Reviewerly/exHarmony huggingface dataset:
Research on Transfer Learning-Based English-Chinese Machine Translation

In order to solve the problems of time-consuming and frequent mistranslations in traditional translation methods, this study designed an English-Chinese machine translation method based on transfer learning. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning. On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The benefit of the proposed method includes a short translation time and fewer mistranslations.
‘You just get sucked into it’: extending the immersion process model to virtual gameplay experiences in managed visitor attractions

This study investigates the immersion process in virtual visitor experiences in the context of a managed visitor attraction. It validates and extends previously developed models of the immersion process from tourism and proposes an underlying structure with four mechanisms driving the immersion process forward. Data was collected through semi-structured in-depth interviews with visitors participating in virtual gameplay at a commercial gaming centre in Oslo, Norway. Findings indicate that the immersion process is comparable across ‘real world’ and virtual visitor experiences and that the process consists of three stages (engagement, engrossment, and transcending involvement), corresponding with involvement triggers, involvement worlds, and the state of immersion. The study extends previous models of the immersion process by identifying the influence of pre-existing involvement as an ‘involvement booster’ enhancing the individual visitor’s immersion process. The findings have implications for how the underlying structure and components of the immersion process can be understood and offers practical implications for experience designers working in the intersection between virtual and ‘real world’ visitor experiences.
Uniform Partitioning of Data Grid for Association Detection

Inferring appropriate information from large datasets has become important. In particular, identifying relationships among variables in these datasets has far-reaching impacts. In this article, we introduce the uniform information coefficient (UIC), which measures the amount of dependence between two multidimensional variables and is able to detect both linear and non-linear associations. Our proposed UIC is inspired by the maximal information coefficient (MIC) [1].; however, the MIC was originally designed to measure dependence between two one-dimensional variables. Unlike the MIC calculation that depends on the type of association between two variables, we show that the UIC calculation is less computationally expensive and more robust to the type of association between two variables. The UIC achieves this by replacing the dynamic programming step in the MIC calculation with a simpler technique based on the uniform partitioning of the data grid. This computational efficiency comes at the cost of not maximizing the information coefficient as done by the MIC algorithm. We present theoretical guarantees for the performance of the UIC and a variety of experiments to demonstrate its quality in detecting associations.
Rule-Based AI System Application on College English Teaching Path Based on Computer-Aided Technology

With the development of the Internet, “Internet Plus” has been widely used in various fields, and the Internet has become a great opportunity to transform CET. People’s demand for education, especially higher education, has also increased rapidly. With the attention and investment of the state in recent years, higher education has developed rapidly, accounting for half of China’s higher education. However, the increase in the number of students has brought great pressure to CET. How to improve the teaching efficiency of large classes is an urgent problem to be solved. The development of sci and tech, especially computer, has brought us new hope. Computer-assisted instruction has been introduced into CET. However, there are some unreasonable points in the design of computer-aided marking system in China, which is not suitable for CET. It is very important to research and design a computer-aided marking system that can expand CET methods and maximize the integration of English instructional resources. This paper introduces the principle, characteristics, and application fields of AI; analyzes the problems faced by CET; and puts forward a CET path based on computer-aided technology.
Machine Learning (ML) based Human Activity Recognition Model using Smart Sensors in IoT Environment

A major progression in sensor-based technologies has resulted in a fast evolution of the Internet of Things (IoT) applications for developing any real-time monitoring systems. Nowadays, an increasing number of aged people living alone dispersed worldwide, and tracking the status of their health function or activity is necessary. In this paper, an IoT-based human activity monitoring model is proposed to continuously check the activities of aged people via smart sensor-based technologies. In this model, vital data are collected by smart sensors or IoT-based devices, and analysis of that data is done by using different machine learning algorithms for detecting any risk in human activity behavior. After evaluating the proposed model, the SVM has attained the highest accuracy of 98.03&#x0025; which is highly effective for our purpose. SVM outperformed all machine learning algorithms used for analysis purposes.
A Novel Approach to Image Forgery Detection

The process of making fake images has become simple with the advancement of powerful editing softwares like Adobe Photoshop, GIMP, some of which are available for free. Image forgery detection is administered with the intention of investigating whether the image under question represents the unmodified captured image, or whether it has been forged. The copy-move forgery detection means finding the part of the picture that was forged i.e., copy-moved to another part of the picture itself. This paper presents a new image forgery detection technique where detecting the cloned digital image forgery is its focus. The proposed approach is used feature extraction methods in both detection and localization stages.
Performance Skew Prediction in HPCC Systems

