Miracle-dz/UniFAR-FTUs huggingface dataset:
Wedderburn components, the index theorem and continuous Castelnuovo-Mumford regularity for semihomogeneous vector bundles

Abstract We study the property of continuous Castelnuovo-Mumford regularity, for semihomogeneous vector bundles over a given Abelian variety, which was formulated in A. Küronya and Y. Mustopa [Adv. Geom. 20 (2020), no. 3, 401-412]. Our main result gives a novel description thereof. It is expressed in terms of certain normalized polynomial functions that are obtained via the Wedderburn decomposition of the Abelian variety’s endo-morphism algebra. This result builds on earlier work of Mumford and Kempf and applies the form of the Riemann-Roch Theorem that was established in N. Grieve [New York J. Math. 23 (2017), 1087-1110]. In a complementary direction, we explain how these topics pertain to the Index and Generic Vanishing Theory conditions for simple semihomogeneous vector bundles. In doing so, we refine results from M. Gulbrandsen [Matematiche (Catania) 63 (2008), no. 1, 123–137], N. Grieve [Internat. J. Math. 25 (2014), no. 4, 1450036, 31] and D. Mumford [Questions on Algebraic Varieties (C.I.M.E., III Ciclo, Varenna, 1969), Edizioni Cremonese, Rome, 1970, pp. 29-100].
Multi-Stage Threat Modelling and Security Monitoring in 5GCN

The fifth generation of mobile networks (5G) promises a range of new capabilities including higher data rates and more connected users. To support the new capabilities and use cases the 5G Core Network (5GCN) will be dynamic and reconfigurable in nature to deal with demand. It is these improvements which also introduce issues for traditional security monitoring methods and techniques which need to adapt to the new network architecture. The increased data volumes and dynamic network architecture mean an approach is required to focus security monitoring resources where it is most needed and react to network changes in real time. When considering multi-stage threat scenarios a coordinated, centralised approach to security monitoring is required for the early detection of attacks which may affect different parts of the network. In this chapter we identify potential solutions for overcoming these challenges which begins by identifying the threats to the 5G networks to determine suitable security monitoring placement in the 5GCN.
Understanding the association between exposure to family planning messages and consistent condom use among never married men in Ghana

Background Despite considerable efforts to promote condom use, sexually active people in sub-Sahara Africa still engage in risky sexual behaviours, with condom use relatively low. With this high vulnerability of these persons to HIV and sexually transmitted infections, research related to exposure to family planning messages to help curb this trend remains sparse. This study examined how exposure to family planning messages in the midst of some socio-demographic factors is associated with consistent condom use among sexually active never married men in Ghana. Methods Data were obtained from the 2014 Demographic and Health Survey (DHS) of Ghana. Only never married men (15–64 years) who have had sexual experience in the last 12 months were included in the analysis (N = 971). Frequencies, percentages, chi-square tests and binary logistic regression analyses were carried out. Results of the binary logistic regression analysis were presented using crude odds ratios (cOR) and adjusted odds ratios (aOR). Results The results showed that only 26.15% of sexually active never married men in Ghana consistently used condom during sex. Men who were exposed to family planning messages were 51% more likely use condom consistently compared to those who are not exposed [aOR = 1.51, CI = 1.04–2.18]. In terms of the covariates, the likelihood of consistent condom use among men in Ghana was lower among those aged 35 years and above compared to those aged 15–24 [aOR = 0.46 CI = 0.21–0.99]. The odds of consistent condom use among men increased with level of education, with men with higher level of education having the highest odds of consistent condom use compared to those with no formal education [aOR = 9.98, CI = 2.05–48.46]. Men of the richest wealth quintile were more likely to use condom consistently compared to those of the poorest wealth quintile [aOR = 2.62, CI = 1.30–5.27]. Higher odds of consistent condom use was found among men who dwelled in the Central, Northern, and Upper East regions compared to those of the Western region. Conclusion Our findings have established a strong association between exposure to family planning messages alongside age, educational level, wealth, and region of residence and consistent condom use. Men exposed to family planning messages were more likely to use condoms consistently. Designed programs should use intervention strategies that focus on interactive and participatory educational activities to improve sexually active men’s interpersonal communication on family planning messages, especially on consistent condom use with their sexual partners.
SeedSortNet: a rapid and highly effificient lightweight CNN based on visual attention for seed sorting

Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting. However the huge computational complexity and massive storage requirements make it a great challenge to deploy them in real-time applications, especially on devices with limited resources. In this study, a rapid and highly efficient lightweight CNN based on visual attention, namely SeedSortNet, is proposed for seed sorting. First, a dual-branch lightweight feature extraction module Shield-block is elaborately designed by performing identity mapping, spatial transformation at higher dimensions and different receptive field modeling, and thus it can alleviate information loss and effectively characterize the multi-scale feature while utilizing fewer parameters and lower computational complexity. In the down-sampling layer, the traditional MaxPool is replaced as MaxBlurPool to improve the shift-invariant of the network. Also, an extremely lightweight sub-feature space attention module (SFSAM) is presented to selectively emphasize fine-grained features and suppress the interference of complex backgrounds. Experimental results show that SeedSortNet achieves the accuracy rates of 97.33% and 99.56% on the maize seed dataset and sunflower seed dataset, respectively, and outperforms the mainstream lightweight networks (MobileNetv2, ShuffleNetv2, etc.) at similar computational costs, with only 0.400M parameters (vs. 4.06M, 5.40M).
INFERENCE FOR STOCHASTIC KINETIC MODELS FROM MULTIPLE DATA SOURCES FOR JOINT ESTIMATION OF INFECTION DYNAMICS FROM AGGREGATE REPORTS AND VIROLOGICAL DATA BY OKSANA

Influenza and respiratory syncytial virus (RSV) are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base the diagnosis of ARI on patients’ symptoms alone, and do not routinely conduct virological tests necessary to identify individual viruses, which limits the ability to study the interaction between multiple pathogens and make public health recommendations. We consider a stochastic kinetic model (SKM) for two interacting ARI pathogens circulating in a large population and an empirically-motivated background process for infections with other pathogens causing similar symptoms. An extended marginal sampling approach based on the Linear Noise Approximation to the SKM integrates multiple data sources and additional model components. We infer the parameters defining the pathogens’ dynamics and interaction within a Bayesian model and explore the posterior trajectories of infections for each illness based on aggregate infection reports from six epidemic seasons collected by the state health department, and a subset of virological tests from a sentinel program at a general hospital in San Luis Potosí, México. We interpret the results and make recommendations for future data collection strategies.
A convergent parallel mixed-method research into the use of the cheat sheet in teacher education: State test anxiety, exam scores and opinions of prospective teachers

The aim of the study is to investigate the use of cheat sheets in teacher education. The research analyzed state test anxiety levels of prospective teachers, the correlation between their cheat sheet evaluation scores and exam scores, and opinions of prospective teachers about the cheat sheet. Thus, the study was conducted as a convergent parallel mixed-method research in which both quantitative and qualitative methods were used simultaneously. The study group consisted of 24 prospective teachers. Data regarding state test anxiety levels of the prospective teachers were gathered just before the exam via State Test Anxiety Scale. The cheat sheet evaluation rubric designed by the researcher was used to evaluate the cheat sheets prepared by the prospective teachers. The prospective teachers’ exam scores were obtained from their responses. The data about opinions of the prospective teachers were collected via Standardized Interview Form developed by the researcher. The findings of the study revealed that prospective teachers’ overall state text anxiety was at “very little” level. There was a statistically significant and positive correlation between the prospective teachers’ cheat sheet evaluation scores and their exam scores. Qualitative findings revealed that the prospective teachers started taking notes at the beginning of the semester to write on their cheat sheets; however, they started to write their cheat sheets the day before the exam. They wrote their cheat sheets in 3-5 hours, and spent 3-5 hours studying for the exam. The prospective teachers rarely used their cheat sheets during the exam; however, the cheat sheet helped them learn the material, and they wanted to use cheat sheets in other courses. The prospective teachers used different strategies while writing their cheat sheets. Based on the conclusions of the study, teacher educators are also recommended to let prospective teachers use cheat sheets during exams to reduce their anxiety, to increase their exam scores, and most importantly, to help them learn the material thoroughly.
False positive anti-hepatitis A virus immunoglobulin M in autoimmune hepatitis/primary biliary cholangitis overlap syndrome: A case report

