Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Public Opinion System in Colleges

With the development of the network, the campus network has become the main method of information dissemination between college teachers and students. Therefore, the public opinion of colleges and universities has rapidly developed. Teachers and students of major universities use the public opinion system to obtain the required information and express their various opinions, but there are also some negative public opinions, which will have a negative impact on the ideology of college students. Therefore, an applicable university public opinion system becomes increasingly important. Based on this, on the basis of querying a large amount of data, through demand analysis and detailed design, the university public opinion system based on Java Web was finally realized. Based on the tens of thousands of college public opinion data crawled from Weibo through the Python crawler written by myself, it has realized the monitoring and data analysis visualization of multiple dimensions of college public opinion, including heat analysis, source analysis, public opinion search, and search term recommendation etc. In order to meet the needs of college public opinion managers, the system implements modules such as user management, role management, menu management, department management, and public opinion display. On the basis of comparing various prediction methods of university public opinion and comprehensively considering the general trend of university public opinion development, a Markov prediction model is introduced to predict the popularity trend of university public opinion. Finally, after testing, the system can run successfully and normally. It is hoped that the college public opinion system will enable relevant departments to understand the public opinion dynamics in real time, correctly guide the direction of public opinion, promptly correct errors and negative public opinion content, and better promote the stable development of the school.
An empirical study on the users' continuous intention of imported cross-border e-commerce platforms based on TAM and perceived risk theory

With the continuous increase of the transaction scale of China's cross-border e-commerce and the expansion of consumers' demand, the imported cross-border e-commerce (ICBEC) platforms have gradually emerged. Although the ICBEC has been enriching the consumers' experience in terms of product types and after-sales services, in the fierce competition, how to continuously attract online shoppers to visit ICBEC platforms has become an urgent problem for the managers. Based on the technology acceptance model and the perceived risk theory, a theoretical model that impacts consumers' continuance intention to ICBEC platforms was proposed. Data were collected by questionnaire, and empirical analysis was carried out with the help of structural equation model. The findings show that perceived ease-of-use significantly affects perceived usefulness, and perceived usefulness, perceived risk, and social impact all significantly affect the users' continuous intention to the ICBEC platforms.
An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers

The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. Firstly, we quantify three crucial factors (resource utilization, resource utilization balancing, and microservice dependencies) in resource scheduling. Then, we propose a multi-objective model to achieve equilibrium in these factors and a multiple fitness genetic algorithm (MFGA) for the balance between resource utilization, resource utilization balancing, and calling distance, where a container dynamic migration strategy in the crossover and mutation process of the algorithm is provided. The simulated results from Container-CloudSim showed the effectiveness of our MFGA.
Irrigation control system‐data gathering in WSN using IOT

Agriculture plays an important role in the growth of agriculture nation like India. The main source of water is essential for agriculture. The irrigation process supplies water, but sometimes the wastage of water may occur. For this reason, the irrigation control scheme in wireless sensor network (WSN) using Internet of things (IoT) is developed to save water and time. This paper presents an ANFIS‐PEGASIS: Irrigation control system‐data gathering in WSN using IoT. The process of the fuzzy inference system (FIS) is utilized for an optimum cluster head (CH) selection that depends on the distance and residual energy. After that, the power‐efficient gathering in sensor information system (PEGASIS) is used for the data collecting process in irrigation structure. PEGASIS is to make a chain between the sensor nodes (SNs), and every node communicates to the CH, and it's transferring the data to the base station (BS). Finally, the decision‐making process can be implemented by adaptive neuro‐FIS (ANFIS) for the automated irrigation process. ANFIS is a useful tool for the decision‐making process. Here, based on the information delivered by the sensors, the decisions are generated using ANFIS for the irrigation system. In the system, the various sensors like humidity, temperature, light intensity, and soil moisture are utilized to sense the different parameters of soil. Based on the value of soil moisture, the land becomes automatically irrigated by ON/OFF, the lamp, and the water pump. The simulation outcomes show that the developed (ANFIS‐PEGASIS) technique outperforms other recent approaches.
An optimal nonlinear companding transform with clipping scheme for universal filtered multicarrier systems

