Reviewerly/exHarmony huggingface dataset:
FL-YOLO: Lightweight Small Target Detection Algorithm Based on Transformer and CNN Hybrid Structure

Although the target detection technology has made amazing progress in the detection of large and medium objects, due to the limited size of small objects, the occlusion between targets and the limitations of the convolutional network itself, small target detection is still as a challenging. In this paper, by improving the YOLOv5s algorithm, a lightweight small target detection FL-YOLO model with a hybrid structure is proposed, and the standard convolution in the original backbone network is replaced by depth-separable convolution and the residual connection method is adjusted. lightweight. A Patch-Attention (PA) module is proposed to extract context information of small objects and insert it into the backbone to enhance the feature extraction ability for tiny target. A new upsampling module named “Transition” is proposed to replace the nearest interpolation in YOLOv5s to minimize information conflict and information redundancy. Finally, the public dataset VisDrone2019 specially constructed for small targets was used to conduct experiments, which verified that the FL-YOLO proposed in this paper is more effective than YOLOv5s.
A Blockchain-based EHR System for Interhospital Communication and Patient Data Ownership Empowerment

Blockchain technology has been utilized in different industries because of its capabilities in terms of data security and its features such as immutability, decentralization, visibility, and traceability. With the frequency of visits to healthcare facilities, it is evident that healthcare is critical to people's lives. Considering such fact and the rise to prominence of E-health, a unique environment for delivering health services through digital and communication technologies, the digital transition of healthcare has initiated and with the level of privacy that health records would require, traditional and established technologies would be unable to accommodate it. For such reason, this study aims to develop a blockchain-based EHR system with an integrated browser-based web application which effectively facilitates the transmission of electronic health records between authorized healthcare organizations and manages the viewership of anonymized patient data. The software development life cycle model used for this study is the waterfall model. To ensure that the system operates as intended, a functionality test and user acceptance testing is performed. For the evaluation of the blockchain network's performance, network tests were conducted, measuring latency and throughput. The results indicate that the system is functioning as intended.
Application of Image Processing Based on Improved Canny Algorithm and Gurukin's Theorem

Placental volume is an important indicator for evaluating fetal health, and automatic identification of placenta and automatic calculation of its volume is of great significance in clinical practice. Due to the complexity and diversity of placental images, domestic and foreign scholars have proposed a variety of image recognition and segmentation methods. Among them, the convolutional neural network method is widely used, but this method has black box theory, which is difficult to explain and requires a large number of training samples. In this context, this paper proposes an improved Canny algorithm based on meshing and OTSU for edge detection, recognition and segmentation of placental images. The placenta volume was calculated by using Gurukin's theorem, and the calculation results were compared with the results of manual calculation by doctors. The results show that the algorithm model proposed in this paper is convenient to calculate, and the error of manual calculation is within 10%. The feasibility of the method has been verified. Especially when the sample size is not enough to use machine learning algorithms, it provides strong support for clinical decision-making.
A Scale-Arbitrary Network for Chinese Painting Super-Resolution

Image super-resolution (SR) plays a crucial role in enhancing low-resolution (LR) images to high-resolution (HR) images. In the context of Chinese painting, SR is of great significance for cultural presentation and post-processing. However, existing methods for super-resolution are limited to specific integer scale factors, and when it comes to the super-resolution of Chinese painting with large scales, the preservation of intricate textural details is compromised. In this paper, we propose a novel super-resolution network capable of handling arbitrary scale factors and preserving rich textural details for Chinese painting. Our network integrates the Meta-RCAN framework with a Scale-Aware Feature Learning Module to achieve superior effects. Furthermore, we introduce Haar loss and degradation loss to better preserve high-frequency features and enhance realistic details and textures. We also introduce global residual learning and fusion to obtain more shallow features. To facilitate our research, we create a large-scale high-performance dataset, Chi-Paint-HD, specifically designed for Chinese painting images. Experimental results demonstrate that our model achieves superior super-resolution performance for arbitrary scale factors, outperforming others models in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and overcoming over-smoothing issues. Additionally, our model achieves excellent quantitative evaluation results and high-quality visual performance for integer scale factors, surpassing previous models that are limited to specific scales.
Factors Related to Information Sharing and Cloud Computing Adoption in SMEs: Evidence from Saudi Arabia

