Miracle-dz/UniFAR-FTUs huggingface dataset:
Music Genre Classification Based on Song Titles with Long Short-Term Memory

This study used deep learning models to classify songs into eight genres based on their titles. The task was challenging because many titles only have three or four words, which may not clearly indicate the genre. The study used a dataset of 50,000 songs and their attributes from Kaggle, and the titles were pre-processed to be compatible with the models. The study compared different deep learning models and found that the Long Short-Term Memory (LSTM) with an extra layer and a dropout was the most accurate, with an accuracy of 42.21%. To the best of the authors' knowledge, there have been no previous studies on music genre classification using song titles, making this study unique in its field.
Surface force apparatus and its application to nanorheological studies

A surface force apparatus has been adapted to operate as a rheometer on a molecular scale. The way it works and the new possibility given to study dynamic properties of very thin films (<1 μm) squeezed between solid surfaces are discussed. Three kinds of tests, enabling the measurement of the viscoelastic properties of confined media in addition to static interactions between the surfaces, are presented. These are: draining—the surfaces are brought together at constant speed; relaxation—the displacement is stopped and the force is decreasing toward a static force; oscillations—the surfaces are submitted to small axial sinusoidal displacements around an average position. Results obtained with dilute polymer solutions moderately confined are presented. Variations of parameters characterizing the terminal zone such as the zero shear viscosity, the average relaxation time, and a parameter connected to the dispersion of relaxation times are presented and qualitatively discussed as a function of the separation .
Efficient ID-Based Key-Insulated Signature Scheme with Batch Verifications using Bilinear Pairings over Elliptic Curves

Abstract Exposure of private keys in cryptographic schemes leads to breakage of security of the scheme as a whole. To avoid the damage caused by key exposure problem, in this paper, we propose an efficient ID-Based Key-insulated Signature (IBKIS) scheme with batch verifications using bilinear pairings over elliptic curves. This scheme updates the signer's private keys from time to time. The proposed scheme is secure in the random oracle model with the assumption that the CDH problem is intractable. We compare the proposed scheme with related schemes.
Tests for comparing elements of a correlation matrix.

In a variety of situations in psychological research, it is desirable to be able to make statistical comparisons between correlation coefficients measured on the same individuals. For example, an experimenter may wish to assess whether two predictors correlate equally with a criterion variable. In another situation, the experimenter may wish to test the hypothesis that an entire matrix of correlations has remained stable over time. The present article reviews the literature on such tests, points out some statistics that should be avoided, and presents a variety of techniques that can be used safely with medium to large samples. Several illustrative numerical examples are provided.
Detection of Cellular Senescence Reveals the Existence of Senescent Tumor Cells within Invasive Breast Carcinomas and Related Metastases

Simple Summary Oncogene-induced senescence constitutes a barrier to carcinogenesis by arresting cells at risk of malignant transformation. However, numerous findings suggest that senescent cells conversely promote tumor growth and metastatic progression, for example, through the senescence-associated secretory phenotype (SASP) they produce. In patients with luminal A and B breast carcinomas, we found broad similarities in the appearance of cancer cells between primary tumors and their corresponding metastases. Lymph nodes from patients with other breast cancer subtypes also revealed senescent tumor cells within metastatic lesions. These results suggest a potential role for senescent breast tumor cells during metastatic progression, raising the question as to whether targeting senescent tumor cells might represent a novel avenue for improved treatment of breast and other cancers. Abstract Oncogene-induced senescence is thought to constitute a barrier to carcinogenesis by arresting cells at risk of malignant transformation. However, numerous findings suggest that senescent cells may conversely promote tumor growth and metastatic progression, for example, through the senescence-associated secretory phenotype (SASP) they produce. Here, we investigated the degree to which senescent tumor cells exist within untreated human primary breast carcinomas and whether the presence of senescent cancer cells in primary tumors is recapitulated in their matched lymph node metastases. For the detection of senescence, we used SA-β-galactosidase (SA-β-gal) staining and other senescence markers such as Ki67, p21, p53, and p16. In patients with invasive luminal A and B breast carcinomas, we found broad similarities in the appearance of cancer cells between primary tumors and their corresponding metastases. Analysis of lymph nodes from patients with other breast cancer subtypes also revealed senescent tumor cells within metastatic lesions. Collectively, our findings show that senescent tumor cells exist within primary breast carcinomas and metastatic lesions. These results suggest a potential role for senescent breast tumor cells during metastatic progression and raise the question as to whether the targeting of senescent tumor cells with anti-senescent drugs might represent a novel avenue for improved treatment of breast and other cancers.
UAV-Assisted Task Offloading in Vehicular Edge Computing Networks

