Reviewerly/exHarmony huggingface dataset:
Non-parameter clustering algorithm based on saturated neighborhood graph

Clustering algorithms play a very important role in the field of data mining and machine learning. However existing clustering methods are sensitive to parameters and outliers. The commonly used clustering methods are restricted by problem of parameter selection that different algorithms need to input one or more different parameters. For overcoming these drawbacks, we propose a non-parameter clustering algorithm based on saturated neighborhood graph known as NPCSNG. NPCSNG algorithm uses mathematic method to preprocess the data set, and then uses the characteristics of SNG adaptive clustering to cluster the data, so as to achieve the purpose of non-parameter clustering. NPCSNG has three main advantages: (1) it does not need to manually set any parameters due to the use of adaptive saturated neighborhood graph; (2) it significantly improves the clustering performance as well as the model robustness, making NPCSNG a more practical approach for real-world scenarios; (3) it can easily adapt to data-sets with complex manifold structure. NPCSNG algorithm solves the problem of parameter selection of clustering algorithm and it broadens the idea of clustering by using the characteristics of graphs. • Adaptive noise detection is performed using the density information of the data. • The saturated neighborhood graph has the characteristic of adaptive clustering. • NPCSNG can easily adapt to datasets with complex manifold structure.
A novel domain adaptation theory with Jensen–Shannon divergence

Domain adaptation aims to alleviate the shift between training and test distribution, where the DA theory is crucial in understanding the success of domain adaptation algorithms. In this paper, we reveal the incoherence between the empirical domain adversarial training and its generally assumed theoretical counterpart based on H-divergence. Concretely, we find that H-divergence is not equivalent to Jensen–Shannon divergence, the optimization objective in domain adversarial training. To this end, we establish a new theoretical framework by directly proving the upper and lower target risk bounds based on the joint distributional Jensen–Shannon divergence. We further derive bidirectional upper bounds for marginal and conditional shifts. Our framework exhibits inherent flexibility for different transfer learning problems, which is usable for various scenarios. From an algorithmic perspective, our theory enables a generic guideline of the unified principles of semantic conditional matching, feature marginal matching, and label marginal shift correction. We employ algorithms for each principle and empirically validate the benefits of our framework.
TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.
3D-VDNet: Exploiting the vertical distribution characteristics of point clouds for 3D object detection and augmentation

Accurate 3D object detection is limited by the sparsity of LiDAR-based point clouds. The vertical distribution characteristics (VDCs) of point clouds in pillars are robust to point-sparsity and provide informative semantic information on objects. Based on this, we propose a novel 3D object detection framework where the VDCs of point clouds are exploited to optimize feature extraction and object augmentation. More specifically, a Spatial Feature Aggregation module is proposed to perform robust feature extraction by decorating pillars with the VDCs. To spatially enhance semantic embeddings, we employ VDCs to construct a voxelized semantic map, acting as an additional input stream. Moreover, we develop an Adaptive Object Augmentation (AOA) paradigm, which adopts the VDC searching of suitable ground regions to “paste” virtual objects, thus avoiding conflicts with new scenes. Extensive experiments on the KITTI dataset demonstrate that our framework can significantly outperform the baseline, achieving 3.74%/1.59% moderate AP improvements on the Car 3D/BEV benchmarks with 38 FPS inference speed. Furthermore, we prove the stable performance of our AOA module across different detectors.
Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

With the rapid development of artificial neural networks, more sophisticated network models and more accurate prediction results are provided for solving engineering applications. In this paper, the weights and thresholds of the feedforward neural network model were optimized using the pelican algorithm, and the optimal solution was output by simulating the pelican predation scheme and assigned as the new parameters of the neural network. A POA-BP network model was proposed, and its better prediction was demonstrated by comparing the fitting and prediction performance with 13 neural network models such as random forest, support vector machine, and wavelet basis by evaluating metrics such as RMSE, MSE, and MAE. To further improve the prediction accuracy, different hidden layer topologies of POA-BP were compared, and the Monte Carlo method was used to obtain seven design variables for the lithium battery shell size parameters, and parameter regression prediction was performed for the structure after the variable density topology optimization used the isotropic material interpolation model (SIMP) with the moving asymptote method by invoking the MinGW-w64 compiler, and the 1-3-1 neural network was selected model to predict each dimension of the battery shell structure, the final shell weight reduction ratio was 18.12% and the first-order intrinsic frequency was increased by 14.56%.
Reliable wavefront reconstruction from a single lateral shearing interferogram using Bayesian convolutional neural network

