Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Ethyl Acetate Extract of Songaricum Rupron on p38 and CREB Characteristics of Ovariectomized Rats

OBJECTIVE To study the effect of ethyl acetate extract of Songaricum Rupron( ECS) on ovariectomization induced learning and memory disorder in rats and its potential mechanism. METHODS SD rats were ovariectomized to establish animal model of learning and memory disorder,and taken ECS by intragastric administration( 3. 3 mg·kg- 1). Morris water maze was used to test the learning and memory ability of rats. HE staining was used to observe morphological change of hippocampal neurons. The p38 and pCREB expression were tested by Western-Blot. RESULTS The latent route of OVX + ECS group in the Morris water maze was significantly shorten,while numbers of crossing platform were markedly increased,compared with OVX group. The neurological form of pyramidal cells in hippocampus were improved by HE staining. CONCLUSION It shows that the p38 expression is reduced while pCREB is increased in OVX + ECS group,which potentially contributes to the improvement of learning and memory disorder due to lack of endogenous estrogen.
Information Security Definition within the Humanitarium Aspect

Contemporary condition of global society informatization and as the consequence the questions of information security are the key issues for individual citizens, companies, as well as for States and countries as a whole. Ensuring information security is carried out in conditions when process participants operate the key components of the term, that is why it is necessary to consider approaches to the concept of “information security” in scientific literature of both Russian and foreign resources. The importance of researching humanitarian aspects of information security should be considered as the priority task due to the direct connection between human and computer society. Humanitarian aspects of information security should draw more attention as the most vulnerable in this area and since the damage is extended to a much larger audience compared to the damage of a single computer. It is also necessary to consider humanitarian aspects in order to predict and prevent possible threats.
Link Prediction for Opportunistic Network Based on the Attraction between Nodes

Opportunistic network is a type of self-organizing network that uses node movements to bring about encounter opportunities for communication, and characterized by sparse node connections and frequent network topology changes. The prediction of node link probability is the key to study the topological changes of opportunistic network. We propose a node-attraction-based link prediction method of opportunistic network (OLPA), which analyzes node properties and the connection relationship of node pairs to obtain the attraction between nodes, and introduces a time decay factor to weight the evolution sequence of attraction to obtain the probability of future connections between nodes. By comparing with LSTM, E-LSTM-D, GCN-GAN link prediction models on multiple real opportunistic network data sets, the propose link prediction method we mentioned has good accuracy.
Rotation Augmentation for Head Pose Estimation Problem

The head pose estimation problem is one of the essential tasks of computer vision. Currently, many solutions use neural networks and show good results. Using data augmentations can significantly improve the quality of the trained neural network. Augmentations that affect the head rotation angles can increase the representativeness of head positions in the dataset. Horizontal flipping and rotating by a certain angle can achieve the desired effect. The rotation data augmentation is well known for the detection problem, but the only horizontal flip was used for the head pose estimation problem in previous papers. This paper proposes a modification of the image rotation augmentation for the head pose estimation problem, which corrects head rotation angles. Formulas for calculating the corrected rotation angles and bounding box are given in this paper. We conducted a series of computational experiments to test the effectiveness of the proposed augmentation. This paper compares proposed augmentation with different angles rotation and baseline, which applies only horizontal flip. The 300W - LP dataset was taken as a training dataset and the AFLW2000 dataset as a validation dataset. The pretrained Resnet50 was taken as a backbone. As the head branch for each angle, regression via classification with bin size equal to one was used. The best result is achieved by rotating at a random angle from −40 to 40 degrees. The accuracy of prediction by MAE metric angles was 3.85, which is close to the result of the best SOTA method with 3.83. The improvement in the accuracy of angle prediction relative to baseline is 22.1 % according to the MAE metric.
A Discrete Operational Point Control-based Adaptive Dynamic Coordination Control Strategy for HEV

This paper proposes a limited discrete operation points control strategy for series hybrid electric tracked-vehicle. First, to optimize the control parameters of rule-based energy management strategy, particle swarm optimization algorithm is employed. the optimization results were applied to determine the optimal switch strategy between operation points of the engine. Second, a dynamic adaptive strategy is also proposed to achieve the coordinated control of the engine-generator set. Finally, with combination of above parts, a detailed energy management strategy and coordinated control system was proposed. The results demonstrate that the discrete operation points control strategy combined with coordinated control strategy can improve the electricity power supply performance, decrease the fluctuations of the system and stabilize the bus voltage and guarantee robust control performance.
The Study on Online Learning Frequency, Learning Engagement and Mental Health of College Students During COVID-19

