Reviewerly/exHarmony huggingface dataset:
Application of deep learning-based ethnic music therapy for selecting repertoire

With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function.
An Iterative Method for the Distance Constraints in a Multi-Sensor Positioning System

The distance constraint can enhance the state estimation performance of a multi-sensor positioning system. However, the existing methods encounter problems such as low state estimation accuracy and high computational complexity. This study proposes an iterative constraint algorithm that effectively solves the distance constraint problem in a multi-sensor positioning system. The proposed algorithm linearizes the distance constraint in each iteration to obtain an approximate linear constraint model. Then, it re-estimates the approximate system state by using the estimation projection algorithm. In the last iteration, the proposed algorithm uses the approximate state estimate as input to estimate a more accurate system state until the iteration is terminated. Three simulations are provided to demonstrate the effectiveness and superiority of the proposed algorithm.
A Theoretical Analysis of Density Peaks Clustering and the Component-Wise Peak-Finding Algorithm

Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density peaks clustering has proved capable in applications, yet little work has been done to understand its theoretical properties or the characteristics of the clusterings it produces. Here, we prove that it consistently estimates the modes of the underlying density and correctly clusters the data with high probability. However, noise in the density estimates can lead to erroneous modes and incoherent cluster assignments. A novel clustering algorithm, Component-wise Peak-Finding (CPF), is proposed to remedy these issues. The improvements are twofold: 1) the assignment methodology is improved by applying the density peaks methodology within level sets of the estimated density; 2) the algorithm is not affected by spurious maxima of the density and hence is competent at automatically deciding the correct number of clusters. We present novel theoretical results, proving the consistency of CPF, as well as extensive experimental results demonstrating its exceptional performance. Finally, a semi-supervised version of CPF is presented, integrating clustering constraints to achieve excellent performance for an important problem in computer vision.
An Adaptive Fuzzy C-Means segmentation and deep learning model for efficient mammogram classification using VGG-Net

Breast cancer mortality can be prevented by only early, accurate mammography screening and diagnosis. Although CNN-based computer-aided diagnosis (CAD) systems for breast cancer have made tremendous progress recently, accurate identification of mammography lesions is still difficult because of poor signal-to-noise ratio (SNR) and physiological features. In this manuscript, an Adaptive Fuzzy C-Means Segmentation and Deep Learning Model for Efficient Mammogram Classification Using VGG-Net (AFCM-DCNN) is proposed. The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image by adjusting the pixel contrast. The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. But in conventional AFCM technique, the centroid values get generated randomly, which consumes more computational time. Hence to enhance the performance of traditional AFCM, centroid value is optimally chosen by means of optimization algorithm. A technique for classifying images called DCNN analyses the input image and categorizes it as either benign, malignant and normal. The method extracts the features of the image and train with VGG-16 Net classifier. The neurons at the output layer have been designed to compute Class Centric Disease Support (CCDS) towards various classes. Accordingly, the mammogram class is identified towards detecting the brain tumor. The performance of the proposed method AFCM-DCNN exhibits higher accuracy of 29.3%, 25.6% and 24.6%, higher sensitivity of 15.4%, 16.6% compared with the existing methods. Therefore, in future work, hope to enhance the performance depending on transfer learning with similar data.
CR-TransR: A Knowledge Graph Embedding Model for Cultural Domain

As a combination of information computing technology and the cultural field, cultural computing is gaining more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project “Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this article proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.
Monocular out-of-plane vibration measurement using parametric homography

Vision-based measurement is an emerging method for vibration tests due to its non-contact nature and high spatial resolution, which make the full-field measurement possible. Conventional vision-based measurement requires two or more cameras to produce an out-of-plane response measurement. However, the additional cost of another camera and synchronization system makes applying this kind of measurement method to real-world engineering projects more challenging. This paper presents a single-camera method for measuring the out-of-plane vibration of plate structures. To accomplish that, the image variation induced by the motion of a plane is represented using a motion-parametric homography. Under the Lucas–Kanade framework, the motion-parametric homographies are estimated separately for each region of interest (ROI) between images, and the out-of-plane displacement response is obtained concurrently. The proposed method’s results are confirmed in good agreement with those of the laser Doppler vibrometer through an impact test on a steel plate structure.
Gated recurrent unit decision model for device argumentation in ambient assisted living

The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
MMDataLoader: Reusing Preprocessed Data Among Concurrent Model Training Tasks

Data preprocessing plays an important role in deep learning, which directly affects the training efficiency. Data preprocessing is performed on the CPU. The preprocessed data are then fed to the models that are trained on the GPU. We observed that data preprocessing on the CPU can potentially create a bottleneck in the entire process of a model training task. In order to tackle this issue, we have developed MMDataLoader, which enables reusing preprocessed data among multiple model training tasks. MMDataLoader automatically constructs a data preprocessing pipeline based on each task’s specific preprocessing workflow, allowing for maximum data reuse and reduced computing workload on the CPU. Unlike conventional data loaders that operate at the task level and provide data provision services to specific training tasks, MMDataLoader operates at the server level and provides data for all concurrently running tasks. We have conducted extensive experiments. The results show that MMDataLoader can significantly increase preprocessing throughput without affecting model convergence when compared to conventional methods where model training tasks are executed concurrently. For instance, with three tasks running, the preprocessing throughput can increase by 1.6x to 3.15x, depending on the tasks being executed and the proportion of preprocessing operations that are shared among them.
An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control

