Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Cross-Project Defect Prediction Using Encoder Networks and Transfer Learning

Heterogeneous cross-project defect prediction (HCPDP) aims to predict defects in new software projects using defect data from previous software projects where the source and target projects have some different metrics. Most existing methods only find linear relationships in the software defect datasets. Additionally, these methods use multiple defect datasets from different projects as source datasets. In this paper, we propose a novel method called heterogeneous cross-project defect prediction using encoder networks and transfer learning (ENTL). ENTL uses encoder networks to extract the important features from source and target datasets. Also, to minimize the negative transfer during transfer learning, we used an augmented dataset that contains pseudo-labels and the source dataset. Additionally, we have used a single dataset to train the model. To evaluate the performance of the ENTL approach, 16 datasets from four publicly available software defect projects were used. Furthermore, we compared the proposed method with four HCPDP methods namely EGW, HDP_KS, CTKCCA and EMKCA, and one WPDP method from existing literature. The proposed method on average outperforms the baseline methods in terms of PD, PF, F1-score, G-mean and AUC.
The role of pudendal nerve block in hemorrhoid surgery: a systematic review and meta-analysis of double-blind randomized controlled trials

Background Pudendal nerve block (PNB) is a commonly used anesthesia method that has been widely used in postoperative analgesia for hemorrhoids in recent years. Therefore, we conducted a systematic review and meta-analysis of double-blind randomized controlled trials (RCTs) to analyze the effectiveness of PNB in postoperative analgesia for hemorrhoids. Methods Relevant data and studies published from inception until August 14, 2023, were retrieved from PubMed, Embase, and Web of Science to evaluate the beneficial effects of PNB for analgesia following hemorrhoidectomy. Results This meta-analysis included 6 double-blind RCTs comprising 501 patients. We evaluated the function of PNB in improving outcomes of postoperative analgesia of hemorrhoids. Visual analogue scale (VAS) scores on postoperative within 6 h (MD, −3.04; 95% CI, −4.13 to −1.95; P < 0.0001), 12 h (MD, −3.14; 95% CI, −3.87 to −2.40; P < 0.0001), and 24 h (MD, −2.25; 95% CI, −2.95 to −1.55; P < 0.0001) were enhanced by the application of PNB, but not in 48 h (MD, −2.54; 95% CI, −5.29 to 0.20; P = 0.07). Conclusion Pudendal nerve block (PNB) could effectively relieve postoperative pain of hemorrhoids. However, our results still need to be confirmed by multi-center clinical studies.
HiCARE: Hierarchical Clustering Algorithm for Road Service Routing Enhancement

The potholes in the road cause substantial monetary and physical damage to the ongoing traffic. Also, it requires extensive maintenance, especially in areas where the temperature may go down below freezing point. One of the major causes of potholes is the rain or running water that is accumulated in the cracks and later on due to low temperature, changes into ice. Then, ice forms a larger volume for the same amount of water after expanding and causes cracks to expand and at some point become a pothole. They are also the cause of traffic congestion. Therefore, pothole repair needs urgent attention and is one of the routine road maintenance tasks. Kansas City is one of the cities with established social data networks for residents to request road services to mitigate the problem. Although the policymakers have not ignored the issue and rudimentary patching policies are in place, unfortunately, that does not provide efficient road maintenance routes. This paper proposes a Hierarchical Clustering Algorithm for Road Service Routing Enhancement (HiCARE). HiCARE is a practical framework, that makes use of open data in order to optimize the route for maintenance. The optimization considers the time and date of reported potholes, locations, traffic situations, weather conditions, type of patch or other repairs needed, and crew availability, to mention some. This research has characterized spatiotemporal pothole density by analyzing the past 16 years of pothole data from the Open Data KC 311 in Kansas City. HiCARE enhances the NP-hard Traveling Salesperson Problem (TSP) by classifying potholes into layers of clusters. HiCARE finds a cluster’s shortest possible pothole route by identifying each cluster group’s entrance and exit pothole points. Moreover, it modifies the final routes to skip any local minima. The empirical research and analysis indicate that HiCARE significantly reduces the traversing distance and is faster in computation time when compared to typical TSP heuristic algorithms for daily resolution scheduling.
The Fusion of Wide Field Optical Coherence Tomography and AI: Advancing Breast Cancer Surgical Margin Visualization

