Reviewerly/exHarmony huggingface dataset:
File allocation among the on trustworthiness areas attached to the distributed environment, using server storage testing method

This paper focuses on the goal of consistency in the distributed environment, specifically the organization’s inability to handle the data storage issues properly. The main drawback of this type of server is that it never keeps track of the connected clients, their capacity, storage size, and more. On the other hand, data redundancy helps the server if any of the connected clients don’t work with the help of available copy system functions. It will improve the system's performance and never affect the system's flow. In the proposed work, each client-side system redundancy component is calculated, which will help the user identify exactly how many units of data transfer take place and the exact number of data losses. So any data loss is immediately notified to the user, and particle transactions are re-initialized to avoid data loss.
A Long-Tailed Image Classification Method Based on Enhanced Contrastive Visual Language

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes are large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses the enhanced momentum contrastive loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrasting visual language-based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and the F1 value by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual language has improved the performance of tail classes and reduced the accuracy difference between the majority and minority classes.
A Critical View on the OQuaRE Ontology Quality Framework

Creating and maintaining high-quality ontologies is crucial as they encapsulate the logic for rule-based artificial intelligence. One way to objectively assess ontologies is ontology metrics, and the research community has already proposed measurement frameworks for assessing ontology quality. Arguably, the most holistic framework available is OQuaRE. Not only does it suggest metrics, but it also links these metrics to quality characteristics and interprets them using a school-like grading system. However, in an effort to implement the framework for the metric calculation software NEOntometrics, serious drawbacks came to light: (1) Important parts of the framework are only available at online resources (thus not part of the scientific record and without ensured permanent availability). (2) The proposing authors have been involved in all applications of the framework and corresponding research, an outside perspective is missing. (3) At times, the meaning of metrics changed throughout the papers without notice, leading to conflicting definitions. (4) A thorough evaluation of the quality grades is missing. This research aims to fill this gap: We first scrutinize the various metric proposals with their conflicting versions and resolve these conflicts. Afterward, we present OQuaRE to its full extent to create a permanent, lasting record. At last, we use a large body of evaluated ontologies to assess whether OQuaREs quality ratings sufficiently cover the ontology models used in the wild.
Deep Multi-view Clustering Based on Graph Embedding

In recent decades, multi-view clustering has garnered more attention in the fields of machine learning and pattern recognition. Traditional multi-view clustering algorithms typically aim to identify a common latent space among the multi-view data and subsequently utilize k-means or spectral clustering techniques to derive clustering outcomes. These methods are time and space consuming while leading to splitting feature extraction and clustering. To address these problems, deep multi-view clustering based on graph embedding is proposed in this paper. First, multiple autoencoders are used to mine complementary information from multi-view and simultaneously seek the common latent representations. In addition, both the validity of the nearest neighbor correlation and the local geometric structure of multi-view data are taken in account and a novel graph embedding scheme is proposed. Specifically, the affinity graph information of the original data is directly applied to the soft assignment of data, which is consistent with the clustering loss, thus improving the performance of multi-view clustering. Numerous experiments conducted on various datasets exhibit the efficacy of our algorithm.
Make Active Attention More Active: Using Lipschitz Regularity to Improve Long Sequence Time-Series Forecasting

Long-term time series prediction aims to accurately forecast the future by analyzing the prevailing temporal patterns derived from historical data inputs. Commonly used techniques for long-term time series prediction using an encoder-decoder architecture incorporating a self-attention-based mechanism have achieved impressive results and remain at the forefront of the field. Furthermore, to address the quadratic time complexity associated with the self-attention mechanism, researchers have sought to exploit the long-tailed distribution of self-attention and use several approaches based on the selection idea to achieve better performance while improving model efficiency. However, these efforts have primarily focused on practical implementations rather than exploring the underlying theoretical principles and reinforcing their effectiveness. Inspired by the increasing disparity between graph nodes in graph neural networks (GNNs), we investigate the impact of the long-tailed distribution of self-attention scores on prediction accuracy in this work. We propose a novel approach to enhance the distinction of self-attention scores and achieve performance improvements. Moreover, we incorporate this approach into a state-of-the-art model and validate its effectiveness through theoretical analysis and visual verification. Our method was extensively tested on four large datasets and ultimately showed superiority over existing methods. In addition to a remarkable average reduction of 18% in MSE and 12% in MAE, our approach also reduced time consumption by 38%.
Fuzzy Neo: A Novel Method for Batch Liquid Temperature Control System Using Simpler Fuzzy Rule and Inference