Vehicular edge computing (VEC) provides an effective task offloading paradigm by pushing cloud resources to the vehicular network edges, e.g., road side units (RSUs). However, overloaded RSUs are likely to occur especially in urban aggregation areas, possibly leading to greatly compromised offloading performance. Inspired by this, this article explores this situation by introducing an unmanned aerial vehicle (UAV) to address the VEC overload problem. Specifically, we formulate a novel online UAV-assisted vehicular task offloading problem to minimize vehicular task delay under the long-term UAV energy constraint. To solve the formulated problem, we first decouple the long-term energy constraint based on the Lyapunov optimization technique. In this way, the problem can be solved in a real-time manner without requiring future information. Then, we construct a Markov chain based on Markov approximation optimization to find out the close-to-optimal UAV-assisted offloading strategies. Furthermore, we derive a mathematical analysis to rigorously demonstrate the offloading performance of the proposed algorithm. Additionally, the simulation results show that the proposed method outperforms the baselines by significantly reducing the vehicular task delay constrained by the long-term UAV energy budget under various system parameters, such as the energy budget and computation workloads.
Utilizing Sentiment Analysis to Enhance the Quality of Online Learning

The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: “Positive,” “Negative,” and “Neutral. ” Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique.
GAN-Based Privacy-Preserving Unsupervised Domain Adaptation

In recent years, the rapid development of deep learning is attributed to the large amount of labeled data brought by the digital age. When there is no labeled data available in some application scenarios, domain adaptation can be used to transfer knowledge from the source domain with labeled data to the target domain without labeled data. In the process of domain adaptation, the target client requires direct access to the source data or model, which would lead to the risk of privacy leakage, e.g., Membership Inference Attacks (MIA). Attackers can collect the prediction vector of the model through black-box access to the source model, and then infer an individual’s membership in the source training dataset. To deal with this privacy issue, we propose a GAN-based Privacy-Preserving Unsupervised Domain Adaptation Framework. Specifically, the target client learns a conditional generator, sends the intermediate results perturbed by differential privacy to the source client, and the source client uses the source model to provide guidance for the generator so that the generator can generate the data corresponding to the input label that is the same as the data distribution in the target domain. We evaluate the performance of our proposed method on digital dataset and office-31dataset, which are popular domain adaptation benchmark datasets, and verify the security by the accuracy and F1-score of Membership Inference Attacks.
Real-Time Sperm Detection Using Lightweight YOLOv5

Malformed sperm is an important cause of male infertility, and sperm morphology analysis (SMA) is an effective means to diagnose sperm morphology. Deep learning assists to enhance performance on precise SMA; however, existing deep learning based SMA methods mostly focus on single cell scale, which presents a challenge for obtaining single-sperm-image datasets (one sperm cell per image). It is also challenging to integrate current object detection models on low-performance devices. This paper presents a lightweight model for sperm detection. By removing 50% of convolutional kernels cutting the large-object-detecting head from YOLOv5s, our model got a similar precision to the original YOLOv3 (mAP.5 of 0.957 and 0.947, respectively), but with a model size of only 2.8 MB (123.6 MB of YOLOv3). There is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.604 with 14.4MB model size); however, our model still shows a significant advantage in reducing the number of parameters. Experimental results also indicated that MS COCO pre-training is helpful in sperm detection tasks, and the mosaic augmentation strongly enhances the precision for all YOLO models.
Small Magellanic Cloud-Type Interstellar Dust in the Milky Way

It is well known that the sight line toward HD 204827 in the cluster Trumpler 37 shows a UV extinction curve that does not follow the average Galactic extinction relation. However, when a dust component, foreground to the cluster, is removed, the residual extinction curve is identical to that found in the SMC within the uncertainties. The curve is very steep and has little or no 2175 Å bump. The position of HD 204827 in the sky is projected onto the edge of the Cepheus IRAS bubble. In addition, HD 204827 has an IRAS bow shock, indicating that it may be embedded in dust swept up by the supernova that created the IRAS bubble. Shocks due to the supernova may have led to substantial processing of this dust. The HD 204827 cloud is dense and rich in carbon molecules. The 3.4 μm feature indicating a C-H grain mantle is present in the dust toward HD 204827. The environment of the HD 204827 cloud dust may be similar to the dust associated with HD 62542, which lies on the edge of a stellar wind bubble and is also dense and rich in molecules. This sight line may be a Rosetta Stone if its environment can be related to those in the SMC having similar dust.
The Design of Reload Cores Using Optimal Control Theory

