Reviewerly/exHarmony huggingface dataset:
A deep learning and multi-objective PSO with GWO based feature selection approach for text classification

Text Classification (TC) is becoming an increasingly important activity due to the vast quantity of documents available on the internet, through emails, and in digital libraries. It's usually accomplished after completing Feature Selection (FS), which entails choosing relevant characteristics to improve classification accuracy. The cost of computing is reduced, and the accuracy of the TC system is improved by diminishing the dimension of feature space. As a result, one of the essential tasks in TC is to detect the optimal feature set. In this work, we have introduced a hybrid framework that is based upon Multi-Objective Optimization (MOO)-based FS and for the classification, we have used a Bidirectional Long Short Term Memory (BiLSTM) network for the TC system. Here we have used a modified version of Particle Swarm Optimization (PSO) with Grey Wolf Optimization (GWO) for the FS task. In this multi-objective FS, the first objective function is to enhance the Classification Accuracy (CA), and the second one is to diminish the selected features. The experimental findings suggest that the proposed hybrid model accomplish competitive performance compared with the other traditional classifier.
Automatic Assisstance System Based on Machine Learning for Effective Crowd Management

So here we are going to study about the machine learning and automatic assistance system in machine leering which help in the effective crowd management Machine learning (ML) is a form of artificial intelligence (AI) that enables software programmers to increase prediction accuracy without being specifically intended to do so. Machine learning techniques use previous data as input to anticipate new output values. Because data is so important, developing improved methods for intelligently managing the now-ubiquitous crowd-powered data-gathering systems is a critical next step toward totally autonomous agents. So here we will discuss about the machine learning and its automatic assistance system in crowd management. Learning for use in a variety of sectors is becoming more common as the availability of data expands over time. Computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so on. Such approaches have already been used in the fields of Smartphone apps, computer equipment, online websites, and cyber security. So here we have reached a conclusion about the automatic assistance system based on machine learning for effective cored management.
Fragmentation Based Hybridized Encryption Scheme for Cloud Environment

Due to the progress of technology., the danger of thefts or threats is rising day by day, which creates a question inside the minds of users while utilizing various technologies such as cloud. Because the cloud provides a variety of services, demand has increased. In order to provide efficient and better service, the cloud network should be protected both from internal and external threats, making it more secure. Multiple security mechanisms were presented for achieving security., and a number of them utilized the concept of fragmentation, in which data is divided into subparts, encrypted, and then kept on various servers. These procedures were adequate for providing security, but they posed a new problem of data loss owing to segmentation. To address the issue of data loss, a new framework was created with a safe environment that prevents the theft of tasks/data provided by the user while simultaneously lowering the risk of data loss. The material was divided into bits and encrypted using a hybrid security approach that mixes SSL and Hexadecimal codes in this system to minimize loss of information using set of criteria. This Pulse rifle structure is reproduced in a local cloud system with a large amounts of data store that includes image, video, and text data of various forms. When opposed to the earlier framework, the results were considerably better off in terms of file sizes after decryption, duration of storage, and loss of data.
Research on Automatic Tracking Method of Marker Points in Sports Image Sequence Based on Feature Matching

research-article Research on Automatic Tracking Method of Marker Points in Sports Image Sequence Based on Feature Matching Author: Wenlong Cheng Jiangxi Vocational Technical College of Industry & Trade, China Jiangxi Vocational Technical College of Industry & Trade, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 847–852https://doi.org/10.1145/3544109.3544366Published:18 July 2022Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Alternate Level Set Evolutions With Controlled Switch for Tooth Segmentation

CBCT images have been widely used in digital orthodontics. In CBCT images, there are some tooth boundaries of blurriness and disappearance, and the teeth are of intensity inhomogeneity. In order to identify these boundaries and segment teeth, this paper develops an approach of alternate level set evolutions with controlled switch via slice-by-slice tooth segmentation: the user selects a voxel inside a tooth to initialize a small circle encircling the voxel, and the curve is automatically expanded until the tooth surface is formed. In each slice, the curve is expanded by two level set evolutions: the first level set evolution handles the intensity inhomogeneity and the second level set evolution solves the boundary blurriness and disappearance of the teeth. Experiments showed that the proposed method successfully segmented 12 CBCT datasets of 356 teeth and outperformed the geodesic active contour method and the popular method and the state-of-the-art method.
Coordinated Development of China’s Regional Economy and Ethnic Diversity under the Background of Big Data and the Internet of Things