Industries and laboratories heavily rely upon temperature-controlling technology due to the dependencies of their systems on temperature. Therefore, temperature control systems were implemented to maintain the stability of the temperature in those systems, including the batch processes. This research aimed to propose and test a novel method for batch liquid temperature control system using Fuzzy Neo which has simpler fuzzy rule and inference compared to the previous methods. The system was developed using an Arduino-based microcontroller to control the heating process of water. The results showed that the implementation of Fuzzy Neo was successfully stabilizing and maintaining the temperature in the system from different temperature references and adapted against the disturbance simulated to the system. The value of RMSE is 0.5347, which showed that the observed value of dimmer frequencies in the system are close to the predicted values calculated using Fuzzy Neo method.
On-Demand and Model-Driven Case Building Based on Distributed Data Sources

The successful application of Case-Based Reasoning (CBR) depends on the availability of data. In most manufacturing companies these data are present, but distributed over many different systems. The distribution of the data makes it difficult to apply CBR in real-time, as data have to be collected from the different systems. In this work we propose a framework and algorithm to efficiently build a case representation on-demand and solve the challenge of distributed data in CBR. The main contribution of this work is a framework using an index for objects and the sources where data about those objects can be found. Next to the framework, we present an algorithm that operates on the framework and can be used to build case representations and construct a case base on-demand, using data from distributed sources. There are several parameters that influence the performance of the framework. Accordingly, we show in a conceptual and experimental evaluation that in highly-distributed and segregated environments the proposed approach reduces the time complexity from polynomial to linear order.
Structural design of heavy floating garbage cleaning robot suitable for small water area

The garbage on small water areas such as parks, lakes and urban rivers is mainly divided into lightweight floating garbage such as garbage bags and leaves and heavy floating garbage such as tires and animal carcasses.The garbage cleaning robot designed in this paper is mainly used to cleaning heavy floating garbage.This paper introduced in detail the structural design process of the garbage cleaning robot, including the design of the hull, the cleaner, the pushing device and the calculation of related parameters.
STADEE: STAtistics-Based DEEp Detection of Machine Generated Text

We present STADEE, a \textbf{STA}tistics-based \textbf{DEE}p detection method to identify machine-generated text, addressing the limitations of current methods that rely heavily on fine-tuning pre-trained language models (PLMs). STADEE integrates key statistical text features with a deep classifier, focusing on aspects like token probability and cumulative probability, crucial for handling nucleus sampling. Tested across diverse datasets and scenarios (in-domain, out-of-domain, and in-the-wild), STADEE demonstrates superior performance, achieving an 87.05% F1 score in-domain and outperforming both traditional statistical methods and fine-tuned PLMs, especially in out-of-domain and in-the-wild settings, highlighting its effectiveness and generalizability.
GAN for Blind Image Deblurring Based on Latent Image Extraction and Blur Kernel Estimation

We propose a GAN for image deblurring based on latent image extraction and blur kernel estimation, with which the single image deblurring assignment is successfully completed. We introduce the FFT to the image of the latent image extraction because the FFT can convert image transformation from spatial to frequency domain; this is a good solution for the convolutional neural network for partial frequency domain knowledge learning and gives a sharper picture; We also apply the cross-scale reproducibility of natural images to the extraction of blur kernel. By adding regularization constraints to the kernel to enhance estimation precision, the estimated kernel of the image is generated via fusing of local kernels after numerous iterations. Meanwhile, a multi-scale discriminator structure combining RSGAN and PatchGAN is used. RSGAN is applied as a global discriminator with more accurate classification criteria, while PatchGAN is applied as a local discriminator to determine the accuracy of local blur kernel. The experiment proves that our work is effective.
Knowledge Graph-Based Approach for Main Transformer Defect Grade Analysis

