Reviewerly/exHarmony huggingface dataset:
Mondrian Conformal Prediction of Boosting for Swarm Behavior Recognition

The relationships between empirical data of agents' movements and swarm behavior signals make it possible to predict swarm behaviors based on empirical data. From the theorem of view, the existing machine learning algorithms produce a function mapping the objects into scores and it is obviously practical demand for real-life data applications to provide uncertainty quantification for the simple prediction results. In this paper, we describe a distributions-free framework to measure the uncertainty of the simple prediction and we apply Mondrian Conformal Predictors to provide a valid set prediction under unconstrained randomness assumption. Both three representative behaviors of swarm real-life datasets, such as Align, Flock, and Group, and the synthesis collective swarm behavior datasets are presented, in addition, the validity check plots are also established for their prediction performance. We choose the modern machine learning algorithm, Boosting, as the role of underlying algorithms to produce the score of prediction rules. Instead of average treatment effect, the proposed method provides the two measurements, confidence, and credibility, for individual treatment effects and the set prediction has guaranteed average coverage in finite samples regardless of the unknown data distributions. Both Real-life and the synthesis datasets experimental results empirically demonstrate that existing methods suffer from a significant embarrassment in uncertainty quantification, while our methods achieve the desired and straightforward to provide the guaranteed probability.
The Design of Multifunctional Online Travel Service Management Platform and the Implementation of MySQL

This article is mainly based on MySQL technology to realize the “multifunctional tourism information service website”. First, ASP.NET is used to dynamically design web pages to publish relevant travel information, ADO.NET technology is used to encapsulate business logic and database operations, and client/server architectures such as JSP, JDBC, and MySQL 2005 database, etc., are used in the development and design of the travel website system based on JAVA WEB technology. This paper mainly studies the demand analysis of the dynamic website system, the system design goal of the travel service website, and sort out the business process and performance requirements of the system, thus increasing the efficiency by 9.7%.
IoT based Smart Dustbin for Waste Management

Increased population creates a large amount of waste in urban areas and hence waste management has become a tedious process. Generally, trash cans will be opened manually to track the waste overflow. To overcome this challenge, this research work has proposed an automatic smart trash can using IoT and sensor-based circuits. A self-contained smart dustbin has been developed to perform multi-tasking. The proposed study has constructed a smart vehicle, which moves automatically in a pre-defined path to collect and dispose the waste in the main collection area. The ultrasonic sensor present in the proposed module detects obstacles in the vehicle's path, while the infrared sensor checks the waste level in the garbage. The inbuilt Wi-Fi module collects the data and regularly stores the data in cloud for future analysis. The proposed smart trash can has the ability to separately dis pose the metal from the normal waste. For preventing waste overflow, the main collection area will be equipped with an IR sensor and a buzzer to alert the concerned person when the trash is nearly full. The implementation of proposed system will save both cost and time by eliminating the manpower required for garbage collection and disposal.
Reliability Analysis and Algorithm of Data Center Network

Folded Clos networks in data center networks architecture have received widespread attention in recent years, and the performance analysis in data center networks is a very important issue. In this paper, we analyze system reliability of folded Clos network, which is defined as the probability that at least one effective subgraph is operating. An effective subgraph is a subgraph of the network where all leaf nodes connect to at least one root node. Next, we define a basic subtree to be an effective subgraph having all nodes contributed to the reliability and show that the system reliability could be calculated by using basic subtrees of folded Clos network. In any data center network, the number of basic subtrees is less than 6% of the number of effective subgraphs, significantly reducing the running time of the accurate algorithm. Given that the calculation of the reliability is NP-hard, we propose a Monte Carlo algorithm to estimate it. The simulations show that the proposed system reliability describes the capability of the network to resist failures well and the proposed algorithms are effective and efficient.
A Comparative Study on Adversarial Attacks for Visible and Infrared Images

In recent years, the booming development of artificial intelligence has driven the development and progress of society. With its excellent performance, deep learning has been widely used in many fields, including some security-sensitive fields such as unmanned systems, auto-driving and so on. Research has found that deep neural networks are vulnerable to adversarial samples, which can lead to misclassification of neural networks and can cause serious safety incidents. Hence, the study of adversarial samples has received increasing attention, and researchers have conducted studies from different perspectives, and relevant research results have emerged. However, most of these studies have focused on visible images, and few have studied infrared images, which play an important role in military and other important fields. In this paper, a comparative study on adversarial attacks for visible and infrared images is presented. A number of adversarial attack experiments are conducted on ResNet18 and VGG16 models for visible and infrared images. By comparing the experimental results, the characteristics of infrared adversarial samples are derived relative to those with visible adversarial samples. Then, a gradient attack method from a frequency domain perspective is proposed. The adversarial perturbation noise is added to the low frequency component of images. It is verified experimentally that the proposed method improves the transferability of infrared adversarial samples compared to some baseline methods.
Machine Learning Based Machine Reading Comprehension - A Survey