The Gulf Cooperation Council region, particularly the Kingdom of Saudi Arabia, is embracing 5G technology and its applications, such as cloud computing. Nevertheless, Small and Medium-sized Enterprises (SMEs) are held back due to the introduction of new technology, which leads to new business models and system dynamics that require a new set of tools (hard skills) and skills (soft skills) to quickly adopt new technologies. Using survey data from 122 respondents and structural equation modeling, the study finds the factors affecting the level of information sharing in organizations can act as a barrier to cloud computing adoption. The study's contribution is that it looks at the effect of information sharing from three intertwined socio-economic, technological, and behavioral perspectives. The results suggest that although technological aspects are important, socio-economical, and behavioral aspects are equally crucial for cloud computing adoption. The study concludes by highlighting the implications for SMEs adopting cloud computing and the need for future research to understand why 5G adoption might not realize its full potential in emerging markets.
Knowledge Graph Completion Using Multiple Embedding Representations for Intelligent Information Extraction from Technical Reports

As a new data structure, knowledge graphs are widely used in search engines, recommendation systems, question and answer systems and other related fields. The knowledge graph is helpful to realize intelligent and digital knowledge service of nuclear power accidents, in which link prediction can solve the discovery and restoration of missing information in Knowledge graph. This is also one of the research hotspot in the field of knowledge graph applications. Currently, the text description of entity information is rarely considered in the completion of nuclear power Knowledge graph. In this paper, we propose MEK-ConvKB (Multi-Embedding Knowledge Graph Prediction based on ConvKB), a reasoning model combined with multi-embedding techniques to improve performance. The embedded expression of Knowledge graph is enhanced by text description in nuclear power accident reports, which improves the accuracy of link prediction and expands the Knowledge graph of nuclear power accidents. The results show that our model can effectively express the semantic association between entities, Our model achieved the best performance compared to the baseline methods., which can provide a research basis for solving the discovery and restoration of missing information in knowledge graphs.
CGIR: a Model of Cross Language Information Retrieval based on Concept Graph by Fusing Attention Mechanism

Cross language information retrieval faces challenges such as language differences, data scarcity, contextual disparities, and machine translation errors. To enhance retrieval accuracy and effectiveness, this paper proposes a similarity evaluation framework called Concept Graph Information Retrieval (CGIR). The framework includes the creation of concept graphs, quantized representations of these graphs, and retrieval processes. The construction process of CGIR incorporates an attention mechanism, significantly boosting its performance and accuracy. Through this fusion, concept graphs offer a comprehensive representation of texts, capturing the essence of the entire content while minimizing the displayed information, all while preserving the original meaning of the text to the fullest extent possible. The experimental results clearly demonstrate that the generated concept graphs effectively function as semantic representations of the entire texts. In comparison to keyword-based, ontology-based, and term-based retrieval methods, CGIR exhibits a remarkable improvement in accuracy, surpassing them by over 10%.
A Named Entity Recognition Method Based on Pre trained Models MBERT and BiLSTM

With the increasingly serious situation of online public opinion, a large amount of text data on the network needs to be processed urgently, and the task of naming entity recognition arises at the historic moment. With the enhancement of people's technology in the field of Natural language processing, the way to extract key information from a large amount of online information and control negative public opinion is becoming more and more effective. This article combines the most advanced pre-training models and deep learning networks, which are based on the combination of MBERT, BiLSTM, and CRF models, effectively improving the efficiency of named object recognition tasks in the field of public opinion.
Minimax rates for sparse signal detection under correlation

Abstract We fully characterize the nonasymptotic minimax separation rate for sparse signal detection in the Gaussian sequence model with $p$ equicorrelated observations, generalizing a result of Collier, Comminges and Tsybakov. As a consequence of the rate characterization, we find that strong correlation is a blessing, moderate correlation is a curse and weak correlation is irrelevant. Moreover, the threshold correlation level yielding a blessing exhibits phase transitions at the $\sqrt{p}$ and $p-\sqrt{p}$ sparsity levels. We also establish the emergence of new phase transitions in the minimax separation rate with a subtle dependence on the correlation level. Additionally, we study group structured correlations and derive the minimax separation rate in a model including multiple random effects. The group structure turns out to fundamentally change the detection problem from the equicorrelated case and different phenomena appear in the separation rate.
A novel approach for lung cancer detection using deep belief networks

