Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Knowledge Graph Representation Learning with Text Information

Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as low-dimensional dense vectors, these tasks can improve efficiency significantly. However, limited by the sparseness and huge scale of the actual structure, representation learning only from a structural perspective can no longer meet the research needs. To improve the performance, researchers introduced auxiliary information into representation learning. This paper focuses on models using text as auxiliary information, dividing all text-combined models into two categories: Closed-world assumption models and Open-world assumption models. The former is limited by the model's demand for the structural representation of the graph itself, and can only predict the entities and relationships that already exist in the knowledge graph. The latter can handle entities that have not previously been seen during model training, and connect brand-new entities to the knowledge graph, which is more in line with the dynamic trend of the knowledge graph in real world. Open-world assumption models can be further subdivided into multiple types according different joint functions, such as alignment function, fusion function, score function and transformation function. This paper summarizes existing methods in detail and looks forward to future possible research directions.
YOLACT in Micro-Assembly Robot System

With the development of micro-assembly systems, the requirements for micro-assembly vision recognition and detection are getting higher and higher. Combining with deep learning, which has made a great splash in image processing, this paper uses the YOLACT instance segmentation algorithm to recognize and segment the targets in microscopic vision. ResNet-101 backbone is used in the experiment to obtain higher detection accuracy and mask quality. The experiment finally achieves an accuracy of 89.43% for the recognition of targets with boundary mAP values higher than 0.5.
Predicting the Risk of Death for Sepsis Based on Within-Class Mixup and Lightgbm

Sepsis is a disease with a high mortality rate in intensive care units. Rapid and accurate identification of the hospitalization risk of sepsis patients is helpful for doctors to intervene in time. To address the problem of complexity, diversity, and imbalance of medical data in hospital, the prediction method, named Mu-Lightgbm, is proposed. Selectively referring to the disease severity scoring system, a public critical care medicine database (MIMIC-III) is used, and demographic information and laboratorial examination data are used as characteristic variables. Firstly, according to the distribution and category of the sample data, the within-class Mixup method is used to augment the sample data to ensure the balance between each class. Secondly, the Lightgbm model for risk prediction is constructed and trained with the processed data. A total of 23741 sepsis patients are collected from the MIMIC-III database with a mortality rate of 19.03%. Five-fold cross-validation shows that the AUC of Mu-Lightgbm model and Lightgbm model are 0.93 and 0.91, respectively. Compared with the existing prediction models, the proposed model performs better in prediction accuracy, which can assist clinicians in more timely treatment.
Siamese Networks for Image Comparison and Discrepancy Localization: Siamese Networks in Exam Proctoring

With the prevalence of online learning and exams, a side camera view overlooking a student's computer screen and work area is recommended to compare the expected view on exam screen to that of student's screen and identify cheating. This work proposes a novel architecture of Siamese convolutional neural networks to automate the proctoring process by simultaneously detecting disallowed activities on computer screen and localizing the region in cases where such activities are carried out in a portion of screen only. While the proposed Siamese detector compares the base image to test image to detect features in the entire image to ascertain discrepancy, the Siamese localizer splits the image area in sections to perform regional feature comparisons to yield discrepant regions. Both components run in a single-shot mode and avoid multi-shot regressions. On a public web screenshot database, the proposed Siamese detector and localizer both achieve impressive results with detector achieving 96.4% accuracy on 20-way full-screen discrepancy detection task and localizer achieving 94.4% accuracy along with mean overall IoU of 0.72 over discrepant segments.
An Enhanced Naïve Bayes Model for Crime Prediction using Recursive Feature Elimination

It is no secret that crime affects everyone and has devastating socio-economic impact with long lasting effects. For this reason, law enforcement agents work tirelessly to reduce crime. However, despite their best efforts, crime still prevails and is constantly on the rise. Predictive approaches have shown positive results in mitigating this problem, hence in this paper, we have adopted the well-known Naïve Bayes algorithm to tackle crime. In this work, we augmented the classic Naïve Bayes with recursive feature elimination and applied it to crime prediction. When compared with the original Naïve Bayes, it was observed that our approach indeed improved the performance of Naïve Bayes by about 30\%. We further bench-marked our Naïve Bayes variant with other predictive algorithms such as Random Forests and Extremely Randomized Trees, and obtained results showed that our variant was equally as good and even better in some instances.
Forest Fire Detection Based on Improved YOLOv5