Machine reading comprehension is an area of NLP that has profited greatly from the advancements in Artificial Intelligence. The goal is to empower machines with the ability to read a comprehension, understand the context, and then answer the questions asked. The comprehension represents unstructured natural language text and it is significantly more complex to answer the questions than the symbolic matching heuristics utilized earlier. The emergence of major advancements in this field has been influenced by the combination of sophisticated neural network models, processing resources, and the availability of massive human annotated datasets. Despite the clear disparity between contemporary Machine Reading Comprehension model and real human reading comprehension, many Machine Reading Comprehension models have already outperformed humans on a variety of benchmark datasets. To progress present MRC models toward “actual” knowledge, existing datasets, assessment measures, and models must be improved. The paper presents a comprehensive survey on the various features of Machine Reading Comprehension systems, their methodology, dataset, and research novelties. In addition, persisting open research questions are identified and directions for future work are highlighted.
Pothole Identification and Dimension Approximation with YOLO Darknet CNN

The automation of daily routine tasks is day-by-day increasing. This is a necessary scenario, especially in today's lifestyle. One such automation is driverless vehicle driving which requires the prompt identification of the potholes present on the roads. A lot of research is present to deal with the real-time pothole identification challenges. However, none of the existing research is capable of achieving prompt accurate prediction. To contribute to the existing literature, this paper proposes a Dark Convolutional Neural Network (Darknet CNN) based model called tiny YOLO (You Only Look Once) for pothole identification and dimension approximation of the potholes in order to alert the driver (or automation system) for the safe driving. The proposed model is implemented for the number of epochs to check the performance of the model. The performance metric used for the model evaluations includes Precision (P), Recall (R), Average Precision (AP), and micro Average Precision (mAP) as evaluation criteria with low GPU memory utilization, bounding box area, object Area, and total area. The model gives the best performance for the 270 epochs. The proposed model is light weighted, which helps the video to store in minimum space and lesser time span.
Logistic Regression based Machine Learning Technique for Phishing Website Detection

Nowadays, many people start switching from offline to online to save their precious time. They started buying products online and made their payments through online transactions across websites. These online buyers are asked to provide details such as their name, address, location, passwords, and other essential bank details on that particular website. The unaware online buyer got caught in these sites, which leads to a process of phishing. They are called phishing websites. This research work has proposed an efficient prediction method based on the machine learning technique to analyze and predict these phishing websites. Novel classification algorithm and techniques are used to analyze and extract the datasets that might maliciously cause phishing. The essential traits are helpful to identify these types of phishing sites such as domain, URL and encryption technique of a website while detecting malicious data. This research work will use a logistic regression algorithm for detecting the phishing website. A logistic regression algorithm is used to provide better performance than the traditional classification algorithm. To protect user sensitive information and for effective, secure transaction payments, many E-commerce enterprises are using this application to stay on the safer side.
A Globally Oriented Weight Integration Attention Module with Different Scales

Convolutional neural networks can enhance the model's capabilities to capture key information by introducing an attention mechanism. This capability allows convolutional neural networks to acquire more focused information from multiple dimensions, such as channel, space, time and branch dimension. Many networks try to obtain more effective channel attention mechanisms by design better network structure. The process of getting a channel attention coefficient result in significant information loss, limits the amount of information acquired. In this work, we propose a globally oriented weight integration attention module, which solves the information loss of the attention mechanism in acquiring attention coefficients by average pooling. The feature map is processed in several dimensions by using dense convolution with various branches, and the attention coefficients are obtained by combining diverse sensory fields through multi-dimensional information fusion. The module's attention coefficients are derived by combining the attention coefficients of two separate attention coefficient branches. The module reduces the information loss of the attention network and adaptively concentrates on different regions. Our GWI attention mechanism module is simple and effective. We evaluated the effectiveness of the GWI attention mechanism module and achieved better results on the dataset of several computer vision tasks.
Decision-making Method Based on Multi-agent Deep Reinforcement Learning

