Reviewerly/exHarmony huggingface dataset:
Secure identity key and blockchain‐based authentication approach for secure data communication in multi‐WSN

Summary Blockchain technology is an effective way to protect data that is handled by wireless sensor network (WSN). The traditional authentication protocol relies on trusted third parties, and most of the WSN paradigm does not allow these conditions, which results in single‐point failure. Sometimes the authentication process is slower, eventually hampers user experience, and thus users are inclined towards other alternatives that can offer multi‐factor authentication. To solve such issues, a secure identity key and blockchain‐based authentication framework is designed using a hybrid blockchain system in multi‐WSN to enhance the system's security. The nodes cooperate in the network to perform specific tasks such that interaction between nodes ensures the legitimacy of node's identity. With hybrid blockchain technology, nodes' identity information is authenticated and stored in the network after verification to establish communication among ordinary nodes and between the ordinary nodes and end user. The proposed method obtains higher security by achieving less computation time, higher detection rate, and limited memory usage of 0.072 s, 92.48%, and 3.925 MB, respectively. The developed authentication method is proved capable of proving resistance in case of different attacks in the network.
MTMVC: Semi-supervised 3D hand pose estimation using multi-task and multi-view consistency

The high performance of state-of-the-art deep learning methods for 3D hand pose estimation heavily depends on a large annotated training set. However, it is difficult and time-consuming to obtain the annotations for 3D hand poses. To leverage unannotated images to reduce the annotation cost, we propose a semi-supervised method based on Multi-Task and Multi-View Consistency (MTMVC) for hand pose estimation. First, we obtain the joints based on heatmap prediction and coordinate regression parallelly and encourage their consistency. Second, we introduce multi-view consistency to encourage the predicted poses to be rotation-invariant. Thirdly, to make the network pay more attention to the hand region, we propose a spatially weighted consistency. Experiments on four public datasets showed that our proposed MTMVC outperformed existing semi-supervised hand pose estimation methods, and by only using half of the annotations, the accuracy of our method was comparable to those of several state-of-the-art fully supervised methods.
Self-attention-based long temporal sequence modeling method for temporal action detection

Temporal Action Detection (TAD) is a basic and complex task in video understanding. It aims at detecting both the localization and category of actions in a video. The anchor-free TAD methods directly predict the action classes at each location and regress the distances to the boundaries. However, current anchor-free models based on convolutional neural network encode spatiotemporal sequences by 3D convolution networks. Due to the limited receptive field and the basic prior of the translation invariance, effective long temporal sequence modeling cannot be achieved. As a result, these methods cannot effectively detect temporal boundaries. To solve this problem, we design a novel end-to-end self-attention temporal enhancement TAD model, which introduces the Temporal Enhancement module to enhance the temporal feature encoding of the videos and expand the receptive field. Extensive experiments demonstrate that the self-attention Temporal Enhancement model yields an effective improvement on previous work, which improves the performance on THUMOS14 by 1.2%, reaching 53.2% on average mAP. Meanwhile, a competitive result of 34.7% average mAP is achieved on ActivityNet-1.3.
CaPTURe: Cartoon Pose Transfer Using Reverse Attention

Most of the previous pose transfer methods require additional input data such as joint keypoints of the target pose extracted by a pre-trained network in a human domain. However, in the fields where cartoon characters are used, such as animation or webtoons, the body proportions and structures of the characters often deviate from those of real-life humans. Therefore, it is not appropriate to utilize a pre-trained network designed for the human domain to extract additional data from these cartoon characters. Even if the network is newly trained in the cartoon domain, expensive data labeling is necessary. As a result, most of the previous pose transfer methods are not suitable for application in the cartoon domain. To address these issues, we propose a cartoon pose transfer network named CaPTURe that can successfully perform pose transfer in the cartoon domain with only target images and no other input data. Here, we incorporate the attention mechanism to accurately generate the desired identity. Additionally, we employ reverse attention to enhance the precision of generating the target pose. This approach eliminates the influence of identity information in the pose feature, enabling our network to focus solely on utilizing pose information. Consequently, through comparative experiments using a cartoon domain dataset, CaPTURe shows significant improvements in quantitative results over the previous state-of-the-art methods. Specifically, it achieves a 4.7% reduction in L1 Distance, a 0.1% improvement in SSIM, and a 0.8% enhancement in LPIPS. Moreover, CaPTURe exhibits superior qualitative performance than the previous state-of-the-art methods. In addition, we demonstrate that CaPTURe is capable of achieving effective pose transfer not just in the cartoon domains, but also in the human domains, as evidenced by our experiments on a human domain dataset.
Do ICTs reduce inequalities in access to professional training in Cameroon?

