Reviewerly/exHarmony huggingface dataset:
A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

Big data refers to the large amount of information that is collected from different areas and shared on the internet.However, this development has led to difficulties in using frequent itemset mining applications.To overcome the issue of frequent data mining, this research has introduced an empirical sampling algorithm using RadeMacher average (ESA-RMA).When considering the size of the initial sample and scheduling the samples, the ESA utilizes the RadeMacher average to bound the samples.Initially, the data is obtained from the dataset of the human activity recognition (HAR) and real time datasets from smartphone gyroscope and accelerometer, then obtained data is pre-processed using the data normalization technique.Then, the ESA is used to select the labelled data and RMA is used to bound the samples.This bounding process defines the upper limit of the input data which helps in the effective mining of frequent item sets.Thus, the data with redundant items are mined out using the proposed ESA-RMA method.The experimental results show that the proposed ESA-RMA has taken a minimum run time of 212 ms for data obtained from smartphone accelerometer which is comparatively lower than the existing Scalable Simple Random Sampling (ScaSRS) with processing time of 362 ms.Similarly, for HAR dataset, the proposed method took processing time of 5.43 s whereas the existing vertical frequent time interval-related pattern (VertTIRP) mining approach took processing time of 7.82 s.
Walk-Spread Algorithm: A Fast and Superior Stochastic Optimization

This work offers a new stochastic optimization i.e., a metaheuristic algorithm combining both directionbased search and neighbourhood search called as walk-spread algorithm (WSA).These two types of searches become the inspiration for its name where the term walk represents the direction-based search while the term spread represents the neighbourhood search.There are two direction-based searches performed in every iteration where each search produces a single child.Meanwhile, there are two neighbourhood searches performed in every iteration where each search produces several children.The global best unit becomes the first reference while two shuffled units become the second reference in performing the direction-based search.Meanwhile, the local search space of the first neighbourhood search is wide while the second one is narrow.The 23 classic functions are chosen as the assessment of WSA where WSA is confronted with the five latest metaheuristics: mixed leader-based optimization (MLBO), golden search optimization (GSO), pelican optimization algorithm (POA), zebra optimization algorithm (ZOA), and attack-leave optimization (ALO).The assessment result shows that the offered WSA achieves the acceptable result so fast.Moreover, WSA is also superior to these five confronters by outperforming MLBO, GSO, POA, ZOA, and ALO in 23,23,22,21, and 21 functions respectively.
Enhancing Endometrial Cancer Detection by Feature Entanglement Image Generator and Multimodal Feature Learning with Attention Mechanism

Endometrial cancer (EC) rate is rising progressively worldwide so early diagnosis of EC using medical imaging is vital to increase a patient's survival rate.To avoid the misdiagnosis of EC on magnetic resonance imaging (MRI) scans by clinicians, an automated staging model based on deep learning has broadly emerged in medical systems.Many deep learning models like convolutional neural networks (CNNs) with transfer learning schemes are developed to classify normal and cancer patients from a sequence of MRI scans.But such models have poor sensitivity to classify EC stages from other benign lesions, resulting inaccurate diagnoses of EC.Also, the classical transfer learning schemes reduce the accuracy of detecting medical images due to the discrepancies in data distribution between the source and target domains.Hence, this article proposes a novel deep EC prediction (DeepECP) model that involves image synthesis and classification processes.First, a feature entanglement generative adversarial network (FE-GAN) is proposed for MRI synthesis that creates a desired MRI sequence according to the complementary features of multiple MRI modalities.Then, a multi-modal CNN with long short-term memory (LSTM) network followed by the fully connected (FC) layer is developed to extract a sequence of cancer features from multi-modal MRI sequences.Moreover, an attention strategy is used to fuse those extracted features and get a final feature vector, which is given to the softmax function to classify EC stages.Finally, the extensive experiments show that the DeepECP model on the TCGA-UCEC and CPTAC-UCEC datasets reaches 93.2% and 93.3% accuracy in detecting EC stages, respectively compared to the support vector machine (SVM), InceptionResNet and CNN models.
QoS based Optimal Path Selection Using Adaptive Sparse Bayesian Find-FixFinish-Exploit-Analyze Based Extreme Learning Machine for SDN based IoT Networks