BACKGROUND Autoimmune hepatitis (AIH) is an immune-mediated liver disease affecting all age groups. Associations between hepatitis A virus (HAV) and AIH have been described for many years. Herein, we report a case of an AIH/primary biliary cholangitis (PBC) overlap syndrome with anti-HAV immunoglobulin M (IgM) false positivity. CASE SUMMARY A 55-year-old man was admitted with manifestations of anorexia and jaundice along with weakness. He had marked transaminitis and hyperbilirubinemia. Viral serology was positive for HAV IgM and negative for others. Autoantibody screening was positive for anti-mitochondria antibody but negative for others. Abdominal ultrasound imaging was normal. He was diagnosed with acute hepatitis A. After symptomatic treatment, liver function tests gradually recovered. Several months later, his anti-HAV IgM positivity persisted and transaminase and bilirubin levels were also more than 10 times above of the upper limit of normal. Liver histology was prominent, and HAV RNA was negative. Therefore, AIH/primary biliary cholangitis (PBC) overlap syndrome diagnosis was made based on the “Paris Criteria”. The patient was successfully treated by immunosuppression. CONCLUSION This case highlights that autoimmune diseases or chronic or acute infections, may cause a false-positive anti-HAV IgM result because of cross-reacting antibodies. Therefore, the detection of IgM should not be the only method for the diagnosis of acute HAV infection. HAV nucleic acid amplification tests should be employed to confirm the diagnosis.
Adaptive Simultaneous Magnetic Actuation and Localization for WCE in a Tubular Environment

Simultaneous magnetic actuation and localization is a promising technology to realize active wireless capsule endoscopy. In this article, an adaptive approach is proposed to efficiently propel and precisely locate a magnetically actuated capsule in unknown complex tubular environments. In order to track the capsule in real time in a large workspace, we improve upon our previous external sensor-based magnetic localization strategies by proposing an adaptive method to automatically activate an optimal subset of sensors during the capsule movement and developing a simplified multiple objects tracking algorithm to estimate the six-dimensional pose of the capsule in real time. Moreover, based on a study of capsule locomotion in tubular environments with different shapes, we propose to dynamically adjust the pose of the actuator to achieve efficient and robust propulsion of the capsule in an unknown tubular environment. The effectiveness of our proposed system is validated in extensive experiments on phantoms and ex vivo animal organs. Compared with the state-of-the-art, our method can realize the closed-loop control of a magnetic capsule without requiring sensing modules to be placed inside the capsule, which can reduce the space constraints and energy consumption of the capsule. Moreover, the system and algorithms for adaptive actuation and localization can provide an improved propulsion speed, workspace range, and localization update frequency as well as a comparable localization accuracy.
A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits

This paper unifies the design and the analysis of risk-averse Thompson sampling algorithms for the multi-armed bandit problem for a class of risk functionals ρ that are continuous and dominant. We prove generalised concentration bounds for these continuous and dominant risk functionals and show that a wide class of popular risk functionals belong to this class. Using our newly developed analytical toolkits, we analyse the algorithm ρ-MTS (for multinomial distributions) and prove that they admit asymptotically optimal regret bounds of risk-averse algorithms under the CVaR, proportional hazard, and other ubiquitous risk measures. More generally, we prove the asymptotic optimality of ρ-MTS for Bernoulli distributions for a class of risk measures known as empirical distribution performance measures (EDPMs); this includes the well-known mean-variance. Numerical simulations show that the regret bounds incurred by our algorithms are reasonably tight vis-à-vis algorithm-independent lower bounds.
Recommending Extract Method Refactoring Based on Confidence of Predicted Method Name

Refactoring is an important activity that is frequently performed in software development, and among them, Extract Method is known to be one of the most frequently performed refactorings. The existing techniques for recommending Extract Method refactoring calculate metrics from the source method and the code fragments to be extracted to order the recommendation candidates. This paper proposes a new technique for accurately recommending Extract Method refactoring by considering whether code fragments are semantically coherent chunks that can be given clear method names, in addition to the metrics used in previous studies. As a criterion for the semantic coherency, the proposed technique employs the probability (i.e. confidence) of the predicted method names for the code fragments output by code2seq, which is a state-of-the-art method name prediction technique. The evaluation experiment confirmed that the proposed technique has higher correctness of recommendation than the existing techniques.
News text classification based on hybrid model of Bidirectional Encoder Representation from Transformers and Convolutional Neural Network

At present, with the rapid development of Internet technology, text data show massive characteristics. Network media and new media platforms have become an important part of journalism and communication, and network news has become one of the important sources of information. In order to meet the needs of online news reading users and improve the efficiency of personalized news recommendation by content distribution platform, it is urgent to effectively manage and utilize online news. Based on this, this paper studies and implements the algorithm for news text classification, and the goal is to build a classification model for news text with higher classification accuracy. In this paper, BERT and CNN algorithm are combined to classify news texts. The core idea is to send BERT as the embedding layer into the CNN model. The experimental results show that this method is better than the simple BERT model or the simple CNN model.
A Non-intrusive loading monitoring method based on Attention mechanism and denoising-Autoencoder

This paper built denoising-Autoencoder (dAE), Attention-dAE and transformerbased regression models based on Non-Intrusive Load Monitoring (NILM) and Deep Learning (DL) methods, and uses them in UK-DALE[1] proving on the dataset, the verification results show that the recognition accuracy of adding Attention mechanism and dAE is significantly improved, and the model parameters are greatly reduced.
Discussion on the temperature influence of Pb/Cu/Ni/Mo precipitation in copper faucets

