Reviewerly/exHarmony huggingface dataset:
Particle Swarm K-Means++ Clustering Method Based on Multiple Differential Privacy Protection Mechanism

By performing K-Means++ clustering on the samples, the sample data set can obtain a better initial clustering center, and the generation process of this center is transformed using the method of differential privacy, and the Gaussian kernel is used in the process of updating the clustering center. The function performs privacy allocation and adds noise, and finally uses the particle swarm optimization algorithm to optimize and the experimental results show that the algorithm has achieved good prediction results, and the indicators are equal to the original algorithm, so that the algorithm can protect the user’s privacy while ensuring the accurate clustering center.
Transforming Relational Databases into OWL Ontology: Methods and Perspectives

In recent years, the amount of data has become steadily larger than it used to be, resulting in the emergence of so-called big data, which makes it very difficult to handle and query data stored in traditional database systems such as relational databases. This led to the need for ontology to simplify and facilitate compliance with data models and querying them both for humans to make them meaningful and for computers to make the process easier. This paper aims to review and discuss methods for mapping an immigrating relational database to OWL ontologies. The results showed that ontology is still unable to produce the data required by a single ontology mapping. Rather, each case needs different rules that can be combined in the future to make a better ontology model along with the help of artificial intelligence and data mining to collect the required data.
Data Migration from Conventional Databases into NoSQL: Methods and Techniques

The variety and velocity of data are being changed by data growth. More processors, more storage space, and more money are needed to handle a huge amount of data. Big data, as it is known, refers to the growth of data. Big data processing cannot be handled by traditional databases such as relational databases. Relational databases store a considerable amount of data, yet they are not completely ready to deal with huge data problems. While traditional relational databases are still used in a large scope of applications, it has recently seen an explosion in the number of new database technologies developed for Big Data serving. The main alternative to relational databases is NoSQL databases. As a result, most companies must use flexible models and techniques to migrate data from relational database systems to NoSQL . This paper aims to investigate the methods and techniques for migrating data from traditional relational databases to NoSQL. We categorize some of the works that have been mentioned in the literature and the database conversion problem, focusing on the migration of a relational database as a source into NoSQL as a target database. The paper provides a comprehensive explanation and evaluation of the existing approaches and tools.
Classifying Esophageal Cancer Using a Hybrid Quantum-Classical Neural Network

Early diagnosis of esophageal cancer is critical for increasing the chances of successful treatment and reducing mortality rates. Traditional machine learning models for diagnosing esophageal lesions use previously collected data on specific local features, but these methods can be slow and computationally intensive. As a result, researchers are exploring alternative training platforms for deep learning models, including quantum computers. These computers can take advantage of the properties of quantum entanglement and superposition to solve complex problems.In this study, we propose a Hybrid Quantum-Classical Neural Network using deep learning and supervised learning techniques in a quantum framework. Our focus is on classifying esophageal cancer to demonstrate the network’s capabilities. We trained a network of two qubits to learn the label of a given dataset and optimize circuit parameters to minimize error. The proposed Hybrid Quantum-Classical Neural Network showed high accuracy in classifying esophageal cancer.
Application of Symbolic Regression to Unsolved Mathematical Problems

This study proposes a method for solving unsolved mathematical games using symbolic regression libraries. We aimed to demonstrate the effectiveness of genetic programming in mathematics in rendering the process of finding formulas more efficient. In the first part of the study, we customized the Python symbolic regression library “gplearn” by adding new features, such as conditional branching. The library uses genetic programming to obtain formulas from data, and we found that the performance of the customized version was better than that of the original. However, the user of this library must be experienced in mathematics to set the conditions for branching. The second part of the study involved the creation of a Swift symbolic regression library using genetic programming. We implemented a new method that combines two criteria for selecting the best formulas: the mean absolute error and the percentage of data described by the formula without error. This new library can discover formulas as good as those discovered using the customized “gplearn” library without requiring specialized knowledge. In some cases, the Swift library discovered formulas that better described the data better than the “gplearn” library.The results of this study suggest the potential for using genetic programming in mathematics and expanding the scope of research on symbolic regression.
Machine Learning Approaches for an Automatic Email Spam Detection

