Reviewerly/exHarmony huggingface dataset:
A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

The automated generation of class diagrams is a crucial task in software engineering, facilitating the understanding, analysis, and documentation of complex software systems. Traditional manual approaches are time and energy consuming, error-prone, and lack consistency. To address these challenges, this research presents an automated proposed approach that utilizes Graph Neural Networks (GNNs), a machine learning algorithm, to generate class diagrams from source code within the context of Model Driven Architecture (MDA) and reverse engineering. A comprehensive case study is conducted to compare the results obtained from the automated approach with manually created class diagrams. The GNN model demonstrates high accuracy in capturing the system’s structure, associations, and relationships. Notably, the automated approach significantly reduces the time required for class diagram generation, leading to substantial time and energy savings. By advancing automated software documentation, this research contributes to more efficient software engineering practices. It promotes consistency, eliminates human errors, and enables software engineers to focus on higher-value tasks. Overall, the proposed approach showcases the potential of GNNs in automating class diagram generation and its practical benefits for software development and documentation.
Manual assembly actions segmentation system using temporal-spatial-contact features

Purpose Accurate segmentation of artificial assembly action is the basis of autonomous industrial assembly robots. This paper aims to study the precise segmentation method of manual assembly action. Design/methodology/approach In this paper, a temporal-spatial-contact features segmentation system (TSCFSS) for manual assembly actions recognition and segmentation is proposed. The system consists of three stages: spatial features extraction, contact force features extraction and action segmentation in the temporal dimension. In the spatial features extraction stage, a vectors assembly graph (VAG) is proposed to precisely describe the motion state of the objects and relative position between objects in an RGB-D video frame. Then graph networks are used to extract the spatial features from the VAG. In the contact features extraction stage, a sliding window is used to cut contact force features between hands and tools/parts corresponding to the video frame. Finally, in the action segmentation stage, the spatial and contact features are concatenated as the input of temporal convolution networks for action recognition and segmentation. The experiments have been conducted on a new manual assembly data set containing RGB-D video and contact force. Findings In the experiments, the TSCFSS is used to recognize 11 kinds of assembly actions in demonstrations and outperforms the other comparative action identification methods. Originality/value A novel manual assembly actions precisely segmentation system, which fuses temporal features, spatial features and contact force features, has been proposed. The VAG, a symbolic knowledge representation for describing assembly scene state, is proposed, making action segmentation more convenient. A data set with RGB-D video and contact force is specifically tailored for researching manual assembly actions.
Optimization of MySQL database

The performance of MySQL, a well-known open-source relational database management system used in a variety of sectors, including e-commerce, finance, and healthcare, can be improved through the use of physical programming and data tuning. While data tuning involves refining the database to increase efficiency, physical programming involves optimizing the physical storage of data. This article gives a general introduction of MySQL and its architecture, looks at the many methods and tools used in physical programming and data tuning, and talks about the advantages of these techniques and how they affect MySQL's performance.
Image encryption algorithm based on ElGamal cryptography and selective random diffusion

Abstract Some current image encryption schemes are independent of the plaintext, leading to a vulnerability to the chosen-plaintext attack under symmetric structure. To address this issue, an asymmetric image encryption algorithm with plaintext correlation is suggested, after analyzing various image encryption schemes using chaotic systems. First, a three-dimensional New Logistic-Sine Map (NewLSM) is designed by coupling Logistic map and Sine map, considering them as seed maps. Analyses prove that the NewLSM has a wider continuous chaotic interval and more complex chaotic behavior than seed maps. Secondly, to enhance the keystream associated with plaintext, a new key acquisition model is constructed, i.e., NewMKG, by combining the hash function SHA-3 with the public key ElGamal cryptography. Then, employing the NewLSM and the NewMKG, a new image encryption algorithm with asymmetric structure is presented using classical framework of confusion-diffusion. In particular, a new diffusion method is proposed after confusion, namely, selective random diffusion (NewSRD). Moreover, experiments and analyses indicate that a good performance can be reached for the proposed algorithm. For example, the value of information entropy for the obtained cipher image is close to eight.
Two level blockchain-based privacy preservation framework in IoT with heuristic fusion mechanism-aided deep learning architecture

