Reviewerly/exHarmony huggingface dataset:
Comparasion of Error Rate Prediction Methods of C4.5 Algorithm for Balanced Data

C45 is a highly effective decision tree algorithm widely used for classification purposes. Compared to CHAID, Cart, and ID3, C4.5 generates decision trees that are easier to understand and does so in a faster manner. This is due to C4.5 selecting attributes based on their information content during each stage of the process. After generating the decision tree model, its performance needs to be evaluated. One commonly used method is the prediction error rate, which assesses the model's performance. The prediction error rate consists of two approaches: the train error rate, which employs the same data for both building and testing the model, potentially leading to overfitting, and the test error rate, which divides the data into training and testing sets. The test error rate includes cross validation techniques such as Leave One Out Cross Validation (LOOCV), Hold Out (HO), and k-folds cross validation. Considering these factors, this research focuses on comparing the three cross-validation methods for predicting error rates applied to the C4.5 algorithm. The study utilizes artificially generated data with a normal distribution, including univariate, bivariate, and multivariate datasets with various combinations of mean differences and correlations. Different correlation structures are applied between two relevant variables and between relevant and irrelevant variables in the bivariate and multivariate data, including three correlation levels: no correlation, moderate correlation, and high correlation. This research findings that k-folds cross validation is the most suitable cross validation method to apply to C4.5.
Soft-Tissue Driven Craniomaxillofacial Surgical Planning

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.
Research on Question Answering for Knowledge graph of Aircraft PHM Fault

A question recognition method based on BERT-BiLSTM-ATT-CRF is proposed to solve the problem of entity recognition difficulties faced by Question answering in the field of aircraft PHM. First, the BERT model is used for preprocessing, and then the entity information in the question is further processed by the BiLSTM-ATT-CRF. Then, the Bayesian classifier is used to obtain the question classification. Finally, the Knowledge graph is used to realize intelligent question answering in the field of aircraft PHM. After experimental comparison, the proposed BERT-BiLSTM-ATT-CRF model has better recognition ability in the field of aircraft PHM, with accuracy, recall, and F1 values of 72.236%, 73.563%, and 72.8995% on the aircraft PHM corpus, respectively. The implementation of intelligent Q&A in the field of aircraft PHM is of great significance for the intelligent development of aircraft PHM.
Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

This study aims to solve the problem of limited learning efficiency caused by information overload and resource diversity in online course learning. We adopt a recommendation algorithm that combines knowledge graph and collaborative filtering, aiming to provide an application that can meet users’ personalized learning needs and consider the semantic information of learning resources. In addition, this article collects and models implicit data in online courses and compares the impact of video and text learning resources on user learning needs under different weights in order to deeply understand the different contributions of video and text learning resources to meeting learning needs. The experimental results show that the video high-weight experimental group performs better than the text high-weight experimental group; students tend to prefer video resources. This experiment can help students cope with the challenges brought by numerous types of learning resources and provide personalized and high-quality learning experiences for learners. At the same time, adjusting and innovating teaching models for teachers has great reference value.
Detection and classification of plasmodium parasites in human blood smear images using Darknet with YOLO

Plasmodium parasites cause malaria, a deadly, contagious, and potentially fatal blood disease spread by mosquitoes. Under a qualified technician&apos;s microscope, the traditional and most common method of diagnosing malaria is visually examining human blood smears for parasite-infected red blood cells. The method, as mentioned earlier, is time-consuming and inefficient, and the diagnosis depends on the examiner&apos;s experience and knowledge. Automated image recognition techniques based on image processing were previously applied to malaria blood smears for diagnosis. However, the practical performance still needs to be improved. Hence, it encouraged researchers to develop malaria detection and diagnostics quickly, easily, and efficiently. The study&apos;s primary goal is to detect cells from images of multiple cells in human blood smears on microscope slides, detect whether a particular cell is infected, and identify parasite species and life cycle stages. YOLOv4 and Darknet53 models were trained to do as such, and were able to achieve a 98.71% for detection accuracy and 97.08%, and 97.36% for parasite type accuracy and life-cycle stage accuracy, respectively. The proposed model also achieved an average runtime of 237.55ms per image in detection, 66.5ms in type classification, and 58.75ms in life-cycle stage classification.
Influence of shortest path algorithms on energy consumption of multi-core processors