In the context of big data and the Internet of Things, the development of the Chinese nation is changing with each passing day, and the regional economic development has also shown a long-handed trend. With the development of the diversity of the Chinese nation, it has become more and more prosperous, which drives the sustainable development of the regional economy, but in the process of regional economic development, it also reflects the various contradictions and problems of my country’s economy in the ethnic minority regional economy. By studying the current status of Chinese nation diversity and regional economic development under the background of big data and the Internet of Things, this article pointed out the problems and contradictions in the development of national diversity and regional economic development, through the use of thermodynamic computing models for data analysis, and based the status quo of the economic development of Xinjiang region is an example, and it proposes measures to promote the coordinated development of the Chinese nation’s diversity and regional economic coordinated development.
An Analysis and Research on Chinese College Students’ Psychological Barriers in Oral English Output from a Cross-Cultural Perspective

English, as China’s second language, is a part of the overall quality of the people. Mastering this universal language in the world is required to communicate with the world. The most important purpose of learning a language is to communicate, and listening and speaking ability is the most important language skill. However, under the influence of the test-oriented education model, English teaching in our country places too much emphasis on the cultivation of reading and writing ability and neglects the training of oral expression ability. In this context, this research proposes an oral English teaching assistance program for both teachers and college students and plans to build an artificial-intelligence-based oral English teaching assistance system. The combination of ability and oral English teaching solves the drawbacks of traditional oral English teaching and establishes a new teaching form for college students’ oral English teaching. Combined with the popular trend of Internet electronic teaching methods, it realizes a free and online platform for learners to correct and improve pronunciation, laying a certain foundation for the development of mobile online English pronunciation learning in the future and solving the psychological barriers to college students’ oral English output.
Intelligent Prediction Model of CET-4 Passing Rate Based on Multisource Heterogeneous Data Mining

With the growing software of English in society, English gaining knowledge is turning into extra and greater important. The software and lookup of fact mining science in English educating and gaining knowledge will turn out to be a new improvement trend. This study is based on the information of non-English majors’ learning situation of Public English course and CET-4 scores. It selects the data related to CET-4 scores as the characteristics, takes CET-4 passing rate as the goal, and makes use of selection tree classification mannequin to predict the passing likelihood of CET-4. After comparison and analysis, it is observed that the multisource heterogeneous statistic mining science can precisely predict students’ grades, which verify the software of multisource heterogeneous information mining technological know-how in university English studying and have an impact on of the mannequin on the end result prediction. The results of College English final examination and gender are the main factors affecting the passing rate of CET4. The higher the score of College English final examination, the higher the passing rate of CET-4. The results of this study show that girls’ passing rate and average score of CET-4 are higher than boys’. Other factors have little influence on CET4 passing rate and can be ignored. There is a significant positive relationship between the scores of CET-4, CET-1, and Putonghua. After analysing the number attributes of the model, blended with the statistical consequences of information analysis, this paper offers some ideas and hints to enhance the instructing administration and the passing charge of CET-4.
Application of Convolutional Neural Network to GIS and Physics

Human life and property are often seriously threatened and lost due to natural disasters such as earthquakes. As a spatial information system, the geographic information system (GIS) can collect, store, and manage the local or whole related physical data of the surface space to be measured with the support of software and hardware. The physical data is collected through GIS for performance testing. The data are collected from the aftershock event records of the Wenchuan earthquake. Among them, 14,000 Wenchuan earthquake events are used as the original data set, and 8,800 aftershock events are used as the test data set. Seismic data involves the detection of multiple physical quantities, which makes the seismic data gradually increase, many data have no obvious linear relationship, and traditional detection methods are difficult to meet the detection requirements. The artificial intelligence method led by a convolutional neural network (CNN) can perform pattern matching on complex nonlinear variables, and models with general characteristics can be generated from different seismic waveforms for the prediction of seismic waveforms. The results show that GIS can effectively intercept and collect seismic physical signals. The training and detection accuracy of CNN combined with GIS physical data is higher than 90%. Compared with traditional training methods, CNN is obviously superior in detection accuracy and recall rate. At the same time, a large number of microseismic events that are easily missed by manual selection can also be found.
Dance Movement Recognition Based on Multimodal Environmental Monitoring Data

