Miracle-dz/UniFAR-FTUs huggingface dataset:
JEDI: Joint and Effective Privacy Preserving Outsourced Set Intersection and Data Integration Protocols

In the era of big data with an ever-increasing amount of data, the demand for data processing is increasing, and various computing service agreements have also been created. While data are widely used, data providers worry that their privacy will be infringed, so the security of the data has also received more attention. Private set intersection (PSI) allows two data-owning parties to jointly compute the intersection of private datasets. The party cannot obtain any other information except for the intersection results. However, Bloom filter (BF) is a naive solution that can be used to potentially obtain PSI, but the verifiability of using BF is usually a concern for effectiveness. Hence, garbled Bloom filter is indeed expected to overcome this concern in two-party setting. We aim for the outsourced scenario where there are many data-owning parties in distributed setting. These parties would like to jointly obtain the set intersection in a private manner supported by an outsourced cloud, and further to obtain data integration. The main challenges are that the previous solutions cannot be directly transformed into an outsourced scenario. In this paper, the proposed joint and effective privacy preserving outsourced set intersection and data integration protocols are called $\textsf {JEDI}$ . They are generic constructions. We start by introducing a basic outsourced PSI protocol from the cryptographic building blocks and new BF and GBF techniques. Then, we modify the basic protocol and propose the full-fledged scheme to achieve an outsourced multi-party PSI protocol. Finally, we analyze the security and execution efficiency aspects to ensure usability.
Improvement of Supercapacitor Performance of In Situ Doped Laser-Induced Multilayer Graphene via NiO

Herein, we have reported a novel strategy for improving the electrochemical performance of laser-induced graphene (LIG) supercapacitors (SCs). The LIG was prepared using a CO2 laser system. The polyimide polymer was the source material for the fabrication of the LIG. The doping process was performed in situ using the CO2 laser, which works as a rapid thermal treatment to combine graphene and NiO particles. NiO was used to improve the capacitance of graphene by combining an electric double-layer capacitor (EDLC) with the pseudo-capacitance effect. The high-resolution transmission electron microscopy, energy-dispersive X-ray spectroscopy, and Raman spectroscopy showed that the structure of the LIG is multilayered and waved. The HRTEM image proves the distribution of NiO fine particles with sizes of 5–10 nm into the graphene layers. The electrochemical performance of the as-prepared LIG was tested. The effect of the combination of the two materials (oxide and carbon) was investigated at different concentrations. The LIG showed a specific capacitance of 69 Fg−1, which increased up to 174 Fg−1 for the NiO-doped LIG. The stability investigations showed that the electrodes were very stable for more than 1000 cycles. This current study establishes an innovative method to improve the electrochemical properties of LIG.
Joint Learning of Segmentation and Overall Survival for Brain Tumor based on U-Net

The prognosis, hence survival, of patients with brain tumors is highly dependent on the size and grade of the tumor. Thus, joint learning of brain tumor segmentation and overall survival of patients with brain tumors can benefit each other. In this work, we explored the feasibility of prediction of patients' overall survival through U-Net guided by the information of brain tumor segmentation. We evaluated the proposed model on the multimodal brain tumor segmentation (BraTS) 2017 challenge dataset. We achieved the mean Dice score of 0.595 for brain tumor segmentation on the test set. The Pearson correlation coefficient for overall survival prediction on the test set was 0.243, indicating promising results for both brain tumor segmentation and overall survival prediction.
Distributed and backstepping boundary controls for port-Hamiltonian systems with symmetries

ABSTRACT A geometric spatial reduction for the port-Hamiltonian models is presented in this paper. It is based on the projection which makes use of the symmetries and on the preservation of the ‘natural’ power pairing for the considered system. Thanks to this reduction, an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC-like) synthesis for infinite dimensional port-Hamiltonian systems is investigated. As for the finite dimensional case, a feedback control transforms the original model into a closed-loop target Hamiltonian model. Both distributed control and boundary control are used. The finite rank distributed control is determined to solve an average IDA-PBC matching equation. A backstepping boundary control is used to stabilize the matching error. The control model chosen to illustrate the approach is the so-called resistive diffusion equation for the radial diffusion of the poloidal magnetic flux.
Greek Orthodox Church Hymns Recognition Using Deep Learning Techniques