The effective maintenance of power grid equipment is critical for ensuring the safe and stable operation of the power grid. In recent years, knowledge graphs have emerged as a powerful tool for representing complex relationships and knowledge in a structured and accessible format. In this paper, we proposed a knowledge graph-based approach for analyzing and diagnosing defects in power grid transformers. We first designed an ontology for defect data in the field of main trans- formers in power grids. The ontology included equipment information, defect descriptions, and industry-standard classification criteria. We then performed named entity recognition(NER) on textual data in the field of main transformers using the Bert-Bilstm-CRF [1–3] model to extract entities. The extracted entity information was represented using the ontology, and the ontology was embedded into a knowledge graph using models such as TransE [4]. We conducted knowledge graph completion experiments to achieve diagnosis and analysis of the defect level. Our experimental results demonstrated that this method efficiently and automatically constructs a knowledge graph of main transformers in power grids. The well-designed ontology and effective knowledge graph completion experiments also support the analysis of defect levels in main transformers in power grids. Additionally, this method can promote the understanding and management of complex systems in the field of power grid equipment.
CSAANet: An Attention-Based Mechanism for Aligned Few-Shot Semantic Segmentation Network

Semantic segmentation, a fundamental job in computer vision, involves identifying and classifying items in an image. However, it is too costly to collect a sizable volume of annotated data for prediction tasks. Few-shot semantic segmentation approaches aim to learn from a short amount of labeled data and generalize to new classes in order to get over this constraint. Learning to distinguish objects from a small sample of labeled samples is the key challenge in this project. Thus, we propose a Channel and Spatial Attention Alignment Network (CSAANet) for better performance in few-shot semantic segmentation. Our approach uses the channel and spatial attention to obtain weighted classifiers for novel classes. The classes in the image may be precisely segregated using the weight classifiers. Additionally, we construct a semantically aligned auxiliary learning module to fully utilize the supporting image information and enhance the learned weights. Experimental findings on few-shot semantic segmentation datasets, PASCAL-5i and COCO-20i, demonstrate that our proposed method outperforms other methods.
CHEST X-RAY IMAGE SEGMENTATION USING MODIFIED DEEPLABV3+ METHOD

COVID-19 is a disease that affects the human respiratory system. The latest developments in September 2022 the number of confirmed cases of COVID-19 worldwide reached 608,328,548 with 6,501,469 patients who died. While in Indonesia confirmed COVID-19 reached 6,408,806 with 157,892 patients who died. Reserve Transcription Polymerase Chain Reaction (RT-PCR) is the most widely used tool. However, the latest RT-PCR test report shows that the RT-PCR test is inadequate. As an alternative, radiographic images such as chest x-rays and CT scans can help detect this. Radiographic images, especially x-rays, need processing to be able to make a diagnosis. Computer Aided Diagnosis (CAD) is a computer assisted diagnosis system that can be used as supporting information in making a diagnosis. To make it easier to make a diagnosis, we need a deep learning model that can help with this. DeepLabV3+ is a method that can carry out the segmentation process. DeepLabV3+ which is an extension of DeepLabV3 with the aim of improving the segmentation results. DeepLabV3+ uses a modified Xception as the backbone. In this study, 1,500 chest x-ray image data were used which were then divided into 80% for training data and 20% for testing data. There are 4 test scenarios in this study, namely with a learning rate of 0.01 without CLAHE, a learning rate of 0,01 and using CLAHE, a learning rate of 0,0001 without CLAHE, and a learning rate of 0,0001 using CLAHE. Of the 4 scenarios the learning rate scenario is 0,01 and using CLAHE gets the highest evaluation results using the Dice Similarity Coefficient (DSC) of 96.91%.
A Hybrid Tourism Recommendation System Based on Multi-objective Evolutionary Algorithm and Re-ranking

The tourism recommendation system (TRS) can intelligently recommend the next attractions for users according to their historical visit records. Most tourism recommendation methods only consider accuracy as an evaluation index, which can no longer meet the needs of users who are increasingly seeking for diversified attractions. In order to solve the problem, a hybrid recommendation model based on multi-objective optimization and re-ranking (MOEA/D-HRR) is proposed in this study. To overcome the limited predictive power of a single recommendation algorithm, MOEA/D-HRR mixes three different classical techniques by weighted summation of their recommendation results, and then improve the diversity of the recommendation list by using the re-ranking algorithm based on Maximal Marginal Relevance (MMR). In addition, we construct a multi-objective optimization problem in which accuracy and diversity are formulated as two objectives, and use a multi-objective evolutionary algorithm (MOEA) to optimize the weights and parameter of the re-ranking algorithm. A lot of experiments on two public tourism datasets and a new tourism dataset were carried out. Experimental results showed that the model could provide users with diversified attractions while ensuring the accuracy of recommendation attraction.
Cluster Equality Validity Index and Efficient Clustering Optimization Strategy