This study explores the integration of Wide Field Optical Coherence Tomography (WF-OCT) with an AI-driven clinical decision support system, with the goal of enhancing productivity and decision making in breast cancer surgery margin assessment. A computationally efficient convolutional neural network (CNN)-based binary classifier is developed using 585 WF-OCT margin scans from 151 subjects. The CNN model swiftly identifies suspicious areas within margins with an on-device inference time of approximately 10 ms for a 420 × 2400 image. In independent testing on 155 pathology-confirmed margins, including 31 positive margins from 29 patients, the classifier achieved an AUROC of 0.976, a sensitivity of 0.93, and a specificity of 0.98. At the margin level, the deep learning model accurately identified 96.8% of pathology-positive margins. These results highlight the clinical viability of AI-enhanced margin visualization using WF-OCT in breast cancer surgery and its potential to decrease reoperation rates due to residual tumors.
Exploring explainable AI: category theory insights into machine learning algorithms

Explainable artificial intelligence (XAI) is a growing field that aims to increase the transparency and interpretability of machine learning (ML) models. The aim of this work is to use the categorical properties of learning algorithms in conjunction with the categorical perspective of the information in the datasets to give a framework for explainability. In order to achieve this, we are going to define the enriched categories, with decorated morphisms, LearnLearn , ParaPara and MNetMNet of learners, parameterized functions, and neural networks over metric spaces respectively. The main idea is to encode information from the dataset via categorical methods, see how it propagates, and lastly, interpret the results thanks again to categorical (metric) information. This means that we can attach numerical (computable) information via enrichment to the structural information of the category. With this, we can translate theoretical information into parameters that are easily understandable. We will make use of different categories of enrichment to keep track of different kinds of information. That is, to see how differences in attributes of the data are modified by the algorithm to result in differences in the output to achieve better separation. In that way, the categorical framework gives us an algorithm to interpret what the learning algorithm is doing. Furthermore, since it is designed with generality in mind, it should be applicable in various different contexts. There are three main properties of category theory that help with the interpretability of ML models: formality, the existence of universal properties, and compositionality. The last property offers a way to combine smaller, simpler models that are easily understood to build larger ones. This is achieved by formally representing the structure of ML algorithms and information contained in the model. Finally, universal properties are a cornerstone of category theory. They help us characterize an object, not by its attributes, but by how it interacts with other objects. Thus, we can formally characterize an algorithm by how it interacts with the data. The main advantage of the framework is that it can unify under the same language different techniques used in XAI. Thus, using the same language and concepts we can describe a myriad of techniques and properties of ML algorithms, streamlining their explanation and making them easier to generalize and extrapolate.
Model Uncertainty for Predicting the Bearing Capacity of Sand Overlying Clay

In this paper, 62 centrifuge tests are collected from the literature and used to perform a statistical evaluation of the model factor for conventional methods to calculate the bearing capacity of dense sand overlying clay. The model factor is defined as a ratio of measured capacity to calculated capacity. The variations of the model factor with input parameters are established as regression equations based on the results of finite-element limit analysis (FELA). Conventional methods that multiply by regression equations are shown to be more accurate on average for all data sets. Further verification exercise from 27 additional centrifuge tests indicates the modified method could also be applicable for medium dense sand overlying clay. The mean and coefficient of variation of the model factor for the modified methods are finally characterized as a lognormal random variable with a mean of 1.06 and coefficient of variation of 0.17. DOI: 10.1061/(ASCE)GM.1943-5622.0000898. © 2017 American Society of Civil Engineers.
Device Authentication using Homomorphic Encryption

: In the digital era, data security in files, databases, accounts, and networks is of utmost importance. Due to the sensitive, private, or protected information they contain, databases are a common target for cyber attacks. To assess threats to data and lower the risk involved with data processing and storage, data security is crucial. Therefore, it is necessary to find solutions to the data security problems. It has become crucial to be up-to-date on different encryption technologies and trends due to the internet's growing sophistication and dependence on internet data transmission. It can help protect confidential information and sensitive data and enhance the security of the system. In this paper, we propose a homomorphic encryption-based device authentication method while safeguarding template data. Homomorphic encryption technology has the capability of computing on encrypted data, making it more difficult for attackers to get their hands on the original template. In this study, the CKKS technique was used, which supports the approximation of real or complex numbers.
Genome-wide detection of Wolbachia in natural Aedes aegypti populations using ddRAD-Seq