Based on the decision-making architecture of information pooling and sharing in the hidden layer, the communication protocol is set manually, and the pooling method is used to integrate the information. Although the problem of communication and extension between agents is solved, it is difficult for tasks lacking prior knowledge to design effective communication protocols. The centralized decision- making architecture based on two-way RNN communication uses the information storage characteristics of two-way RNN structure. It can self learn the communication protocol between agents, which overcomes the rigid requirement of task prior knowledge in communication protocol design. The action distribution of a single agent is used as the output of the multi- agent network to replace the joint action distribution, and the global state information in the environment is used as the input instead of simply inputting the local information to different agents. The effectiveness of the method is verified by an example.
Deep Neural Networks for Detecting Hate Speech

With increased social media activity, hate speech on the Internet has become increasingly prevalent. It is a hazardous and damaging form of internet content that targets a group or individual based on their religion, race, or sexual orientation. As a result, it has garnered increasing attention from researchers. This article will examine current research trends, data sources, and methodologies and recommend future study directions. The subject of hate speech was determined at the start of 2020, and it includes attacks on minorities, religion, women, the general election agenda, and politics. Individuals have experimented with various methodologies and models and discovered several characteristics that fulfill the exclusion requirements. However, these methodologies and features do not imply that they will perform well in detecting hatred. The data collection, selected features, number of categories, and mutually exclusive categories all significantly impact the classification performance of hate speech.
Health Status Evaluation of Bearing Based on DE-ELM

Be directed against the nonlinear dynamic characteristics of bearing vibration signals and the low accuracy of reliability evaluation, a bearing health evaluation method based on extreme learning machine(ELM) optimized by differential evolution algorithm (DE-ELM) is proposed. Firstly, the vibration signal is preprocessed, and the importance of each feature is calculated by using the Gini coefficient in the random forest to select the time domain features and frequency domain features with high weight. Then the health status is divided by hierarchical clustering. Finally, the health status of bearing is evaluated by DE-ELM. DE-ELM not only has simple structure and fast convergence speed, but also can make up for the decline of classification accuracy of extreme learning machine, caused by the random generation of parameters. From experimental results, comparing with the traditional ELM model, the optimized model has higher classification accuracy and better health state recognition effect.
Adaptive Multi-feature Fusion Improved ECO-HC Image Tracking Algorithm Based on Confidence Judgement for UAV Reconnaissance

Owing to the advantages of target tracking algorithm based on correlation filtering on the efficiency in speed and the tracking accuracy, it has been widely applied in the image real-time tracking, especially in the field of the video surveillance, the human-computer interaction, and the intelligent transportation. However, considering the tracking timeliness and the ability to deal with deformation, position changes and occlusion in complex background, especially in the application for the unmanned aerial vehicle (UAV) reconnaissance, an adaptive multi-feature fusion improved tracking algorithm with confidence judgement strategy is proposed in this paper. On the basis of the efficient convolution operators handle-crafted (ECO-HC) method, which is the best algorithm with excellent performance based on correlation filtering, an adaptive multi-feature strategy and a strategy of confidence state recognition with confidence judgement and relocation are described in detail. After quantitative and qualitative comparison tests with other advanced algorithms, the results fully verify the superiority in tracking accuracy and robustness against the interference of the complex background.
Image-Based Visual Servoing of a Fully-Actuated Hexacopter

In this paper, a novel image-based visual servoing (IBVS) approach is proposed for a fully-actuated hexacopter, which allows to maintain a stable attitude with the horizontal movement. In contrast to the existing methods, the key insight is to integrate the IBVS method to the fully-actuated hexacopter, which can alleviate the field of view (FOV) constraint. Specifically, the nonlinear hierarchical position and attitude controller is designed based on its dynamic characteristics. In the outer loop, an new IBVS method with perspective moments is employed to design the force input. The inner loop utilizes the quaternion to generate the torque input. Comparative simulations are conducted to verify that the proposed method can relax the FOV constraint greatly.
An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