Traditional forest fire detection based on sensing devices has a small range, low sensitivity and high false alarm rate, while existing forest fire detection methods based on deep learning have poor identification effect and inaccurate identification of small smoke and fire targets in practical application. To solve the above problems, this paper proposes an improved YOLOv5 network forest fire detection method. Firstly, the image is divided into multiple grids. The adaptive anchor setting optimization method is used to generate the adaptive anchor parameters through the training of neural network, so that the network has certain pertinence in forest fire detection. Then CSPDarknet-53 was used to extract the feature network and train to get the optimal weight model, so as to detect the fireworks target in the image. In order to solve the problem of actual forest scenarios, the accuracy of the model is improved by building a high-quality forest fire data set. Using the CIoU loss function to improve the network detection capability, and the combination of Soft-NMS and DIoU-NMS post-processing to improve the inhibition effect of redundant bounding box and reduce the recall rate. The experimental results show that the improved algorithm has a higher detection speed than the traditional network in forest fire detection, and the model has a good multi-scale fire detection performance, which has a certain application value in the real scene forest fire detection.
Detection of Diffuse Auroral Events Based on LSTM

Discrete and diffuse aurora are important categories of auroras optically observed on the ground. Some of the mechanisms and regularities of diffuse auroras are still unclear. In this work, we formulate the automatic diffuse aurora detection to learn a classifier to recognize an all-sky auroral image sequences into diffuse and non-diffuse aurora with artificial intelligence technique. Unlike the discrete aurora, the luminosity of diffuse aurora is relatively weak, the boundaries are blur and the shapes are random. In view of this, we introduced the Long-Short Term Memory (LSTM) method to characterize the dynamic features of the aurora. For the flicker features between frames, we use CNN to simultaneously learn the transient temporal and spatial characteristics of the all-sky image sequence. Experimental results show that the proposed method can efficiently and accurately recognize diffuse aurora, which make it feasible to statistically analyze the diffuse aurora events based on a great amount of auroral image data.
Privacy Concerns for Visual Assistance Technologies

People who are blind share their images and videos with companies that provide visual assistance technologies (VATs) to gain access to information about their surroundings. A challenge is that people who are blind cannot independently validate the content of the images and videos before they share them, and their visual data commonly contains private content. We examine privacy concerns for blind people who share personal visual data with VAT companies that provide descriptions authored by humans or artifcial intelligence (AI). We frst interviewed 18 people who are blind about their perceptions of privacy when using both types of VATs. Then we asked the participants to rate 21 types of image content according to their level of privacy concern if the information was shared knowingly versus unknowingly with human- or AI-powered VATs. Finally, we analyzed what information VAT companies communicate to users about their collection and processing of users’ personal visual data through their privacy policies. Our fndings have implications for the development of VATs that safeguard blind users’ visual privacy, and our methods may be useful for other camera-based technology companies and their users.
ViT-based VQ-VAE Generative Network for Accompaniment Generation

In recent years, accompaniment generation has become one of the hottest topics in deep learning and many researchers work on it. However, most prior works are unstable and lack harmony, which makes the results they generate far from satisfying to human ears. To address this problem, we propose to combine accompaniment generation and music theory. Firstly, according to the characteristic of accompaniment that accompaniment itself has many fixed patterns, we employ VQ-VAE as our generative framework. The component called codebook of VQ-VAE has the capability to learn the common patterns in accompaniment, which coincides with the idea of pattern writing in the music of accompaniment. Moreover, accompaniment is composed of multiple tracks and every track consists of a sequence of musical notes, which means that accompaniment has both temporal and spatial information. So, we introduce ViT as the encoder and decoder of VQ-VAE because of this model's capability of simultaneously capturing time sequence information as well as harmony information between the tracks in the same time slice. Objective and subjective evaluations demonstrate the above features of our work, and the quality of our generated results reach the state-of-the-art level.
The Modified Gravitation-based Algorithm for Community Detecting in Dynamic Social Networks

