Reviewerly/exHarmony huggingface dataset:
A Multiple-Path Learning Neural Network Model for Code Completion

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today’s integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task.
ERC721M: A Web3 Token Standard for REIT Companies

In today’s rapidly growing economy with everincreasing inflation, the purchasing power of money keeps decreasing as time pass by therefore it becomes important to invest money to counter the growing inflation. Real estate investment has been a proven hedge against inflation over many decades. The Real Estate sector despite so much contribution to the financial system has seen relatively less innovation in terms of technology. Blockchain has shown a great affinity towards managing real estate properties and all the transactions involved. Investment in this sector usually requires huge capital as a result, small retail investors are not able to invest. This is where Real Estate Investment Trust (REIT) comes to the rescue of retail investors. This paper focuses on the ERC721M token standard and proposed architecture as to how a REIT company can make use of ERC721M to design their Decentralized Asset organization, or simply DAO, which will help them in adapting web3 technologies and bring automation in their procedure of operations with maximum transparency, We have also done a comparative analysis of ERC721Mv/s Standard Deployment and why ERC721M is the best token standard to be suited for creating smart contract interface for such an operation.
3D Reconstruction and Texture Optimization Based on Differentiable Rendering

Due to noise and quantization errors inherent in depth cameras, RGB-D based 3D reconstruction algorithms inevitably suffer from defects such as texture blurring and ghosting. However, current texture optimization methods tend to only focus on optimizing one aspect, such as camera pose, geometry, or texture, without considering the highly coupled relationship between them. As a result, it is difficult to achieve global optimality. To address this issue, we propose a joint optimization framework based on differentiable rendering that simultaneously optimizes camera pose, geometry, and texture. By fully exploiting the complementary relationship between these aspects, we are able to reconstruct textures with high fidelity while recovering fine geometric details. First, we propose an optimized geometry model combined with adaptive subdivision, where we selectively subdivide the model only in texture-rich regions to increase the number of vertex facets. Then, we perform geometry optimization to recover high-frequency geometric details evenly in the presence of large geometric errors without incurring computational costs. After the pose correction and geometric model optimization, we use pixel regeneration pipelines to generate clear texture atlases. Comparative evaluations with state-of-the-art texture optimization methods on a public dataset demonstrate the effectiveness of our proposed approach in reducing errors in the reconstruction of 3D models, generating clear textures, and recovering high-frequency geometric details.
Few-Shot Custom Speech Synthesis with Multi-Angle Fusion

In this paper, we propose the TDNN-VITS model: an efficient custom speech synthesis system that can synthesize the speech of arbitrary target speakers, well-improving speech quality as well as reducing the amount of adaptive data. Our model consists of a speaker encoding module, which aims to extract speaker timbre information, and a TTS module, which is based on the fully end-to-end VITS model, but improved according to the problems of custom speech. To better improve the speech quality and speaker similarity, we propose a multi-angle fusion speaker embedding approach. With only 10 speech data and about one minute, good results can be achieved by just fine-tuning for half an hour. The experimental results show that our model is improved compared to the previous classical model, and good speech naturalness and speaker similarity are obtained. The ablation experiments also show the effectiveness of our multi-angle fusion.
Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement.
FedWM: Federated Crowdsourcing Workforce Management Service for Productive Laziness

Federated crowdsourcing, as a dynamic privacy-preserving distributed machine learning approach, has attracted significant research attention recently. Compared to federated learning (FL), clients can dynamically collect and label fresh data as required, and train model on the updated data. Existing research has mainly focused on incentivizing clients to spend more effort on data collection and labelling in order to improve FL model performance. However, as data collection and labeling require human effort, they need to balance work and rest. This need has been overlooked by existing federated crowdsourcing research. In this paper, we propose the Federated Workforce Management (FedWM) approach to bridge this important gap. It first measures the contribution of each client to the FL model, and estimates the urgency collecting new labelled data based on the rate of change of the contribution. Then, FedWM computes the working time taking into account of the client’s maximum productivity and self-reported mood. Finally, it takes both the urgency level of obtaining new data and clients’ productivity into consideration to provide scheduling services that advise the clients on work-rest balance in a given time slot based on Lyapunov optimization. Through theoretical analysis, we provide the performance bounds of FedWM. Through extensive experiments based on real-world datasets, we demonstrate that FedWM achieves significantly more advantageous tradeoffs between client rest and FL model performance compared to existing approaches. To the best of our knowledge, it is the first federated crowdsourcing framework designed to achieve productive laziness.
Forensic Analysis of Car Accident Using MobileNet and Optical Flow