Precipitation of harmful substances in the faucets has always been the focus of attention, this experiment selected the four metal elements of Pb/Cu/Ni/Mo as the research object, and studied the precipitation in the faucets which were extracted at the ambient temperature of 20°C, 40°C and 60°C. The results showed that the Pb and Mo precipitation in the faucets increased with the temperature rise, while the Cu and Ni in most samples were the least precipitated at 60°C. Detection found that the metals precipitation among different batches and different samples in the same batch varied widely, mainly affected by the processing technology and the material of faucets.
Cloud data security and various security algorithms

The term Cloud computing is a most trending and promisingtechnology. It is the most basic need for many organizations. Cloud technology has many advantages as user can access the files from anywhere over the internet. It has also a very known feature of using the resources according to their demand and user does need to pay extra charges for the unused resources. In Cloud Computing user do not need to carry all their documents with them as cloud access is global. But as Cloud computing means sending data over internet so sometimes there may be security issues and threat of intruder attack at the time of data transmission. As traditional banking industry is totally changed and now the total control in the hand of customer rather than the bank. In banking industry cloud computing provides various features viz; cost saving, business continuity, business agility and time saving. but as data in bank is the most crucial data so cloud security is the first priority for every bank before adopting cloud. security at cloud is the biggest obstacle for the banking industry to adopt this trending technology and its services. So there is need to provide security to the cloud. This paper aims on cloud basic methodology and analyze some security algorithms and their applicability in cloud banking environment.
A Bi-level Security Mechanism with Encryption and User Authentication through Hardware Token for Cloud Computing Based Web Application

— In contrast to traditional solutions, cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. Although commercial CSP provides secured data storage to the client data, it is a research challenge to satisfy the user’s requirement of confidentiality and authentication. Cloud security solutions typically have single security architecture but have many customers with different demands. Even multi-tenancy feature of cloud gives rise to security issue related to data and also the authentication of users. Though cloud service provider solves this problem by strong encryption algorithms for data security, the security achieved, works at single level. We propose a system with bi-level security mechanism which uses encryption at cloud service level along with application controller level security and system level security which provides transient user authentication with a small hardware token. This token continuously monitors the user’s presence over a wireless connection link to the machine through which user is accessing cloud. The system enforces the security guarantees, although at a cost of performance.
Challenges in IoT Technology Adoption into Information System Security Management of Smart Cities: A Review

Article history: Received: 25 December, 2020 Accepted: 20 February, 2021 Online: 10 March, 2021 Sustainable urban development and utilization of Internet of Things (IoT) technology is driving cities globally to evolve into Smart Cities (SC). The power of IoT services and applications will enable public agencies to provide personalized services to the citizens and inevitably improves their much-needed quality of life. However, although the use of IoT technology proves to be advantageous to citizens, it is not without challenges, particularly concerning with the management of information security. As agencies prepare towards SCs with the utilization of IoT, their Information Systems (IS) security management is even more critical. Current IS security management approaches must be reviewed and potentially revise appropriately in tandem with the increasing commercial use of the IoT technology. Therefore, this paper aims to discuss challenges in the IS management specifically in protecting and assuring information accuracy and completeness. Document analysis on relevant literature has been carried out to identify and analyse the challenges. The result discusses that the IS security management for IoT-enabled SC is challenged in five aspects: governance, integrity, interoperability, personalization, and self-organizing. Considerations of these challenges will support SC development concerning the IS security management in IoT-enabled SC.
Research on PD monitoring and location method of automatic directional substation

The accurate positioning of partial discharge can effectively avoid the occurrence of insulation failure of power equipment, but most of the existing positioning devices are fixed antenna array, which is difficult to achieve multi angle discharge monitoring in space, and the positioning method is single and the positioning accuracy is not high. Therefore, this paper proposes an automatic directional monitoring device for partial discharge source, and proposes a precise positioning method based on fixed point iteration method. With this positioning method as the main method and the automatic directional device as the auxiliary method, the spatial multi angle positioning of partial discharge source is realized. Firstly, PD signal recognition algorithm is used to identify the fault of PD signal, and then the steering gear rotation angle is determined by the angle correlation calculation formula, so as to accurately identify the position and direction of PD source. On this basis, four antenna array positioning algorithm is used to accurately locate PD source. Finally, through the field simulation test, it is found that the angle error of automatic orientation is 3°. The position error of PD source is 0.19m, which verifies the feasibility of the proposed method and device.