SDN (software defined network) is a networking approach which enables the IoT (internet of things) to be maintained through programming.The assessment of best routing path satisfying the requirements of QoS (quality of service) possess a significant role in networking.Several methods have been employed by the researchers for maintaining the necessities of QoS.But due to certain limitations like data latency, protocol complexity and bursty traffic which affects the network performance.In the proposed work, adaptive sparse bayesian-find fix finish exploit analyze-extreme learning machine (ASB-F3EA-ELM) is employed with four steps such as cluster formation, cluster head selection, rule caching policy and optimal path selection.The cluster formation is performed by using grid structures for forwarding the data packets.To avoid data transfer from all the IoT nodes, process of cluster head selection for each cluster is performed by modified aquila optimizer (MAO).A prominent rule caching policy is implemented for reducing rule caching cost in SDN based IoT architecture.The best routing path for effective data transfer is performed by the approach of ASB-F3EA-ELM.The performances of proposed approach are analyzed in the NS2 platform.The outcomes are estimated in terms of bandwidth, rule caching cost, delay, throughput, network lifetime and packet loss rate.When comparing to the existing methods, the proposed work shows better output in satisfying the requirements of QoS as it achieved improvement in throughput of 3.89% to 7.97%, packet loss rate reduced from 0.11% to 0.07%, network lifetime increased by 13.75% to 27.5% as compared to PSO, GA, ACO and GWO methods.
A Study of Dance Movement Capture and Posture Recognition Method Based on Vision Sensors

With the development of technology, posture recognition methods have been applied in more and more fields. However, there is relatively little research on posture recognition in dance. Therefore, this paper studied the capture and posture recognition of dance movements to understand the usability of the proposed method in dance posture recognition. Firstly, the Kinect V2 visual sensor was used to capture dance movements and obtain human skeletal joint data. Then, a three-dimensional convolutional neural network (3D CNN) model was designed by fusing joint coordinate features with joint velocity features as general features for recognizing different dance postures. Through experiments on NTU60 and self-built dance datasets, it was found that the 3D CNN performed best with a dropout rate of 0.4, a ReLU activation function, and fusion features. Compared to other posture recognition methods, the recognition rates of the 3D CNN on CS and CV in NTU60 were 88.8% and 95.3%, respectively, while the average recognition rate on the dance dataset reached 98.72%, which was higher than others. The experimental results demonstrate the effectiveness of our proposed method for dance posture recognition, providing a new approach for posture recognition research and making contributions to the inheritance of folk dances. Doi: 10.28991/HIJ-2023-04-02-03 Full Text: PDF
3D reconstruction method based on the multi-polarization superposition coding phase pattern of LRR objects

Conventional research in structured light measurements has utilized light intensity as a channel for information. The polarization of light can be used as an additional channel of information. In this paper, a method based on the superposition of multiple polarization states is proposed to encode structured light. By building a polarization model between the color of light and the polarization states, polarized structured light containing phase information is obtained without rotating the polarizer. It is demonstrated that the method improves the waveform quality of stripes and the accuracy of the 3D reconstruction results when measuring highly reflective objects.
Management of Digital and Intellectual Technologies Integration in Education Informatization

The aim of the study is to analyze, systematize, and formulate scenarios for managing the integration of digital and intelligent technologies in the informatization of education based on the influence of the factors of the existing external environment. It was shown that digital transformation is a process of digital technology integration into all aspects of business activities, requiring fundamental changes in technology, culture, operations, and principles of creating new products and services. Simulation models of digital and intelligent technologies in informatization of education are proposed. Possible scenarios for the development of the education system are described: inertial and transformational. A new viable base scenario is proposed, which can be called a divergent, or school dilution scenario. It is illustrated that these three rather general scenarios show the possible place and role of digital and intellectual technologies in the changes taking place in the informatization of education today.
Exploring the Full Potential of Collaborative Learning and E-Learning Environments in Universities: A Systematic Review