In this article, an exponential companding (EC) scheme, which can reduce PAPR without raising the average output power level, is proposed for universal filtered multi carrier (UFMC) signals. However, EC scheme degrades the BER performance. Therefore, an optimal nonlinear companding transform named as “modified exponential companding” (MEC), which can use two companding levels based on a threshold value, is proposed. The proposed MEC scheme shows enhanced PAPR and BER performances than EC scheme. Moreover, the MEC provides more design flexibility and freedom with an unchanged average output power level. Furthermore, a hybrid scheme is also proposed to improve PAPR performance, where clipping operation is employed on MEC signal based on an appropriate clipping threshold. Simulation results illustrate that proposed MEC scheme offers improved system performance with a net gain of 7.22 dB than conventional EC and μ‐law techniques. Besides, the proposed hybrid scheme with clipping ratio 1.3 proffers enhanced PAPR reduction of 8.85 dB and improved net gain of 7.95 dB. Also, the proposed EC, MEC, and hybrid schemes can provide lesser spectrum side‐lobe levels than μ‐law. Finally, the UFMC system with the proposed hybrid scheme is verified by employing wireless open‐access research platform (WARP) as hardware over real‐time channel.
Experience Sharing in a Traffic Scenario

Travel apps become more and more popular giving information about the current trafﬁc state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, learning effects add to this information. In this paper, we analyse the effects on the overall network, if adaptive driver agents share their aggregated experience about route choice in a reinforcement learning (Q-learning) setup. Drivers share what they have learnt about the system, not just information about their current travel times. We can show in a standard scenario that experience sharing can improve convergence times for adaptive driver agents.
Characterisation of nutritional quality traits of a chickpea (Cicer arietinum) germplasm collection exploited in chickpea breeding in Europe

Abstract. Seed and flour characteristics of 79 chickpea (Cicer arietinum L.) accessions from a representative collection of the germplasm used by European breeders were evaluated. The accessions were grouped according to desi or kabuli types and by different seed traits (size, shape, colour, surface). The variation in nutritional composition was assessed by principal component analysis (PCA) of data from 13 quality parameters. The first PCA component discriminated the accessions by basic composition (protein, fibre, fat) plus δ-tocopherol, and the second by carotenoid composition (zeaxanthin). Whereas desi types showed higher protein and fibre, kabuli accessions exhibited higher fat contents. The majority of accessions analysed showed very low (<1%) resistant starch content. Higher carotenoid concentration was obtained in desi-type accessions and it was related to specific seed traits: small seed size, angular shape and black colour. Besides discrimination between desi and kabuli groups, the detected associations of classes of shape, size and colour seed traits can be explored in chickpea-quality breeding programs. Several accessions showed higher concentrations of α-tocopherol (>200 μg g–1). LEGCA728, with green colouring in the seed coat and cotyledons, showed exceptional lutein concentration (28.32 μg g–1). We conclude that the chickpea germplasm in use by European breeders presents high potential for improvement of nutritional and health-benefit components not yet routinely implemented in the breeding of this important pulse crop.
Plant species of community interest identified in the Calimani-Gurghiu mountains (Mures County, Romania)

The aim of this study was to verify in the field the existing information about chorology, to select the category of endangered plants and to highlight the main populations of valuable species. The territory under study is located in the CălimaniGurghiu Mountains, the area belonging to Mureş County. For each species of community interest data were recorded about the systematics of species, the area, the population, the area of habitat/habitats suitable for species, the conservation status and the general trend of conservation status, abundance. As a result of this field research, nine plant species of community interest were identified (Angelica palustris, Arnica montana, Campanula serrata, Galanthus nivalis, Iris aphylla, Lycopodium alpinum, L. annotinum, L. clavatum, L. complanatum). These species belong to phytocenosis framed into 8 types of Natura 2000 habitats of community interest (6520, 6430, 6230*, 7140, 8220, 9410, 9130, 91V0). The general trend of conservation status is unfavourable-inadequate, the populations of the species of community interest are predominantly in a good and very good state of preservation, but in the future the populations are expected to decrease. Besides species of community interest, within the study area 30 plant species important in phytogeographical terms and rare species present in the national red lists were identified.
Cell Culture Established from Warts of Bovine Papilloma