With the rapid growth of internet users, spam emails have become a major problem. Spammers can easily create fake profiles and email accounts by pretending to be genuine people in the sent emails. The spammers target people who are unaware of such scams. In today’s environment, email is a simple, quick, and cost-effective way to communicate but has various security threats which are necessary to identify to maintain security. This situation necessitates having an inbuilt spam filtering system to use email effectively without being worried about losing personal details. The goal of this work is to discover and predict spam emails early by using various classifiers. Machine learning methods provide the most accurate spam classification. This article contributes towards the development of a spam detection model by using multiple classification methods to tackle spam email challenges and helps in the technological progress in privacy & security. This model employs classification technologies such as naive bayes, K*, J48, and random forest. Conclusively, when the random forest model has been used as a prediction classifier, the output of this model has shown the highest accuracy of 95.48%.
Α ranking model based on user generated content and fuzzy logic

User Generated Comments and ranking based on it, has been studied extensively due to its impact on consumers and businesses. Literature highlights that platforms' rankings might be deceiving due to fake reviews or rates that do not correspond to reality, but existing ranking models are a 'black box' thus, they cannot be assessed. This study fills in this gap by proposing a step-by-step ranking model which is based on comments rather than rates or the theoretically established criteria (e.g., food, service, atmosphere), which are not capable to portray the complexity of the dining experience. The proposed ranking model extracts topics from qualitative data (documents) and converts them into quantitative values through the lens of fuzzy logic. The topics are used as the ranking space while, pairwise comparisons between items (restaurants) are performed to extract a ranking ladder. We applied our model to TripAdvisor's documents from restaurants in Athens, Greece.
Deep learning methods for intra-day cloudiness prediction using geostationary satellite images in a solar forecasting framework

Accurate solar resource forecasting remains a challenge. Electricity grid applications require both days-ahead and intra-day prediction. Satellite-based methods are known to be the best option for hourly intra-day solar forecasts up to some hours ahead. An adapted Deep Learning (DL) method has been recently reported to outperform the traditional Cloud Motion Vectors (CMV) strategy. This article analyzes the utilization of a well-documented computer vision DL architecture, the U-Net in various forms, for the satellite Earth albedo forecast problem (cloudiness), a straightforward proxy for solar irradiance forecast. It is shown that the U-Net performs better than advanced and optimized CMV techniques and previous art IrradianceNet, setting it at the state-of-the-art. The tests are done over the Pampa Húmeda region of southeast South America, an area in which challenging cloud conditions are frequent. The data for this study are GOES-16 visible channel images. These images present a finer spatial (≃1 km/pixel) and temporal (10 min) resolution than previously explored data sources for solar forecasting. Moreover, the image size used here is ×4 bigger (1024 × 1024 pixels) and the predictions reach further into the future (5 h) than in previous works. The analysis includes several ablation studies, involving different architectures, optimization objectives, inputs, and network sizes. The U-Net is optimized for direct and differential image prediction, being the latter a better-performing option. More notably, the U-Net models are shown to be able to predict cloud extinction, something that has been a barrier for CMV methods.
The domination effect of the intelligent environment in the catastrophe mechanism of investor behavior

In recent years, investors have benefited from the convenience of intelligent recommendation information push, but have exhibited over-dependence on intelligent systems. In some scenarios, investors' trust and behavioral decisions are even dominated by information provided by the intelligent environment, which can lead to polarization and frequent reversal of investor behavior. To explore the mechanism of this frequent change, this work combined qualitative simulations with catastrophe theory, and used text data on investor sentiment, confidence, and behavior changes to construct a catastrophe model of investor behavior. The results show that neutral-type investors are more likely to be affected by information push under the dominance effect of intelligent systems, which results in catastrophe in behavior. Meanwhile, due to the dominance of the intelligent system for some investors, even if the direction of change of the investor's internal trust level is inconsistent with the external situational disturbance, it may still lead to catastrophe behavior. Finally, the catastrophe region of investor behavior is explored through an in-depth analysis of fitted catastrophe model parameters. This can lay the foundation for financial institutions' guidance mechanisms and control strategies for investor behavior in the intelligent era.
Nonconvex Rician noise removal via convergent plug-and-play framework

