Reviewerly/exHarmony huggingface dataset:
Approximation Analysis of Convolutional Neural Networks

In its simplest form, convolution neural networks (CNNs) consist of a fully connected two-layer network g composed with a sequence of convolution layers T .Although g is known to have the universal approximation property, it is not known if CNNs, which have the form g • T inherit this property, especially when the kernel size in T is small.In this paper, we show that under suitable conditions, CNNs do inherit the universal approximation property and its sample complexity can be characterized.In addition, we discuss concretely how the nonlinearity of T can improve the approximation power.Finally, we show that when the target function class has a certain compositional form, convolutional networks are far more advantageous compared with fully connected networks, in terms of the number of parameters needed to achieve the desired accuracy.
Evaluation of the Effect of Virtual Simulation Teaching on Learning Behavior of College Students

As a new learning model that breaks the barriers of time and space, virtual simulation covers a variety of learning behaviors. To evaluate the effect of virtual simulation teaching on college students’ learning behavior, with the help of a course integration model which reduces the omissions in the process of data collection, the data collection process of college students on the online platform was constructed. The collected data before storage were preprocessed which reduces the repetition rate of the original data of learning behavior, and the correlation between the collected data and the actual learning behavior was analyzed. Results show that learners’ specific process of online learning can be summarized into four stages: login trajectory, core learning, social interaction, and learning evaluation. Then, the actual final examination scores of the college students who participated in the test are all above 90 points, and their academic achievements are significantly improved. The use of the virtual simulation teaching method also enhances the overall satisfaction of college students with experimental courses, and the accuracy of the analysis results of course results is 99%, with better results. Conclusions are helpful to build college students' active learning consciousness of virtual simulation teaching courses.
Surgical Instrument Detection Algorithm Based on Improved YOLOv7x

The counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only improve efficiency, but also reduce medical disputes and promote the development of medical informatization. However, during the counting process, surgical instruments may be densely arranged or obstruct each other, and they may be affected by different lighting environments, all of which can affect the accuracy of instrument recognition. In addition, similar instruments may have only minor differences in appearance and shape, which increases the difficulty of identification. To address these issues, this paper improves the YOLOv7x object detection algorithm and applies it to the surgical instrument detection task. First, the RepLK Block module is introduced into the YOLOv7x backbone network, which can increase the effective receptive field and guide the network to learn more shape features. Second, the ODConv structure is introduced into the neck module of the network, which can significantly enhance the feature extraction ability of the basic convolution operation of the CNN and capture more rich contextual information. At the same time, we created the OSI26 data set, which contains 452 images and 26 surgical instruments, for model training and evaluation. The experimental results show that our improved algorithm exhibits higher accuracy and robustness in surgical instrument detection tasks, with F1, AP, AP50, and AP75 reaching 94.7%, 91.5%, 99.1%, and 98.2%, respectively, which are 4.6%, 3.1%, 3.6%, and 3.9% higher than the baseline. Compared to other mainstream object detection algorithms, our method has significant advantages. These results demonstrate that our method can more accurately identify surgical instruments, thereby improving surgical safety and patient health.
On Simulating Skewed and Cluster-Weighted Data for Studying Performance of Clustering Algorithms

In this article, extensions to the recently introduced concept of pairwise overlap between mixture components are proposed. The notion of overlap is useful for studying the systematic performance of clustering algorithms. Existing methods can be used for simulating elliptical data according to pre-specified overlap characteristics. First, an approach to simulating skewed clusters with a desired overlap is proposed. Next, an extension to measuring overlap in cluster-weighted models is considered. Thus, this article provides important extensions to the existing methods for simulating heterogeneous data for studying the systematic performance of clustering algorithms. Supplementary materials for this article are available online.
Correlations in Quantum Network Topologies Created with Cloning

With progress in quantum technologies, the field of quantum networks has emerged as an important area of research. In the last few years, there has been substantial progress in understanding the correlations present in quantum networks. In this article, we study cloning as a prospective method to generate three party quantum networks which will help us to create larger networks. We analyze various quantum network topologies that can be created using cloning transformations. This would be useful in situations wherever the availability of entangled pairs is limited. In addition to that, we focus on the problem of distinguishing networks created by cloning from those that are created by distributing independently generated entangled pairs. We find that there are several states that cannot be distinguished using the Finner inequalities in the standard way. For such states, we propose an extension to the existing Finner inequality for triangle networks by further increasing the number of observers from three to four or six depending on the network topology. This takes into account the additional correlations that exist in the case of cloned networks. In the last part of the article, we use tripartite mutual information to distinguish cloned networks from networks created by independent sources and further use squashed entanglement as a measure to quantify the amount of dependence in the cloned networks.
IMPACT OF CORRUPTION ON ECONOMIC GROWTH: AN EMPIRICAL EVIDENCE FROM NIGERIA

