Reviewerly/exHarmony huggingface dataset:
Geometry and mixture models

In this article we look at the geometry of mixture models. This geometry can be affine, convex, differential, information, or algebraic. While mixture models are simple to define, highly interpretable, and widely used in statistical practice, their underlying statistical properties are complex and still not completely theoretically resolved. This complexity often has a geometric cause and resolution. The family presents interesting challenges to the development of a complete geometric theory of inference. Throughout we illustrate the discussion with simple, and often visual, models for illustration and to aid intuition.
Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach

Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confident set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.
Weak Pseudo-Bosons and Related Bi-coherent States

One of the essential characteristics of pseudo-bosonic operators satisfying the commutation relation ( 2.14 ), briefly [a, b] = 1 1, is that there is no reason, in general, for a and b to be related by some adjoint operation, as for ordinary CCR, ( 2.9 ).
Eye Blinking Detection Test

The eye blink detection software detects the eye of the user using the camera and detects whether the user has blinked his/her eye or not. Applications of the software involve developing better security programs that involve iris scanning. Developing a better working locking mechanism for smartphones, that are widely using face unlock systems.
End-to-End Plankton Database Collection System

Monitoring living plankton status has greatly developed owing to machine vision technology. One of these developments is the automatic video acquisition system, which can capture high-resolution information and record considerable details of a scene. However, existing optical sensors cannot usually obtain a whole image, in which all details of plankton are fully clear, due to the influence of many factors, such as depth-of-field limitation of lenses, movement of plankton, and large difference in plankton scale. The captured video also needs to be filtered to eliminate the redundancy before using for the sparsity of the sample. This procedure is time-consuming and costly. Therefore, in this article, we develop an end-to-end plankton database collection system that can directly generate complete, clear plankton images from video. First, the regions of interest of the plankton are extracted. Then, the same plankton which appears in successive frames is identified to reconstruct the clearest morphological and structure features through their detailed information. Experimental results indicate that the proposed system can effectively compress the original data. The proposed fusion method also outperforms the state-of-the-art methods, especially for images with anisotropic blur. Furthermore, this system can monitor the abundance and distribution of marine plankton with the help of an embedded computing platform.
A Case Study of Clustering and Visualization With Clickstream Data Using UX2Vec

Along the trend of digital transformation, brand website does not only promote brand image, but it also provides an experience in the path-to-purchase. With the incorporation of ecommerce capabilities on brand websites, we will need to revisit how we do website analytics to suit the new needs. The increased online usage allows for big data analysis with real-time behavioural data. This paper presents a new methodology for web analytics. Based on a case study of real-life brand store website in Hong Kong, the exploration study employs the new use of machine learning technique – UX2Vec, an unsupervised learning methodology for vectorization and embedding, plus k-Means clustering algorithm to discover insights and suggest improvement. An analysis was performed with 30-day data of 69,648 page view records gathered from the website. The proposed method led to a successful clustering result to characterize the website based on machine learning technique.
Home Appliance Review Analysis Via Adversarial Reptile

Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspects of the product are most discussed. In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We found that the macro-F1 is improved from 68.6% (BERT fine-tuned model) to 70.3% (p-value 0.04).
Attention-based Multi-View Stereo Network

The convolutional neural networks (CNNs) is a basic component in deep learning-based multi-view stereo (MVS) network for feature extraction, which effectively extract feature representations on single image scale. A broad range of prior research has exploited feature pyramids to compute a multi-scale feature representation, seeking to strengthen semantics at all scales. In this paper, we focus to improving the quality of representations by explicitly modelling the interdependencies between channels throughout its feature hierarchy. We propose to add a lightweight attention module to the feature pyramids, allowing us inferring high resolution depth maps to achieve better reconstruction results. Using attention module in Cas-MVSNet, our method only slightly increases the complexity and computational burden of the model, and achieves a more complete point cloud and less noise results on the DTU benchmark.
Application of Genetic Algorithm in Evolutionary Music