Over the last decade, the volume of data has been growing at a larger rate in comparison to the processing power available. The advent of distributed computing was essential in being able to handle these vast amounts of data. However, the distribution of data across the systems may not be uniform and gives rise to the problems of data skew and performance skew. A key challenge is to estimate the effective performance skew of a set of queries based on the data skew of the dataset on a multi-computing cluster. We use HPCC Systems, a modern big data management and analysis tool. Methods used to measure the impact of performance skew on the performance of queries on a HPCC cluster are heavily dependent on human interpretation. This project aims to automate the process of skew prediction by analyzing the execution graphs of a job on the HPCC Systems cluster and predicting the probable performance skew for a given set of queries using a Random Forest Regressor Model.
The uncertainty of boundary can improve the classification accuracy of BI‐RADS 4A ultrasound image

The Breast Imaging-Reporting and Data System (BI-RADS) for ultrasound imaging provides a widely used reporting schema for breast imaging. Previous studies have shown that in ultrasound imaging, 90% of BI-RADS 4A tumors are benign lesions after biopsies. Unnecessary biopsy procedures can be avoided by accurate classification of BI-RADS 4A tumors. However, the classification task is challenging and has not been fully investigated by existing studies. For benign and malignant tumors of BI-RADS 4A, the appearances of intra-class tumors are highly variable, the characteristics of inter-class tumors is overall-similar. Discriminative features need to be found to improve classification accuracy of BI-RADS 4A tumors.In this study, we designed the network using the clinical features of BI-RADS 4A tumors to improve the discrimination ability of network. The boundary information is embedded into the input of the network using the uncertainty. A fine-grained data augmentation method is used to find discriminative features in tumor information embedded with boundary information. Two mathematical methods, voting-based and variance-based, are used to define the uncertainty of boundary, and the differences of these two definitions are compared in a classification network.The dataset we used to evaluate our method had 1155 2D grayscale images. Each image represented a unique BI-RADS 4A tumor. Among them, 248 tumors were proven to be malignant by biopsy, and the remaining 907 were benign. A weakly supervised data augmentation network (WS-DAN) was used as the backbone classification network, which showed competitive performance in finding discriminative features. Using the auxiliary input of the uncertain boundaries defined by the voting method, the area under the curve (AUC) value of our method was 0.8347 (sensitivity = 0.7774, specificity = 0.7459). The AUC value of the variance-based uncertainty was 0.7789. The voting-based uncertainty was higher than the baseline (AUC = 0.803), which only inputs the original image. Compared with the classic classification network, our method had a significant effect improvement (p < 0.01).Using the uncertain boundaries defined by the voting methods as auxiliary information, we obtained a better performance in the classification of BI-RADS 4A ultrasound images, while variance-based uncertain boundaries had no effect on improving classification performance. Additionally, fine-grained network helped find discriminative features comparing with the commonly used classification networks.
Table Tennis Capture System Based on Image Recognition and Modeling

This article takes table tennis as the research object, mainly extracts a large number of table tennis video trajectories, and combines the kinematic analysis method to reduce the noise of the extracted target to improve the accuracy of the table tennis trajectory and obtain the table tennis trajectory. The parameter group is used to simulate the trajectory of table tennis; based on the MATLAB environment to realize the table tennis trajectory simulation that provides the initial velocity and coordinates, complete the capture of the table tennis drop point. This article uses the two-dimensional information in the image to estimate the parameter values that form the three-dimensional information and then calculates the three-dimensional information based on the estimated parameter values. The unscented Kalman filter is used to estimate the trajectory parameters and rotation parameters of table tennis, and the algorithm for calculating and updating markers in real time is proposed, which reduces the influence of calculation errors on the estimation process and improves the accuracy of parameter estimation. First, establish a table tennis movement model and choose a suitable model to describe the translation and rotation movement of the table tennis. Then, based on the extended Kalman filter algorithm and the unscented Kalman filter algorithm, the ball flight trajectory estimation algorithm is established. Finally, an actual image acquisition system is built to collect trajectory information and surface information images from the actual table tennis movement. Processing is performed to obtain the positions of the center of the ping pong ball and the marked points on the corresponding sensors. Simulation and experimental results show that the proposed algorithm can effectively estimate the trajectory, linear velocity, and angular velocity parameters of a table tennis ball, and the selection of the initial value has little effect on the algorithm.
Semantic similarity based food entities recognition using WordNet