Fine motion recognition is a challenging topic in computer vision, and it has been a trendy research direction in recent years. This study combines motion recognition technology with dance movements and the problems such as the high complexity of dance movements and fully considers the human body’s self-occlusion. The excellent motion recognition content in the dance field was studied and analyzed. A compelling feature extraction method was proposed for the dance video dataset, segmented video, and accumulated edge feature operation. By extracting directional gradient histogram features, a set of directional gradient histogram feature vectors is used to characterize the shape features of the dance video movements. A dance movement recognition method is adopted based on the fusion direction gradient histogram feature, optical flow direction histogram feature, and audio signature feature. Three components are combined for dance movement recognition by a multicore learning method. Experimental results show that the cumulative edge feature algorithm proposed in this study outperforms traditional models in the recognition results of HOG features extracted from images. After adding edge features, the description of the dance movement shape is more effective. The algorithm can guarantee a specific recognition rate of complex dance movements. The results also verify the effectiveness of the movement recognition algorithm in this study for dance movement recognition.
Intent-based 5G UPF configuration via Kubernetes Operators in the Edge

The expected growing number of edge clouds in the telecommunication industry requires new types of configuration management approaches in order to deal with the increased complexity. The Kubernetes Operator pattern widely used for lifecycle management of cloud native applications could be also applied to network management and configuration. In this paper we present our approach on using Kubernetes Operators to automatically adapt the configuration of the edge-located User Plane Functions (UPFs) to intents coming from an Edge Application. The owner of the Edge Application does not need to deal with network-related configuration as the chain of Kubernetes Operators manage it. Furthermore, we also provide numerical results about the speed of automatic configuration.
Multidimensional directional derivatives and AM-FM dual-band image demodulation by higher order Teager-Kaiser operators

In this work we tackle the problem of demodulation of multidimensional AM-FM signals by multidirectional higher order Teager-Kaiser operators. This new class of operators extends those reported in the literature, which are restricted to partial derivatives. We illustrate this class of operators on signals made up of two components (bi-band signals). We show that this mixture of bi-band multidimensional AM-FM signals extends a solution of the fourth-order generating differential equation (GDE). This result generalizes the findings reported in [1] to multidimensional case. Coefficients of GDE expressed in terms of Teager-Kaiser energy operators of higher orders, yield estimates for the instantaneous amplitude and frequency of each component of the multidimensional signal in any direction. To resolve the sign ambiguity problem of the estimated frequency components of the frequency vector, a relative signs estimation strategy is presented. Three frequency estimation algorithms are developed. To show the effectiveness of the introduced new class of operators and the interest of directional derivatives, demodulation results on synthetic and real images are presented and analyzed. We compare our results to methods of the literature. Capability of the methods to track signal demodulation is measured by the norm of demodulation error and also by the well known structural similarity index measure. Even the aim of this paper is focused on bi-band multidimensional AM-FM signals, we report promising preliminary results on tri-band signals showing that the present work can be extended to more than two components.
RDOD: A Robust Distance-based Technique for Outlier Detection

Outlier detection is an important topic in data mining and has been employed in various sciences. Numerous outlier detection methods have been proposed so far; one of the most prominent categories of these methods is based on k-nearest neighbor (k NN). In the present study, an efficient and robust distance-based outlier detection method is presented. One of the main challenges of methods based on k-nearest neighbor is their high dependency on parameter k. The proposed method, however, reduces the sensitivity to k while maintaining the high preciseness of the algorithm. The proposed method was evaluated in two-dimensional synthetic and multidimensional real datasets and compared with some state-of-the-art algorithms in the field. The results of the experiments proved the effectiveness of the proposed method.
An Enhanced Chaotic System Based Color Image Encryption using DNA Encoding