This systematic review examines the challenges and potential solutions to improve the implementation of collaborative learning and e-learning environments in universities. The article analyzes 200 relevant articles published between 2014-2022 using a qualitative approach to identify themes and sub-themes related to collaborative learning and e-learning environments. The study reveals various learning methods employed, including MOOC (Massive Open Online Course), digital mind maps, and tripartite learning, but also highlights challenges such as lack of digital infrastructure, low-quality internet networks, and inadequate practical tools. The research emphasizes the need for effective leadership and management in vocational institutions and recommends further research on curriculum development, learning evaluation methods, lecturer performance assessment, and improving digital infrastructure and educator training. Collaboration with industry and other training institutions is also suggested to improve students' employability and entrepreneurship skills.
A comparative study of different denoising and enhancement techniques for blood cell images

Blood cell images play a vital role in medical diagnosis and treatment. However, these images are often affected by noise during slide preparation or images acquisition. The presence of noise can lead to incorrect diagnosis and treatment. Furthermore, it may also affect the output of next image processing applications and can have an impact on the accuracy of results. The primary aim of this paper is to discuss different types of denoising and enhancement methods for blood microscopic images which enhance the visual quality of image by minimizing the noise and restoring the small details, such as texture, edges, corners and contrast. The research study of denoising and enhancement methods are based on the removal of Poisson noise present in blood microscopic images. The denoising and enhancement may assist the radiologists in diagnosis and disease prediction. The comparative study is evaluated on self-acquired datasets in terms of full reference, no reference and human visual system-based performance metrics.
A Shortcut Enhanced LSTM-GCN Network for Multi-Sensor Based Human Motion Tracking

Multi-sensor based motion tracking is of great interest to the robotics community as it may lessen the need for expensive optical motion capture equipment. However, the traditional convolution algorithms have difficulty adapting to the data due to the changes of joints’ relative position during motion. The time-series networks often used in the past ignore the spatial characteristics of sensors. We tackle this challenge by combining long short-term memory (LSTM) with graph convolution network (GCN), adding the prior knowledge of sensor distribution, and integrating it into the motion law through the adjacency matrix. This article proposes a novel shortcut enhanced LSTM-GCN network (SE-LSTM-GCN). It connects LSTM and GCN in sequence and extracts temporal and spatial features of data. At the same time, the shortcut is used in the network to enhance the output of two middle layers and to restore the filtered information. Our experimental results on two different motion tracking datasets show that the proposed network is able to learn the mapping relationship with better universality, less tracking error, and without increasing much training time, and can better perform human motion tracking tasks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Accurate and real-time multiple soft sensors motion tracking suits are more accepted for their low cost. However, the soft-sensor based motion tracking is not comparable to the traditional optical equipment in prediction error. To this end, we present a novel network shortcut enhanced LSTM-GCN (SE-LSTM-GCN), consisting of shortcuts, long short-term memory (LSTM), and graph convolution network (GCN). The LSTM solves the non-linear and hysteresis of soft strain sensors, and GCN is integrated into the network since the knowledge of sensor location can be put into the adjacency matrix generated by the k-nearest neighbor (KNN). While shortcuts are used to enhance the output of middle layers to form combined features. Experimental results on two public datasets show that the proposed network is superior to competing algorithms in terms of prediction error. The network can be deployed in embedded devices, such as VR gloves to provide a better gaming experience. The current algorithm is based on the relationship between sensor data and distance. In future research, we will focus on adding other human kinematics laws to the network.
Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure

Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.
Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model

&lt;p&gt;The infrastructure of smart cities is intended to save citizens’ time and effort. After COVID-19, one of such available infrastructure is electronic shopping. Online consumer reviews have a big influence on the electronic retail market. A lot of customers save time by deciding which products to buy online by evaluating the products’ quality based on user reviews. The goal of this study is to forecast if reviews based on reviews representation mining will be helpful while making online purchases. Predicting helpfulness is used in this suggested study to determine the usefulness of a review in relation to glove vector encoding of reviews text. Using an encoding-based convolution neural network and a bidirectional gated recurrent unit, the authors of this study constructed a classification model. The suggested model outperformed these baseline models and other state-of-the-art techniques in terms of classification outcomes, reaching the greatest accuracy of 95.81%. We also assessed the effectiveness of our models using the criteria of accuracy, precision, and recall. The outcomes presented in this study indicate how the proposed model has a significant influence on enhancing the producers’ or service providers’ businesses.&lt;/p&gt;
P2KG: Declarative Construction and Quality Evaluation of Knowledge Graph from Polystores