This study is considered the first in the country on growing of bovine papilloma in cell culture from skin warts of cattle in order to detect papilloma virus, and to establish a cell line of transformed cells for further studies. In this study the warts were surgically removed from cows showing lump lesions on skin of abdomen, neck and udder, and transferred aseptically to laboratory by transport media. Trypsin and collagenase enzyme were used to dispersed papilloma cells from fragments of warts, Dulbecco’s modified Eagle’s medium (DMEM) and Rosswell Park Memorial Institute (RPMI)-1640 Medium (Gibco, were used with 10% fetal calf serum for culturing of papilloma cells. Cultured cell were first noticed after 3-4 days post incubation(PI) and appeared as epithelial and fibroblastic cells. After 7days post inoculation clones of transformed epithelial cells start to appear and after (20-25) days it became a complete monolayer. Successful secondary culture was achieved by using trypsin-versene solution(TV). Further studies is needed to detect the virus from cultured cell by PCR and ELISA techniques beside preparation of vaccine from cell culture for treatment of papillomas in infected cattle. ةیرقبلا ةیملحلا مارولأا نم يولخلا عرزلا تابثإ
Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Compressive Sensing (CS) is a widely used technique in biomedical signal acquisition and reconstruction. The technique is especially useful for reducing acquisition time for magnetic resonance imaging (MRI) signal acquisitions and reconstruction, where effects of patient fatigue and Claustrophobia need mitigation. In addition to improving patient experience, faster MRI scans are important for time sensitive imaging, such as functional or cardiac MRI, where target movement is unavoidable. Inspired from recent research works on multi-layer convolutional sparse coding (ML-CSC) theory to model deep neural networks, this work proposes a multi-layer basis pursuit framework which combines the benefit from objective-based CS reconstructions and deep learning-based reconstruction by employing iterative thresholding algorithms for successfully training a CS-MRI restoration framework on GPU and reconstruct test images using parameters of the trained model. Extensive experiments show the effectiveness of the proposed framework on four MRI datasets in terms of faster convergence, improved PSNR/SSIM, and better restoration efficiency as compared to the state of the art frameworks with different CS ratios.
Player Satisfaction Model and its Implication to Cultural Change

Game refinement theory has been studied to derive a measurement of game sophistication. Recently, it has been developed as physics in mind, which may relate to the state of player’s feelings such as satisfaction and comfort in mind. This article explores a link between game refinement theory and reinforcement schedule. A notion of winning hardness to achieve the goal (<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>) of a gamified activity, relative to the reinforcement schedules (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>), is proposed through the physics in mind measures, which is utilized to quantify an activity’s enjoyment. By applying these new measurements to various gaming activities, the sense of player satisfaction and sophistication in game-playing is induced. Indicators for cultural changes and their implications to game-playing landscapes and experiences were established based on evidence from various well-known games. From such findings, the link between game refinement theory and reinforcement schedule may imply that classifying the games according to our mind’s psychological activities is vital in design decisions that largely influences people’s quality of life.
Experimental Results of Maritime Target Detection Based on SVM Classifier

A radar target detection algorithm based on Support Vector Machine (SVM) Classifier is proposed in this paper for the problem of target detection of high resolution range profile (HRRP). In solving nonlinear and high-dimensional pattern recognition, the SVM classification algorithm proposed based on statistical theory shows many advantages. the basic idea of SVM can be summarized as transforming the input space into a high-dimensional space by the nonlinear variation defined by the inner product, and then finding the optimal classification plane in this new space. The experimental results show the radar target detection algorithm based on SVM classifier can detect targets successfully in different clutter environment and has good performances.
Translational Investigation of Turmeric for Arthritis Treatment: A Review of Lessons Learned

Turmeric rhizome has been used for centuries to combat inflammation, including joint inflammation in arthritis. However, scientific evidence of efficacy and mechanism of action and a systematic evaluation of the biological activity of turmeric components for the treatment of inflammation have been lacking. This review will summarize the results of recently published studies that we have conducted to address each of these specific areas of inquiry using an animal model of rheumatoid arthritis.
Disclosing Product Availability in Online Retail

Problem definition: Online retailers disclose product availability to influence customer decisions as a form of pressure selling designed to compel customers to rush into a purchase. Can the revelation of this information drive sales and profitability? We study the effect of disclosing product availability on market outcomes—product sales and returns—and identify the contexts where this effect is most powerful. Academic/practical relevance: Increasing sell-out is key for online retailers to remain profitable in the presence of thin margins and complex operations. We provide insights into how their information-disclosure policy—something they can tailor at virtually no cost—can contribute to this important objective. Methodology: We collaborate with an online retailer to procure a year of transaction data on 190,696 products that span 1,290 brands and 472,980 customers. To causally identify our results, we use a generalized difference-in-differences design with matching that exploits one policy of the firm: it discloses product availability only for the last five units. Results: The disclosure of low product availability increases hourly sales—they grow by 13.6%—but these products are more likely to be returned—product return rates increase by 17.0%. Because returns are costly, we also study net sales—product hourly sales minus hourly returns—which increase by 12.5% after the retailer reveals low availability. Managerial implications: The positive effects on sales and profitability amplify over wide assortments and when low-availability signals are abundantly visible and disclosed for deeply discounted products whose sales season is about to end. In addition, we propose a data-driven policy that exploits these results by using machine learning to prescribe the timing of disclosure of scarcity signals in order to boost sales without spiking returns.
A Data Concept Map for the Data Driven Enterprise Using Smart Technologies