Deep learning techniques have found numerous applications in the sector of cultural and religious heritage. Greek Orthodox Church hymns are examples of Byzantine religious tradition. Their performance is governed by stringent rules not observed in other cultures. This work trains a novel shallow convolutional neural network to identify Greek Orthodox Church hymns using a private dataset. For ten of the most frequently chanted hymns, the deep learning model achieves over 98% accuracy. AlexNet and MobileNetV2 are trained on the same data to evaluate their performance. Additional analysis is conducted to validate the obtained results.
Post stroke special clinic in primary care: How well the patients with stroke can be managed in the community?

Optimal control of modifiable cardiovascular disease risk factors for patients with stroke is essential to prevent further ischemic events. The aim of this study was to evaluate the performance of the Post-Stroke Special Clinic (PSSC), a public primary care setting run by family specialists, by testing the control of blood pressure, low density lipoprotein, haemoglobin A1c if diabetic and smoking, among patients who had stroke and follow-up in PSSC. A retrospective single arm pre-post study was conducted among Chinese ischemic stroke patients who attended PSSC from 1st January, 2015 to 31st December, 2018 and had follow-up till one year after last incidence of stroke. At one year after last incidence of stroke, there were significant decrease in blood pressure, low density lipoprotein, haemoglobin A1c and the proportion of smokers compared with baseline (all P<0.05). There were 19 out of 326 cases had recurrent stroke within one year, yielding an annual recurrence rate of 5.8%. Findings of this study on the improvement in cardiovascular disease risk factor control and the relatively low one-year recurrence rate of stroke underlined the positive role of primary care on stroke management.
Monitoring Ischemic Changes in Electrocardiograms using Dickinson-Steiglitz Discrete Hermite Functions

Ischemic heart disease is one of the main common causes of death and an electrocardiogram (ECG) is used in the investigation of the heart disease. A method of real time detection of ischemic features from long term ECG signals based on DickinsonSteiglitz discrete Hermite expansions is proposed. In this paper, the discrete Hermite functions were generated as eigenvectors of the extended tridiagonal matrix that commutes with the centered Fourier matrix. Each ECG complex was extracted from the long term ECG and expanded in terms of Hermite functions using a simple dot product. These coefficients were found to contain information about the shape of the corresponding ECG complex. The first 50 coefficients were used to reconstruct the ECG complex. These 50 coefficients were fed as input to train a committee Neural Network classifier to identify ST-segment and T-wave changes, which are one of the ischemic features in the ECG complex. The trained network was tested with long term ECG records from the MIT-BIH database. The performance was analyzed in terms of sensitivity and specificity. This results were compared with the ones that came from the same method used with a tridiagonal Hermite matrix (T matrix).
Synthesis of fractional-order PI controllers and fractional-order filters for industrial electrical drives

This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral U+0028 PI U+0029 unit, with a non-integer order integral action, while the latter can be of integer or non-integer type. To satisfy robustness and dynamic performance specifications, the feedback controller is designed by a loop-shaping technique in the frequency domain. In particular, optimality of the feedback system is pursued to achieve input-output tracking. The setpoint pre-filter is designed by a dynamic inversion technique minimizing the difference between the ideal synthesized command signal U+0028 i.e., a smooth monotonic response U+0029 and the prefilter step response. Experimental tests validate the methodology and compare the performance of the proposed architecture with well-established control schemes that employ the classical PIbased symmetrical optimum method with a smoothing pre-filter.
Recognition of H2AK119ub plays an important role in RSF1-regulated early Xenopus development