The Internet of Things (IoT) connects more objects used in the modern world. To connect IoT nodes, it needs maintenance services, robustness, frictionless authentication, and high security. Blockchain emerges as a workable solution by providing those important features. Several authentication problems, maintenance, and security with IoT systems are solved because of blockchain technology. The blockchain-based IoT network is an open source, and everyone may see encrypted keys and transactional information. So, from this public network, anyone can take crucial information about users. A new blockchain-adopted IoT-based privacy preservation approach is developed to provide higher security with less utilization of the cost requirements. Here, two levels of data privacy is accomplished by utilizing the deep learning approach. The first level of privacy is used for authenticating a person, where the authentication among collected data is performed using Modified Adaboost and Long Short-Term Memory (MABLSTM), and the authenticated data is stored in the blockchain database. After that, the second level of privacy is obtained by performing data encoding with the encoder part of Autoencoder, and further Elliptic Curve Cryptography (ECC) is used to encrypt the data. Hence, the encrypted data is given to the data decryption process, and this has been subjected to the decoder part of the autoencoder to reconstruct the original data. Moreover, the parameters in the autoencoder and the parameters within the MABLSTM are optimized with the help of the developed Modified Levy Flight Distribution with Grasshopper Optimization (MLFD-GO). However, the parameters like LSTM and Adaboost are optimized with the help of the MLFD-GO algorithm to enhance the performance in terms of accuracy and precision for providing the higher level authentication scheme to provide high level security. Finally, the performance of the developed blockchain-based privacy preservation approach is validated over recently developed techniques.
Sequential Gaussian co-simulation of tectono-geochemical anomaly for concealed ore deposit prediction

Tectono-geochemical data measured from tectonites host a wealth of information that is closely related to the accumulation and dispersion of ore-forming materials, while structure system is the only pathway to connect the deep orebody with the surficial tectono-geochemical anomaly, and both have correspondence and consistency. Based on the geological precondition that structure system and ore-forming process are closely associated with each other, the study proposed a method of sequential Gaussian co-simulation of tectono-geochemical anomaly, which integrates the tectono-geochemical anomaly with the ore-controlling structure system to solve two basic geological prospecting problems that are closely associated with ore-forming space and ore-forming material migration. A case study for concealed Pb–Zn prospecting based on tectono-geochemical data in the Huangzhuguang ore cluster (China) was employed to examine the potential applications of the proposed method. Firstly, centered logratio transformation was employed to remove the closure effect of tectono-geochemical data and to make the data conform to an approximate normal distribution. Secondly, sequential Gaussian co-simulation was applied to improve the simulation results of the tectono-geochemical anomaly by introducing the ore-controlling structure system. Thirdly, the probability distribution of tectono-geochemical anomalies was quantified by uncertainty modeling technique. The case study demonstrated that the proposed method can significantly improve the performance of the primary variable in predicting concealed ore deposits.
Graph embedding based multi-label Zero-shot Learning

Multi-label Zero-shot Learning (ZSL) is more reasonable and realistic than standard single-label ZSL because several objects can co-exist in a natural image in real scenarios. Intra-class feature entanglement is a significant factor influencing the alignment of visual and semantic features, resulting in the model's inability to recognize unseen samples comprehensively and completely. We observe that existing multi-label ZSL methods place a greater emphasis on attention-based refinement and decoupling of visual features, while ignoring the relationship between label semantics. Relying on label correlations to solve multi-label ZSL tasks has not been deeply studied. In this paper, we make full use of the co-occurrence relationship between category labels and build a directed weighted semantic graph based on statistics and prior knowledge, in which node features represent category semantics and weighted edges represent conditional probabilities of label co-occurrence. To guide the targeted extraction of visual features, node features and edge set weights are simultaneously updated and refined, and embedded into the visual feature extraction network from a global and local perspective. The proposed method's effectiveness was demonstrated by simulation results on two challenging multi-label ZSL benchmarks: NUS-WIDE and Open Images. In comparison to state-of-the-art models, our model achieves an absolute gain of 2.4% mAP on NUS-WIDE and 2.1% mAP on Open Images respectively.
Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining.This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update.Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability.Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer.
Measuring the quality of projections of high-dimensional labeled data

