Reviewerly/exHarmony huggingface dataset:
Fault detection of microwave source in high-power microwave heating system based on wavelet analysis

The microwave source queue of high-power microwave heating system has only binary detection of whether there are running or not. Online real-time detection of the working status and changes of the microwave source queue is necessary in order to improve the economy and reliability of its queue. A fault detection method of microwave source queue in high power microwave heating system based on wavelet analysis is presented. Firstly, the fault of microwave source queue is judged according to the change of electric field intensity. Secondly, by acquiring the continuous wavelet transform of the input signal and output signal at multiple scales to calculate the residual signal generated by the extreme value, and the detection is completed according to the change of the residual signal. Finally, the wavelet time-frequency spectrum of the residual signal is drawn to obtain the feature information and perform visual analysis of the detection results. Three fault states of microwave source are simulated respectively, the effectiveness of the method is verified by simulation and result analysis. The continuous wavelet transform analysis method is used to detect the fault of the microwave source queue, which can accurately detect the fault location and time of microwave source queue online.
Material Identification System with Sound Simulation Assisted Method in VR/AR Scenarios

We propose a new model using transfer learning method in motivation of specifying and identifying material of an object by knocking in VR/AR applications. Different from the traditional contact knocking material/object identification method, we apply the sound simulation method to enlarge the training dataset containing various models and materials in real-world scenarios. Our approach is based on Domain-Adversarial Training of Neural Networks that learns from the pre-collected simulated and corresponding real knocking sound to extract their common features determined by different materials. Given the scanned 3D model and the real knocking sound from users, we present an incremental learning model using the features extracted by pre-trained transfer learning model to generate the final material classifier. We perform an overall evaluation showing that our system achieves around 93.3% accuracy of identifying materials, which is much higher than the accuracy mentioned in previous work.
Correlation Filter for Object Tracking Method Based on Multi-Template Update

Aimed at the current tracking algorithm such as object occlusion, severe deformation, motion blur and background confusion, a tracking method based on multiple template updates is proposed to improve the robustness of the algorithm. First, a response graph quality evaluation index is proposed to evaluate the reliability of the tracking result of the current frame. When the tracking result is unreliable, the model update is stopped immediately, and the tracker can find the object again when the object reappears. However, the indicator will always remain within a reliable range when the object is continuously blocked. At this time, if you stop the update tracking of the model, it will drift due to lack of information. In order to solve the above problems, the algorithm in this chapter adopts a multi-template tracking strategy—adding several additional filters to track the object. The proposed algorithm is compared with several recent state-of-the-art tracking algorithms on OTB100 benchmark datasets (online object tracking benchmark). Especially, the pro-posed algorithm greatly improves its basic algorithm in AUC and Precision on some complex environments of partial occlusion, severe deformation, motion blur, background clutter and illumination variation, which has a better tracking performance.
Mapping Mobile Learning Experience in Chinese Language Learning

Advances in mobile technologies have greatly enhanced Chinese language learning. In applying mobile technologies to Chinese language learning, on top of evaluating their impact, one of the key areas is to map where different components of mobile learning experience lie in leveraging mobile technologies for enhancing Chinese language learning. Equipped with the framework of mobile experience, this chapter provides a unique approach to serve as a stepping stone that would lead to more discoveries of innovative and effective ways for mapping mobile learning experience in applying mobile technologies to Chinese language learning around the world.
SPLReePlan - Automated Support for Software Product Line Reengineering Planning

The extractive adoption of Software Product Lines (SPL) relies on the reuse of the already developed systems, employing a reengineering process. However, due to the diversity of options found in the daily practice of SPL development, rigorous planning of scenarios is critical to perform SPL reengineering. This diversity is the result of different organizational aspects, such as team experience and product portfolio. Hence, a proper planning process must consider technical and organizational aspects, however, most existing studies in the field do not take into account organizational aspects of the companies. In this work, we present SPLReePlan, an automated framework to aid the SPL reengineering planning taking into account technical and organizational aspects. Our framework is supported by a web-based tool, ready to be used in the industry. To investigate how flexible is SPLReePlan to support the SPL reengineering planning in diverse situations, we extracted eight different scenarios from the SPL literature, which are used as input for the evaluation of SPLReePlan. The results indicate that SPLReePlan can be satisfactorily customized to a variety of scenarios with different artifacts, feature retrieval techniques, and reengineering activities. As a contribution, we discuss the lessons learned within the evaluation, and present challenges that were faced, being a source of information for tool builders or motivating new studies.
Image inpainting with gradient guidance

To generate images with complete structure and clear content, a gradient-guided image inpainting method is proposed by introducing the gradient branch to guide the image inpainting. At the same time, in order to better fuse the branch features of gradient map and repair results of generator network, a feature equalization module with attention mechanism is introduced, to effectively balance features and inhibit learning unimportant feature information. Finally, in order to avoid using KL divergence and JS divergence to measure the distribution gap between two samples, this paper uses Wasserstein distance to measure the sample gap, and designs the adversarial-discriminative network based on WGANGP. Experiments on Paris StreetView and CelebA datasets show that our method can obtain satisfactory repair results with complete structure and clear content.
A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove called SmartScan, which creates a 3D virtual model of a real object, is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object's surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, having a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.
High School Physics Teaching Design to Implement IEP Individual Education Plan-take”Curve motion” as An Example