Modern multi-core processors, operating systems and applied software are being designed towards energy efficiency, which significantly reduces energy consumption. Energy efficiency of software depends on algorithms it implements, and, on the way, it exploits hardware resources. In the paper, we consider sequential and parallel implementations of four algorithms of shortest paths search in dense weighted graphs, measure and analyze their runtime, energy consumption, performance states and operating frequency of the Intel Core i7-10700 8-core processor. Our goal is to find out how each of the algorithms influences the processor energy consumption, how the processor and operating system analyze the workload and take actions to increase or reduce operating frequency and to disable cores, and which algorithms are preferable for exploiting in sequential and parallel modes. The graph extension-based algorithm (GEA) appeared to be the most energy efficient among algorithms implemented sequentially. The classical Floyd-Warshall algorithm (FW) consumed up to twice as much energy, and the blocked homogeneous (BFW) and heterogeneous (HBFW) algorithms consumed up to 52.2 % and 21.2 % more energy than GEA. Parallel implementations of BFW and HBFW are faster by up to 4.41 times and more energy efficient by up to 3.23 times than the parallel implementation of FW and consume less energy by up to 2.22 times than their sequential counterparts. The sequential GEA algorithm consumes less energy than the parallel FW, although it loses FW in runtime. The multi-core processor runs FW with an average frequency of 4235 MHz and runs BFW and HBFW with lower frequency of 4059 MHz and 4035 MHz respectively.
Data augmentation via context similarity: An application to biomedical Named Entity Recognition

In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.
A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.
Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI

Generative AI (GenAI), encompassing image generation and large language models (LLMs), has opened new avenues for gameplay experiences. This paper introduces "1001 Nights", a narrative game centered on GenAI. Drawing inspiration from Wittgenstein's note, "The limits of my language mean the limits of my world", the game exemplifies the concept of language as reality. The protagonist, Shahrzad, possesses a unique power: specific keywords, such as "sword" or "shield", when spoken by others in tales, materialize as tangible weapons, serving as battle equipment against the King. Players guide the LLM-driven King in co-creating narratives, with GPT-4 employing LLM reasoning methods to ensure story consistency. As these narratives progress, the depicted world is dynamically generated and visualized through Stable Diffusion, blurring the boundaries between narrative and in-game reality. This fusion of interactive storytelling combines gameplay paradigms and story together with dynamic content generation. Players not only aim to alter Shahrzad's fate from the original folklore, but also leverage the power of natural language to shape the game's world. With this example, we propose the term "AI-Native games" to categorize innovative games where GenAI is fundamental to the game's novel mechanics and very existence.
Improved Broad Learning System for Birdsong Recognition

Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, dMFCC, and dsquence is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition.
Anticipatory Hand Glove: Understanding Human Actions for Enhanced Interaction

Perceived real-time interactions between humans and a metaverse often require synchronizing actions in the virtual and real world. Latency is a main blocking factor due to processing and transmitting data over long distances because cloud data centres that deploy metaverse locate far away. We present an anticipatory computing solution to predict precisely human actions grasping objects based on hand kinesthetics leveraging smart gloves with IMU and flexion sensors. We demonstrated that the gain from the anticipation of up to several hundred milliseconds could compensate for computing and transmission latency, enabling immersive interactions over extremely long distances. Our solution is flexible and is free from locking into particular smart glove vendors, making it easy for future extensions.
Eye Blinking for Command Generation Based on Deep Learning

Due to progress in the field of deep learning in order to find and track objects through the use of computer vision in the service of large segments of the population, as it was adopted in the field of serving people b with special needs for the sake of dialogue and implementation of many requests in this research, a series of commands for use by people with special needs with speech problems or paralysis, where the ability to use the eye blink is very useful for social communication, were developed. In this research, the orders needed by the target people with special needs were studied, and (11) commands were identified that can be increased according to the intended sample. A table of commands was built depending on the length of the eye blink period. By creating a modified CNN: Convolutional Neural Network structure and training it on 2 different database, deep learning was used to identify and determine if the eye is closed or open (mrlEye2018 and Closed Eye in the Wlid:CEW).It was then followed by a test on all of the chosen examples, in different contexts and at various ages. The test on the data yielded excellent results, with 99% percent accuracy on data from test samples and 97.5% and 96% percent accuracy on training data in each case. To check the cases (11) suggested commands, the proposed system was evaluated on a collection of videos taken in real time and under normal recording circumstances through a camera, and the correctness of generating the codes that was proposed was 94% percent on four tries in each of the above instances.
The application of social recommendation algorithm integrating attention model in movie recommendation

To improve the accuracy of recommendations, alleviate sparse data problems, and mitigate the homogenization of traditional socialized recommendations, a gated recurrent neural network is studied to construct a relevant user preference model to mine user project preferences. Through the Preference Attention Model Based on Social Relations (PASR), this study extracts user social influence preferences, performs preference fusion, and obtains a Recommendation Algorithm Based on User Preference and Social Influence (UPSI). The study demonstrates that the UPSI algorithm outperforms other methods like the SocialMF algorithm, yielding improved recommendation results, higher HR values, and larger NDCG values. Notably, when the K value equals 25 in Top-K recommendation and using the CiaoDVDs dataset, the NDCG value of the UPSI algorithm is 0.267, which is 0.120 higher than the SocialMF algorithm's score. Considering the user's interaction with the project and their social relationships can enhance the effectiveness of recommendations. Unlike other variants, the UPSI algorithm achieves a maximum hit rate HR value of 0.3713 and NDCG value of 0.2108 in the Douban dataset. In the CiaoDVDs dataset, the maximum hit rate HR value of UPSI is 0.4856, 0.0333 higher than UPS-A, 0.0601 higher than UPS, and 0.0901 higher than UP. Research methods can effectively improve the homogenization problem of traditional socialized recommendations, increase algorithm hit rates and NDCG values. Compared to previous studies, research methods can more fully explore the preference correlation between users, making recommended movies more in line with user requirements.
RF Fingerprint Identification for 5G Mobile Device Based on Transient Features