Purpose This work aims to assess the effects of information and communication technology (ICT) on inequalities in access to professional training (PT) in Cameroon. Design/methodology/approach This study used data from the fourth Cameroonian Household Survey (ECAM 4), the concentration index (CI) calculations and the Wagstaff et al. (2003) decomposition. Findings The preliminary results show that the CI calculations by groups of individuals reveal the existence of significant inequalities in favour of the poor. This is the case for all groups of individuals who use ICT tools, namely radio, internet, telephone and television. The results of the Wagstaff et al. (2003) decomposition reveal that an equitable distribution of income between those who use and those who do not use the telephone, radio and internet reduces inequalities in access to FP in favour of the poor. Originality/value Despite the wealth of literature devoted to the study of inequalities in access to education, the consideration of PT is still very marginal. In Cameroon, the literature devoted to the study of inequalities in access to PT is still almost non-existent, probably because of a low level of interest in the scientific community. However, as just seen, PT is a tool for combating unemployment, particularly in economies where the informal sector is important, insofar as the proportion of unemployed and inactive people is very low amongst the ones that have taken a PT course. Moreover, studies on the effects of ICT on inequalities in access to PT are still rare in the literature.
Module for arbitrary controlled rotation in gate-based quantum algorithms

Almost all gate-based quantum algorithms involving controlled rotation modules have faced the same challenges of limited quantum resources in the noisy intermediate-scale quantum (NISQ) era. Based on quantum phase estimation, this paper proposes a general module for arbitrary controlled rotation in gate-based quantum algorithms, which can be applied in the Harrow–Hassidim–Lloyd (HHL) algorithms and some other quantum algorithms. Numerical results show that the proposed module only requires a small number of auxiliary qubits to ensure the high fidelity of the HHL algorithm. Compared with the polynomial fitting function method, within a certain error range, the module only needs fewer quantum gates to realize the controlled rotation module of the HHL algorithm. Since the gate complexity of the module is associated with phase estimation, the gate complexity is also polynomial if the diagonal unitary matrix in phase estimation can be decomposed efficiently.
DMPH-Net: a deep multi-scale pyramid hybrid network for low-light image enhancement with attention mechanism and noise reduction

Abstract Aiming at the problems of color bias and noise enhancement on the output image by the traditional low-light image enhancement algorithm, we propose a deep multi-scale pyramid hybrid network (DMPH-Net) algorithm that fuses attention mechanism and multi-scale pyramid is proposed in this paper. The algorithm uses DecomNet to decompose reflectance and light components, and uses multi-scale illumination attention module to fuse light and reflectance for the decomposed low-light reflectance to improve the realism and details of reflectance; by using five-layer feature pyramid and kernel selection in PRID-net module to achieve the fusion of contextual information between different scale feature layers, while effectively removing the enhanced of noise, and the added color loss effectively suppresses the color bias of the output image; using multi-scale cascading and channel attention mechanisms to adjust the illumination and fuse the illumination ratios, effectively enhancing the brightness, texture, and other feature information in the image. The DMPH-Net algorithm is experimentally validated on LOL and no-reference LIME, MEF, and NPE datasets, and the objective evaluation metrics PSNR $$\uparrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>↑</mml:mo> </mml:math> , SSIM $$\uparrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>↑</mml:mo> </mml:math> , LIPIPS $$\downarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>↓</mml:mo> </mml:math> , and NIQE $$\downarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>↓</mml:mo> </mml:math> are 23.3772, 0.8442, 0.1386, and 3.5966 on LOL dataset. The objective evaluation metrics NIQE on the reference-free datasets LIME, NPE, and MEF are 3.0735, 3.1711, and 2.9464, respectively. The experiments show that the DMPH-Net algorithm maintains high image details and textures in image enhancement and denoising, effectively enhances low-light images, and reduces noise and color bias of images. Compared with RUAS, UnRetinex-Net, and other enhancement algorithms, it improves in objective evaluation metrics PSNR, SSIM, LIPIPS, and NIQE.
Chained-DP: Can We Recycle Privacy Budget?

Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inject random noise into users' vectors when communicating with users and the central server. Due to the privacy-utility trade-off, the privacy budget has been widely recognized as the bottleneck resource that requires well provisioning. In this paper, we explore the possibility of privacy budget recycling and propose a novel Chained-DP framework enabling users to carry out data aggregation sequentially to recycle the privacy budget. We establish a sequential game to model the user interactions in our framework. We theoretically show the mathematical nature of the sequential game, solve its Nash Equilibrium, and design an incentive mechanism with provable economic properties. Our numerical simulation validates the effectiveness of Chained-DP, showing that it can significantly save privacy budget as well as lower estimation error compared to the traditional LDP mechanism.
Proactive Auto-scaling For Delay-sensitive Service Providers Over Public Clouds

As a new design mechanism for improving service quality and user experience, a common practice is to deploy the delay-sensitive services in a public cloud. However, this method is also faced with the challenge of serving the fluctuating resource demands through timely acquisition of enough instances. In this paper, we systematically study the problem of proactively purchasing cloud resources for delay-sensitive service providers over the public clouds which are under a flexible pricing model, so that the total cost can be minimized over the long run. We formulate the Proactive Cloud Resource Scaling Cost Minimization (PCRSCM) problem, in which we take the prediction, purchasing and deployment cost into consideration. This problem can be proved to be NP-hard and we propose the Proactive Online Instance Purchase (POIP) algorithm to solve the problem. We also evaluate the performance of POIP through real trace-driven simulations and real testbed. Our evaluation shows that POIP significantly reduces delay by more than 40% when compared to the reactive method and it costs about 20%, 25%, 30% less than state-of-the-art methods, such as POLAR, CMPVS and APOP, respectively.
Digital Technologies in Geography Teaching

This article presents several examples of digital technologies, such as the use of augmented reality, virtual reality, mLearning, web-based GIS, online applications, and the like, which can be used in the educational process of geography. We also present the methodologies created within the national project called IT Academy - Education for the 21st Century, which tries to achieve the educational goal through inquiry-oriented teaching and digital technologies. We discuss in more detail the methodology focused on field teaching using mLearning and digital technologies.
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance.
tMPT: Reconfiguration across Blockchain Shards via Trimmed Merkle Patricia Trie

Sharding is one of the most promising techniques that can improve the scalability and storage issues of blockchain systems. In the past few years, many sharding protocols have been proposed to contribute to the technique matrix of blockchain sharding such as the coordination mechanisms of blockchain shards, the handling of cross-shard transactions, the security guarantee of blockchain shards, etc. Although those previous solutions are crucial for sharded blockchains, we still have not found any systematic implementation of shard reconfiguration, which determines the security of a sharded blockchain because the shuffling of blockchain nodes could prevent malicious nodes from corrupting a shard. However, the implementation of shard reconfiguration is not easy. When reallocating blockchain nodes to designated shards, typical challenges include the following: i) how to synchronize a large size of state data for a newly arrived node, and ii) how to mitigate the large reconfiguration latency of blockchain shards while keeping the liveness and consistency properties of a blockchain system. To overcome those challenges, we propose a dedicated protocol for shard reconfiguration in blockchain sharding using trimmed Merkle Patricia Trie (tMPT). The proposed tMPT-based protocol is designed to guarantee the high efficiency of the reconfiguration of blockchain shards while ensuring the uninterrupted services of the sharded blockchain. We implement the proposed tMPT-based reconfiguration protocol in a prototype, which enables the functionality of blockchain sharding. We then deploy our prototype in Alibaba Cloud. The experimental results show that the proposed tMPT-based protocol outperforms the existing methods in terms of reconfiguration efficiency. For example, the throughput of the proposed protocol shows 198% higher than Ethereum's full sync method.
Sıtma Hastalığının Otomatik Tespiti için EfficientNet Tabanlı Segmentasyon Modellerinin Performans Analizi

Malaria is a disease caused by the Plasmodium parasite, which is common in the tropics. The traditional methods commonly used to diagnose malaria, one of the world's deadliest diseases, are microscopic diagnostic methods in which blood samples taken from suspected individuals are manually examined, or rapid diagnostic tests that are sensitive to human errors. These processes are inexpensive, but experienced and qualified clinicians are needed. Due to this shortcoming, modern diagnostic tools are crucial in the struggle against the disease. In this study, an approach based on deep learning methods was used, which offers beneficial solutions in the diagnosis of disease from medical images. In the proposed approach, U-Net, Pyramid Scene Parsing Network, LinkNet, and Feature Pyramid Network segmentation methods were modified with 8 different pre-trained variants of the EfficientNet deep learning model to obtain improved models. In the malaria segmentation performed with these models, the highest Dice score of 91.50% was achieved in the use of the U-Net model with EfficientNetB6. This model offers a faster and more robust solution to detecting parasites compared to traditional methods.
The Crumbling Wall between Crypto and Non-crypto Markets: Risk Transmission through Stablecoins