Restoring images corrupted by Rician noise is a challenging issue in medical image processing. In the existing methods, the model-driven method can not recover the images well, and the learning-based methods lack good interpretability. In this paper, we propose a plug-and-play (PnP) method to remove Rician noise. Due to the statistical properties of Rician distribution and the implicit deep image priors, the problem is non-convex. We present a convergent PnP method to address these issues by an adaptively relaxed alternating direction method of multipliers. Theoretically, we give some useful mathematical properties and the global linear convergence of the proposed method by an adaptive relaxation strategy. Experimental results show that the proposed method outperforms the existing state-of-art traditional and learning-based methods.
Imbalance knowledge-driven multi-modal network for land-cover semantic segmentation using aerial images and LiDAR point clouds

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel Imbalance Knowledge-Driven Multi-modal Network (IKD-Net) to extract features from multi-modal heterogeneous data of aerial images and LiDAR directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized using a joint loss function. While we were developing IKD-Net, we also established a new dataset called the National Agriculture Imagery Program and 3D Elevation Program Combined dataset in California (N3C-California), which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper’s submission. Our code and N3C-California dataset are available at https://github.com/wymqqq/IKDNet-pytorch.
Graph inference algorithms as high-performance tools for detecting geochemical anomalies related to mineralization from geochemical exploration data

Geochemical exploration data usually consists of a small number of labeled data (cells containing known deposits) and a large number of unlabeled data (cells containing none of known deposits). How to make full use of a small number of labeled data and a large number of unlabeled data to build a high-performance anomaly detection model for identifying mineralization anomalies from geochemical exploration data is a challenge faced by geochemical exploration practitioners. The graph inference algorithms including label propagation and label spreading can efficiently build high-performance semi-supervised classification models by learning from sparse labeled and abundant unlabeled data in machine learning. For this reason, the two graph inference algorithms were used to build the semi-supervised classification models to detect Fe-mineralization anomalies from stream sediment survey data of 1: 50,000 scale in the Chengde area (Hebei Province, China). The label propagation model and label spreading model were compared with the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. The data processing time of the label propagation algorithm and label spreading algorithm is 64.26 s and 28.21 s, respectively; and the data processing time of the Gaussian mixture model and logistic regression algorithm is 37.79 s and 18.98 s, respectively. The area under the receiver operating characteristic curves (AUCs) of the label propagation model and label spreading model are 0.979 and 0.999, respectively; and the AUCs of the Gaussian mixture model and logistic regression model are 0.800 and 0.817, respectively. Therefore, the data modeling efficiency of the label propagation model and label spreading model is comparable to that of the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. However, the performance of the label propagation model and label spreading model are significantly better than that of the Gaussian mixture model and logistic regression model. It can be concluded that the two graph inference algorithms are potentially useful high-performance tools for detecting mineralization anomalies from geochemical exploration data.
LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language Understanding

NLP research on logical reasoning regains momentum with the recent releases of a handful of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing tasks over large Pre-trained Language Models (PLMs) and downstream tasks like question-answering and dialogue systems. In this paper, we release LogiQA 2.0. The dataset is an amendment and re-annotation of LogiQA in 2020, a large-scale logical reasoning reading comprehension dataset adapted from the Chinese Civil Service Examination. We increase the data size, refine the texts with manual translation by professionals, and improve the quality by removing items with distinctive cultural features like Chinese idioms. Furthermore, we conduct a fine-grained annotation on the dataset and turn it into a two-way natural language inference (NLI) task, resulting in 35k premise-hypothesis pairs with gold labels, making it the first large-scale NLI dataset for complex logical reasoning. Compared to Question Answering, Natural Language Inference excels in generalizability and helps downstream tasks better. We establish a baseline for logical reasoning in NLI and incite further research.
Using Tabu Search to Avoid Concave Obstacles for Source Location

Recently, using a particle swarm optimizer (PSO) to guide robots in a source location problem has attracted widespread interest. While being navigated by PSO, robots are easily trapped into U-shape-like concave obstacles such that they move back and forth cyclically and fail to locate a correct source. Existing obstacle avoidance strategies perform well when robots have information about all obstacles. Yet in many real scenes, robots have no prior information. This work proposes a novel PSO based on Tabu Search (PSO-TS) for robots to locate multiple sources. Instead of traditionally setting obstacles as tabu objects, PSO-TS innovatively sets trapping areas as tabu objects such that robots do not need prior knowledge or expensive hardware and much time to obtain obstacle information. The weighted average velocity of a robot is employed to determine if it is stuck inside an obstacle-induced area. If so, a rectangular tabu area is set to push robots out of the area and prevents robots from searching the same area again. The proposed method can be embedded into various source location algorithms to improve their performance. Its obstacle avoidance capability is proved. Finally, experimental results show the algorithmic compatibility, environmental adaptability and obstacle avoidance performance of the proposed method.
Blood Cell Detection Method Based on Improved YOLOv5