In recent years, evolutionary computing has been gradually applied in the field of music creation. Based on the analysis of music coding and genetic operation, genetic algorithm is applied to music creation, so that the computer can automatically complete the composition, and finally realize the evolutionary music operation by optimizing the way of crossover.
A Deep Method Renaming Prediction and Refinement Approach for Java Projects

During the process of software development and maintenance, developers would regularly refactor existing source code to improve efficiency and maintainability. Among various code refactoring activities, method renaming often happens within the whole project evolution process. To perform method renaming, developers should first identify the exact methods that should be renamed, which is generally tedious and error-prone through manual analysis. Towards this end, researchers have proposed some approaches to automatically recommend candidate methods for renaming. To further improve the performance of existing techniques, in this paper, we propose a novel approach that fully leverages historical code changes and overlapping relationships among code entities to identify renaming opportunities for methods. Specifically, we first embed methods into vectors and incorporate overlapping relationships among code entities by using different attention heads in a deep learning network. Then, we apply these obtained vectors to train a classifier to predict potential renaming opportunities for methods. Finally, we utilize historical renaming activities of related code entities to further refine the predicted results. Experimental results on 114,398 methods from 10 open source Java projects show that our approach could outperform the state-of-the-art approach by achieving an average F-measure of 80.02%. To better validate the effectiveness of our approach, we also explore the performance of some major components of our approach. For example, we find that employing related code entities help to improve the performance of our approach by 40.40% in terms of the average F-measure.
Sentence Semantic Matching Model Based on Fusing Local and Global Features

To improve the performance of sentence semantic matching, we propose a model by fusing local and global semantic features for it. The model first captures local and global features from sentences by CNN and BiLSTM. Then, the model runs an alignment operation based on an attention mechanism to generate local and global fusion features. Next, using the fusion features of sentences to be matched, the model generates the interactive features between them by alignment operation. Finally, the fusion features and interactive features of each sentence are concatenated into new features on which Max and Avg pooling operations are performed to extract features as input for a classifier to calculate the final matching result. We evaluate the effectiveness of our model on a public dataset. The experimental results demonstrate that our model achieves better performance on the dataset compared with other state-of-the-art models aiming for sentence semantic matching.
Problems and Prospects of Using Remote Learning Technologies in Different Countries

Modern society is faced with such unprecedented challenges as COVID-19, which in the globalization context can lead to a collapse in all activity spheres, including education. During the lockdowns, which will be similar to those undertaken against the pandemic backdrop, there is a need for a forced transition from class teaching to online forms. At the pandemic beginning, enough time has passed to assess the accumulated experience, comprehend the problems, both those that were resolved and those that could not be resolved promptly. The study made it possible to compare the effectiveness of using different virtual environments for organizing training for engineers, as well as to analyze the training content and its differences from what is used in traditional class training. Different students' groups were selected for the case study in order to compare their participation in the process with traditional and online forms of education, as well as their performance. In addition, an analysis was carried out of how the education form affects the quality of their projects and recommendations were formulated for the further development of new education forms.
Towards the Constraint Learning and Optimization Approach to Document Clustering

This research proposed autonomous constraint learning from a document collection to incorporate these constraints into an effective document clustering process. Constraint Clustering is based on semi-supervised approach towards document clustering, where some prior knowledge about the collection is readily available for clustering. The paper proposes algorithms based on sampling to find three different kind of constraints from the document collection (i) instance level (ii) cluster level and (iii) corpus level. The constraints integrated into constraint K-Mean produced multiple clusters satisfying the constraints. A boosting method is suggested to adaptively learn the constraint's priorities, constraint satisfaction criteria and optimal clustering solution from multiple possible solutions. The proposed algorithms are implemented and tested over standard text mining dataset. The evaluation measures for testing the algorithm involves purity, entropy, and F-measure. This experimental studies achieved encouraging results for constraint learning and on average, 6% improvements are achieved on clustering results.
Data Mining Analysis of Career Planning Based on Artificial Intelligence Technology