Transferring data, particularly digital images, over unsecured communication channels has overgrown in the past years. Digital images such as medical images have certain information. Thus, these data should remain protected and inaccessible. According to discussed issues and numerous attack methods, implementing a high secure encryption algorithm is confronted with crucial issues and challenges. This paper proposes a new efficient color image encryption based on a chaotic system and DNA encoding following one round process. Initially, a hash function generates a secret key to be used in the confusion and diffusion stages simultaneously. Subsequently, the pixels' values are changed in the confusion phase through DNA encoding and XORing. Ultimately, the adjacent pixels' positions are shifted by permutation boxes generated from the chaotic system, and the cipher image is achieved. The experimental results and security analysis proves that our scheme has significantly higher security than other methods and can resist statistical attacks, differential attacks, and occlusion attacks.
Fastener counting method with an improved Blendmask

The fastenings market is large. We design an automatic visual counting system, which obtains the clear image of fasteners through the combination of shaker and visual system. Datasets including in sparse and dense conditions are constructed. An improved Blendmask instance segmentation network is proposed to realize fastener counting. And we propose an improved Blendmask instance segmentation network to realize fastener counting. The experimental test is carried out by using the constructed data set. Compared to many other state-of-the-art, the proposed method can get brilliant estimations.
Enabling the CUDA Unified Memory model in Edge, Cloud and HPC offloaded GPU kernels

The use of hardware accelerators, based on code and data offloading devoted to overcoming the CPU limitations in cores, is one of the main distinctive trends in high-end computing and related applications in the last decade. However, while code offloading is convenient for performance improvement, becoming a commonly used paradigm, memory access and management are a source of bottlenecks due to the need to interact with different address spaces. In this regard, NVidia introduced the CUDA Unified Memory model to avoid explicit memory copies between the machine hosting the accelerator device and the device itself and vice-versa. This paper shows a novel design and implementation of the support to the CUDA Unified Memory in open-source GPGPU virtualization services. The performance evaluation demonstrates that the overhead due to the virtualization and remoting is acceptable considering the possibility of sharing CUDA-enabled GPUs between various and heterogeneous machines hosted at the edge, in cloud infrastructures, or as accelerator nodes in an HPC scenario. A prototype implementation of the proposed solution is available as open-source.
DeepAnna: Deep Learning based Java Annotation Recommendation and Misuse Detection

Annotations have been widely used in Java programs to support additional compile-time, deployment-time, and runtime processing. Developers use annotations to delegate repetitive logics such as object initialization and request forwarding to compilers and runtime frameworks. Therefore, these annotations are important for the correct execution of programs. In practice, however, developers often find it hard to correctly use annotations and the misuse of annotations has led to real bugs in Java programs. In this paper, we conduct an empirical study on Stack Overflow questions to investigate the major development frameworks that are involved in questions about Java annotations and the main problems encountered by developers in the use of Java annotations. Based on the findings of the study, we propose DeepAnna, a deep learning based Java annotation recommendation and misuse detection approach. Based on a corpus of Java programs with intensive use of annotations, DeepAnna trains a deep learning based multi-label classification model by considering both the structural and textual contexts of source code. DeepAnna can recommend annotations at both class level and method level. Our evaluation with a large corpus of open-source Java projects shows that DeepAnna outperforms state-of-the-art text multi-label classification approaches in annotation recommendation and can effectively detect annotation misuses. Based on our analysis, we submit 85 bug-fixing pull requests for annotation misuses in open-source projects and 20 of them have been accepted and merged.
MetBERT: a generalizable and pre-trained deep learning model for the prediction of metastatic cancer from clinical notes.

Distant metastasis is the major cause of cancer-related deaths; however, early diagnosis of cancer metastasis remains a significant challenge. The recent advances in pre-trained natural language processing models coupled with the accumulation of publicly available Electronic Health Records (EHR) data provide an unprecedented opportunity to computationally tackle the challenge. Here, we fine-tuned multiple state-of-the-art BERT-based models using discharge summaries from the open MIMIC-III dataset and derived MetBERT, a novel model tailored to predict cancer metastasis from clinical notes. MetBERT achieved high performance (AUC=0.94) on our in-house validation dataset, suggesting its high generalizability. In addition, MetBERT enabled determining the date of cancer metastasis using the rich information in clinical notes and therefore could be potentially deployed as a tool for early diagnosis. Finally, we interpreted MetBERT at different scales and revealed a possible association between radiation therapy and metastasis risk in multiple cancer types.