Polycomb group (PcG) proteins are key regulators of gene expression and developmental programs via covalent modification of histones, but the factors that interpret histone modification marks to regulate embryogenesis are less studied. We previously identified Remodeling and Spacing Factor 1 (RSF1) as a reader of histone H2A lysine 119 ubiquitination (H2AK119ub), the histone mark deposited by Polycomb Repressive Complex 1 (PRC1). In the current study, we used Xenopus laevis as a model to investigate how RSF1 affects early embryonic development and whether recognition of H2AK119ub is important for the function of RSF1. We showed that knockdown of Xenopus RSF1, rsf1, not only induced gastrulation defects as reported previously, but specific targeted knockdown in prospective neural precursors induced neural and neural crest defects, with reductions of marker genes. In addition, similar to knockdown of PRC1 components in Xenopus, the anterior-posterior neural patterning was affected in rsf1 knockdown embryos. Binding of H2AK119ub appeared to be crucial for rsf1 function, as a construct with deletion of the UAB domain, which is required for RSF1 to recognize the H2AK119ub nucleosomes, failed to rescue rsf1 morphant embryos and was less effective in interfering with early Xenopus development when ectopically expressed. Furthermore, ectopic deposition of H2AK119ub on the Smad2 target gene gsc using a ring1a-smad2 fusion protein led to ectopic recruitment of RSF1. The fusion protein was inefficient in inducing mesodermal markers in the animal region or a secondary axis when expressed in the ventral tissues. Taken together, our results reveal that rsf1 modulates similar developmental processes in early Xenopus embryos as components of PRC1 do, and that RSF1 acts at least partially through binding to the H2AK119ub mark via the UAB domain during development.
Novel Environmental Magnetic Field Measurement Using A Drone

In this research, a self-made three-axis magnetic field sensor device is designed and installed on a drone, which will be able to measure a large area of environmental magnetic fields. The designed magnetic field sensor adopts the air-coil sensing principle, and is equipped with a 3D printed sensor structure to ensure the orthogonality of the three-axis sensing directions. In addition, the drone will generate electromagnetic field, which will affect the measurement data of the designed three-axis magnetic field sensor. Therefore, a two-stage signal processing mechanism is designed to extract the sensing signal (fundamental and harmonic components) that only belongs to the environmental magnetic field. Finally, the measured magnetic fields are transmitted back to the remote monitoring system via ZigBee wirelessly. Since an aerial camera is installed in the drone, and the operator can remotely control the drone to measure the magnetic field of a large-scale path.
A clustering method for power time series curves based on improved self-organizing mapping algorithm

To solve the clustering task of power time series curves, we propose an improved self-organizing mapping algorithm (improve SOM). We optimized the process of feature extraction before SOM algorithm considering the massive and noisy nature of power load data: firstly, we used wavelet transform to denoise and reduce the interference of noise on the clustering process; then extracting power features from denoised power time series curves; then use the improved SOM clustering algorithm to cluster based on the extracted power features. The clustering algorithm proposed in this paper can deal with the clustering task of a large amount of power data and has good accuracy and robustness.
Analysis of the need for ICT (Information and Communication Technology) based biology teaching materials at the high school level