Unstructured text processing is the first step for several applications such as question answering systems, information retrieval, and recipe classification. In the field of recipe classification, number of frameworks have been proposed. However, it is still very tedious and time consuming to extract the food items from the unstructured text and then process for classification. In this research, an automatic food item detection from unstructured text is proposed based on semantic sense modeling. The candidate nouns are detected which can be food items and then the similarity of those nouns is computed with possible food categories. The candidate noun is treated as food item if the similarity is high. For similarity between possible food item and food category is computed by WordNet ontology. The proposed framework is evaluated on benchmark datasets and competitive performance have been achieved. The F-score on large dataset that contains around 20 K recipes is 0.89 which is improved from 0.56.
Analysis of Training Function for NNARX in Solar Radiation Prediction Modeling

Neural Network Autoregressive Model with Exogenous Input (NNARX) is one of the Evolutionary Neural Network that had been used to develop a prediction model for solar radiation which involves a dynamic non-linear for a time-series based prediction. The whole process of Training, Testing, and Validation of NNARX is carried out by using a Training Function. Since there are several improvements that had been made in enhanced the prediction results using NNARX, it is the best to setup an analysis on the Training Function in finding the best to suit the solar radiation prediction modeling. In this paper, the analysis of the Training Function algorithm for solar radiation prediction modeling development using NNARX is carried out using MATLAB R2019a software. Each Training Function algorithm will be used in modeling development and their prediction output will be compared with the actual output. Based on the results, it is shown that Levenberg–Marquardt Training Function is the best Training Function algorithm for NNARX in solar radiation prediction modeling with the coefficient of determination value, R2 of 0.93423.
Image processing for automatic road inventory

Abstract Road image analysis is an important task for automatic road inventory. The determination geometric dimensions for the road and the identification road objects are subprocess of constructing a road digital image. In this article, two algorithms for solving different subtasks of automatic road image inventory are proposed. The first algorithm identifies road signs. A convolutional artificial neural network is used in this algorithm. The training set for the neural network is prepared. A computer experiment to determine the recognition effectiveness of road signs has been conducted. The second algorithm defines the edges of the pavement. The algorithm consists five stages. The edges of the road are modeled as straight lines. The result allows you to automatically determine the width of the road.
Effective hyperparameter optimization with proxy data for multi-organ segmentation

The purpose of this paper is to introduce a practical framework of using proxy data in automatic hyperparameter optimization for 3D multi-organ segmentation. The automated segmentation of abdominal organs from CT volumes is a main task in the medical image analysis field. Much research has been investigated to handle this task based on the immense experience of machine learning. Deep learning approaches require enormous experiments to design the optimal configurations for the best performance. Automatic machine learning (AutoML) using hyperparameter optimization to search the optimal training strategy makes it possible to find the appropriate settings without much deep experience. However, biases of training data can be highly related to the AutoML performance and efficiency. In this paper, we propose an AutoML framework that uses pre-selected proxy data to represent the entire dataset which has the potential to reduce the computation time needed for efficient hyperparameter optimization in searching learning. Both quantitative and qualitative results showed that our framework can effectively build more powerful segmentation models than manually designed deep-learning-based methods and AutoML, which use carefully tuned hyperparameters and randomly selected training subsets, respectively. The average Dice score for 10-class abdominal organ segmentation was 85.9%.
Research on the Detection Countermeasures of Telecommunication Network Fraud Based on Big Data for Killing Pigs and Plates