A formal approach for the optimization of the final design of reload cores has been devised and verified. The method is based on applying the calculus of variations (Pontryagin's principle) to the normal flux and depletion system equations. The resulting set of coupled system, Euler-Lagrange (E-L), and optimality equations are solved iteratively. This is done by assuming a loading pattern for the old fuel, first solving the system equations, and then the E-L equations. The pattern is then modified by using the optimality (or Pontryagin) condition, and the process is repeated until no further improvements can be made. A computer program, OPMUV, implementing these procedures has been written and verified. The code can handle two-dimensional, quarter-core symmetric configurations with up to 241 assemblies and 4 nodes per assembly with modified one-group theory. It also has the capability of optimizing over the entire depletion cycle as well as just at the beginning of cycle (BOC). The results show that the procedure does work. In all cases tried, the method led to a reduction in nodal peaks of 1 to 3% over the final designer-obtained loading pattern within a couple of iterations. These savings carry over to comparable reductions in pin peaksmore » when the optimized patterns are used in four-group, fine-mesh calculations. Since the changes on each iteration are limited to ensure convergence, the method is thus well suited for the final fine tuning of the normally obtained patterns to gain an extra few percent in power flattening. « less
A Compact circularly polarized Beidou antenna loaded with U-slot parasitic patch

With the continuous development of the Beidou navigation system, the demand for Beidou terminal antennas is also increasing. This paper introduces a compact microstrip patch antenna working in the Beidou B2 frequency band. The bandwidth is broadened by means of probe coupling feeding and loading of parasitic patches. The proposed antenna has a profile of 4.6 mm (0.018 $\lambda_{0}$), and occupies an area of 80 mm $\times 80$ mm (0.31$\lambda_{0} (\times 0.31) \lambda_{0}$). The simulated impedance bandwidth with $\vert \mathrm{S}11 \vert \lt -10$ dB is 7.7%, respectively. The simulated 3 dB axial-ratio bandwidth is more than 1.5% for B2 bands. This is a small wideband patch antenna with wide application prospects.
The ‘German Job Miracle’ and Its Impact on Income Inequality: A Decomposition Study

In the last 15 years before the COVID-19 crisis, Germany has experienced a strong and continuous increase in employment - the ‘German job miracle'. During this period, income inequality, which had previously increased sharply, remained relatively stable. This paper analyzes the impact of employment changes on disposable income inequality between 2004 and 2015 and gives an answer to the question why inequality remained constant despite the dramatic increase in employment. It is the first study to examine the effect of changing labor supply patterns due to changes in policies, wages and preferences, as well as the role that labor market constraints have played for inequality of disposable income. It finds that inequality would have increased further due to a transforming population structure, but increasing employment and policy changes almost completely offset this development. The results show that employment growth due to the reduction of labor market constraints has been more important in slowing down the increase in inequality than changes in labor supply © 2023, Mühlhan
Towards Development of a Novel DevOps Platform for Ultra-Large-Scale Commercial Applications: Part 2-Key Technologies and Applications

As a promising and effective method, DevOps (development and operations) methodology has been widely investigated and used in the software industry over the past years. In this study, we have successfully built an end-to-end and innovative DevOps platform of the whole process, namely the YunDao platform. To provide better services, four key technologies have been implemented, including large-scale tenant management technology, multilayer pipeline technology, security detection technology and hybrid deployment capability technology, to improve the quality, security and delivery speed of software release. The YunDao platform has been put into ultra-large-scale applications and was successfully utilized by hundreds of companies. The results indicated that the YunDao platform can improve the efficiency and security of software development and shorten the software development cycle simultaneously. This investigation forms a significant step towards the tailoring and development of novel ultra-large-scale DevOps platform.
Atg8 family proteins, LIR/AIM motifs and other interaction modes

The Atg8 family of ubiquitin-like proteins play pivotal roles in autophagy and other processes involving vesicle fusion and transport where the lysosome/vacuole is the end station. Nuclear roles of Atg8 proteins are also emerging. Here, we review the structural and functional features of Atg8 family proteins and their protein-protein interaction modes in model organisms such as yeast, Arabidopsis, C. elegans and Drosophila to humans. Although varying in number of homologs, from one in yeast to seven in humans, and more than ten in some plants, there is a strong evolutionary conservation of structural features and interaction modes. The most prominent interaction mode is between the LC3 interacting region (LIR), also called Atg8 interacting motif (AIM), binding to the LIR docking site (LDS) in Atg8 homologs. There are variants of these motifs like “half-LIRs” and helical LIRs. We discuss details of the binding modes and how selectivity is achieved as well as the role of multivalent LIR-LDS interactions in selective autophagy. A number of LIR-LDS interactions are known to be regulated by phosphorylation. New methods to predict LIR motifs in proteins have emerged that will aid in discovery and analyses. There are also other interaction surfaces than the LDS becoming known where we presently lack detailed structural information, like the N-terminal arm region and the UIM-docking site (UDS). More interaction modes are likely to be discovered in future studies.
In Vitro Tracking of Human Umbilical Vein Endothelial Cells Using Ultra-Sensitive Quantum Dot-Embedded Silica Nanoparticles