The reconstruction of the original wavefront from its sheared information is an ill-posed inverse problem in the sense of Hadamard. In this paper, we present a new technique for reconstructing the wavefront from a single lateral shearing interferogram using a Bayesian convolutional neural network. With the synthesized data, the trained network can robustly invert the physical model, recover the spectral leakage problem caused by the shear operation, and reconstruct the desired phase distribution. In addition, a quantitative pixel-level uncertainty measure of the reconstruction results is provided simultaneously, which can be used as an indicator to judge the validity of reconstruction accuracy in the absence of ground truth. Numerical experiments are carried out to investigate the robustness and accuracy of the method, in which the effects of different noise levels and shear distances on reconstruction accuracy are evaluated. We further show that the uncertainty maps are highly indicative of the true error and properly calibrated. Finally, optical tests on reconstructing the topology of object deformation have confirmed that our method can considerably increase the accuracy of wavefront reconstruction as compared with the two baseline methods.
Affine Layer-Enabled Transfer Learning for Eye Tracking with Facial Feature Detection in Human–Machine Interactions

Eye tracking is an important technique for realizing safe and efficient human–machine interaction. This study proposes a facial-based eye tracking system that only relies on a non-intrusive, low-cost web camera by leveraging a data-driven approach. To address the challenge of rapid deployment to a new scenario and reduce the workload of the data collection, this study proposes an efficient transfer learning approach that includes a novel affine layer to bridge the gap between the source domain and the target domain to improve the transfer learning performance. Furthermore, a calibration technique is also introduced in this study for model performance optimization. To verify the proposed approach, a series of comparative experiments are conducted on a designed experimental platform to evaluate the effects of various transfer learning strategies, the proposed affine layer module, and the calibration technique. The experiment results showed that the proposed affine layer can improve the model’s performance by 7% (without calibration) and 4% (with calibration), and the proposed approach can achieve state-of-the-art performance when compared to the others.
Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the training, the learning performance with a constant discount factor can be limited. For the purpose of obtaining acceptable learning performance consistently, this paper proposes an adaptive rule for the discount factor based on the advantage function. Additionally, how to use the advantage function in both on-policy and off-policy algorithms is presented. To demonstrate the performance of the proposed adaptive rule, it is applied to PPO (Proximal Policy Optimization) for Tetris in order to validate the on-policy case, and to SAC (Soft Actor-Critic) for the motion planning of a robot manipulator to validate the off-policy case. In both cases, the proposed method results in a better or similar performance compared with cases using the best constant discount factors found by exhaustive search. Hence, the proposed adaptive discount factor automatically finds a discount factor that leads to comparable training performance, and that can be applied to representative deep reinforcement learning problems.
Application of LSTM Neural Network Technology Embedded in English Intelligent Translation

With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compared with other deep learning algorithms such as 3RNN and GRU, the LSTM-SA neural network algorithm has faster convergence speed and lower loss value, and the loss value is finally stable at about 8.6. Under the three values of adaptability, the accuracy of LSTM-SA neural network structure is higher than that of LSTM, and when the adaptability is 1, the accuracy of LSTM-SA neural network improved the fastest, with an accuracy of nearly 20%. Compared with other deep learning algorithms, the LSTM-SA neural network algorithm has a better translation level map under the three hidden layers. The proposed LSTM-SA model can better carry out English intelligent translation, enhance the representation of source language context information, and improve the performance and quality of English machine translation model.
Network Sentiment Analysis of College Students in Different Epidemic Stages Based on Text Clustering