Dimensionality reduction techniques, also called projections, are one of the main tools for visualizing high-dimensional data. To compare such techniques, several quality metrics have been proposed. However, such metrics may not capture the visual separation among groups/classes of samples in a projection, i.e., having groups of similar (same label) points far from other (distinct label) groups of points. For this, we propose a pseudo-labeling mechanism to assess visual separation using the performance of a semi-supervised optimum-path forest classifier (OPFSemi), measured by Cohen's Kappa. We argue that lower label propagation errors by OPFSemi in projections are related to higher data/visual separation. OPFSemi explores local and global information of data distribution when computing optimum connectivity between samples in a projection for label propagation. It is parameter-free, fast to compute, easy to implement, and generically handles any high-dimensional quantitative labeled dataset and projection technique. We compare our approach with four commonly used scalar metrics in the literature for 18 datasets and 39 projection techniques. Our results consistently show that our proposed metric consistently scores values in line with the perceived visual separation, surpassing existing projection-quality metrics in this respect.
IMU-assisted robotic structured light sensing with featureless registration under uncertainties for pipeline inspection

Laser profilometry and structured light sensors are being increasingly deployed for pipeline inspection as they provide the operator with a precise 3D map that can enable visual detection and direct insight into the integrity of the pipe. The focus of the presented paper is the design of an integrated robotic structured light sensing system used to improve the performance of 3D defect reconstruction for pipeline inspection while accommodating the uncertainty seen in a real-world environment. Point cloud registration of the consecutive 3D frames is a key factor in building this 3D map; therefore, a comprehensive featureless registration approach is proposed first, which is proven more efficient than conventional feature-based registration algorithms. Wheel odometry from the developed robotic platform and inertial measurements are integrated into the registration algorithm to enhance the 3D reconstruction performance for sensor stabilization. An intensity-based threshold searching method is further applied to retrieve the reconstructed defect size. Lastly, the uncertainties of the structured light sensing are investigated for the total reconstruction uncertainty and estimated measurement uncertainty to be quantified in order to illustrate the measurement precision. The efficacy of the proposed algorithms are supported by experimental results of pipeline inspection.
Context-Aware Attribute Based Access Control for Cloud-based SCADA Systems

Cloud-based SCADA (Supervisory Control and Data Acquisition) systems enable seamless access to internet of things (IoT) operational data for employees at control facilities and operators working on the field for automated control and monitoring of industrial infrastructure. In this work, we introduced a novel approach based on ciphertext policy attribute-based encryption (CP-ABE) to ensure secure and fine-grained access control over data stored in the cloud. Our proposed scheme considers static and dynamic attributes of the users to devise an access policy. Contextual locks are introduced for defining constraints on dynamic attributes. These locks are independent of the user's attribute sets; thus, they don't require decryption keys to be associated with dynamic attributes. This avoids system overhead with frequent changes in contextual parameters. To protect the confidentiality of the access policy, we obfuscate its attributes during the data-sharing process. Moreover, our proposed scheme prevents key escrow attacks on cloud-stored data. Additionally, fog servers are employed to verify the user's contextual attributes and reduce the computational overhead of decryption for end users. Our scheme enhances the security and integrity of remote process control and monitoring in industrial systems while leveraging the benefits of real-time data analysis and decision-making.
A reputation-aware hierarchical aggregation framework for federated learning