Web extensible markup language (XML) data quality analysis using clustering is necessary in many real time web applications. In huge applications XML is the standard format to explore the web data. In existing approaches the XML web data is clustered by using Weighted Fuzzy C means (WFCM),LSI(Latent Semantic Indexing),LSI*(Enhanced Latent Semantic Indexing) and K-means clustering approach. The existing approach generates degrades the efficiency of the performance. The performance of the clustering is increased by the novelty technique Improved Fuzzy Clustering Method (I-FCM)in this article. I-FCM is the machine learning method that clusters the XML data better than the other existing methods. First, using keyword the features are extracted from the XML web repository. Next, cluster the extracted features using I-FCM approach. Finally, necessary performance parameters in the clustering of XML data are analyzed. This article used the platform MATLAB to demonstrate the proposed approach. For the experimental purpose the LF-AMAZON dataset is utilized. The parameters accuracy, purity, entropy, and search time is utilized to measure the performance of the novelty technique. This performance is compared with the existing approach. The output of the novelty work shows the better performance and robust result is generated than the existing method.
UCSAM: A UAV Ground Control System Architecture Supporting Cooperative Control Among Multi-form Stations based on MDA and Container Cloud Platform

An open, unified and loosely coupled service oriented software architecture of UAV Ground Control System (UGCS) based on container cloud platform is proposed in this paper, which is called “UCSAM”, and can support the collaborative control of UAV among multi-form ground control stations, such as portable, mobile and fixed stations. UCSAM establishes a kind of software computing environment based on the component framework conforming to the OSGi specifications on the client side, the container cloud platform on the server side, and the middleware conforming to the DDS and other transmission protocols between the two, so that the application software components on the client side and the FDSCs(Functional Domain Service Components) on the server side only rely on standardized APIs, so as to realize the architecture features of resource allocation on demand and cross platform portability. On the other hand, UCSAM defines PSM based on Open Application Model (OAM), including message model, service model and application model, which separates FDSC development from the operation and maintenance complexity of infrastructure software. Through a set of tool chains, it realizes the conversion from PIM based on XMI format to PSM based on OAM, code generation based on template and PSM, CI/CD based on GitOps, and dynamic deployment based on AppStore, so as to achieve the architectural goal of dynamic generation of capabilities, and the agile software development mode based on MDA and DevOps. Lastly, several study cases including UAV cooperative application, development of functional domain service components, and application deployment and operation are briefly discussed to verify this architecture's correctness and feasibility.
Feature Correlational Attention Network for Image Inpainting

While existing methods based on generative model are able to restore missing contents with semantical coherency, the lack of combining the correlations between different levels results in the artifacts or blurry textures in the ultimate results. In this paper, we present a Feature Correlational Attention Network (FCAN) that leverages layers' correlations for better predictions. To be specific, we employ the U-Net as the basic framework to build our network, in which we implement each layer with the partial convolution method. Inside the correlational attention module, we choose the feature maps of the intermediate layer at both textural and structural levels, and perform a fusion block to emerge new correlational feature maps. An attention module is utilized to reconstruct generated patches with the kernels extracted from intermediate layers' features. Experiments on two benchmark datasets illustrate that our model generates promising results and outperforms the state-of-the-art methods.
A Survey on Machine Learning Approaches used in Breast Cancer Detection

Breast cancer is growing increasingly detrimental to women's lives. Breast cancer, along with cervical cancer, is the leading cause of death among women. Early disease detection is one of the mostly preferred preventive measures. The cancerous cells can be detected by using a number of screening approaches. Biomedical imaging plays a critical role in determining a patient's disease. Image processing techniques are widely used to detect the affected region in medical images. This article discusses about different screening tests that are used to detect breast cancer (BC) and also the machine learning algorithms used to perform breast cancer diagnosis.
An Optimized Association Rule Data Mining Algorithm

Among data mining algorithms, Apriori association rule data mining algorithm is one of the most widely used algorithms. The algorithm is faced with the problems such as low accuracy of algorithm recommendation, single support and difficulty in setting the support threshold in the operation of current e-commerce recommendation systems. Based on the above problems, this paper proposes an association rule data mining algorithm combining multi-item support tree and support. The algorithm uses recursive iteration to generate conditional database and dynamically adjust the minimum support during mining to obtain frequent item sets, which greatly improves the efficiency of data mining and the accuracy of recommendation. Experiments show that the proposed association rule data mining algorithm with multi-item support greatly changes the number of association rules, thus improving the efficiency of data mining and the accuracy of recommendation.
A Novel Nonlinear Chaotic Mapping Based on Cosine-Exponent

Chaotic systems have been widely used in many fields such as information encryption and complex signal modulation in recent years, because of the initial conditions sensitivity in the chaotic systems and the pseudo-randomness of chaotic sequences. Aiming at the problems of low complexity and low randomness of typical one-dimensional chaotic mapping, a new chaotic mapping based on cosine and exponential function is designed in this paper. Simulation results show that the randomness, complexity and balance of the new chaotic map are better than those of the typical one-dimensional chaotic map. And it has a good application prospect.