This paper uses big data analysis to conduct in-depth analysis and research on the investigation countermeasures of the pig-killing dish telecommunication network fraud. This paper takes the case of “piggy bank” as a perspective, starting from analyzing the concept and characteristics of “piggy bank” type network fraud, analyzing the current phenomenon of this type of network fraud crime, and analyzing the current situation of the relevant laws, to explore the judicial situation regarding this type of network fraud crime, and to explore the problems and dilemmas in the judicial determination of such network fraud crimes, the determination of evidence, the determination of principal and accessory in joint crimes, the determination of crime amount, the determination of attempt and attempt form, and the determination of related crimes. Based on the analysis of the problems, the judicial determination process of “piggyback” network fraud is improved in terms of perfecting the evidence determination process, combining the facts of the whole case to determine the joint crime, accurately characterizing the associated criminal acts, regulating the judicial determination rules of the crime amount, and strictly applying the criteria of the circumstances of attempt and completion. This paper explores the forward shift of investigation work, anticipating telecommunication network fraud crimes in advance or the middle of the matter through active investigation, taking technical countermeasures, especially early warning and dissuasion to avoid the occurrence of telecommunication network fraud crimes, and reversing the unfavorable situation of passive investigation in the past. Finally, it focuses on the strategy and innovation points of the use of network investigation methods in telecom fraud crimes and proposes the investigation method of tracing the money chain and information chain along the line and cross-complementing the two lines at the same time according to the characteristics contained in telecom fraud crimes, relying on multiple groups such as public security, hackers, and network volunteers to jointly combat in a variety of network environments.
Device scheduling and channel allocation for energy-efficient Federated Edge Learning

Federated Edge Learning (FEEL) is a promising distributed machine learning paradigm in the era of edge intelligence, which supports to learn the knowledge in the dataset on the premise of protecting users' privacy. However, this learning paradigm has a big defect. As the training process is carried out at the user end and is very power-consuming, the learning task is a serious challenge for mobile devices with limited battery capacity, which may also hinders the implementation of FEEL. In practical applications, FEEL usually needs to comply with the requirements for training delay and model performance, and may also be affected by the inter-cell interference which is common in the cellular networks. However, the current works only consider the demand for training delay. In this paper, we consider the implementation of FEEL in a general cellular network, and propose an empirical assumption to characterize the relationship between model performance and training data, based on which, a workload constraint is added to the formulated problem to guarantee the model performance. For the formulated problem that contains a summation term of integral variables and an interference term with complex structure at the denominator of the objective function, we propose a device scheduling and channel allocation strategy, also called double-greedy strategy, to obtain its suboptimal solution with low computational complexity. Simulation results verify the advancement of our proposed strategy relative to the existing works, that is, achieving the best energy efficiency on the premise of ensuring the model performance. This advancement makes our strategy more flexible to satisfy the possible various requirements of service providers for model performance.
Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

The fine-grained task of emotion analysis, emotion cause extraction, is a current research hotspot. It aims to discover the underlying reasons behind the emotional expression in texts. Most of the existing work regards the task as an independent text clause classification problem, ignoring the relationship between the clauses and failing to use the indicative relationship between emotional sentences and emotional cause sentences. The existence of these problems greatly affects the accuracy of the task. In this work, an emotion cause extraction method based on a hierarchical network emotional assistance mechanism is proposed. This method uses a hierarchical network composed of bidirectional gated recurrent units, attention mechanism, and graph convolutional networks to capture clause context information, deep semantic information, and structural information between clause neighborhoods. At the same time, by enhancing the emotional information representation of the graph convolutional network nodes, the clause features of the text emotional keywords are introduced into the discovery of candidate cause sentences. Thus, a model of the deep neural network combined with the emotional assistance mechanism is established. Compared with the existing methods, the model established in this paper has better classification performance on the Chinese emotion cause dataset.
Smart Phone Based Bidirectional Communication System for Hearing &amp; Speaking Impaired Community and Normal Society

In day-to-day life, deaf and dumb people are facing a lot of battles in speaking with typical individuals. Sign language plays a very important role for the deaf and people interact with the outside world. Due to the nonattendance of wonderful gesture-based communication interpreters still it leads the communication break. Powerful collaboration possible only if the deaf and dumb persons nearby sign language translator. To overcome this problematic circumstance in our approach utilizing smartphones for establishing active communication with normal people. The main effort of our proposed framework is to design smart mobile for helping the deaf and the dumb community to convey their thoughts anywhere they are located so it reduces the communication hole among normal, hearing, and speech-impaired people. Smartphones act as an interface to translate sign language to speech and vice versa. The various gestures-based signs captured automatically and translating sign language to speech. To process an image of gestures using several image processing techniques and deep learning models. Investigation result shows that this smartphone-based dynamic hand gesture recognition model provides an exact and effective system for the interaction between non-signer and signer.