Reviewerly/exHarmony huggingface dataset:
Restoration of Atmospheric Turbulence-Degraded Short-Exposure Image Based on Convolution Neural Network

Ground-based remote observation systems are vulnerable to atmospheric turbulence, which can lead to image degradation. While some methods can mitigate this turbulence distortion, many have issues such as long processing times and unstable restoration effects. Furthermore, the physics of turbulence is often not fully integrated into the image reconstruction algorithms, making their theoretical foundations weak. In this paper, we propose a method for atmospheric turbulence mitigation using optical flow and convolutional neural networks (CNN). We first employ robust principal component analysis (RPCA) to extract a reference frame from the images. With the help of optical flow and the reference frame, the tilt can be effectively corrected. After correcting the tilt, the turbulence mitigation problem can be simplified as a deblurring problem. Then, we use a trained CNN to remove blur. By utilizing (i) a dataset that conforms to the turbulence physical model to ensure the restoration effect of the CNN and (ii) the efficient parallel computing of the CNN to reduce computation time, we can achieve better results compared to existing methods. Experimental results based on actual observed turbulence images demonstrate the effectiveness of our method. In the future, with further improvements to the algorithm and updates to GPU technology, we expect even better performance.
Agile User Stories’ Driven Method: A Novel Users Stories Meta-model in the MDA Approach

The mastery of the development cycle of a project, especially in software development, has become a need and an unavoidable necessity to produce a deliverable with value. Thus, to satisfy the requirements of users who have become more volatile. In this regard, the Agile manifesto, or Manifesto for Agile software development, has been proposed to bring increased flexibility and pragmatism to the delivered products by giving values and fundamental principles to revolutionize software development processes incrementally, but without handling the technical framework of their productions. So, our defiance in this paper is to add a technical framework to agile software development processes by proposing a user stories’ meta-model driven method in the Model Driven Architecture (MDA) approach. This approach will lead us to put the models and their meta-models at the center of the development process of any software system to facilitate the development process based on the construction and the transformation of models tasks in the Agile context.
Research on Mechanism Design and Kinematic Characteristics of Self-Propelled Photovoltaic Cleaning Robot

A hydraulic drive-based self-propelled photovoltaic panel cleaning robot was developed to tackle the challenges of harsh environmental conditions, difficult roads, and incomplete cleaning of dust particles on the photovoltaic panel surface in photovoltaic power plants. The robot has the characteristics of the crawler wheel drive, rear-wheel-independent turning and three-point-independent suspension design, which makes it adhere to the walking requirements of complex environmental terrains, more flexible in turning and automatically levelling so that the stability of the boom mechanism during walking can be ensured. The kinematics model of the upper arm structure equipped with the end cleaning device was built, and the optimized Circle chaotic map and nonlinear weight factor were introduced to enhance the search ability and convergence speed of the sparrow algorithm. Furthermore, the boom running track was optimized in combination with the seven-order non-uniform B-spline curve. Through optimization, the running time of the boom was reduced by 18.7%, and the cleaning efficiency of photovoltaic panel surface was increased. The effectiveness of self-propelled photovoltaic panel cleaning robot cleaning and the reliability of time-optimal trajectory planning were confirmed through simulation and experiment.
A neuro-symbolic system over knowledge graphs for link prediction

Neuro-Symbolic Artificial Intelligence (AI) focuses on integrating symbolic and sub-symbolic systems to enhance the performance and explainability of predictive models. Symbolic and sub-symbolic approaches differ fundamentally in how they represent data and make use of data features to reach conclusions. Neuro-symbolic systems have recently received significant attention in the scientific community. However, despite efforts in neural-symbolic integration, symbolic processing can still be better exploited, mainly when these hybrid approaches are defined on top of knowledge graphs. This work is built on the statement that knowledge graphs can naturally represent the convergence between data and their contextual meaning (i.e., knowledge). We propose a hybrid system that resorts to symbolic reasoning, expressed as a deductive database, to augment the contextual meaning of entities in a knowledge graph, thus, improving the performance of link prediction implemented using knowledge graph embedding (KGE) models. An entity context is defined as the ego network of the entity in a knowledge graph. Given a link prediction task, the proposed approach deduces new RDF triples in the ego networks of the entities corresponding to the heads and tails of the prediction task on the knowledge graph (KG). Since knowledge graphs may be incomplete and sparse, the facts deduced by the symbolic system not only reduce sparsity but also make explicit meaningful relations among the entities that compose an entity ego network. As a proof of concept, our approach is applied over a KG for lung cancer to predict treatment effectiveness. The empirical results put the deduction power of deductive databases into perspective. They indicate that making explicit deduced relationships in the ego networks empowers all the studied KGE models to generate more accurate links.
Convolutional Long Short-Term Memory Network Model for Dynamic Texture Classification: A Case Study