This study examines the impact of corruption on economic growth in Nigeria from 1986 to 2019.The study used the Johansen cointegration test and vector error correction tests for the data analysis.The study shows that increases in the level of corrupt practices significantly inhibit economic growth in Nigeria in the long run but are insignificant in the short run at the 5% level of significance.The study further reveals that there is a weak transmission effect of corruption on economic growth in Nigeria through household consumption, foreign direct investment, domestic investment, government spending, export and import of goods and services in Nigeria.The study recommends that in fighting corruption, Nigeria requires good and virtuous leaders who are honest with integrity, discipline and trustworthiness, the creation of employment, and the upgrading of Nigeria police among others and that the Nigerian government should advance the use of anti-corruption agencies such as the Independent Corrupt Practices Commission to properly investigate corrupt practices and to apportion appropriate sanctions, strengthen its institutions, and intensify efforts to create more agencies beside the Economic and Financial Crimes Commission and Independent Corrupt Practices Commission to address cases of corrupt practices in the economy.
Remote Sensing Image Synthesis via Semantic Embedding Generative Adversarial Networks

Generating photo-realistic remote sensing images conditioned on semantic masks has many practical applications like image editing, detecting deep fake geography, and data augmentation. Although previous methods achieved high-quality synthesis results for natural images like faces and everyday objects, they still underperform in remote sensing scenarios in terms of both visual fidelity and diversity. The high data imbalance and high semantic similarity of remote sensing object categories make the semantic synthesis of remote sensing images more challenging than natural images. To tackle these challenges, we propose a novel method named Conducted Semantic EmBedding GAN (CSEBGAN) for semantic-controllable remote sensing image synthesis. The proposed method decouples different semantic classes into independent Semantic Embeddings, which explores the regularities between classes to improve visual fidelity and naturally supports semantic-level. We further introduce a novel tripartite cooperation adversarial training scheme that involves a conductor network to provide fine-grained semantic feedback for the generator. We also show that the proposed semantic image synthesis method can be utilized as an effective data augmentation approach on improving the performance of the downstream remote sensing image segmentation tasks. Extensive experiments show the superiority of our method compared with the state-of-the-art image synthesis methods.
Fine-grained detection on the public's multi-dimensional communication preferences in emergency events

<h2>Abstract</h2> With the rapid development of Internet technologies, the public can participate in the information communication of emergency events more conveniently and quickly. Once an emergency occurs, the public will immediately express and disseminate massive information about the causes, processes and results of the emergency. In the process of information communication, the public often adopts diversified communication modes, and then shows differential communication preferences. The detection of the public's communication preferences can more accurately understand the information demands of the public in events, and then contribute to the rational allocation of resources and improve the processing efficiency. Therefore, this paper conducted finer-grained mining on the public's online expressions in multiple events, so as to detect the public's communication preferences. Specifically, we collected the public's expressions related to emergency events from the social media and then we analyzed the expressions from multiple dimensions to obtain the corresponding communication features. Finally, based on the comparative analysis of diversified communication features, static and dynamic communication preferences were obtained. The experimental results indicate that the public's communication preferences do exist, which is universal and consistent. Meanwhile, constructing a better social environment and improving people's livelihood are the fundamental strategies to guide public opinion.
Experimental hierarchy of two-qubit quantum correlations without state tomography

A Werner state, which is the singlet Bell state affected by white noise, is a prototype example of states, which can reveal a hierarchy of quantum entanglement, steering, and Bell nonlocality by controlling the amount of noise. However, experimental demonstrations of this hierarchy in a sufficient and necessary way (i.e., by applying measures or universal witnesses of these quantum correlations) have been mainly based on full quantum state tomography, corresponding to measuring at least 15 real parameters of two-qubit states. Here we report an experimental demonstration of this hierarchy by measuring only six elements of a correlation matrix depending on linear combinations of two-qubit Stokes parameters. We show that our experimental setup can also reveal the hierarchy of these quantum correlations of generalized Werner states, which are any two-qubit pure states affected by white noise.
Dense station-based potential assessment for solar photovoltaic generation in China