Cluster analysis is an important research direction in data mining. The research on Cluster analysis can be divided into two parts: cluster validity index and clustering algorithm. We propose a novel external cluster validity index called CEI (Cluster Equality Index) and an efficient strategy called COS (Clustering Optimization Strategy). CEI constructs the equal confusion matrix by treating all clusters equally with greedy cluster pairing. Then it is used to match the clustering results and the ground truth labels for the clustering quality evaluation. COS is designed to judge whether a cluster should be reclustered by comparing the density of clusters’ centroids with that at their edges. This optimization strategy can be combined with existing clustering algorithms to further improve the quality of their clusters. The public available artificial datasets and real-world datasets are deployed in experiments. The results show that CEI offers more accurate evaluation compared with other indexes, and COS can be further improve the quality of clustering.
Unsupervised Few-Shot Learning via Positive Expansions and Negative Proxies

Few-shot learning has made significant progress recently thanks to pre-training methods and meta-learning approaches. These methods, however, require an extensive labeled dataset that is difficult to obtain. We propose an unsupervised few-shot learning method based on positive expansions and negative proxies to fully utilize abundant unlabeled data. Our approach learns meaningful representations through self-supervised pre-training on unlabeled data using a simple but effective positive and negative sampling strategy. Specifically, we sort the negative queue in descending order based on similarity to the query embedding and then select the top N negatives as positive extensions. Behind these N negatives, we choose M negatives as proxies. We further incorporate this sampling strategy into a novel contrastive loss function. We learn the representation by minimizing the distance between query and positive extensions while maximizing the distance to negative proxies. Our approach greatly narrows the performance gap between supervised and unsupervised learning in two widely used few-shot benchmarks. Under a linear evaluation protocol, our method also achieves performance comparable to current SOTA self-supervised learning methods.
Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext

Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small.
Recognizing Functional Pragmatics of Chinese Discourses on Data Augmentation and Dependency Graph

The study of discourse functional pragmatic structure attaches importance to the function of discourse units. Existing models have poor performance in the functional pragmatics recognition of minority categories and ignore discourse dependency structure to enhance the representation of discourse units. To address the above issues, we propose a Functional Pragmatic Recognition model based on Dependency Structure (FPRDS) to recognise the functional pragmatic structures of Chinese discourses. Specifically, we first propose a data augmentation approach based on adversarial training and subtree swapping to enhance the recognition performance of minority categories. And then we use graph convolutional networks to incorporate the discourse dependency information to better enhance the representations of discourse units. The experimental results show that our FPRDS outperforms the state-of-the-art models, especially for minority categories.
Optical pseudo-image encryption with enhanced plaintext correlation

In this research process, in order to improve the ability of interference-based optical image encryption system to resist chosen plaintext attacks, we propose an optical pseudo-image encryption algorithm to enhance the correlation of plaintext. In this work, we introduce an addictional key, which is highly correlated with the secret image. The key can dynamically update the random phase plate and break the linear relationship between the input and output of the optical encryption system, so that the system will not be cracked by chosen plaintext attacks. Furthermore, the number of keys is reduced through improved sine chaos mapping, so that the encryption system can reduce the amount of key storage data while improving the security of the system.
Improved tests of genuine entanglement for multiqudits

We give an improved criterion of genuine multipartite entanglement for an important class of multipartite quantum states using generalized Bloch representations of the density matrices. The practical criterion is designed based on the Weyl operators and can be used for detecting genuine multipartite entanglement in higher dimensional systems. The test is shown to be significantly stronger than some of the most recent criteria.
Komparasi Teknik Hyperparameter Optimization pada SVM untuk Permasalahan Klasifikasi dengan Menggunakan Grid Search dan Random Search

Classification is one of the important tasks in the field of Machine Learning. Classification can be viewed as an Optimization Problem (Optimization Problem) with the aim of finding the best model that can represent the relationship/pattern between data with labels. Support Vector Machine (SVM) Is an algorithm in Machine Learning used to solve problems such as Classification or Regression. The performance of the SVM algorithm is strongly influenced by parameters, for example error prediction in non-linear SVM results in parameters C and gamma. In this study, an analysis of the technique was carried out to obtain good parameter values using Grid search and Random Search on seven datasets. Evaluation is done by calculating the value of accuracy, memory usage and validity test time of the best model by the two techniques.