The digital world is witnessing the rapid growth of various data such as multimedia content. This enormous volume of information resources is required to be well structured and classified to help machines infer relevant information. Dynamic texture classification is among the essential requirements for multimedia content understanding and plays a vital role in major computer vision applications such as traffic monitoring, face recognition, and surveillance. However, dynamic texture classification brings new challenges to the field of computer vision and has limited literature compared to static texture classification. This paper is another contribution to fill this gap by investigating video classification using textures. In doing so, we have compiled and prepared a dataset of short videos with different textures from various sources, including the DynTex database. These videos are classified according to five categories, namely Clouds/Steam, Fire, Flags, Trees, and Water. Subsequently, the multiclass categorization is performed using the convolutional long short-term memory network (ConvLSTM)-based classifier. The ConvLSTM is a recurrent neural network (RNN) model, just like the LSTM, but the internal matrix multiplications are exchanged with convolution operations. Hence, The ConvLSTM is kind of a combination of Convolution and LSTM. Finally, the experiments have shown that the ConvLSTM succeeded in capturing the spatial features from each texture and making accurate predictions.
Enumeration for a Large Number of Sources Based on a Two-Step Difference Operation of Linear Shrinkage Coefficients

A novel and computationally efficient source enumeration algorithm is proposed for large-scale arrays with a small number of samples, by employing a two-step difference operation of linear shrinkage (LS) coefficients of sample covariance matrix (SCM) in large-dimensional scenarios. It is firstly proved that the difference between noise LS coefficients tends to zero and there exists a clear gap between the last signal LS coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\hat{\alpha } ^{(d - 1)}}$</tex-math></inline-formula> and the first noise LS coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\hat{\alpha } ^{(d)}}$</tex-math></inline-formula> in relatively high signal-to-noise ratio (SNR) cases for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m, n\to \infty$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m/n\to c\in (0,\infty)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> are the antenna number, sample number and source signal number, respectively. With this property, the first-step difference operation is designed to achieve initial source enumeration. Further considering relatively low or medium SNRs, the second step yields an improved estimation result and is capable of estimating a large number of sources. Furthermore, the applicability of the representative LS coefficients based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {SCD_{heur}}$</tex-math></inline-formula> algorithm under various values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> is analyzed, and a more general condition for guaranteeing its effectiveness is provided. Simulation results are provided, which are consistent with the theoretical analysis.
A comprehensive systematic and bibliometric review of the IoT-based healthcare systems

In the healthcare sector, the growth in technology has had a huge effect. Besides, when introduced to the world of healthcare, the Internet of Things (IoT) will simplify the transition by helping physicians closely track their patients, allowing rapid recovery. Aged patients/people should be intensively checked, and their loved ones must be aware of their wellbeing periodically. Therefore, using IoT in healthcare will simplify the lives of physicians and patients alike. Hence, this study explored a comprehensive review of intelligent IoT-based embedded healthcare systems. The papers around intelligent IoT-based healthcare systems printed until Dec-2022 are studied, and some research lines are suggested for the upcoming researchers. Thus, this study's innovation will apply healthcare systems based on IoT to include certain strategies for the future deployment of new generations of IoT-based health technology. The findings revealed that IoT is beneficial for governments to strengthen society's health and economic relations. Besides, owing to novel functional principles, IoT needs modern safety infrastructure. This study is helpful for prevalent and useful electronic healthcare services, health experts, and clinicians.
Implementation of Metaverse for Modeling The Ship Bridge Simulator Based on Virtual Reality As Educational Purposes, Case Study: Tanjung Priok Port Area

Abstract The development of virtual reality technology has increased in recent years with the characteristics and benefits of virtual reality that can help solve complex problems that exist in the real world. Virtual reality technology has been widely applied as a means of education. The ship bridge simulator is one of the supporting tools for training shipping cadets, but this tool has a relatively high cost. Therefore, this research aims to model the ship bridge simulator. The initial stage in this research is to model the ship bridge simulator belonging to the Surabaya Shipping Polytechnic (Politeknik Pelayaran Surabaya) with a case study of Tanjung Priok Port, referred to as the metaverse area. The process is to convert real objects into 3D assets using Blender software and develop them into a virtual reality application package designed using Unity software. The results obtained at the initial stage of this research are to provide a virtual forward and backward feature of the ship in the metaverse area. This virtual reality application has achieved its goals based on user ratings involving about 30 respondents. It is evidenced by its rating score of 908 out of 1050, which shows that this virtual reality application is included in the excellent category.
Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.
ESarDet: An Efficient SAR Ship Detection Method Based on Context Information and Large Effective Receptive Field

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on contextual information and a large effective receptive field (ERF), is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements to it. First, CAA-Net, which has a large ERF, is proposed to better merge the contextual and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.
Human Pose Estimation via an Ultra-Lightweight Pose Distillation Network