To achieve carbon neutrality before 2060, China is vigorously promoting the development of solar photovoltaic (PV) systems to replace traditional power supplies dominated by fossil fuels. A detailed potential assessment for solar PV generation will contribute to constructing and integrating a new power system with a high proportion of solar energy. In this study, we combined high-density and high-accuracy station-based solar radiation data from more than 2400 stations and a solar PV electricity generation model to map the technical potential for solar PV generation in China, while simultaneously considering land constraints through geographic information system technology. We found that the total installable capacity is at least 44,614.6 GW for China as a whole, resulting in an annual electricity generation potential of 72.7 PWh. However, the spatial distribution of solar PV potential does not match the electricity demand in China. Northwest China accounts for more than 95% of the total PV technical potential of China, but the power load centers are mainly located in eastern, southern, and central China, accounting for more than 70% of the total electricity consumption of the country. The challenges of solar PV development in China include grid integration and transmission from resource centers to load centers. The establishment and planning of new power systems based mainly on clean energy should facilitate the integration of fluctuating solar resources in China. This study will contribute to the planning and construction of solar PV systems in China.
Protocols and Guidelines to Enhance the Endpoint Security of Blockchain at User’s End

Blockchain is a distributed, immutable ledger that is used to record transactions, track assets, and build trust.Prominent among the several desirable assurances is enhanced security.It appears to be secure in the sense that no one can tamper with data.Despite the fact that Blockchain can provide a tamper-proof record of transactions, it is not immune to ever-increasing attacks.Endpoint insecurity risks jeopardize users' privacy, sensitive data, and system resources.Sensitive information leads to the disclosure of the private key, which may lead to losses including cryptocurrency from the user's wallet.Previous experiences reveal and enable us to witness many typical attacks.In such cases, the invaders injected malicious codes and gained access to the user's email, after which they used the user's sensitive data for their nefarious purposes.They have been continuously benefited by the malicious codes.As a result, we review vulnerabilities, hacking and the risks associated with it.Some simplistic measures and security enhancement environments are identified.Thus, we highlight a few highly simplistic necessary protocols, recommendations, and technology as a solution to avoid endpoint exploitation at the user's end.
Pemetaan penelitian information retrieval system menggunakan VOSviewer

Information retrieval systems help filter information, so that the process of finding suitable information becomes fast and precise, but there are ways to find more effectively and efficiently with Information retrieval systems using bibliometric methods with the VOSviewer application. The research objectives are to map the trends of research results on the topic of information retrieval system in the library, to map the research results on the topic of information retrieval system in the library based on co-authorship, and to map the research results on the topic of information retrieval system in the library based on co-occurrence. The research method uses descriptive bibliometric analysis by using the Scopus scientific journal database. The results of research on the topic of information retrieval systems in libraries are fluctuating where the most research results occurred in 2008 with publications totaling 13 articles and the lowest publications were seen in 2010 and 2014 with only 1 article produced. The level of author productivity is at 13 authors who produce a total of 2 articles. Co-authorship analysis is useful to see the mapping of research topics through relationships or collaborations between authors. The number of authors analyzed is 278, of which there are only 13 authors who show relationships between other authors. Co-occurrence analysis is useful for displaying bibliometric networks between keywords in a visual form. There are 326 keywords taken and 18 keywords have an occurrence relationship. Based on the bibliometric analysis conducted, it can be seen the distribution of research topics related to information retrieval systems in libraries, including research keywords that are still rarely researched, such as metadata and user studies. This can be used as an opportunity for further research topics.
Cloud computing to audit quality- evidence from the Kingdom of Saudi Arabia

This study investigates how the use of cloud computing systems, compared to on-site ERP systems, affects audit quality. A survey of 430 auditors in Saudi Arabia was conducted to collect data on the impact of cloud computing and on-site ERP systems on audit quality. The collected data was analyzed using statistical techniques to determine the impact of these systems on audit quality. The study found that cloud computing and on-site ERP systems improved audit accuracy, audit process efficiency, report quality, and audit evidence reliability. The results of this study have significant implications for enhancing audit quality. The study suggests that auditors should consider incorporating cloud computing systems to improve the quality of audits while also boosting the efficiency of the audit process. The findings of this study help auditors make informed decisions about using cloud computing systems to improve their audit quality and meet the growing demand for reliable and accurate financial audits. However, the study's limitations include not considering other stakeholders' views and not evaluating the costs of implementing cloud computing systems. Future research can build upon the findings of this study and address its limitations to provide a more comprehensive understanding of the impact of cloud computing systems on audit quality.
Next Generation Automated Reservoir Computing for Cyber Defense