Constructing knowledge graphs from heterogeneous data sources and evaluating their quality and consistency are important research questions in the field of knowledge graph. We propose mapping rules to guide users to translate data from relational and graph sources into a meaningful knowledge graph, and design a user-friendly language to specify the mapping rules. Given the mapping rules and constraints on source data, equivalent constraints on the target graph can be inferred, which is referred as data source constraints. Besides this type of constraints, we design other two types: user-specified constraints and general rules that a high-quality knowledge graph should adhere to. We translate the three types of constraints into uniform expressions in the form of graph functional dependencies and extended graph dependencies, which can be used for consistency checking. Our approach provides a systematic way to build and evaluate knowledge graphs from diverse data sources.
Pattern Recognition and Noncommutativity in Decision Making

Psychological and sociological studies have shown that the order of questions affects the likelihood of obtaining final answers. We studied the influence of image presentation on the efficiency of pattern recognition using the Gollin test. It was found that the correctness/incorrectness of the answer to the first presented image with a larger contour filling affected the subsequent probability of recognizing an image with less contour filling. A qualitative comparison of the obtained results with a quantum-like model and neural network model of pattern recognition in the additional learning mode was also carried out.
OBSTACLE AVOIDANCE METHOD FOR ELECTRIC TRACTOR BASED ON IMPROVED DYNAMIC WINDOW APPROACH WITH PRIORITY OF ENERGY CONSUMPTION

In order to solve the real-time obstacle avoidance problem in electric tractor operation, an improved dynamic window approach (DWA) based on optimal energy consumption is proposed for electric tractor obstacle avoidance. Firstly, energy consumption model of tractor is established based on the transmission system of electric tractor, then energy consumption evaluation sub-function is introduced to improve the evaluation function of original DWA algorithm, and finally, the trajectory is evaluated and the optimal solution of the trajectory is determined by using new evaluation function. Based on the kinematics model of YL254ET electric tractor in Yancheng Yueda, a model predictive controller is designed. The obstacle avoidance planning and tracking control of electric tractor are simulated jointly on Simulink and CarSim simulation platform. Finally, the obstacle avoidance planning test is carried out. The simulation and experimental results show that after the algorithm improvement, the energy consumption of electric tractors is reduced, the generated path is smoother, and the lateral error is smaller.
Typing Records, Maps, and Structs

Records are finite functions from keys to values. In this work we focus on two main distinct usages of records: structs and maps. The former associate different keys to values of different types, they are accessed by providing nominal keys, and trying to access a non-existent key yields an error. The latter associate all keys to values of the same type, they are accessed by providing expressions that compute a key, and trying to access a non-existent key usually yields some default value such as Null or nil. Here, we propose a type theory that covers both kinds of usage, where record types may associate to different types either single keys (as for structs) or sets of keys (as for maps) and where the same record expression can be accessed and used both in the struct-like style and in the map-like style we just described. Since we target dynamically-typed languages our type theory includes union and intersection types, characterized by a subtyping relation. We define the subtyping relation for our record types via a semantic interpretation and derive the decomposition rules to decide it, define a backtracking-free subtyping algorithm that we prove to be correct, and provide a canonical representation for record types that is used to define various type operators needed to type record operations such as selection, concatenation, and field deletion.
Saturating quantum efficiency of SNSPDs with disorder manipulation of NbN films

Abstract Quantum efficiency is one of the most important performance metrics for superconducting nanowire single-photon detectors (SNSPDs). Specifically, near-infrared NbN-SNSPDs with high quantum efficiency are extremely desirable in quantum communications. However, due to the high energy gap of NbN, it is difficult to achieve a saturated quantum efficiency. In this paper, we systematically investigated the primary determinants of fabricating highly saturated NbN-SNSPD by changing the stoichiometric ratio during the growth of NbN thin films. Through electron beam lithography and reactive ion etching processes, NbN-SNSPDs with a saturated quantum efficiency were fabricated. It is worth noting that the saturated quantum efficiency is observed to be closely related to the stoichiometric ratio of NbN films. Artificially increasing the disorder in NbN films can enhance the probability of superconducting phase transition during photon detection. Our work provides a consistently simple and effective method for the fabrication of highly efficient quantum devices, which is crucial for achieving higher precision in future quantum communications.