Based on the demands of the learning curriculum in the classroom, it must create an atmosphere that is active, creative, analytical, and critical in solving problems through the development of thinking skills. However, the involvement of students in learning activities in class is still low. The research aims to determine the need for ICT-based biology teaching materials and the constraints in applying them. The research used a qualitative descriptive approach, the subjects of the research were class X students, biology teachers, ICT teachers, and Deputy Principal for Curriculum. The sampling technique used purposive sampling technique. The research procedure goes through three stages, namely preparation, implementation, and member check. Based on the research results, the biology learning process still uses contextual models and lecture methods. It is also that 83.3% of students said they needed ICT teaching materials to understand the material, concretize abstract material, and increase interaction in the learning process. Constraints in implementing ICT-based teaching materials are classified into physical and non-physical facilities. Physical facilities in the form of learning equipment, while non-physical means lack time and lack of training in designing ICT-integrated learning. Teacher expertise in ICT-based learning needs to be trained so that students can be more active in the learning process.AbstrakBerdasarkan tuntutan kurikulum pembelajaran di kelas harus menciptakan suasana yang aktif, kreatif, analisis, dan kritis dalam pemecahan masalah melalui pengembangan keterampilan berpikir. Namun keterlibatan peserta didik dalam kegiatan pembelajaran di kelas masih rendah. Penelitian bertujuan mengetahui kebutuhan bahan ajar biologi berbasis TIK dan kendala dalam menerapkannya. Penelitian menggunakan pendekatan deskriptif kualitatif, subjek penelitian adalah peserta didik kelas X, guru biologi, guru TIK dan Wakil Kepala Sekolah Bidang Kurikulum. Teknik pengambilan sampel menggunakan teknik purposive sampling. Prosedur penelitian melalui tiga tahap yaitu persiapan, pelaksanaan, dan member check. Berdasarkan hasil penelitian, proses pembelajaran biologi masih menggunakan model kontekstual dan metode ceramah. Diketahui pula 83,3% peserta didik mengungkapkan membutuhkan bahan ajar TIK untuk memahami materi, mengkonkretkan materi yang abstrak, serta meningkatkan interaksi dalam proses pembelajaran. Kendala dalam menerapkan bahan ajar berbasis TIK digolongkan menjadi dua yaitu sarana fisik dan non fisik. Sarana fisik berupa peralatan belajar, sedangkan secara non fisik adalah kekurangan waktu dan belum terlatihnya dalam mendesain pembelajaran terintegrasi TIK. Keahliah guru dalam melaksanakan pembelajaran berbasis TIK perlu dilatih agar peserta didik dapat lebih aktif dalam proses pembelajaran.
Gaussian Model Based Multichannel Separation

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Gaussian model based multichannel separation Alexey Ozerov, Hirokazu Kameoka
Soft Pneumatic Muscles: Revolutionizing Human Assistive Devices with Geometric Design and Intelligent Control

Soft robotics, a recent advancement in robotics systems, distinguishes itself by utilizing soft and flexible materials like silicon rubber, prioritizing safety during human interaction, and excelling in handling complex or delicate objects. Soft pneumatic actuators, a prevalent type of soft robot, are the focus of this paper. A new geometrical parameter for soft artificial pneumatic muscles is introduced, enabling the prediction of actuation behavior using analytical models based on specific design parameters. The study investigated the impact of the chamber pitch parameter and actuation conditions on the deformation direction and internal stress of three tested soft pneumatic muscle (SPM) models. Simulation involved the modeling of hyperelastic materials using finite element analysis. Additionally, an artificial neural network (ANN) was employed to predict pressure values in three chambers at desired Cartesian positions. The trained ANN model demonstrated exceptional performance. It achieved high accuracy with training, validation, and testing residuals of 99.58%, 99.89%, and 99.79%, respectively. During the validation simulations and neural network results, the maximum errors in the x, y, and z coordinates were found to be 9.3%, 7.83%, and 8.8%, respectively. These results highlight the successful performance and efficacy of the trained ANN model in accurately predicting pressure values for the desired positions in the soft pneumatic muscles.
System optimization for index modulated OFDM

This paper introduces the system optimization scheme of OFDM-IM, an emerging modulation technology that combines OFDM technology and modulation index technology to improve spectral efficiency and system reliability while ensuring high-speed transmission. However, as a new technology, OFDM still has shortcomings. This paper investigates three system optimization schemes for OFDM-IM: he deep learning-based OFDM-IM optimization scheme, MIMO-OFDM-IM, and LDPC coding-assisted subcarrier index protection scheme. It is found that these three optimization schemes are indeed helpful for optimizing the performance of OFDM-IM through system simulation experiments. The IM-OFDM system optimization based on deep learning is very helpful for IM-OFDM in terms of channel estimation, signal detection, and modulation classification. MIMO-OFDM-IM can increase channel capacity and signal transmission reliability. By protecting the subcarrier index, LDPC codes can effectively reduce the subcarrier error rate and improve the reliability and performance of IM-OFDM systems. Due to their different technical characteristics, the three optimization schemes have different advantages. They also have their application scenarios in which they excel. However, considering these technologies are relatively new, there are still certain drawbacks to these three technologies. Researchers need to continue to study them in-depth and gradually overcome them.
Strain Modulation of Electronic Properties in Monolayer SnP2S6 and GeP2S6