It is a fact, that important information related to systems’ behavior and dynamics, can be revealed as time passes. Observing changes over time, can often lead to the detection of patterns and trends that might not be immediately apparent from a single system’s snapshot. Additionally, the concept of time can be essential in understanding cause-and-effect relationships. Observing how changes in one variable over time affect changes in another, can gain insights into the causal relationships between different system components. Time series analysis of data that change over time, can be a powerful tool for understanding complex systems in the field of cyber security. Reservoir Computing (RC) is a Machine Learning technique, using a fixed and randomly generated high-dimensional dynamic system, called a Reservoir, to transform and classify input data. The reservoir acts as a nonlinear and temporal filter of the input data, which is then readout by a linear output layer. Continuous-Time Reservoir Computing (CTRC) is a type of recurrent neural network, aiming to model the continuous-time dynamics of the network’s neurons. It is particularly useful for applications where time is critical and it can provide insights into the underlying system’s dynamics. This paper proposes a next-generation CTRC for cyber defense, where the reservoir neurons are modeled as continuous-time dynamical systems. This means that their behavior is described by a system of differential equations that change over time. In order to model the drift phenomenon, identify the abnormal changes in the data, and adaptively stabilize the learning system. The CTRC parameters are optimized using the Reinforcement Learning (RL) method. The proposed system, as proved experimentally, has several advantages over discrete systems, including the ability to handle signals with high sampling rates and to effectively capture real cyber security signals’ continuous nature.
Graph-Clustering Anonymity Privacy Protection Algorithm With Fused Distance-Attributes

Abstract Clustering anonymity is a common social network data privacy protection scheme, which is based on graph-clustering. Many existing graph clustering methods mainly focus on the relationship between the structure and attributes of nodes, and the difference between them due to the metric usually causes the problem of poor clustering results. To address the shortcomings in the above graph-clustering methods, a graph-clustering anonymity method implemented with fused distance-attributes (GCA-DA) is proposed. Firstly, the algorithm quantifies the distance and attribute similarity between nodes separately and balances the metric differences between them to calculate the integrated similarity. Then all the nodes in the graph are clustered into clusters according to the integrated similarity between two nodes, each of which contains no fewer than k nodes. Finally, all the clusters are anonymized. In this method, the attribute generalization for every cluster can prevent attacks by the background knowledge of structure and attributes. In addition, the attributes are divided into numerical and non-numerical attributes to measure them separately, therefore can maintain the usability of the data better. Experiment results demonstrate the effectiveness in improving the quality of clustering and reducing information loss.
Shape reconstruction for undetectable regions of abdominal organs based on a graph convolutional network

Although computed tomography (CT) and magnetic resonance imaging (MRI) produce high-resolution images, during surgery or radiotherapy, only low-resolution cone-beam CT and low-dimensional X-ray images are usually obtained. Furthermore, because the duodenum and stomach are filled with air, it may be hard to accurately segment their contours, even on high-resolution images. Although denoising and image enhancement can be used to assist in the segmentation and reconstruction of 3D images, some regions of organs may remain difficult to detect. To approach this issue, we propose a graph convolutional network (GCN) to reconstruct organs that are hard to detect on medical images. The method uses a framework to transform an initial template into an estimated shape of the patient’s organs based on previous information attainted from different detectable organs from different patients. To overcome the inaccurate estimation results of triangular surface mesh data with the GCN, we propose a method to add internal vertices and edges, which has the effect of improving the calculation accuracy by enhancing the topological structure of the mesh data. In this study, the organ data of 124 patients who underwent pancreatic tumor treatment were used to verify the predictive effect of the proposed method. The 3D organ contours were defined by board-certified radiation oncologists and used as the ground truth surface meshes. For 100 randomly selected detectable feature vertices, the average Euclidean distance error of the liver was 3.72 mm. The topological enhancement proposed in this study reduced the prediction error of the liver by an average of 0.84 mm.