Due to the increased awareness of lung cancer, researchers have created many algorithms that can recognise the disease in its early stages using a variety of Machine Learning (ML) techniques. Clinicians can manage incidental or screen-found ambiguous pulmonary nodules with the help of machine learning-based models for lung cancer prediction. Such methods might be able to lower the variability in nodule classification, enhance decision making, and eventually decrease the proportion of benign nodules that do not need to be followed. This study proposes a novel lung cancer detection method based on Magnetic Resonance Imaging (MRI). Using ML to classify features in MRI scans, this technology is useful for the early detection of lung cancer. The performance was further enhanced using featureselection methodologies. The images were divided into segments using the FBSO feature selection method, and deep learning techniques were used to analyze the three standard datasets, S1, S2 and S3. In this investigation, 98.9% classifier optimality and 96.7% accuracy were attained. This new approach demonstrated excellent dependability and was found to be the most effective classifier system compared with previous studies.
Postdigital Afterlife

This entry begins by exploring the context, understanding, and the development of postdigital afterlife. It examines postdigital afterlife in relation to symbolic immortality. It describes the shift from digital immortality to postdigital afterlife and then explains current perceptions of postdigital afterlife. It then provides a summary of postdigital afterlife creation and the ways in which this has developed over time. The final section presents some of the ethical and legal complexities of creating and manging a postdigital afterlife and postdigital immortal.
Generative adversarial networks: Techniques, area covered, and challenges

Generative Adversarial Networks (GANs) have emerged as powerful frameworks for generating realistic and diverse data samples in various domains including computer vision, natural language processing and audio synthesis. In this study, a comprehensive overview of GANs, their key components, the training process, and the evolution of the field are presented. Different variations in GAN architectures and their applications across domains are discussed. Furthermore, the challenges and open research directions in GANs, their stability, mode collapse, evaluation metrics, and ethical considerations are analysed. The aim of this review is to provide a comprehensive understanding of GANs and their strengths, limitations, and potential future developments.
Automatic Attendance System Using Facial Recognition Using the ADA Boost Algorithm

All industries are seeing constant technological advancement. We anticipate that educational institutions will integrate some of the new technologies into their regular operations to reduce student workloads. Despite its importance, recording attendance is sometimes a time-consuming operation; nevertheless, face recognition is one technology that offers promise for streamlining this vital but often laborious task. This paper investigates face recognition using an ensemble method with the goal of accurately counting attendance in photos taken with a variety of cameras. Used a facial recognition algorithm modelled after Haar's to analyze a group photo of the class. An ensembler processes the image to generate an attendance record once faces of students have been identified. Open CV can detect faces automatically in photographs while still retaining part of the original data. Although several methods exist for identifying people's faces today, this study focuses in on one approach: AdaBoost-based facial recognition. When the stated monitoring system is operating at full capacity, it is accurate to within 2%.
Influencia del liderazgo en gestión del conocimiento y eficiencia hospitalaria

To analyse the effect of leadership style on knowledge management in hospitals and hospital efficiency based on the opinion of experts in hospital management, applying fuzzy cognitive maps (FCM).FCM are relational models that can be used to graphically represent expert opinion and knowledge to infer cause-effect relationships between different concepts. The use of FCM as a simulation tool allows the evaluation of possible scenarios based on different leadership styles in hospitals.In the resulting augmented matrix, standardized effects range from 0.02 to 0.84, with the highest value representing the strongest relationship between knowledge exploitation and hospital efficiency. From the viewpoint of experts, knowledge creation within the hospital also influences hospital efficiency. Regarding variables reflecting leadership characteristics, positive effects have been identified, though with varying intensities, between authority, benevolence, and charisma, both in terms of knowledge creation and exploitation, as well as hospital efficiency. The transformational leadership style is associated with coefficients having higher values for knowledge management and hospital efficiency.Experts suggest that hospitals with authoritarian leadership styles would exhibit lower levels of knowledge creation and management, as well as lower hospital efficiency. On the other hand, they associate hospitals managed with a paternalistic leadership style with better values in both knowledge creation and exploitation, as well as hospital efficiency, compared to the authoritarian leadership style. Finally, they attribute the highest levels in aspects related to knowledge management and hospital efficiency to the transformational leadership style.
Energy-Efficient and Communication-Aware Resource Allocation in Container-Based Cloud with Group Genetic Algorithm