With in-depth research on the complex network, people have a deeper understanding of the physical significance and mathematical characteristics of social networks. Communities are clusters of closely connected nodes within a social network. Community detection is an important task to understand the characteristics of social networks. The gravitation-based algorithm (GBA) is proposed in our previous work to simulate the process of community evolution based on Newton's law of universal gravitation. Based on GBA, we present a modified gravitation-based algorithm (MGBA) in this paper. MGBA uses the degree of nodes in the network to represent the mass of nodes, redefines the distance parameter in the law of gravity according to the close structural characteristics of the community, and introduces the concepts of the parent node and the center node. MGBA includes the MAGB algorithm and the MAFMG algorithm. MAGB is used to detect the community structure and find the center community. MAFMG algorithm is used to find the max gravity of the community. On this basis of MGBA, we present a dynamic modified gravitation-based algorithm (DMGBA) which is based on incremental clustering. After using MGBA to detect the community structure in static networks. If edges are added or removed, DMGBA will be used to redivide the dynamical communities. For adding a new edge, add the neighbor nodes of two related nodes, modify the quality of each node, and then put these nodes into a collection. For deleting old edges, delete the neighbor nodes of two related nodes, and modify the mass of each node. Change the central node of related nodes to itself, and put them into the collection if they are still in the network. The experimental results show that MGBA algorithm achieves 1% higher F1-score in less time than GBA in static large-scale social networks, DMGBA can get good partitions of communities in much less time than comparison methods.
MolEvolvR: A web-app for characterizing proteins using molecular evolution and phylogeny

Studying proteins through the lens of evolution can reveal conserved features, lineage-specific variants, and their potential functions. MolEvolvR (https://jravilab.org/molevolvr) is a novel web-app enabling researchers to visualize the molecular evolution of their proteins of interest in a phylogenetic context across the tree of life, spanning all superkingdoms. The web-app accepts multiple input formats – protein/domain sequences, homologous proteins, or domain scans – and, using a general-purpose computational workflow, returns detailed homolog data and dynamic graphical summaries (e.g., phylogenetic trees, multiple sequence alignments, domain architectures, domain proximity networks, phyletic spreads, co-occurrence patterns across lineages). Thus, MolEvolvR is a powerful, easy-to-use web interface for computational protein characterization.
A Measurement of Vehicle Attitude Based on Single Tri-Axial Acceleration Transducer & ANN

Vehicle bodywork attitude measurement has been important in automotive technology.This paper presents a non-gyro bodywork attitude measurement program.Using a three-axis acceleration sensor,its output reflects real-time body posture through BP neural network.Program skillfully made physical model and the algorithm simple according to the characteristics of motor cars.Taking advantage of the nonlinear function in learning and generalization ability of ANN(Artificial Neural Net),the system made the input three-axis inertial acceleration values of harmful components to be filtered and the system error to be compensated so as to mapoutput Attitude.And finally,it presents the data of experiments.By taking full advantage of ANN which is precessing in parallel,and good at dealing with problem uncertainty,this program provides a solution of calculating real-time bodywork attitude,while with minimal hardware overhead.
Properties, Genetics and Innate Immune Function of the Cuticle in Egg-Laying Species

Cleidoic eggs possess very efficient and orchestrated systems to protect the embryo from external microbes until hatch. The cuticle is a proteinaceous layer on the shell surface in many bird and some reptile species. An intact cuticle forms a pore plug to occlude respiratory pores and is an effective physical and chemical barrier against microbial penetration. The interior of the egg is assumed to be normally sterile, while the outer eggshell cuticle hosts microbes. The diversity of the eggshell microbiome is derived from both maternal microbiota and those of the nesting environment. The surface characteristics of the egg, outer moisture layer and the presence of antimicrobial molecules composing the cuticle dictate constituents of the microbial communities on the eggshell surface. The avian cuticle affects eggshell wettability, water vapor conductance and regulates ultraviolet reflectance in various ground-nesting species; moreover, its composition, thickness and degree of coverage are dependent on species, hen age, and physiological stressors. Studies in domestic avian species have demonstrated that changes in the cuticle affect the food safety of eggs with respect to the risk of contamination by bacterial pathogens such as Salmonella and Escherichia coli. Moreover, preventing contamination of internal egg components is crucial to optimize hatching success in bird species. In chickens there is moderate heritability (38%) of cuticle deposition with a potential for genetic improvement. However, much less is known about other bird or reptile cuticles. This review synthesizes current knowledge of eggshell cuticle and provides insight into its evolution in the clade reptilia. The origin, composition and regulation of the eggshell microbiome and the potential function of the cuticle as the first barrier of egg defense are discussed in detail. We evaluate how changes in the cuticle affect the food safety of table eggs and vertical transmission of pathogens in the production chain with respect to the risk of contamination. Thus, this review provides insight into the physiological and microbiological characteristics of eggshell cuticle in relation to its protective function (innate immunity) in egg-laying birds and reptiles.
A Dual Role of Heme Oxygenase-1 in Tuberculosis

