, smart ticketing systems are the most of the hour. Of the 94,000 kr. is an exorbitant salary of 23,500 kr. spent on paper that can be subsidized using effective alternative techniques [2,3]. The smartphone, which has a profound effect on people's daily routine, can be used for paperless ticketing. The verification of identity documents for 22 million people during their daily visit requires a lot of human resources and all this can be simplified digitally by linking the website with a database of detailed cards, with local museums contributing a large share of this revenue. This proposal will also help in cracking down on those who visit in such a fake manner. By implementing this new web roach, E-ticketing systems will be able to implement Smart Ticketing systems and effective authentication techniques
An Optimized Cosine Similarity-Based Attention Gate for Temporal Sequence Patterns Recognition

Abstract The attention gate is often employed in various applications, such as recommender systems, to determine the correct context of the input data. Adopting the attention gate has been proven to improve prediction accuracy successfully. However, for a temporal sequence problem such as sensor-based Sign Language Recognition (SLR), it is still challenging to integrate the attention gate into the solution since the processing input is only in the form of a sine waveform. In our work, we propose an optimized cosine similarity-based attention gate to manipulate the sine wave signal while improving the recognition of temporal sequence data. We also evaluate and compare the various distance calculations and determine the best one for the SLR application. Adopting a distance-based attention gate has successfully achieved the recognition accuracy of 99% while reducing the error rate to 5%.
An empirical investigation of the impact of foreign direct investment on economic growth in Ethiopia

The main objective of this study is to empirically investigate the impact of foreign direct investment on economic growth of Ethiopia by using a time series data for the period 1992–2019. Other explanatory variables like trade openness, human capital, national saving and gross capital formation were incorporated in the model. For the purpose of undertaking the study, Autoregressive Distributed Lag (ARDL) econometric model was employed. Moreover, a Toda-Yamamoto Causality test was performed to identify the direction of the causality between economic growth and foreign direct investment. Findings from the study show that, both in the short run and long run, foreign direct investment has a positive and significant impact on economic growth. As an outcome of Toda-Yamamoto Causality test shows, there is a unidirectional causality running from economic growth to foreign direct investment. On the basis of the findings, recommendations are made for the government authorities to expand infrastructural facilities for enabling a free movement of the investors to the remote and marginalized areas to further expand investment activities that result in higher economic growth.
Real-time recommendation method of online education resources based on improved decision tree algorithm

In order to improve the accuracy of online education resources recommendation and reduce the recommendation time, this paper introduces the improved decision tree algorithm to design a real-time recommendation method of online education resources. We calculate the information gain rate of online educational resources and collect the dataset of educational resources to obtain the attribute information of online educational resources. The specific attribute branches of users are matched according to the information gain rate, and the pruning structure of incomplete data feature attributes of online education resources is constructed by discretisation method. C4.5 algorithm is used to improve the discreteness of decision tree algorithm, and the improved decision tree is used to classify online education resources. The real-time path of online resource recommendation is simulated to realise real-time resource recommendation. The results show that this method can improve the real-time recommendation effect of educational resources.
Blockchain in Peer-to-Peer Platforms: Enhancing Sustainability and Customer Experience in Tourism

Blockchain technology is emerging as a high-impact solution for the tourism industry, a topic chosen for its growing research relevance and potential to revolutionise the tourism sector in several areas. This study examines how the combination of Blockchain technology and P2P platforms advances sustainability and marketing in the tourism accommodation market. It attempts to fill a gap in the literature by focusing on its application in two areas, namely digital markets and technology, which are expanding. The originality of this research lies in its comprehensive review of blockchain applications in tourism from a practical point of view, which has been largely unexplored in the existing literature. Through a bibliometric review of forty-two papers, various Blockchain applications were identified, such as improving transparency, trust, and efficiency in hotel operations and eliminating intermediaries to reduce costs. The adoption of smart contracts and the use of cryptocurrencies have also emerged as key trends. These findings highlight the transformative potential of Blockchain technology to build trust between hosts and guests, streamline processes, and improve the customer experience. However, they emphasise the need for the careful planning and consideration of the challenges associated with implementing this technology. Future research should further explore the specific applications of Blockchain technology in tourism to optimise its impact on industry and ensure long-term sustainability.
Quantum interferometric power versus quantum correlations in a graphene layer system with a scattering process under thermal noise

Abstract Cutting-edge quantum processing technology is currently exploring the remarkable electronic properties of graphene layers, such as their high mobility and thermal conductivity. Our research is dedicated to investigating the behavior of quantum resources within a graphene layer system with a scattering process, specifically focusing on quantum interferometric power (QIP) and quantum correlations, while taking into account the influence of thermal noise. To quantify these correlations, we employ measures like local quantum uncertainty (LQU) and logarithmic negativity (LN). We examine how factors like temperature, inter-valley scattering processes strength, and other system parameters affect both QIP and quantum correlations. Our results reveal that higher temperatures lead to a reduction in QIP and non-classical correlations within graphene layers. Moreover, it is noteworthy that QIP and LQU respond similarly to changes in temperature, whereas LN is more sensitive to these variations. By optimizing system parameters such as band parameter, wavenumber operators and scattering processes strength, we can mitigate the impact of thermal noise and enhance the quantum advantages of graphene-based quantum processing