The nanoscale spatiotemporal resolution of single-particle tracking (SPT) renders it a powerful method for exploring single-molecule dynamics in living cells or tissues, despite the disadvantages of using traditional organic fluorescence probes, such as the weak fluorescent signal against the strong cellular autofluorescence background coupled with a fast-photobleaching rate. Quantum dots (QDs), which enable tracking targets in multiple colors, have been proposed as an alternative to traditional organic fluorescence dyes; however, they are not ideally suitable for applying SPT due to their hydrophobicity, cytotoxicity, and blinking problems. This study reports an improved SPT method using silica-coated QD-embedded silica nanoparticles (QD2), which represent brighter fluorescence and are less toxic than single QDs. After treatment of QD2 in 10 μg/mL, the label was retained for 96 h with 83.76% of labeling efficiency, without impaired cell function such as angiogenesis. The improved stability of QD2 facilitates the visualization of in situ endothelial vessel formation without real-time staining. Cells retain QD2 fluorescence signal for 15 days at 4 °C without significant photobleaching, indicating that QD2 has overcome the limitations of SPT enabling long-term intracellular tracking. These results proved that QD2 could be used for SPT as a substitute for traditional organic fluorophores or single quantum dots, with its photostability, biocompatibility, and superior brightness.
The Detection and Recognition of Electrocardiogram's Waveform Based on Sparse Decomposition and Neural Network

Electrocardiogram(ECG) is an important method of diagnosis.Using computer to detect and recognize all kinds of wave of ECG can release doctor' s tension coming from more and more ECG data,and decrease error because of tiredness,negligence, and subjective warp.Based on Gabor dictionary modified and the particle swarm optimization,ECG data are decomposed by the theory of sparse decomposition.Comparing of original ECG,its solution vector of sparse decomposition is very sparse(only a few components are nonzero). Every nonzero value corresponds to an atom selected form dictionary,the atom represents the characters of ECG.Based on magnitude of nonzero value in the solution vector and waveform of atom corresponding,some information,such as amplitude,wave width,waveform,position,can be found out.Then using prior knowledge of ECG,it can be made sure which kinds of waveform represented by atom(P wave,QRS complex or T wave). And then,it is constructed that training sample of neural network(NN). Passing through training,NN will complete automatic detection and recognition of ECG.Experiments have proved that this algorithm could complete P wave,QRS complex and T waves' automatic detection and recognition of ECG,at the same time.
Evaluation of the Performance of Autonomous Visual Perception Units in Challenging Environments

With the application of intelligent systems is expanding, the reliability and security of intelligent systems have higher requirements. This paper studies the performance trends of deep models in visual perception unit of intelligent systems. First of all, explicitly quantifiable image data is generated by image generation models for foggy weather and random vibrations. And the multi-level challenging environment datasets are generated based on image data. The performance trends of the deep models in synthetic challenging environments are verified based on classical object detection models. The results show that the influence of different environmental stresses on the object detection performance of the visual perception unit varies greatly under challenging weather conditions. Exploring the performance trend of AI model is of great value and significance for evaluating the reliability and safety of visual perception system in the future.
Object Detection from 3D Point Cloud Using Deep Learning (SFM)

This paper proposes a cost-effective way for the object detection and classification of objects modeled as 3D renders, via Deep Learning. 3D modeling is the process of manipulating edges, vertices, and polygons in artificial 3D space that creates mathematical coordinated representations of the surface. In this research, we propose to use a stereo camera and a 2D laser scanner (LiDAR) for the construction of 3D object models. We created a 3D model of an object using a stereo camera. Video of objects was captured maintaining the right angles all the time. Then with the help of Intel Real Sense Viewer, a 3D polygon mesh was created, which was converted to a point cloud. A two-dimensional (2D) laser scanner was used to make several chunks of 2D scans from various sides of the object. We then fused the point cloud of the obtained chunks to build a 3D model. We then combined the point clouds obtained from both sources using the Iterative Closest Point (ICP) algorithm. The fused point cloud resulted in the formation of a denser and crispier dataset to be used for Deep Learning. The aforementioned deep learning algorithm, Point Net, encodes sparse point cloud data efficiently and shows very strong performance on par with the state of the art. We have formed a dataset using stereo camera, LIDAR and ICP among which we have obtained the highest accuracy results from ICP algorithm dataset.