Microservice is a new architecture for application development that makes applications more flexible to deploy, extend and update compared to monolithic architectures. As container-based clouds rapidly gained popularity in recent years, more microservices are deployed in containers and composed of complex and elaborated applications for users. The challenges of microservices deployment in a container-based clouds arise from two-level resource allocations to not only minimize the overall energy consumption but also to reduce the communication data volume between microservices in physical networks to improve application performance. However, there is still a lack of research that considers these two important challenges jointly during microservice composition and resource allocation. Motivated by this, in this work, we propose a genetic algorithm-based algorithm, namely EC-GGA, to not only minimize the energy consumption in cloud data centers but also minimize the communication data volume of applications. We compare EC-GGA with several state-of-the-art algorithms to demonstrate the effectiveness of our proposed algorithm.
Niagara: Scheduling DNN Inference Services on Heterogeneous Edge Processors

Intelligent applications heavily rely on deep neural network (DNN) inference services executed on edge devices to fulfill functional prerequisites while safeguarding user data privacy. However, the execution of such DNN services on resource-constrained edge devices poses a significant challenge: low throughput of inference tasks. To this end, this paper proposes Niagara, a novel system designed to maximize system throughput by judiciously scheduling DNN inference services on heterogeneous processors available on edge devices. Niagara faces two critical challenges: uncertain workload dynamics and high scheduling complexity. To effectively address these challenges, Niagara employs a predictive model to anticipate incoming workload patterns and orchestrates the allocation of services across heterogeneous processors through a combination of offline scheduling optimization and online service dispatching strategies. We have implemented Niagara and conducted thorough experiments. The results demonstrate that Niagara surpasses state-of-the-art approaches by elevating DNN inference throughput by up to 4.67 $$\times $$ , all while satisfying the same stringent inference latency requirements. Furthermore, Niagara has been successfully deployed in real-world power supply substations to detect violations, ensuring uninterrupted, accident-free operation during its six-month deployment period.
Deep Learning Model for Personalized Web Service Recommendations Using Attention Mechanism

The big volume of candidate Web services and their differences make it hard for developers to discover a set of appropriate ones for mashup creation. Thus, recommending suitable services is a vital problem. Service recommendation methods should not only meet the functional needs of users but also consider contextual features like application domain and service performances to provide more personalized recommendations. In this paper, we propose an attention-based deep learning model for service recommendation. It makes service recommendation based on service characteristics and user feed-backs. Specifically, we build a service network, which learns to intelligently discover services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and mashups, and a non-functional attention mechanism that considers service qualities to guide the selection of the most appropriate ones and improves user satisfaction. Experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com.
Distributing Quantum Computations, by Shots

Quantum Process Units (QPUs) are becoming more widely accessible to the public. Nonetheless, they still are very susceptible to noise and feature only a small amount of qubits, making it possible to only execute short quantum computations. Facing this problem, several approaches were proposed to make the most of the present situation, either by distributing the Quantum load, sending different Quantum programs to different QPUs or by distributing Quantum program fragments, by cutting a Quantum program into multiple smaller chunks. Here, we propose a change of perspective. Due to the probabilistic nature of Quantum Mechanics, it is usually required to iterate the execution of a Quantum program numerous times or shots. We suggest considering the shots dimension while determining how to distribute quantum computations. In this paper, we design and develop a methodology to distribute the shots of a Quantum program among many QPUs. Exploiting multiple QPUs improves the resilience to potential QPUs failures. Our solution also enables users to directly encode, through a proposed DSL, their own distribution strategies according to their needs and considered scenarios, offering an expressive and customisable approach. Finally, we showcase a prototype implementation and discuss a life-like use case that can only be addressed by relying on our approach.
A Middleware for Hybrid Blockchain Applications: Towards Fast, Affordable, and Accountable Integration

Hybrid blockchain architectures combine centralized applications, like enterprise systems, with (public) blockchain to implement additional functionality, such as tamper-proof record keeping. To reduce the latency and cost of using a public blockchain, these systems may rely on batching of transactions or general-state channel networks. While reducing costs, the former increase the latency. With the latter, only major state updates are recorded on-chain, while most transactions history remains only on the channels. This paper describes a novel solution that combines the benefits of both approaches to decrease the latency and cost of hybrid blockchain applications. We propose to combine a local blockchain that runs on a centralized server to provide near-immediate state update confirmation, with a batching mechanism sending transactions to a public blockchain for record-keeping at a most convenient time. We also introduce a dispute mechanism promoting the prompt delivery of correct batches to the public blockchain by the application provider, thereby deterring malicious behaviours. The solution is motivated by a fintech use case, for which we also show the implementation of a prototype and an experimental evaluation of the latency and cost savings.