The All Children with Disabilities Education Act, established by the United States Congress in 1975, was the first federal law in the history of the United States primarily directed at the education of children with disabilities. This is a major statute in the history of disability education in the United States, and its passage has fundamentally transformed the face of disability education in the United States. Since then, Congress has reauthorized and changed the legislation five times in response to the legislation's implementation and practical demands, with the goal of ensuring that all impaired children have the right to free and adequate public education.
Teacher-Student Network for Low-quality Remote Sensing Ship Detection

Most CNN-based detectors rely on high-quality images, and the detection performance may be damaged as the image quality decreases. To enhance ship detection for low-quality images, we propose a distillation network which utilize a “teacher” detector with high-quality input images to guide the training of the original “student” detector. In this way, the student detector can learn the advantage information from the teacher and thus achieves improved detection performance. We feed images with different sampling ratios to our network, and the experimental results on HRSC2016 dataset validate the effectiveness of our method. Moreover, we apply our method to different backbones, and the experimental results demonstrate the generality of our method.
A Multi-Robot Cooperative Searching Algorithm in Unknown Environments based on Neural Network

A neural network algorithm are proposed for complete coverage navigation which only choose the maximum activity of peripheral neurons as a guide. However, cooperation is the key to utilizing the potential of multirobot systems. In this paper, we present a market method based on neural network of dynamic task allocation for groups of such robots. Robots bid for tasks depend on its fitness and release those overdue tasks, which strengthens cooperation and improves search efficiency. The simulation results show that our method can realize the cooperation among individuals, improve the area coverage rate, reduce the number of repeated coverages, and improve the area coverage efficiency of swarm robots.
High-definition processing of remote sensing images based on CUT-CycleGAN

High-definition remote sensing images are more and more widely used in research and life. However, due to hardware conditions and transmission rate limitations, it is too expensive to directly obtain high-definition original images. So, it has become a research hotspot on how to use algorithms to receive high-definition remote sensing images from low-resolution images. In view of the existing super-resolution methods for remote sensing images, the dependence on a large number of matching low-resolution and high-resolution(LR-HR) data sets and the slow network training time. In this paper, contrast learning is used for unpaired image-to-image conversion model (CUT-CycleGAN), which uses cyclic consistency to achieve the purpose of training using unpaired images, and adds a contrast learning framework to effectively shorten CycleGAN's training time and to improve efficiency. The experiment selects SRGAN, CycleGAN, EDSR, and FSRCNN four existing super-resolution methods to compare with the method in this paper. The results show that the training time of CUT-CycleGAN is reduced by nearly 55.7%, and after training with unpaired images, the quality of the generated high-definition images is good enough.
GSW: A High Performance IQA Index Based on Global and Saliency Window Similarity

Abstract Perceptual image quality assessment (IQA) attracts significant attention in recent years. It is proved that both global score and an image’s visual saliency (VS) are consistent with subjective evaluation. The global quality score reflects the consistency of the overall structure between two images, and VS map contributes complementarily in evaluating perceptual quality. This paper presents an effective IQA index based on global and saliency window similarity, namely GSW. It chooses VS map as an image feature and uses a special strategy to draw the saliency window with largest significancy. Meanwhile, the background area is taken into account to guarantee the robustness of the quality score. Experimental results on four most widely used databases verify that, compared with state-of-the-art IQA methods, GSW performs consistently well can provide more accurate quality prediction with a low computational complexity.
An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis

Multi-speaker spoken datasets enable the creation of text-to-speech synthesis (TTS) systems which can output several voice identities. The multi-speaker (MSPK) scenario also enables the use of fewer training samples per speaker. However, in the resulting acoustic model, not all speakers exhibit the same synthetic quality, and some of the voice identities cannot be used at all. In this paper we evaluate the influence of the recording conditions, speaker gender, and speaker particularities over the quality of the synthesised output of a deep neural TTS architecture, namely Tacotron2. The evaluation is possible due to the use of a large Romanian parallel spoken corpus containing over 81 hours of data. Within this setup, we also evaluate the influence of different types of text representations: orthographic, phonetic, and phonetic extended with syllable boundaries and lexical stress markings. We evaluate the results of the MSPK system using the objective measures of equal error rate (EER) and word error rate (WER), and also look into the distances between natural and synthesised t-SNE projections of the embeddings computed by an accurate speaker verification network. The results show that there is indeed a large correlation between the recording conditions and the speaker’s synthetic voice quality. The speaker gender does not influence the output, and that extending the input text representation with syllable boundaries and lexical stress information does not equally enhance the generated audio across all speaker identities. The visualisation of the t-SNE projections of the natural and synthesised speaker embeddings show that the acoustic model shifts some of the speakers’ neural representation, but not all of them. As a result, these speakers have lower performances of the output speech.
Do Critical Components Smell Bad? An Empirical Study with Component-based Software Product Lines