Accident investigation is an effective aspect of reconstructing car accident cases by utilizing video recorded by a third party using closed-circuit television (CCTV). The video is processed using computer vision and analyzed to determine the perpetrator or object whose movement is abnormal or anomalous. One by one, frames from CCTV are examined to identify anomalies in the movement of car objects on the highway that were caught on CCTV cameras. This is performed to ascertain the cause of the car accident. In this paper, we propose the use of the MobileNet model to classify car accident images extracted from CCTV. In addition, we apply the optical flow technique to detect flow for object direction movement. This paper combines MobileNet and optical flow methods to obtain high performance in car accident classification. The MobileNetV2 model in the collision class achieves precision of 97.27%, recall of 98.94%, and F1 score of 98.10%. In addition, the optical flow model shows that the MAPE value is 1.82%.
SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks

Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.
The Effectiveness of Resampling Method for Handling Class Imbalances in Software Defect Prediction

Defect prediction is crucial for software products to be high-quality and reliable. Class imbalance, however, in which one class does much better than the other, poses a significant challenge to flaw prediction models. This inequality often results in discriminatory behavior towards the majority class, resulting in poor performance in identifying the defects of the minority class. By undersampling the dominant class, oversampling the minority class, or combining the two, resampling entails changing the distribution of the dataset. This study aims to develop a robust and accurate model that can overcome the limitations of class imbalance and improve overall crash prediction performance. Logistic regression, a widely used classification algorithm, offers interpretability and flexibility, making it suitable for defect prediction. This research investigates the effectiveness of the resampling technique in conjunction with logistic regression to deal with the class imbalance in defect prediction software. Accuracy and UAC measurement result from the t-test for 12 MDP datasets show that Logistic Regression+Sample (Bootstrapping) works much better than Logistic Regression, with an average accuracy of 90.78% and an average AUC of 0.81.
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users’ conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method.
Multi-Sensor Data Fusion Using Triangulation Method and K-Means Algorithm

A combined group of items that have similar data sets is known as a cluster. The principal objective of data fusion is to combine several pieces of information to find out a more precise result. Generally, Electro-Optical Sensors(EOS) are used to find out the position of a flying object. A single sensor can only provide the direction of an object, therefore at least two sensors are required for the identification of the target location. Tracking information from various sensors is combined and calculated the position of the moving object. In this paper, we presented a study to improve the accuracy of a moving target by using the triangulation method with the help of the K-means clustering algorithm. The primary focus is to identify and eliminate the effect of an erroneous sensor measurement by applying K-means clustering. The accurate position of the flying object can be obtained from the cluster formed by combining a group of all similar measurements. A comparison study of errors with respect to the actual value and calculated value were drawn to validate the results.
Analysis of Hadley Chaotic System and Its Application in Digital Image Encryption

Data security has become crucial in the current digital era, as a lot of important information is stored in digital form. One method that can be utilized in data security systems is chaotic systems, such as the Hadley chaotic system. The initial research in this paper involves the analysis of phase diagrams and bifurcation diagrams of the Hadley system, which is utilized as a random number generator for the XOR method to produce cipher data. This paper conducts an analysis to evaluate the effectiveness of the algorithm used for image encryption, including histogram analysis, correlation analysis, entropy analysis, sensitivity analysis, and noise attack analysis. The findings suggest that the Hadley system has the potential to be used as a data transmission masking technique based on the research findings. Numerical simulations using Python are conducted in this paper to support the research findings. It is expected that this research can contribute to enhancing communication security in the digital era.
Fast Image Denoising Using Peer Group Filtering Applied to Weather Forecasting in Radar Imagery

This paper deals with impulse noise reduction in digital color images, with an application to radar imagery. Impulse noise is a random perturbation of the pixel intensity in either one or all of the color channels of the image. It is caused due to sensor sensitivities or atmospheric effects and impedes image perception and quality. The vector median filters are by far the most popular of the filters for denoising (or reducing) impulse noise in images. These filters work on the principle of similarity detection within a window, or block of pixels to determine a suitable pixel to replace the test pixel, which is usually the centre pixel. These filters, however, are time consuming as the window is made to sequentially slide across all the pixels in the image. A recent variant of this class of filters are the peer group filters, in particular the fast peer group filter. This filter applies the similarity detection procedure over only a set of those pixels that are peers (or members) to the test pixel. This membership criterion facilitates in accelerating the filtering process through minimising the computation to within the member pixels in the window. This approach, albeit being the fastest to date in the class of vector median filters, is still computationally expensive as it involves sequential windowing across the entire window. This paper proposes a faster variant to this technique. The key idea here is to apply a heuristic anomaly detection technique over the rows of the image to determine the anomalous (noisy) pixels and then apply the peer group filter over only those anomalies. This approach reduces the order of computation and ensures accelerated filtering by virtue of processing only those pixels that are determined to be noisy. The efficacy of the proposal is measured using a combined measure of time and accuracy.
Intelligent phase detection of signals on varying SNR using machine learning algorithms