In order to analyze the evolution trend of public opinion in emergencies and explore its evolution law, this paper constructs a network sentiment analysis model based on text clustering, where the emotion analysis part is based on the pretraining BERT model and BiGRU model, in which BERT is used as the word embedding model to extract the feature vector of emotional text and BiGRU is used to extract the context of the text feature vector to accurately identify the sentiment polarity of public opinion data. In addition, the K-means clustering algorithm and Kolmogorov-Smirnov Z test were used to divide the different epidemic stages. Compared with other methods, the model proposed in this paper has a great degree of improvement in accuracy, recall, and F1 score index, which provides an opportunity reference and effective detection means for schools at all levels to carry out timely mental health education and psychological intervention for students.
Laplacian smoothing gradient descent

We propose a class of very simple modifications of gradient descent and stochastic gradient descent leveraging Laplacian smoothing. We show that when applied to a large variety of machine learning problems, ranging from logistic regression to deep neural nets, the proposed surrogates can dramatically reduce the variance, allow to take a larger step size, and improve the generalization accuracy. The methods only involve multiplying the usual (stochastic) gradient by the inverse of a positive definitive matrix (which can be computed efficiently by FFT) with a low condition number coming from a one-dimensional discrete Laplacian or its high-order generalizations. Given any vector, e.g., gradient vector, Laplacian smoothing preserves the mean and increases the smallest component and decreases the largest component. Moreover, we show that optimization algorithms with these surrogates converge uniformly in the discrete Sobolev $$H_\sigma ^p$$ sense and reduce the optimality gap for convex optimization problems. The code is available at: https://github.com/BaoWangMath/LaplacianSmoothing-GradientDescent .
Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism

With the continuous progress of image retrieval technology, in the field of image retrieval, the speed of a search for a desired image from a great deal of image data becomes a hot issue. Convolutional Neural Networks (CNN) have been used in the field of image retrieval. However, many image retrieval systems based on CNN have a poor ability to express image features, resulting in a series of problems such as low retrieval accuracy and robustness. When the target image is retrieved from a large amount of image data, the vector dimension after image coding is high and the retrieval efficiency is low. Locality-sensitive hash is a method to find similar data from massive high latitude data. It reduces the data dimension of the original spatial data through hash coding and conversion, and can also maintain the similarity between the data. The retrieval time and space complexity are low. Therefore, this paper proposes a locality-sensitive hash image retrieval method based on CNN and the attention mechanism. The steps of the method are as follows: using the ResNet50 network as the feature extractor of the image, adding the attention module after the convolution layer of the model, and using the output of the network full connection layer to retrieve the features of the image database, then using the local-sensitive hash algorithm to hash code the image features of the database to reduce the dimension and establish the index, and finally measuring the features of the image to be retrieved and the image database to get the most similar image, completing the content-based image retrieval task. The method in this paper is compared with other image retrieval methods on corel1k and corel5k datasets. The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved. It also has higher robustness in different scenarios.
A statistical approach to study the spatial heavy metal distribution in soils in the Kushk Mine, Iran

Abstract The present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), principal component analysis (PCA), and spatial distribution patterns, to perform element distribution evaluation within the area. The distributions of heavy metals in the entire area were exhibited in the form of maps. The average concentrations of Cd, Cu, Mn, Fe, Zn, and Pb were found to be 0.39, 0.26, 5.3, 4.1, 51.9, and 40.9 mg.kg −1 , respectively. Based on the PCA and HCA findings, the heavy metals were divided into two groups. The first group included Pb, Cd, Zn, and Cu. In the first group, altered threshold‐surpassing anthropogenic and lithogenic pollution was found to be the main factor accounting for Pb and Zn. The second group involved Fe and Mn, which could be impacted by either anthropogenic and lithogenic factors. Furthermore, the geo‐statistical results demonstrated higher contents of the heavy metals in the south of the mine and in the vicinity of the mine tailings. It may be concluded from the results that the heavy metal contents of the area are impacted by anthropogenic and lithogenic factors.
Multi-scale fusion framework via retinex and transmittance optimization for underwater image enhancement