Background Wolbachia, an endosymbiotic bacterium, is globally used to control arboviruses because of its ability to block arboviral replication and manipulate the reproduction of Wolbachia host, Aedes aegypti. Polymerase chain reaction (PCR)-based Wolbachia detection has been recently reported from natural Ae. aegypti populations. However, due to the technical limitations of PCR, such as primer incompatibility, PCR-based assays are not sufficiently reliable or accurate. In this study, we examined double digestion restriction site-associated DNA sequencing (ddRAD-Seq) efficiency and limitations in Wolbachia detection and quantification in field-collected Ae. aegypti natural populations in Metro Manila, the Philippines, compared with PCR-based assays. Methods A total of 217 individuals Ae. aegypti were collected from Metropolitan Manila, Philippines. We separated it into 14 populations consisting of 7 female and male populations. We constructed a library for pool ddRAD-Seq per population and also screened for Wolbachia by PCR assays using wsp and 16S rRNA. Wolbachia density per population were measured using RPS17 as the housekeeping gene. Results From 146,239,637 sequence reads obtained, 26,299 and 43,778 reads were mapped across the entire Wolbachia genome (with the wAlbA and wAlbB strains, respectively), suggesting that ddRAD-Seq complements PCR assays and supports more reliable Wolbachia detection from a genome-wide perspective. The number of reads mapped to the Wolbachia genome per population positively correlated with the number of Wolbachia-infected individuals per population based on PCR assays and the relative density of Wolbachia in the Ae. aegypti populations based on qPCR, suggesting ddRAD-Seq-based semi-quantification of Wolbachia by ddRAD-Seq. Male Ae. aegypti exhibited more reads mapped to the Wolbachia genome than females, suggesting higher Wolbachia prevalence rates in their case. We detected 150 single nucleotide polymorphism loci across the Wolbachia genome, allowing for more accurate the detection of four strains: wPip, wRi, TRS of Brugia malayi, and wMel. Conclusions Taken together, our results demonstrate the feasibility of ddRAD-Seq-based Wolbachia detection from field-collected Ae. aegypti mosquitoes.
A Novel Deep Multi-Instance Convolutional Neural Network for Disaster Classification From High-Resolution Remote Sensing Images

The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images.
THE USE OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR EDUCATION AND SCIENTIFIC RESEARCH

Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GAI), is increasingly being used in education and scientific research. This article explores the use of AI applications, such as ChatGPT, Scite, and Litmaps in education and scientific research. This kind of application offers several advantages but should be used with certain caution. Any inference must be verified made by AI tools to ensure their validity. While current AI technologies can quickly analyze vast amounts of data or represent networks of citations, their results may be influenced by the quality and comprehensiveness of databases. Thus, human experts must review and verify the results carefully to ensure their accuracy and originality. Additionally, academic institutions should educate students and researchers about the risks of utilizing GAI models while providing comprehensive guidelines to prevent plagiarism.
Planning and projecting critical human resource needs : the relationship between hope , optimism , organizational commitment , and turnover intention among U . S . Army reserve soldiers

The U.S. Army Reserve struggles with retention and faces difficulties with soldier turnover. The intent of the study was to assess key psychological variables such as optimism and hope to establish how those traits might impact soldiers' level of organizational commitment and turnover intention in the Army. The sample consisted of 124 U.S. soldiers selected from an Army Reserve unit in Houston, TX. The results of the study indicate that hope and optimism correlate with affective commitment but not with continuance commitment. In addition, higher hope, optimism, affective commitment, and continuance commitment all resulted in correlations with lower turnover intention. Affective commitment and turnover intention possessed the strongest relationship. Significant levels of hope and optimism with affective commitment supports the idea that soldiers who are emotionally attached to the organization will most likely stay. Results of this study could provide Army Human Resource Command a basis for a strategy to retain key personnel.
A Synergistic Antibacterial Study of Copper-Doped Polydopamine on Ti3C2Tx Nanosheets with Enhanced Photothermal and Fenton-like Activities

In response to the trend of drug−resistant and super bacteria, the existing single antibacterial methods are not sufficient to kill bacteria, and the development of multifunctional antibacterial nanomaterials is urgent. Our study aims to construct copper−doped polydopamine−coated Ti3C2Tx (CuPDA@Ti3C2Tx) with an enhanced photothermal property and Fenton−like activity. The nanocomposite hydrogel consisting of CuPDA@Ti3C2Tx and alginate can improve the antioxidant activity of two−dimensional MXene nanosheets by coating them with a thin layer of PDA nanofilm. Meanwhile, Cu ions are adsorbed through the coordination of PDA−rich oxygen−containing functional groups and amino groups. Calcium ions were further used to crosslink sodium alginate to obtain antibacterial hydrogel materials with combined chemotherapy and photothermal therapy properties. The photothermal conversion efficiency of CuPDA@Ti3C2Tx is as high as 57.7% and the antibacterial rate of Escherichia coli reaches 96.12%. The photothermal effect leads to oxidative stress in bacteria, increases cell membrane permeability, and a high amount of ROS and copper ions enter the interior of the bacteria, causing protein denaturation and DNA damage, synergistically leading to bacterial death. Our study involves a multifunctional synergistic antibacterial nanodrug platform, which is conducive to the development of high−performance antibacterial agents and provides important research ideas for solving the problem of drug−resistant bacteria.
Streaming ASR Encoder for Whisper-to-Speech Online Voice Conversion