The phase detection as a part of phased-locked loop (PLL) circuit is a critical component in any telecommunication systems. With the advancement of modern-era technologies, the researchers are trying to replace the complicated hardware circuitry with the machine learning algorithms with high degree of accuracy thereby making the system more compact. This paper introduces efficient techniques for machine learning (ML) classifiers that are able to classify a time-domain input signal for the accurate detection of phase shift w.r.to a reference signal. The incoming input signal is synchronised with the reference signal by using a pseudo-random pilot sequence present in the signal itself. The proposed schemes replicate the phase detection circuit with ML based software model without compromising the efficacy in terms of the receiving system functionality. The ML model designed is able to detect the phase of an incoming signal upto 93% accuracy in Additive white Gaussian noise (AWGN) channel.
Image Super-resolution with Enhanced Channel Attention Residual Network

Convolutional neural networks (CNN) have been successful in the field of image super-resolution, achieving state-of-the-art results. While Transformer-based neural networks are becoming increasingly popular, CNN-based deep neural networks still have great potential in this field. In this study, we optimized and adapted Residual Channel Attention Networks (RCAN) for image super-resolution. Specifically, we proposed an enhanced channel attention residual block and introduced a multi-spectral channel attention mechanism into the network, which leads to an improved performance of the attention mechanism. Experimental results demonstrate that our proposed Enhanced Channel Attention Residual Network (ECARN) achieves significant performance improvement compared to previous models. Our approach represents a promising direction for further research into image super-resolution using CNN-based deep neural networks with attention mechanisms. (Abstract)
Radar radiation source signal sorting method based on meta learning

Radar radiation source sorting and identification methods based on in-depth learning have received considerable attention in recent years, but there are time and resource constraints in practice. To solve this problem, this paper presents a method of radar radiation source signal sorting based on meta-learning. Data samples for signal sorting are generated by acquiring the three-dimensional entropy, image coefficient, high-order cumulants, bandwidth and carrier frequency characteristics of the signal. A signal classification model based on adaptive channel attention is constructed. Model-Agnostic Meta-Learning (MAML) meta-learning mechanism is used to enhance the generalization performance of the model to meet the task requirements of radar signal sorting under small sample conditions. The experimental results show that this method can effectively improve the low recognition rate of small sample radar radiation source signals, and achieve a good classification accuracy under the condition of a small sample of training data.
Cross-level Relational Graph Contrastive Learning for Bundle Recommendation

Different from the traditional recommendation system which regards a single item as an atomic unit, the purpose of bundle recommendation is to recommend a set of items to users. In recent years, some work often uses the model based on GNN to solve the bundle recommendation problem. However, compared with the traditional recommendation models, the GNN-based bundle recommendation has a more serious data sparse problem, which significantly reduces the performance of the model. Meanwhile, due to the lack of modeling ability of complex interactive information and dependent information, it is still far from enough to learn the representation of users and bundles. In view of the advantages of contrast learning in mining its own supervisory signals, we propose a model named Cross-level relational graph Contrastive Learning for Bundle Recommendation (CL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BRec). Different from previous contrastive learning methods, our method learns representations of users and bundles from multiple relational graphs of different levels through a crossview contrastive learning paradigm. Specifically, we regard the user-item/bundle graph as a local interaction relation view, the bundle-item graph as a local dependency relation view, and the user-bundle-item tripartite graph as a global structure view to build a cross-level bundle recommendation model. Extensive experiments conducted on two benchmark datasets demonstrate that CL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BRec outperforms the state-of-the-art methods by over 12.56% and 8.6%, respectively.
Graph Convolution Network for Superpixel and Stereo Matching

We propose a method to predict depth using stereo images. Existing methods often suffer from pixel mismatch in small targets and ill-posed regions. To address this problem, we use superpixel segmentation to group pixels with similar color information into larger units, and learn the weight information between adjacent superpixels through a graph convolutional network. We then use the learned superpixel information to guide the disparity map prediction of the stereo matching architecture. Furthermore, we introduce superpixel pooling and superpixel correlation cost volume to constrain the stereo matching features, providing an effective architecture for cost aggregation. By incorporating superpixel information, we improve the accuracy of existing stereo matching models. Experimental results demonstrate that the proposed method significantly improves the performance of the depth prediction.