Component-based software product line (SPL) consists of a set of software products that share common components. For a proper SPL product composition, each component has to follow three principles: encapsulating a single feature, restricting data access, and be replaceable. However, it is known that developers usually introduce anomalous structures, i.e., code smells, along the implementation of components. These code smells might violate one or more component principles and hinder the SPL product composition. Thus, developers should identify code smells in component-based SPLs, especially those affecting highly interconnected components, which are called critical components. Nevertheless, there is limited evidence of how smelly these critical components tend to be in component-based SPLs. To address this limitation, this paper presents a survey with developers of three SPLs. We inquire these developers about their perceptions of a critical component. Then, we characterize critical components per SPL, and identify nine recurring types of code smells. Finally, we quantitatively assess the smelliness of the critical components. Our results suggest that: (i) critical components are ten times more prone to have code smells than non-critical ones; (ii) the most frequent code smell types affecting critical components violate several component principles together; and (iii) these smell types affect multiple SPL components.
Human pose recognition based on multiple features and random forest algorithm

Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition.
An Interpretability Analysis of Travel Decision Learning in Cyber-Physical-Social-Systems

In the few decades, the rise of Internet and social media has fundamentally changed the management, control, and operation modes of modern engineering and social systems like defense, energy, critical health care and transportation.
Multi-instance Conditional Autoencoder a data driven compression model for strongly correlated datasets

Multimedia compression is an essential data processing procedure to any Content Delivery Network (CDN). Autoencoder is a data driven model which compresses the high dimensional inputs into a low dimensional latent representation creating a bottleneck impelling the model to learn a compression which is able to reconstruct the input with minimum error. The model searches for patterns in the data to reduce the input dimensionality fitting the input through the bottleneck while minimizing the reconstruction loss. Conventional autoencoder's latent representation exploits intra-sample correlation without taking advantage of the inter-samples correlation. Our model, Multi-instance Conditional Autoencoder, compresses many files to comprise one latent representation, and then restore each file individually in the decoding phase. We achieved higher compression rate compared to traditional autoencoder in case of audio files, Javanese and VoxCeleb datasets, where data correlation is sufficiently high.
Establishing Causality to Detect Fraud in Financial Statements

In this work, two approaches have been presented to derive the important variables that an auditor should watch out for during the audit trials of a financial statement. To achieve this goal, machine learning modeling is leveraged. In the first approach, important features or variables are derived based on ensemble method and in the second approach, an explainable model is used to corroborate and expand the conclusions derived from the ensemble method. A dataset of financial statements that was labeled manually is utilized for this purpose. Four important measures, namely, random forest recommendations of first approach, random Forest Explaner -pvalue, random Forest Explainer-first multi-way importance plot and random Forest Explainer-second multi-way importance plot, are employed to derive the important features. A final list of six variables is derived from these two approaches and four measures
Investigating the Use of Machine Learning Techniques in a Random Physical System

Machine learning (ML) techniques have seen increasing use in recent years in complementing traditional HPC solutions to physical systems problems. While the scientific community has been rapidly adopting such techniques, it is still unclear how different ML techniques compare in terms of accuracy. In this paper we address this question by designing and training a neural network and comparing its performance to traditional classification models using as a case study a non-interacting quantum system on a graph structure. We build a classifier with the ability to distinguish extended from localized quantum states based on their different structure and compare it with other commonly used ML classifiers. Our results show high accuracy for certain ML models in most cases, whereas others are less effective.
Review Paper on Anomaly Detection in Data Streams

Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.
How Empirical Research Supports Tool Development

Empirical research provides two-fold support to the development of approaches and tools aimed at supporting software engineers. On the one hand, empirical studies help to understand a phenomenon or context of interest. On the other hand, studies compare approaches and evaluate how software engineers could benefit from them. Over the past decades, there has been a tangible evolution in how empirical evaluation is conducted in software engineering. This is due to multiple reasons. First, the research community has matured a lot thanks also to guidelines developed by several researchers. Second, the large availability of data and artifacts, mainly from the open-source, has made it possible to conduct larger evaluations, and in some cases to reach study participants. This keynote will first overview how empirical research has been used over the past decades to evaluate tools, and how this is changing over the years. Then, we will focus on the importance of combining quantitative and qualitative evaluations, and how sometimes "depth" turns out to be more useful than just "breadth". We will also emphasize how research is not a straightforward path, and negative results are often an essential component for future advances. Last, but not least, we will discuss how the role of empirical evaluation is changing with the pervasiveness of artificial intelligence methods in software engineering research.
Deep learning-based browser record analysis research

With the vigorous development of the Internet to combat criminal activities such as black and gray production, the problem of data classification is gradually being taken seriously. Therefore, by modeling and analyzing the browser history records that have been acquired, in which Chinese word separation in the field of neuro-linguistic programming (NLP) is used for word separation, feature extraction using a vocabulary table model, and classification processing by a neural network algorithm. Simulation experiments on browser history data through feature extraction and neural networks are conducted to train the accuracy of the model for analyzing browser history records and classifying the test data.