Low contrast, poor color saturation, and turbidity are common phenomena of underwater sensing scene images obtained in highly turbid oceans. To address these problems, we propose an underwater image enhancement method by combining Retinex and transmittance optimized multi-scale fusion framework. Firstly, the grayscale of R, G, and B channels are quantized to enhance the image contrast. Secondly, we utilize the Retinex color constancy to eliminate the negative effects of scene illumination and color distortion. Next, a dual transmittance underwater imaging model is built to estimate the background light, backscattering, and direct component transmittance, resulting in defogged images through an inverse solution. Finally, the three input images and corresponding weight maps are fused in a multi-scale framework to achieve high-quality, sharpened results. According to the experimental results and image quality evaluation index, the method combined multiple advantageous algorithms and improved the visual effect of images efficiently.
Deja Vu: semantics-aware recording and replay of high-speed eye tracking and interaction data to support cognitive studies of software engineering tasks—methodology and analyses

The paper introduces a fundamental technological problem with collecting high-speed eye tracking data while studying software engineering tasks in an integrated development environment. The use of eye trackers is quickly becoming an important means to study software developers and how they comprehend source code and locate bugs. High quality eye trackers can record upwards of 120 to 300 gaze points per second. However, it is not always possible to map each of these points to a line and column position in a source code file (in the presence of scrolling and file switching) in real time at data rates over 60 gaze points per second without data loss. Unfortunately, higher data rates are more desirable as they allow for finer granularity and more accurate study analyses. To alleviate this technological problem, a novel method for eye tracking data collection is presented. Instead of performing gaze analysis in real time, all telemetry (keystrokes, mouse movements, and eye tracker output) data during a study is recorded as it happens. Sessions are then replayed at a much slower speed allowing for ample time to map gaze point positions to the appropriate file, line, and column to perform additional analysis. A description of the method and corresponding tool, Deja Vu, is presented. An evaluation of the method and tool is conducted using three different eye trackers running at four different speeds (60 Hz, 120 Hz, 150 Hz, and 300 Hz). This timing evaluation is performed in Visual Studio, Eclipse, and Atom IDEs. Results show that Deja Vu can playback 100% of the data recordings, correctly mapping the gaze to corresponding elements, making it a well-founded and suitable post processing step for future eye tracking studies in software engineering. Finally, a proof of concept replication analysis of four tasks from two previous studies is performed. Due to using the Deja Vu approach, this replication resulted in richer collected data and improved on the number of distinct syntactic categories that gaze was mapped on in the code.
Virtual reality for the promotion of historical empathy: A mixed-methods analysis

Historical empathy may be enhanced by virtual reality (VR) technologies, which provide varying degrees of immersion into other time periods and places. This study explored the effects of combining semi-immersive and fully immersive VR with a follow-up writing task to promote historical empathy with adult learners. Thirty-six participants were randomly assigned to view a brief historical film on either a flatscreen or a head-mounted device (HMD). Afterward, participants were again randomly assigned to either a first-person perspective or a factual recall writing prompt before then responding to questionnaires gauging their situational interest. Quantitative and qualitative data were analyzed to provide a holistic interpretation of participants' development of historical empathy. Results suggest that although type of writing task remains instrumental in promoting historical empathy, immersive VR with an HMD also plays a promising role. Our findings pose important implications for post-secondary, museum, and teacher educators interested in scaffolding VR experiences to promote historical empathy.
Exploring Remote Communication through Design Interventions: Material and Bodily Considerations

Communication over distance unfolds often in the form of video calls, digital messages or letters, and is frequently explored in HCI through digital, tangible and hybrid interactions. In this paper, we present five design interventions with the aim to rethink current strategies for remote communication by using and repurposing analogue, non-digital and bodily means. In an autoethnographic study, we implement and explore those interventions with our family members and friends. We present and discuss the collected insights and individual experiences from the interventions in depth to articulate reflections on communication over a distance that intend to inspire playful, material-driven, bodily ways of remote connectedness. Finally, we emphasize the value of reducing complexity to strengthen (remote) connectedness, creating bodily connections over physical distance that reduce pressure on users, and allowing participants to challenge the boundaries of not using digital means.