Career planning, as a means that can not only realize education management, but also promote personal self-development and full development of personality, is now being used more and more widely in colleges and universities in our country. This article first analyzes the implementation of career planning education in colleges and universities. The nature of higher vocational education determines the needs of development, which is an inevitable trend of social development. Analyze the employment data of the research objects, explain the employment situation of graduates of higher vocational colleges, the impact of career planning on the employment of students in higher vocational colleges, their existence and causes, and proceed from reality to demonstrate careers. The importance of career planning to the development of higher vocational students. Compared with the same period last year, there is a shortage of about 35% of the units that recruit chemical majors, and the relative competitiveness of students has increased a lot. On the contrary, the units of administrative majors have increased, and there is more room for students to choose. Career planning education is not only a long-term educational process, but should also be carried out in a timely and targeted manner according to the emergence of student problems.
Image Watermark Combining with Discrete Wavelet Transform and Singular Value Decomposition

In this paper, using the spectrum characteristics of discrete wavelet transform (DWT) and the features of image singular value decomposition (SVD), an image watermarking algorithm is proposed, which combined with the contrast sensitivity characteristics of human visual system (HVS). In the algorithm, the scrambled watermark is embedded into the singular value matrix of image with some intensity, and its inverse process is used to extract the watermark, by using HVS characteristics. The algorithm is verified by simulation, and tested by compression, shearing, Gaussian noise, and median filter attacks, and compared with 8 reported watermarking algorithms on the anti-attack performance. The results indicate that under the strong compression attack with a quality factor of 20%, the NC value of the extracted watermark can still reach 0.8467, and PSNR and SSIM of the watermarked image can reach 26.1793 dB and 0.8517, which has better robustness than the 8 existing watermarking algorithms. The comprehensive results indicate that the proposed algorithm can commendably balance the relationship among robustness, visual transparency and watermark embedding amount in the process of watermark embedding.
Exploring Teacher-Student Learning Approach for Multi-Lingual Speech-to-Intent Classification

End-to-end speech-to-intent classification has shown its advantage in harvesting information from both text and speech. In this paper, we study a technique to develop such an end-to-end system that supports multiple languages. To overcome the scarcity of multi-lingual speech corpus, we exploit knowledge from a pre-trained multi-lingual natural language processing model. Multi-lingual bidirectional encoder representations from transformers (mBERT) models are trained on multiple languages and hence expected to perform well in the multi-lingual scenario. In this work, we employ a teacher-student learning approach to sufficiently extract information from an mBERT model to train a multi-lingual speech model. In particular, we use synthesized speech generated from an English-Mandarin text corpus for analysis and training of a multi-lingual intent classification model. We also demonstrate that the teacher-student learning approach obtains an improved performance (91.02%) over the traditional end-to-end (89.40%) intent classification approach in a practical multi-lingual scenario.
Comparison of Approaches to the Circuits Design for DWT with CDF 9/7 Wavelet

Discrete wavelet transform (DWT) is widely used in modern science and technology to solve a wide range of signal and image processing problems and digital communications. The high growth rates of quantitative and qualitative characteristics of digital information lead to the need to improve information processing methods and increase the efficiency of their implementation. Specialized hardware circuits are used to solve this problem since they can significantly enhance the characteristics of DWT implementation devices. Calculations for DWT are organized using multiple approaches that differ in the priorities of resource consumption. This paper proposes a comparative analysis of state-of-the-art approaches to the circuits design for DWT with Cohen-Daubechies-Feauveau 9/7 wavelet. The evaluation results using the “unit-gate” model showed that the optimized direct implementation of DWT exceeds the lifting scheme by 4.59 times in computational speed and requires 9.55% fewer hardware costs when implemented on modern digital devices such as Field-Programmable Gate Arrays.
Leveraging Transformer Self Attention Encoder for Crisis Event Detection in Short Texts