Cross-device federated learning (FL) involves FLClients sharing their model updates to a global server for aggregation, which may result in a single point of failure as it becomes cumbersome for a global server to handle many FLClients. Hierarchical aggregation (HA) places another layer of aggregation (at edge servers) between FLClients and the global server. Although HA reduces the communication cost in aggregation, it does not help reduce the communication cost incurred by resource constrained FLClients while sharing their local models with the edge servers. This paper proposes a novel reputation-aware hierarchical aggregation framework (FedRaHa) that employs a reputation-based method to select clients' updates for aggregation as to minimize unnecessary local update exchanges. FedRaHa is evaluated using benchmark datasets such as MNIST, Fashion-MNIST, and real-world Chest Xray dataset. The results show that FedRaHa achieves the highest accuracy of 86 % and reduces the communication cost by 27.15 % as compared with the state-of-the-art.
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Recently certain few-Lee weight additive codes over some mixed alphabet rings with optimal or minimal Gray images are constructed using suitable defining sets. Inspired by these works, we construct two special classes of linear codes over Z2[u] with u2=0 by using the mixed alphabet ring Z2Z2[u] and employing simplicial complexes having one or more maximal elements. We show that these codes have few-Lee weights by determining their Lee weight distributions. As applications, we are getting the 1st order binary Reed-Muller codes of length 23m−2 (where m>1) and some infinite families of binary self-orthogonal minimal codes via Gray map.
Detail Feature Inpainting of Art Images in Online Educational Videos based on Double Discrimination Network

Abstract In order to improve the image detail restoration effect of online education videos and improve the quality of online education images, a method for image detail restoration of online education videos based on dual discriminant networks is proposed. Utilize the grayscale changes in the selected spatial domain to enhance the detailed features of the original video art image, and construct a dual discriminant generative adversarial network model to repair the enhanced image details. Using a U-Net structured generation network to generate images similar to detail regions, and using image detail features as input, the initial image restoration is completed by training the generation network. Improve the authenticity and consistency of output image details through global and local discriminant networks. The experimental results show that this method has a good image enhancement effect in restoring image detail features in online education video art, improving the dynamic range and grayscale contrast of the image, and has a significant effect on image detail repair. The average peak signal-to-noise ratio of this method is 30.108, and the average structural similarity is 0.961, proving that the repair effect of this method is good. The average PSNR of this method under Gaussian noise interference is 29.68, which proves the robustness of this method.
Blockchain-Enabled Lightweight Fine-Grained Searchable Knowledge Sharing for Intelligent IoT

With the rapid development of the Internet of Things (IoT), millions of IoT devices are constantly generating massive amounts of data. The development of artificial intelligence (AI) and edge computing makes it possible to conduct data analysis and knowledge mining efficiently among IoT edge devices. Knowledge, including intermediate results and training models obtained by large amounts of redundant data, is the core and foundation of edge intelligence. However, the sharing and utilization of knowledge still face a series of security and privacy issues, such as illegal knowledge access, knowledge tampering, and privacy leakage. To address these issues, in this paper, we propose a blockchain-based knowledge storage and sharing architecture that enables secure knowledge management in intelligent IoT. We first design a permissioned blockchain-based decentralized and trusted knowledge storage scheme, which includes the on-chain encrypted knowledge storage and an improved Delegated Proof of Stake (DPoS) consensus protocol. Besides, we propose a lightweight attribute-based searchable encrypted knowledge sharing mechanism, in which fine-grained and privacy-preserving knowledge collaboration is achieved through smart contracts and keyword search. Moreover, we reduce the computing overhead of edge devices through the design of partial outsourcing decryption. Finally, we analyze the security performance of our system as well as verify its practicality and ability to reject dishonest servers by simulation.
Recent Advances in Text-to-Image Synthesis: Approaches, Datasets and Future Research Prospects

Text-to-image synthesis is a fascinating area of research that aims to generate images based on textual descriptions.The main goal of this field is to generate images that match the given textual description in terms of both semantic consistency and image realism.While text-to-image synthesis has shown remarkable progress in recent years, it still faces several challenges, mainly related to the level of image realism and semantic consistency.To address these challenges, various approaches have been proposed, which mainly rely on Generative Adversarial Networks (GANs) for optimal performance.This paper provides a review of the existing text-to-image synthesis approaches, which are categorized into four groups: image realism, multiple scene, semantic enhancement, and style transfer.In addition to discussing the existing approaches, this paper also reviews the widely used datasets for text-to-image synthesis, including Oxford-102, CUB-200-2011, and COCO.The evaluation metrics used in this field are also discussed, including Inception Score, Fréchet Inception Distance, Structural Similarity Index, Rprecision, Visual-Semantic Similarity, and Semantic Object Accuracy.The paper also offers a compilation of the performance of existing works in the field.