The emergence of COVID-19 has significantly changed the teaching environment of universities resulting in lots of studies on college students' online learning engagement and learning performance. However, few studies have focused on the relationship between learning engagement and college students' physical and mental health. The aim of this study was to explore online learning frequency, learning engagement, mental health and how about the relationship between them, with the method of online questionnaire survey (N=268). The study finds that there is a significant positive correlation between positive affect and learn engagement (emotional engagement, cognitive engagement, behavioral engagement), and a significant negative correlation between negative affect and learn engagement. The study also finds some differences between students from urban and rural areas, that is mental health level of college students in urban areas is generally lower than that in rural areas, and the learning engagement of college students in urban areas are all significantly lower than that in rural areas.
Detecting Fashion Apparels and their Landmarks

Apparel landmarks are the functional key-points on the apparels that can be used for a more discriminative visual analysis of the apparel images. Such a framework can facilitate apparel alignment in displaying apparel images on the websites for recommendation systems, apparel image retrieval, apparel style transfer, trend analysis or help build a system to ensure dress code in a particular environment. However, challenges such as background clutter, human poses, scales apparel variation and lighting can render such a task difficult. We present a conceptually simple, flexible, and general framework for apparels’ landmark detection that can also be simultaneously used for apparel detection. In addition to the position of the landmarks in the apparels, we also classify the landmarks as visible or occluded in the same framework. We perform all these tasks in parallel using multi-task learning. Our proposed convolutional neural network is end-to-end differentiable and simple to train, since all these tasks are performed on the same architecture without any additional parameters to learn. The fashion landmark detection task is similar to joint localization and detection problems like human pose estimation, hence our approach extends stacked hourglass architecture, originally proposed to solve human pose estimation. Over the past few years, many modifications have been proposed to improve this architecture. We also compare the performances of some of these different variations of stacked hourglass architectures. These architectures leverage both global and local features captured by the deep convolutional neural networks to better localize the apparel in the image as well as the landmarks in those apparels.
Topic trends and user engagement on Instagram

This paper studies how engagement with a post on an online social media platform is correlated with the choice of topics and global topic trends. We leverage a large-scale dataset, comprised of over three million public posts from influencer profiles on Instagram. Based on a hashtag-based methodology to detect topics within posts and thematic trends over time, we distinguish between features that can be leveraged by the content creators, such as usage of trending hashtags within a specific topic of interest, from features mostly beyond their control, such as global topic popularity trends. Our analysis revealed that both kinds of features are relevant to predicting the success of a post, and the results differ according to the size of the follower base of the influencers. We hope that these and other results of our study provide valuable information about influence dynamics and strategies for content personalization on online social media platforms.
A High-Order-N Frequency-Multiplier Using Pulsed Oscillator: Modeling and Optimization

This work focusses on the modeling of the Phase Noise (PhN) through the building blocks of a high-order-N frequency multiplication circuit that uses Pulsed injection Locking Oscillator (P-OSC). The proposed analytical expressions allow predicting the transformation of PhN through different blocks of the frequency multiplier. To cope with the inherent nonlinear characteristics of frequency multiplication circuits, simplified nonlinear equivalent circuits (NEC) are used to loosen the time and memory constraints of the numerical simulations. The analytical results show good agreements with Harmonic Balance simulations of NEC. This work helps determining the circuit key parameters controlling Phase Noise and output integrated RMS Jitter for optimization purposes.
An Inclined Bounding Box Method for Detecting Parking Spaces in Open Parking Lots using Aerial Images

With the increasing population in urban areas, finding a vacant parking space has always been the biggest problem for drivers, which causes a series of issues such as energy waste and environmental pollution. Most outdoor parking lots cannot provide details of vacant parking spaces due to limited sensor technology in outdoor environments (i.e., poor resistance to external interference due to heat, light or signal noise). Although some studies are based on images captured by cameras, the existing vacancy detection methods using fixed-position surveillance cameras are not flexible and accurate enough for wide use. We propose an Inclined Bounding Box (IBB) method for detecting vacant parking spaces using aerial images. The IBB method takes advantage of the feature-description ability of deep Convolutional Neural Network (CNN) to identify the status of parking spaces and develops an inclined bounding boxes calibration algorithm on the top layer of CNN architecture. The proposed IBB method can automatically identify parking spaces, locate vacant parking spaces with any direction and detect multiple parking spaces simultaneously. The experimental results show that the proposed IBB method is more accurate than the three counterpart methods.
Event-triggered Secure Group Consensus of Second-order Multi-agent Systems under Periodic DoS Attacks