In this paper, we propose an radio frequency (RF) fingerprint extraction method based on transient features for 5G mobile phones. From the perspective of network access security, the RF fingerprint of the physical random access channel (PRACH) signal has been studied. We find that the same PRACH preamble from the same device has the same transient features, while different PRACH preambles from the same device have different transient features. However, with a sufficiently large training set, classification can be achieved with an accuracy of 75.8% in the mixed-preamble case. 4 mobile phones are used in the experiment and a complete network access system has been built through OpenAirInterface.
Personalized Learning Resource Recommendation using Differential Evolution-Based Graph Neural Network: A GraphSAGE Approach

This paper proposes a novel personalized recommendation algorithm for learning resources based on differential evolution(DE) and graph neural networks(GNN). By representing learners and learning resources as graph data and incorporating a multi-head attention mechanism, we have developed an effective method for personalized recommendation. The differential evolution algorithm is utilized to optimize model hyperparameters, resulting in improved recommendation performance. We conducted experiments on a widely used personalized learning resource dataset, comparing our method with several classical recommendation algorithms. The results demonstrate significant advantages of our approach in terms of accuracy, recall, $\Gamma 1$ score, and RMSE value.
Composite AI for Behavior Analysis in Social Interactions

Social interactions are fundamental to human life. Accurately identifying and interpreting verbal and non-verbal cues is essential for analyzing human behavior and human-machine interactions. The complexity of these interactions, along with the different communication signals, and their varying frequencies is a challenge that Deep Neural Networks cannot yet address. Composite AI, which combines Deep Learning (DL) and traditional AI methods, emerges as a potential approach. We propose a framework comprising three main modules: feature extraction, episode detection, and activity interpretation. The feature extraction module uses DL methods to extract relevant information about the environment, interacting partners, and context of the interaction. By combining these features with rule-based systems, we restrict the analysis to key episodic components, effectively reducing the false positive detection rate. Classifiers are then used to identify and recognize specific sub-events. We show our system’s effectiveness in detecting communication cues on recorded sessions of a standardized autism diagnostic test (ADOS-2) involving autistic children, where detecting such cues can be highly challenging. In this scenario, we achieve 87% and 89% accuracy for verbal and non-verbal requests, respectively, and zero false positives. This comprehensive framework can significantly enhance social interaction analysis, enabling more effective automated event analysis. We also highlight the importance of sub-event segmentation and the components that could be improved with additional algorithmic combinations or data-driven improvements of the detection systems.
Research on infrared insulator image recognition based on improved YOLOv5

As the scale of transmission lines is expanding, the safety of transmission line operation is getting more and more attention; insulators as an important component of transmission lines, the use of insulators greatly affects the safe operation of transmission lines. The traditional depth recognition algorithm can not achieve effective recognition of infrared images, in order to achieve rapid recognition of insulators in complex environments, this paper proposes the recognition of infrared insulator images based on the improved YOLOv5 depth neural network detection algorithm. Firstly, Ghost convolution was introduced into the backbone network to speed up detection and network lightweighting; secondly, to enhance the multi-scale convergence of networks, improved GAM attention module added behind the neck network; in addition, the network introduces an EIOU loss function for convergence; finally, validation of this improved algorithm with the collected infrared insulator dataset. The results show that the improved algorithm in this paper achieves 91.2% accuracy and 92.1% mAP on the infrared insulator dataset, which improves 2.3% and 4.1% compared with the test results of YOLOv5 model, simultaneous detection speed up to 91 FPS, which meets the real-time requirement.
Measuring nonstabilizerness via multifractal flatness

Universal quantum computing requires nonstabilizer (magic) quantum states. Quantifying the nonstabilizerness and relating it to other quantum resources is vital for characterizing the complexity of quantum many-body systems. In this work, we prove that a quantum state is a stabilizer if and only if all states belonging to its Clifford orbit have a flat probability distribution on the computational basis. This implies, in particular, that multifractal states are nonstabilizers. We introduce multifractal flatness, a measure based on the participation entropy that quantifies the wave-function distribution flatness. We demonstrate that this quantity is analytically related to the stabilizer entropy of the state and present several examples elucidating the relationship between multifractality and nonstabilizerness. In particular, we show that the multifractal flatness provides an experimentally and computationally viable nonstabilizerness certification. Our work unravels the direct relation between the nonstabilizerness of a quantum state and its wave-function structure.