In recent years, two-dimensional (2D) materials have attracted significant attention due to their distinctive properties, including exceptional mechanical flexibility and tunable electronic properties. Via the first-principles calculation, we investigate the effect of strain on the electronic properties of monolayer SnP2S6 and GeP2S6. We find that monolayer SnP2S6 is an indirect bandgap semiconductor, while monolayer GeP2S6 is a direct bandgap semiconductor. Notably, under uniform biaxial strains, SnP2S6 undergoes an indirect-to-direct bandgap transition at 4.0% biaxial compressive strains, while GeP2S6 exhibits a direct-to-indirect transition at 2.0% biaxial tensile strain. The changes in the conduction band edge can be attributed to the high-symmetry point Γ being more sensitive to strain than K. Thus, the relocation of the conduction band and valence band edges in monolayer SnP2S6 and GeP2S6 induces a direct-to-indirect and indirect-to-direct bandgap transition, respectively. Consequently, the strain is an effective band engineering scheme which is crucial for the design and development of next-generation nanoelectronic and optoelectronic devices.
Different Structures—Similar Effect: Do Substituted 5-(4-Methoxyphenyl)-1H-indoles and 5-(4-Methoxyphenyl)-1H-imidazoles Represent a Common Pharmacophore for Substrate Selective Inhibition of Linoleate Oxygenase Activity of ALOX15?

Mammalian 15-lipoxygenases (ALOX15) are lipid peroxidizing enzymes that exhibit variable functionality in different cancer and inflammation models. The pathophysiological role of linoleic acid- and arachidonic acid-derived ALOX15 metabolites rendered this enzyme a target for pharmacological research. Several indole and imidazole derivatives inhibit the catalytic activity of rabbit ALOX15 in a substrate-specific manner, but the molecular basis for this allosteric inhibition remains unclear. Here, we attempt to define a common pharmacophore, which is critical for this allosteric inhibition. We found that substituted imidazoles induce weaker inhibitory effects when compared with the indole derivatives. In silico docking studies and molecular dynamics simulations using a dimeric allosteric enzyme model, in which the inhibitor occupies the substrate-binding pocket of one monomer, whereas the substrate fatty acid is bound at the catalytic center of another monomer within the ALOX15 dimer, indicated that chemical modification of the core pharmacophore alters the enzyme–inhibitor interactions, inducing a reduced inhibitory potency. In our dimeric ALOX15 model, the structural differences induced by inhibitor binding are translated to the hydrophobic dimerization cluster and affect the structures of enzyme–substrate complexes. These data are of particular importance since substrate-specific inhibition may contribute to elucidation of the putative roles of ALOX15 metabolites derived from different polyunsaturated fatty acids in mammalian pathophysiology.
ANALYSIS OF VELOCITY AND TEMPERATURE PROFILES ACROSS VERTICAL FLAT SURFACE OF RADIATORS DURING NATURAL CONVECTION

Heat transmission through a vertical flat surface of a radiator is strongly influenced by temperature and velocity profiles along such surface. The velocity and temperature profiles correspond to natural convection and depend on Grashof and Prandtl number. In order to obtain velocity and temperature profiles of current natural convection problem, continuity equation, momentum equation and energy equation have been utilized. These partial differential equations have been transformed into two ordinary differential equations of third degree by means of independent dimensionless variables and of the stream function. Such equations are solved numerically and the results are compared with results of an analytical solution.