Analyzing content generated on social media has proven to be a powerful tool for early detection of crisis-related events. Such an analysis may allow for timely action, mitigating or even preventing altogether the effects of a crisis. However, the high noise levels in short texts present in microblogging platforms, combined with the limited publicly available datasets have rendered the task difficult. Here, we propose deep learning models based on a transformer self-attention encoder, which is capable of detecting event-related parts in a text, while also minimizing potential noise levels. Our models' efficacy is shown by experimenting with CrisisLexT26, achieving up to $$81.6\%$$ f1-score and $$92.7\%$$ AUC.
Testing Autonomous Driving System based on Scenic

Autonomous driving system develops rapidly in recent years, which also brings many critical issues. Object detector is one of the most important modules in autonomous driving system, how to ensure its quality is an urgent problem to be solved. Many studies have been carried out to perturb the input images by mutation to test object detectors. But digital adversarial perturbations, e.g., changing image pixels, may never happen in the physical world. Therefore, a metamorphic testing approach for object detectors based on Scenic is proposed in this paper. An abstract scene in Github repository of Scenic and object detector Yolact were used in the experiments. The experimental result shows that the proposed approach can obtain a minimum of 43% and a maximum of 96% defect detection rates under different confidence probabilities.
A Survey on Different Approaches to Automating the Design Phase in the Software Development Life Cycle

Software design is a basic plan of all elements in the software, how they relate to each other in such a way that they meet the user requirements. In software development process, software design phase is an important phase as it gives a plan of what to do and how to do it during the implementation phase. As the technology is evolving and people's needs in the technological field are increasing, the development of software is becoming more complex. To make the development process somewhat easy, it is always better to have a plan which is followed throughout the process. In this way, many problems can be solved in the design phase, for which a number of tools and techniques are present. One is known as Design Patterns. In software engineering, a design pattern is a general solution to commonly occurring problems in software design. A design pattern isn't a finished design that can be transformed directly into code.
Script event prediction based on pre-trained model with tail event enhancement

Script event prediction is a big challenge and its goal is to predict the subsequent event based on the observed events. Since an event is described by text, the pre-trained models have been applied for event representation. However, the embedding based on the pre-trained models is sensitive to the short text format of events, and the existing works do not handle it well. In addition, previous models pay more attention to the semantic similarity but ignore the factors of emergencies. The turning event at the tail of the event chain can easily affect the follow-up direction. This paper proposes a new preprocessing method: cleaning, alignment, and connection, which helps to obtain richer event representations. On this basis, we concatenate the embedding of the CLS token and event sequence to integrate the semantic and temporal features of the event chain. To deal with the problem of event turning, we propose a tail event enhancement module. It adds the transition probability of tail events and candidate events into prediction layer, so as to avoid pay only attention to the semantic feature. The results of a large number of comparative experiments and ablation experiments confirm the superiority of our model compared with the baselines.
Parameter Estimation of Linear Observation Equation in Neural Network Learning Algorithm

In our daily life, we often hear that some people are highly sensitive to neural network models. In this case, people will train them. However, when the amount of data is small or a large amount of information needs to be stored, it is easy to be deleted. Therefore, how to solve the linear prediction problem has become one of the topics that are difficult to study and need to be further discussed. In this paper, the parameter estimation of linear observation equation is deeply studied based on neural network learning algorithm. Firstly, this paper discusses the principle of neural network and studies the characteristics of neural network learning algorithm. Then, aiming at the changes of linear observation equation parameters, this paper designs experiments to test the stability and performance of linear observation equation parameter estimation under the optimization of neural network learning algorithm. Finally, the test results show that the estimation of linear observation parameter equation optimized by neural network algorithm is basically above 85, with small amplitude change and good stability. In contrast, the estimation of linear observation parameter equation under the support of traditional algorithm changes greatly and has poor stability.