Data Driven Innovation (DDI) is one of the main pillars leading to improved productivity and economic growth for companies. The adoption and use of DDI may determine their competitive advantages and innovation capabilities to exploit data. However, it is known that the key to DDI lies in the data itself. Comparing the use of data and the creation of innovation, the way how data flows, is collected and integrated is no less pressing. In this paper, we present the Data Concept Map to take into account the human factor for digitalization challenges and benefit organizations, especially for small- and medium sized enterprises, in terms of saving costs, improving flexibility and marching towards Data Driven Enterprise. We introduce the concept of utilizing smart technologies as a channel to work with humans for expediting data collection, system integration and information flow. An experimental case study is conducted for a manufacturing application to demonstrate the effectiveness of the proposed approach.
DP-ANN: A new Differential Private Artificial Neural Network with Application on Health data (Workshop Paper)

Privacy of the individual data, especially in the Health data, is very sensitive and important. Privacy preserving Machine learning is emerging as one of the solutions for the security of data with the utility to create knowledge. In this paper, we have proposed a differential private artificial neural network (DP-ANN) and shows its application to predict the spread and the peak number of COVID-19 cases. We proposed a differential private artificial neural network (DP-ANN) in which laplacian noise has been introduced at activation function level and it has been compared with existing privacy ideas at error function and weights level of ANN. Results show that DP-ANN model with the private activation function produces the result similar to the base ANN model.
A Multi-view Deep Convolutional Neural Network for Reduction of False Positive Findings in Breast Cancer Screening

Screening mammography is commonly the only imaging exam allowing early-stage detection of breast cancer. The early detection is, in fact, associated with a decreased breast cancer mortality rate amongst women. However, false positive recall is one of the main limitations of screening practices and it is often associated with unnecessary workups and biopsies. To tackle this issue and improve the medical image classification performance in order to carry out a screening/diagnosis task, we propose to use a multi-view deep convolutional neural network - the proposed network can extract discriminative features from Cranial Caudal (CC) and Medio-Lateral Oblique (MLO) views for each breast of a patient (a set of four images). We experiment it on an augmented-data based subset selected from the open Digital Database for Screening Mammography (DDSM) using 5400 images. We show how the proposed method can lead to a better performance than the state-of-the-art ones, especially in terms of prediction accuracy and false positive rate reduction. In fact, The results show statistically significant reduction in false findings without increasing false negative cases. Our method achieves a specificity rate of 98% and an accuracy rate of 98.88%. Index Terms–Mammography, Breast cancer diagnosis, False positive findings, Deep learning, Multi-view deep convolutional neural network.
GEMINIVIRUSES AS MODELS TO STUDY THE ESTABLISHMENT AND MAINTENANCE OF DNA METHYLATION

ii Dedication.vii Acknowledgements . viii Vita . xi List of Figures . xviii CHAPTERS: CHAPTER 1: INTRODUCTION . 1 1.1 GEMINIVIRUSES.1 1.1.1 Geminivirus virion structure and genome organization.2 1.1.2 Geminivirus satellite DNAs . 6 1.1.3 Geminivirus proteins.6 1.1. 4 Geminivirus infection cycle: transmission through replication. 10 1.1. 5 Geminivirus Transcription . 13 1.2 RNA SILENCING AS A DEFENSE MECHANISM. 15 1.2. 1 RNA silencing pathways in plants. 16 1.2. 2 Post-transcriptional gene silencing (PTGS) . 18 1.2. 3 Transcriptional gene silencing (TGS) and the methylation pathway . 21 1.2. 4 Systemic spread of the silencing signals. 24 1.2. 5 TGS as a defense against geminiviruses . 27 xiii 1.2. 6 Suppression of PTGS by geminivirus proteins . 30 1.2. 7 Suppression of TGS by geminivirus proteins. 35 1.2. 8 Novel suppressor activities of the geminivirus AL2 protein. 39 1.3 CHROMATIN STRUCTURE AND EPIGENETICS . 40 1.3. 1 Nucleosome structure and assembly. 40 1.3. 2 Histones modifications and variants . 42 1.3. 3 Chromatin remodelers . 46 1.3. 4 Pol IV and Pol V mediate chromatin formation and spread DNA methylation in plants . 48 1.3. 5 RdDM and the potential role of RNA Polymerase II . 53 1.3. 6 Geminivirus chromatin . 54 1.3. 7 Geminivirus chromatin as a model to study the RdDM pathway. 55 CHAPTER 2 . 65 ANALYSIS OF GEMINIVIRUS AL2 PROTEIN IDENTIFIES NEW SUPPRESSOR ACTIVITIES AND DEVELOPMENTAL CHANGES IN THE ROLE OF ADK IN THE N. BENTHAMIANA METHYL CYCLE. 65 2.