Iron metabolism is vital for the survival of both humans and microorganisms. Heme oxygenase-1 (HO-1) is an essential stress-response enzyme highly expressed in the lungs, and catabolizes heme into ferrous iron, carbon monoxide (CO), and biliverdin (BV)/bilirubin (BR), especially in pathological conditions which cause oxidative stress and inflammation. Ferrous iron (Fe2+) is an important raw material for the synthesis of hemoglobin in red blood cells, and patients with iron deficiency are often associated with decreased cellular immunity. CO and BR can inhibit oxidative stress and inflammation. Thus, HO-1 is regarded as a cytoprotective molecule during the infection process. However, recent study has unveiled new information regarding HO-1. Being a highly infectious pathogenic bacterium, Mycobacterium tuberculosis (MTB) infection causes acute oxidative stress, and increases the expression of HO-1, which may in turn facilitate MTB survival and growth due to increased iron availability. Moreover, in severe cases of MTB infection, excessive reactive oxygen species (ROS) and free iron (Fe2+) due to high levels of HO-1 can lead to lipid peroxidation and ferroptosis, which may promote further MTB dissemination from cells undergoing ferroptosis. Therefore, it is important to understand and illustrate the dual role of HO-1 in tuberculosis. Herein, we critically review the interplay among HO-1, tuberculosis, and the host, thus paving the way for development of potential strategies for modulating HO-1 and iron metabolism.
The Symbiosis of Virtual Reality Exposure Therapy and Telemental Health: A Review

Phobias and related anxiety are common and costly mental health disorders. Experts anticipate the prevalence of phobias will increase due to the COVID-19 pandemic. Exposure therapies have been established as effective and reliable treatments for anxiety, including recent innovations in virtual reality-based exposure therapy (VRET). With the recent advent of telemental health (TMH), VRET is poised to become mainstream. The combination of VRET and TMH has the potential to extend provider treatment options and improve patient care experiences. In this narrative review, we describe how recent events have accelerated VRET + TMH, identify barriers to VRET + TMH implementation, and discuss strategies to navigate those barriers.
Non-Spinal Neuromodulation of the Lumbar Medial Branch Nerve for Chronic Axial Low Back Pain: A Narrative Review

Chronic low back pain remains highly prevalent, costly, and the leading cause of disability worldwide. Symptoms are complex and treatment involves an interdisciplinary approach. Due to diverse anatomical etiologies, treatment outcomes with interventional options are highly variable. A novel approach to treating chronic axial low back pain entails the use of peripheral nerve stimulation to the lumbar medial branch nerve, and this review examines the clinical data of the two different, commercially available, non-spinal neuromodulation systems. This review provides the clinician a succinct narrative that presents up-to-date data objectively. Our review found ten clinical studies, including one report of two cases, six prospective studies, and three randomized clinical trials published to date. Currently, there are different proposed mechanisms of action to address chronic axial low back pain with different implantation techniques. Evidence suggests that peripheral nerve stimulation of the lumbar medial branch nerve may be effective in improving pain and function in patients with chronic axial low back pain symptoms at short and long term follow up, with good safety profiles. Further long-term data is needed to consider this intervention earlier in the pain treatment algorithm, but initial data are promising.
From Expert to Elite? — Research on Top Archer’s EEG Network Topology

It is not only difficult to be a sports expert but also difficult to grow from a sports expert to a sports elite. Professional athletes are often concerned about the differences between an expert and an elite and how to eventually become an elite athlete. To explore the differences in brain neural mechanism between experts and elites in the process of motor behavior and reveal the internal connection between motor performance and brain activity, we collected and analyzed the electroencephalography (EEG) findings of 14 national archers and 14 provincial archers during aiming and resting states and constructed the EEG brain network of the two archer groups based on weighted phase lag index; the graph theory was used to analyze and compare the network characteristics via local network and global network topologies. The results showed that compared with the expert archers, the elite archers had stronger functional coupling in beta1 and beta2 bands, and the difference was evident in the frontal and central regions; in terms of global characteristics of brain network topology, the average clustering coefficient and global efficiency of elite archers were significantly higher than that of expert archers, and the eigenvector centrality of expert archers was higher; for local characteristics, elite archers had higher local efficient; and the brain network characteristics of expert archers showed a strong correlation with archery performance. This suggests that compared with expert archers, elite archers showed stronger functional coupling, higher integration efficiency of global and local information, and more independent performance in the archery process. These findings reveal the differences in brain electrical network topologies between elite and expert archers in the archery preparation stage, which is expected to provide theoretical reference for further training and promotion of professional athletes.