The crypto and non-crypto markets used to be separate from each other. However, we argue that risks are now being transmitted across the two markets via stablecoins, which are pegged to non-crypto assets and hold a crucial role in crypto trading. Employing copula-based CoVaR approaches, we identify significant risk spillovers between stablecoins and cryptocurrencies, as well as between stablecoins and non-crypto markets. We also find that the risk spillovers between crypto and non-crypto markets become significant only after stablecoins are widely accepted. This finding underscores the function of stablecoins as a bridge for risk transmission across these distinct markets, while also highlighting the progressively blurring boundaries between the two spheres. Additionally, we document that the risk spillovers from the US dollar to the crypto market are stronger than vice versa, indicating an ongoing process of re-dollarization in the crypto market.
Confiscation of Electronic Evidence Cyber Crime in Business and Law Collaboration After Pandemic Achieving Sustainable Legal Development

This paper aims to find out how the confiscation of digital evidence in cybercrime cases from the perspective of the COVID-19 pandemic uses postpandemic business and legal collaboration to encourage sustainable legal development.The analysis highlights how the use of cloud storage as a storage results in crime, in the form of ATM owner privacy data.Such as case studies that show how digital evidence, as a form of information technology advancement, allows the wearer to cross the jurisdictional boundaries of each country.Jurisdictional issues on the internet are closely related to law enforcement issues in each country.As a world without borders, applying jurisdiction on the internet is not easy.There needs to be certainty about the law that will be applied in a world without borders.The jurisdiction of a country as we know it can be developed and used in a world without borders.This study examines the subject matter through a normative juridical approach, legal approach, case approach, qualitative analysis, examines the principle of legal certainty philosophically.By analyzing the theory of criminal law and confiscation.Business and legal collaboration in the form of cloud storage service products will provide advantages and benefits for all parties.There needs to be certainty about the law that will be applied in a world without borders.The jurisdiction of a country as we know it can be developed and used in a world without borders.
Impact of Organizational Learning on Nurse Performance with Organizational Commitment and Citizenship Behavior (OCB) as a Mediation Variable and in Leadership Moderation

The role of the hospital in supporting public health is very strategic because the hospital and the number of people in need are very disproportionate.This study aims to analyze the role of nurses in hospitals who currently handle individual participants and health insurance participants who are often underestimated and receive different services.The unit of analysis studied in this study is a nurse at a hospital in Jakarta that serves BPJS patients, which is a referral hospital for BPJS patients and actively serves BPJS patients.This study uses hypotheses based on theoretical studies and empirical studies as research studies.The paradigm used is quantitative (positivism).This research was conducted with a quantitative approach to 295 hospital nurses in the city of Jakarta who served BPJS patients.The data analysis process in this study used LISREL software to test the effect between constructs and test the fit of the model.The result of this study shows organizational learning, organizational commitment, and OCB because they have an important influence on nurse performance.The organizational commitment variable becomes the central variable because it has a direct influence and is able to mediate organizational learning on nurse performance.Leadership is proven to fail in moderating organizational learning and OCB on nurse performance.The implications of this research can also contribute to government and private hospitals in terms of applying the concepts of organizational learning, organizational commitment, OCB and leadership in improving the performance of nurses in hospitals.
A Deep GCN Approach Based on Multidimensional Projections and Classification to Cybercrime Detection in a True Imbalanced Problem with Semisupervision

In the financial world, some transactions can be fraudulent. Appropriate detection for such these transactions could help to reduce them. An illegal trade detection can be made through intrinsic features and by using structural information related to the way followed during its lifecycle. Fusing both heterogeneous information sources could contribute to accuracy improvement. Nowadays, graph-based technologies allow the fusion of geometric information linked to nodes and structural information related to transitions between nodes. The article aims to validate new Deep Learning technologies based on graphs and Directed Graph Convolutional Networks (DGCNs) in a problem. The structural-graph information problem is that near nodes are related to all the direct or indirect linked nodes that allow them to connect from one node to some other. Each node is closer to the other the lower is the number of ways required to reach the second one, and the greater is the weight of assumed ways. On the other hand, another relevant information is the intrinsic features related to each one of the nodes, which must be fused with the structural information in new hidden features that aggregates both kinds of information for each of the nodes. Moreover, the problem to be modeled also has two main intrinsic issues related to cybercrime, which are common in other types of problems. These two main problems are: firstly, a truly imbalanced situation occurs between fraud and non-fraud transactions. Secondly, missing labels appear in many nodes for a human without knowledge of the actual node situation. Finally, the paper shows an easy way to fuse information from model performance and cost risks that allows us to establish the corresponding threshold for the output model to distinguish between Licit and Illicit transactions.