Whispered speech is a quiet voice without vocalization. One of the common cases of using whispered speech is a technique that can help overcome stuttering. But whispered speech can be uncomfortable and difficult to understand in everyday communication. To address these problems, we propose a method of low-delayed whisper-to-speech voice conversion, which can be useful in real life communication of people with disordered speech. As part of our research, we study the impact of streaming Automatic Speech Recognition models on the quality of voice conversion, comparing different streaming models and methods for model adaptation to streaming settings, and showing the importance of using such models in cases of low-delayed voice conversion.
A multi-scale pyramid feature fusion-based object detection method for remote sensing images

ABSTRACT Object detection is a basic and challenging task in remote sensing image analysis that has received extensive attention in recent years. Feature fusion is one of the key steps in object detection. Most existing methods of feature fusion first complete the preliminary fusion of feature maps of different scales through ‘add’ or ‘concat’ operations, followed by using a single-scale convolution to further improve the fusion effect. However, due to the fact that multi-level features exhibit multi-scale representations, the fusion effect of existing methods is limited. To improve the efficiency of feature fusion, we propose a multi-scale pyramid feature fusion network, which performs multi-scale learning through multi-scale convolution kernels to complete multi-level feature fusion more effectively. Then we propose a lightweight decoupled head, which alleviates the conflict between the classification task and the localization task. We conducted experiments on the dataset of object detection in aerial images (DOTA) dataset and the HRSC2016 dataset to verify our proposed methods. The results show that the performance of our proposed methods is better than other existing methods, with an mAP of 73.3%, 67.6%, 65.0%, and 96.7% on the DOTA1.0, DOTA1.5, DOTA2.0, and HRSC2016 datasets, respectively. Meanwhile, the parameter quantity of the proposed model is 10.3 M, and the inference time is 5.1 ms, which meets the requirement of lightweight and ensures the timeliness of detection.
Developing machine learning models to predict methane and nitrogen oxide
 engine-out emissions from a heavy-duty natural-gas engine

—Heavy-duty engine manufacturers must comply with challenging and more stringent emission and greenhouse gas (GHG) regulations. Predicting engine emission behavior in system-level models with reasonable accuracy is advantageous for engine and powertrain development. Machine learning (ML) models are promising alongside 3D physics-based and one-dimensional models. In this study, five different ML models are trained using experimental engine data for emission prediction of methane (CH 4 ) and nitrogen oxides (NOx). The models are compared with an existing phenomenological engine model (GT-Power). The ML models include linear regression, Ridge regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results show that the RF model outperforms other models and a one-dimensional model regarding NOx emission prediction. The results of RF NOx and CH 4 emission prediction in the test set fit with 80% accuracy (±20 error margin). Also, 95% of test data points have less than 10% error compared to real experimental data.
Critical Insight into Pretransitional Behavior and Dielectric Tunability of Relaxor Ceramics

This model discussion focuses on links between the unique properties of relaxor ceramics and the basics of Critical Phenomena Physics and Glass Transition Physics. It indicates the significance of uniaxiality for the appearance of mean-field type features near the paraelectric-to-ferroelectric phase transition. Pretransitional fluctuations, that are increasing up to the size of a grain and leading to inter-grain, random, local electric fields are responsible for relaxor ceramics characteristics. Their impact yields the pseudospinodal behavior associated with “weakly discontinuous” local phase transitions. The emerging model redefines the meaning of the Burns temperature and polar nanoregions (PNRs). It offers a coherent explanation of “dielectric constant” changes with the “diffused maximum” near the paraelectric-to-ferroelectric transition, the sensitivity to moderate electric fields (tunability), and the “glassy” dynamics. These considerations are challenged by the experimental results of complex dielectric permittivity studies in a Ba0.65Sr0. 35TiO3 relaxor ceramic, covering ca. 250 K, from the paraelectric to the “deep” ferroelectric phase. The distortion-sensitive and derivative-based analysis in the paraelectric phase and the surrounding paraelectric-to-ferroelectric transition reveal a preference for the exponential scaling pattern for ε(T) changes. This may suggest that Griffith-phase behavior is associated with mean-field criticality disturbed by random local impacts. The preference for the universalistic “critical & activated” evolution of the primary relaxation time is shown for dynamics. The discussion is supplemented by a coupled energy loss analysis. The electric field-related tunability studies lead to scaling relationships describing their temperature changes.