Multi-agent reinforcement learning (MARL) algorithms based on trust regions (TR) have achieved significant success in numerous cooperative multi-agent tasks. These algorithms restrain the Kullback–Leibler (KL) divergence (i.e., TR constraint) between the current and new policies to avoid aggressive update steps and improve learning performance. However, the majority of existing TR-based MARL algorithms are on-policy, meaning that they require new data sampled by current policies for training and cannot utilize off-policy (or historical) data, leading to low sample efficiency. This study aims to enhance the data efficiency of TR-based learning methods. To achieve this, an approximation of the original objective function is designed. In addition, it is proven that as long as the update size of the policy (measured by the KL divergence) is restricted, optimizing the designed objective function using historical data can guarantee the monotonic improvement of the original target. Building on the designed objective, a practical off-policy multi-agent stochastic policy gradient algorithm is proposed within the framework of centralized training with decentralized execution (CTDE). Additionally, policy entropy is integrated into the reward to promote exploration, and consequently, improve stability. Comprehensive experiments are conducted on a representative benchmark for multi-agent MuJoCo (MAMuJoCo), which offers a range of challenging tasks in cooperative continuous multi-agent control. The results demonstrate that the proposed algorithm outperforms all other existing algorithms by a significant margin.
Real and Synthetic Punjabi Speech Datasets for Automatic Speech Recognition

Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.
Fractal modeling of oil and gas geochemical data in the Taiwan Strait Basin

This study investigated the fractal characteristics of oil and gas indicators in the Taiwan Strait Basin using multifractal methods. The results revealed that these indicators can be categorized into five groups through cluster analysis, principal component analysis, and multifractal analysis. Acidolysis hydrocarbon indicators exhibited strong multifractal characteristics, while other indicators showed weak multifractal characteristics. The high values of Polycyclic aromatic hydrocarbons (F) indicators were mainly found in the southwest direction of the study area, whereas the high values of total aromatic hydrocarbons and their derivatives (UV) indicators were distributed in the eastern part. Erosion carbonate (ΔC) indicators were predominantly located in the western part, while the distribution of acid extractable hydrocarbons (A) and pyrolytic hydrocarbons (P) indicators was sporadic. The distribution of Erosion carbonate (ΔC) indicators in the Jinjiang Depression resembled that in the Jiulongjiang Depression, but the distribution of other indicators varied. The S-A method was employed to identify oil and gas anomaly target areas and predict and evaluate favorable prospect zones in the basin. This study provides a theoretical foundation for future oil and gas exploration and evaluation in the Taiwan Strait.
The nexus of supply chain performance and blockchain technology in the digitalization era: Insights from a fast-growing economy

This article extends the coverage of previous studies and addresses persistent gaps in the literature by assessing the simultaneous effects of adopting blockchain technology (BCT) on supply chain performance. It empirically examines the indirect effect of BCT adoption on supply chain performance through the mediating variables of supply chain agility, adaptability, transparency and coordination, based on the expanded triple A supply chain concept. The study analyzes data from manufacturing firms listed on the Indonesian Stock Exchange. The findings align with propositions derived from the Resource Based View (RBV), in which capabilities viewed as mediators—rather than testing for direct effects of BCT on organizational performance—are the most likely scenario for multifaceted supply chain operations. This study contributes to the ongoing debate on the relationship between BCT and organizational performance. The research offers insights for policymakers and blockchain vendors into this relationship.
Performance evaluation of generative adversarial networks for generating mugshot images from text description

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
An answer set programming-based implementation of epistemic probabilistic event calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.
Fractal modeling for geochemical data of deep-sea surface sediments: A case study from Zhongsha Island, Southern China Sea

This study applied principal component analysis (PCA), concentration-area (C-A) and spectrum-area (S-A) fractal models to identify geochemical element combination anomalies related to heavy metals from the data for 43 geochemical elements in surface sediments of the Zhongsha Islands. The relationships between these 43 elements were first examined by applying correlation and cluster analysis to the surface sediment sample dataset. The multifractal spectral functions of the 43 elements were then calculated and all elements were classified using the curve shape of the spectral function. The results of classification were consistent with that of cluster analysis, thereby verifying the reliability of fractal analysis of the study area. PCA was then used to identify element combination anomalies and to assess high-value areas of element contents, and the S-A model was used to distinguish between the anomaly and background signal. The results of this multiple-analysis approach show that the element combination anomaly is mainly located in the deep-sea plain area, with a few elevated values in the Zhongsha South Basin. This study showed that quantitative evaluation of anomalies of multi-element associations can effectively delineate a target area and that anomalies of element associations extracted by the S-A fractal method can provide a more accurate outline areas containing high contents of combined elements. This study can act as a theoretical reference for the environmental assessment of the Zhongsha sea area.
Performance counter dataset for behavioural biometric purpose

In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.