In this paper, an event-triggered group consensus pinning control strategy is proposed for the second-order nonlinear multi-agent systems (MASs) with directed communication graph under periodic denial-of-service (DoS) attacks, which does not require the MASs to satisfy the in-degree balance condition. On this basis, the state error systems under periodic DoS attacks are established. In addition, it should be pointed out that the control strategy includes the selection method of pinning nodes under the group consensus framework. Furthermore, the sampled-data-based event-triggered mechanism (ETM) reduces the excessive consumption of system resources. Finally, simulation examples are given to verify the effectiveness of the control strategy under periodic DoS attacks with different duration.
A Novel Framework for Hierarchical Fault Detection Under the Supervision of Key Performance Indicators

This paper proposes a novel key performance indicators (KPIs) supervised hierarchical fault detection framework for model-free linear dynamic systems. In the ground process layer, a bank of residual generators are constructed with the aid of the subspace identification method, based on which the faulty process variables are isolated and the corresponding fault distribution matrix is derived. In the upper KPIs supervised layer, the dynamic relationship between the high level KPIs and the low level process variables is identified and the fault detection is realized by checking the geometric property of the fault distribution matrix transmitted from the ground layer. A numerical example is provided in the end to demonstrate the effectiveness of the proposed fault detection framework.
Towards Understanding iOS App Store Search Advertising: An Explorative Study

With millions of apps competing in the market, one major issue for app developers is to get their apps discovered by intended users. iOS App Store Search Advertisement (ASA) is a mechanism that provides app developers a chance to increasing the awareness of their apps within the iOS App Store by bidding related searching keywords. However, as one of the most efficient methods to promoting apps, ASA has not been touched by our research community, to the best of our knowledge. To fill the void, in this paper, we present the first large scale explorative study on understanding iOS app store search advertising. Specifically, we have created the first dataset on ASA promotion, covering over 47K popular apps across 22 categories and over 2.8 million ASA bidding keywords during the first week of 2021. Based on this dataset, we investigate the adoption of ASA in the wild and the characteristics of the bidding keywords. Furthermore, we created a large-scale app-keyword relation graph, based on which we infer the app competitive relations. We observed two types of app competitive relations, i.e., explicit competition that the advertised apps intended to attract users who search the corresponding target apps by embedding their names into the ASA keywords, and implicit competitions that apps share a large portion of bidding keywords with others. Our efforts reveal interesting implications to stakeholders in the mobile app ecosystem, including app markets and app developers, etc. our study is the first step towards understanding and improving the in-market mobile app promotion mechanisms.
Charge Transport through Polyaniline Incorporated Electrically Conducting Functional Paper

The hopping charge transfer between polarons and bipolarons and metal-like band structure in polaron lattice of polyaniline makes it of high scientific worth. With above views polyaniline has been deposited on paper by a low cost chemical method to make the paper electrically conductive and functional. The prepared conductive papers were characterized using X-ray diffraction measurement, FTIR, UV–vis–NIR spectrophotometric measurement, and electrical conductivity measurement. Impressive electrical properties with good optical transparency were obtained with a highest light transmittance of 94.0% at 550 nm and electrical sheet resistance of 8.92 × 105 Ω/□. In addition, repeated cycles of deformation of the electrically conductive polyaniline incorporated flexible paper do not produce any noticeable change in its electrical conductivity and optical absorbance. The electrically conductive papers are of significant interest in preparing flexible and foldable circuits. The polyaniline-deposited conductive pape.
Laser Fired Contacts in multicrytalline silicon solar cells

The roadmap for the development of silicon solar cells requires the introduction of passivating contacts to obtain higher efficiencies as well as to reduce the cost of production to be industrially implemented. In this context, Laser Fired Contact (LFC) on p-type silicon wafers have been shown to be an effective technique to improve efficiencies, due to their ability to reduce recombination losses on the back surface of crystalline silicon solar cells. These studies have mainly focused on high quality monocrystalline silicon wafers and there are not enough developments made with multicrystalline silicon (mc-Si) wafers. Therefore, in this work, we present the optimization of the LFC process on p-type mc-Si and its application to two types of silicon devices: diffusion and heterojunction solar cells. These rear contacts have led to improved efficiencies for both types of solar cells over similar devices with thermalized aluminum rear contacts and without back passivation. These results illustrate the enormous potential of these localized laser-contacts created for mc-Si solar cells, perfectly compatible with a lower cost industrial production.
Fast HBM Access with FPGAs: Analysis, Architectures, and Applications