Most current pose estimation methods have a high resource cost that makes them unusable in some resource-limited devices. To address this problem, we propose an ultra-lightweight end-to-end pose distillation network, which applies some helpful techniques to suitably balance the number of parameters and predictive accuracy. First, we designed a lightweight one-stage pose estimation network, which learns from an increasingly refined sequential expert network in an online knowledge distillation manner. Then, we constructed an ultra-lightweight re-parameterized pose estimation subnetwork that uses a multi-module design with weight sharing to improve the multi-scale image feature acquisition capability of the single-module design. When training was complete, we used the first re-parameterized module as the deployment network to retain the simple architecture. Finally, extensive experimental results demonstrated the detection precision and low parameters of our method.
Relation extraction: advancements through deep learning and entity-related features

Capturing semantics and structure surrounding the target entity pair is crucial for relation extraction. The task is challenging due to the limited semantic elements and structural features of the target entity pair within a sentence. To tackle this problem, this paper introduces an approach that fuses entity-related features under convolutional neural networks and graph convolution neural networks. Our approach combines the unit features of the target entity pair to generate corresponding fusion features and applies the deep learning framework to extract high-order abstract features for relation extraction. Experimental results from three public datasets (ACE05 English, ACE05 Chinese, and SanWen) indicate that the proposed approach achieves F1-scores of 77.70%, 90.12%, and 68.84%, respectively, highlighting its effectiveness and robustness. This paper provides a comprehensive description of the approach and experimental results.
Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis

Multiview subspace learning (MvSL) has attracted extensive research interest in many real-world applications, e.g., pattern recognition and computer vision. Existing MvSL approaches seek a discriminant common subspace by simultaneously maximizing the arithmetic mean of the between-class covariance and minimizing the global average of the within-class covariance. However, the arithmetic mean normally assigns equal weight to all between-class distances. Then, classes with small distances overlap with each other, and the largest between-class distance dominates the common subspace. In addition, the global average of within-class covariance (which is computed by the sum of within-class distances for all classes) significantly differs from the within-class covariance of a single class. This will inevitably cause the internal details of each class to be ignored and lead to lower recognition accuracy. To circumvent these issues, we develop a productive strategy to take more consideration of the relationship between any two classes and propose a novel Multiview Latent Structure Learning (MvLSL) model, which incorporates the weighted harmonic mean of pairwise between-class scatter and pairwise within-class scatter. Due to the adjustable dimensions of the common subspace, MvLSL can be naturally extended for dimensionality reduction (DR), and we utilize a reconstruction term to suppress data degradation and improve the DR performance. Our models are tested on six benchmark datasets with five evaluation criteria and compared with existing state-of-the-art methods, indicating our models’ effectiveness and superiority.
Protecting Data and Queries in Cloud-Based Scenarios

Abstract The availability of cloud services offered by different providers brings several advantages to users and companies, facilitating the storage, sharing, and processing of data. At the same time, the adoption of cloud services brings new security and privacy risks and challenges. As a matter of fact when leveraging cloud-based services for data storage and processing, data owners loose direct control on their data. Data and queries over them could then be at risk for both potentially improper exposure, compromising their confidentiality, or tampering, compromising their integrity. In this paper, we discuss the main issues to be addressed for guaranteeing data security and privacy in cloud-based storage and processing. We illustrate the different challenges to be considered and the research directions toward their solutions.
Evaluation of Innovation and Entrepreneurship Education Based on Fuzzy Comprehensive Evaluation: A Case Study of Water Management

Entrepreneurship related to water environment governance is an important way to promote sustainable development. Water Management majors in some Chinese universities have set up courses on innovation and entrepreneurship to organize students to carry out innovation and entrepreneurship practice. But it is necessary to evaluate the effects of such education. Based on fuzzy comprehensive evaluation (FCE), this paper evaluates the performance of innovation and entrepreneurship education in water environmental management major in universities. Firstly, the fuzzy evaluation index was selected, and the single factor matrix was set by calculating the evaluation weight. In case of a college, the FCE evaluation model was constructed, and the bidirectional top-level processing was used to realize the performance evaluation. The test results show that: the organizational leadership and curriculum content of the case college are good, and other indicators such as success conversion rate are in the average level. It is believed that more efforts should be made for colleges to improve the entrepreneurial environment.
Entanglement of a cavity with a three-level atom and a graphene membrane in the presence of a laser field

This paper examines the entanglement and coherence of a hybrid quantum system in the presence of an external laser field. The hybrid system is composed of a single-mode cavity field containing a vibrating graphene membrane and a superconducting qutrit in a cascade configuration. Specifically, we investigate how the coupling of qutrit-graphene, detuning parameters, and external field coupling affects different measures, such as negativity tangle, algebraic tangle, geometric tangle, and the l1 norm of coherence. Our results demonstrate that the upper bounds of tangle measures and coherence decrease with increasing the detuning parameters. The external field amplifies the detrimental impact caused by detuning. Furthermore, heightened qutrit-graphene coupling elevates the maximum bounds of tangle and coherence quantifiers, while also increasing the frequency of oscillations.