In order to solve the problems of low accuracy and missed detection in traditional blood cell data detection tasks. This paper proposes and implements the blood cell detection method based on the YOLOv5 (YOLOv5-ALT). The goal of this research is to enhance the accuracy of the detection with the YOLO techniques. This work presents the method overcomes the shortcomings of the existing method by introducing the attention mechanism in the feature channel, modifying SPP module in YOLOv5 backbone feature extraction network, and changing the bounding box regression loss function. Based on the deep learning object detection algorithm, each evaluation index is compared to evaluate the effectiveness of the model. Experimental results show that the mAP@0.5, Precision and Recall of the YOLOv5-ALT reaches 97.4%, 97.9% and 93.5%. This method is more in line with the effectiveness of the blood cell detection task.
Quantum state transfer: interplay between gate and readout errors

Quantum networks consist of quantum nodes that are linked by entanglement, and quantum information can be transferred from one node to another. Operations can be applied to qubits of local nodes coordinated by classical communication to manipulate quantum states, and readout/measurement will be employed to obtain results. Here, we use quantum circuits to simulate quantum state transfer between two nodes connected in a linear geometry through other nodes. We explore the interplay between gate and readout errors on the performance of state transfer. We find that the nominal success probability is not necessarily a monotonic function of the two error rates and employ numerical simulations and analytic tools to understand their interplay.
RGB-D Salient Object Detection Using Saliency and Edge Reverse Attention

RGB-D salient object detection is a task to detect visually significant objects in an image using RGB and depth images. Although many useful CNN-based methods have been proposed in the past, there are some problems such as blurring of object boundaries and inability to detect important parts of objects, leading to a decrease in detection accuracy. In this paper, we propose RGB-D salient object detection using Saliency and Edge Reverse Attention(SERA), which combines the fusion of saliency and edge features with reverse attention. The reverse attention process has the effect of making it easier to capture the boundaries of objects and undetected objects by inverting the up-sampled saliency features and weighting other saliency features, and the edge reverse attention process has the effect of making salient object regions stand out by inverting the edge features and weighting saliency features. The interaction between the salient object features and the edge features enhances each other’s features and refines the information on the boundary of objects and salient object regions. In addition, to make it easier to refer to the global information of an image, we introduced the Multi-Scale Interactive Module(MSIM), which is capable of acquiring information at rich scales by converting feature maps to different resolutions and interacting with them. In addition to the salient object output, supervised learning is applied to multiple edge outputs of each resolution to improve the accuracy of both salient objects and boundary areas. Experimental results on five benchmarks show that the proposed method quantitatively performs better than the conventional method, and qualitatively improves the sharpness of object boundaries and the accuracy of detecting important parts of objects.
Self-attention Mechanism at the Token Level: Gradient Analysis and Algorithm Optimization

The self-attention mechanism is a feature processing mechanism for structured data in deep learning models. It has been widely used in transformer-based deep learning models and has demonstrated superior performance in various fields, such as machine translation, speech recognition, text-to-text conversion, and computer vision. The self-attention mechanism mainly focuses on the surface structure of structured data, but it also involves attention between basic data units and self-attention of basic data units in the deeper structure of the data. In this paper, we investigate the forward attention flow and backward gradient flow in the self-attention module of the transformer model based on the sequence-to-sequence data structure used in machine translation tasks. We found that this combination produces a "gradient distortion" phenomenon at the token level of basic data units. We consider this phenomenon a defect and propose a series of solutions to address it theoretically. Furthermore, we conduct experiments and select the most robust solution as the Unevenness-Reduced Self-Attention (URSA) module, which replaces the original self-attention module. The experimental results demonstrate that the "gradient distortion" phenomenon exists both theoretically and numerically, and the URSA module enables the self-attention mechanism to achieve consistent, stable, and effective optimization across different models, tasks, corpora, and evaluation metrics. The URSA module is both simple and highly portable.