Over the past few decades, the gap between rapidly increasing computational power and almost stagnating memory bandwidth has steadily worsened. Recently, 3D die-stacking in form of High Bandwidth Memory (HBM) enabled the first major jump in external memory throughput in years. In contrast to traditional DRAM it compensates its lower clock frequency with wide busses and a high number of separate channels. However, this also requires data to be spread out over all channels to reach the full throughput. Previous research relied on manual HBM data partitioning schemes and handled each channel as an entirely independent entity. This paper in contrast also considers scalable hardware adaptions and approaches system design holistically. In this process we first analyze the problem with real world measurements on a Xilinx HBM FPGA. Then we derive several architectural changes to improve throughput and ease accelerator design. Finally, a Roofline based model to more accurately estimate the expected performance in advance is presented. With these measures we were able to increase the throughput by up to 3.78× with random and 40.6× with certain strided access patterns compared to Xilinx’ state-of-the-art switch fabric.
Grasping Detection Based on YOLOv3 Algorithm

In order to achieve multi-object grasping detection, this paper draws on the YOLOv3 algorithm, which has good performance in the field of object detection, and designs a grasping detection model based on deep learning. This model can recognize and locate multi-object in real time, and can predict the grasping point and the grasping deflection angle at the same time, which can realize end-to-end object positioning and grasping detection. For the specific application scenario, the corresponding dataset is automatically produced through data enhancement, which greatly saves the cost and time of manual collection and labeling. In the actual scene, we identify and locate the object and estimate the grasping point and grasping deflection angle of the object at the same time. 99.5% of the objects can be detected on the self-made data set. This method can greatly improve the accuracy of grasping detection in a specific scene.
Destination-Based Cooperative Jamming in Security UAV Relay System with SWIPT

Aiming at the energy-constrained UAV relay system with eavesdroppers, a physical layer security transmission scheme combining power splitting and time switching is proposed. The energy-constrained UAV relay uses Simultaneous Wireless Information and Power Transfer (SWIPT) technology to simultaneously receive energy and information from the radio frequency signal sent by the base station, and uses the collected energy to send the information to the destination node. At the same time, the destination node uses a full-duplex mode to receive information and send interference signals to the eavesdropper, so as to maximize the system security rate. Through simulation, the influence of different factors such as artificial noise power, power allocation ratio, and source emission power on the safety performance of the system is analyzed. The simulation results show that the proposed power division and time switching scheme can increase the system security rate under certain conditions. Compared with the gradient descent algorithm, the proposed alternate iteration optimization algorithm can reduce the number of iterations.
A numerical study on the enhancement and suppression of crystal nucleation

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DNA Identifications After the 9/11 World Trade Center Attack

The attack on the World Trade Center on 9/11/2001 challenged current approaches to forensic DNA typing methods. The large number of victims and the extreme thermal and physical conditions of the site necessitated special approaches to the DNA-based identification. Because of these and many additional challenges, new procedures were created or modified from routine forensic protocols. This effort facilitated the identification of 1594 of the 2749 victims. In this Policy Forum, the authors, who were were members of the World Trade Center Kinship and Data Analysis Panel, review the lessons of the attack response from the perspective of DNA forensic identification and suggest policies and procedures for future mass disasters or large-scale terrorist attacks.
An Area-Efficient SPHINCS+ Post-Quantum Signature Coprocessor

The significant advances in the area of quantum computing of the past decade leave no doubt about the fact that quantum computers are an actual threat to cryptography. For this reason, a lot of efforts have been made lately in designing so-called post-quantum cryptographic primitives. The adoption of these schemes depends on the future capability of post-quantum cryptographic schemes to offer performances and functionalities similar to their classical counterparts. In particular, a milestone towards standardization is the implementation on FPGA of cryptographic primitives which leads to an efficient execution. We contribute in this respect by providing an area-efficient FPGA implementation of SPHINCS+, a post-quantum signature scheme which guarantees very high security, allowing its deployment into embedded systems such as hardware security modules, IoT devices or nanosatellites.