Reviewerly/exHarmony huggingface dataset:
GLOBAL DIGITALIZATION AND TRANSFORMATION OF HUMANITIES EDUCATION

The authors believe that This problem is closely related to the implementation of innovations.. But it itself is faced with a huge number of problems, one of which is improving the quality of its functioning and guaranteeing the results of its activities. Role of higher education in this process is key. This problem is closely related to the implementation of innovations. The authors conceptually changing field of modern educational practices, aspirations and attempts to humanize and humanize education, taking into account the realities of the information society are favorable factors in creating and implementing the concept of humanities education, which allows different ways of implementation Based on a critical approach to reforming the current system of higher education the paper touches upon the need to change the process informatization of humanities education. Innovation in education should be understood as an innovation designed to resolve the current problem situation in order to optimize the educational process, improve its quality or organize favorable conditions for the assimilation of material by students. The analysis of the processes associated with the fact that the course of transferring the Ukrainian economy from a raw material development path to an innovative one is extremely important. The development of humanities education in the information society is aimed at implementing an interdisciplinary approach, i.e. should ensure the effectiveness of the development and application of humanities knowledge and form a conscious responsible choice in a variety of cultural meanings, cultural self-determination.
Achieving the Promise of Confidential Computing

The confidentiality and integrity of data are cornerstone properties of information technology security.The use of cryptography in conjunction with more traditional access control and other separation mechanisms is now wide-spread, well understood, and accepted when protecting data at rest or in motion.As we rely more and more on computing environments that we may not directly control, such as those we see today with various cloud computing models, there is heightened interest in the use of these technologies to protect data that is actively in use.This interest and related trend, which is becoming known as Confidential Computing (and several other names), has its roots in the long and rich history of computer security.I will discuss the role and ongoing evolution of hardware-based trusted execution environments -such as SGXin achieving these goals, the progress we've made and the challenges we and the industry still face, all through the lens of my personal experience and what we are seeing from Intel's cloud and data center customers and partners. Bio:Ron Perez is an Intel Fellow and Chief Security Architect in the Office of the CTO at Intel.He is responsible for overall Intel security architecture and cross-business unit security technology roadmap alignment, with a focus on Confidential Computing and platform security for the disaggregated, heterogeneous, and distributed data center.He leads a team of senior technology leaders driving Intel's foundational security technologies for server platforms and roots-of-trust, hierarchical memory subsystems, supply chain and life cycle integrity, and trusted execution environments in collaboration with Intel Labs and world-wide strategy, planning, engineering, design, and validation teams.An industry veteran, Ron joined Intel in 2017 with a breadth of experience spanning security, semiconductors, and cloud computing.Most recently, he
Feature Reconstruction and Metric Based Network for Few-Shot Object Detection

In the object detection task, deep learning-based methods always need a large amount of annotated training data. However, annotating a large number of images is labor-intensive. In order to reduce the dependency of expensive annotations, we propose a novel end-to-end object detection network~(FM-FSOD) that integrates metric learning and meta-learning to tackle the few-shot object detection task. FM-FSOD is a class-agnostic detection model that can accurately recognize novel categories without fine-tuning on testing data. Specifically, to quickly learn the characteristics of novel categories, we propose a meta-representation module (MR module) to learn from intra-class mean prototypes and acquire the ability to reconstruct high-level features with the meta-learning method. To further conduct the similarity of features between support prototypes and query ROI features, we propose Pearson metric module (PR module), which serves as a classifier. Compared with the previous standard cosine distance module, the PR module enables the model to acquire the robust ability for large bias features. We have conducted extensive experiments on benchmark datasets FSOD, MS COCO, and PASCAL VOC to demonstrate the feasibility and efficiency of our model. Comparing with the previous methods, FM-FSOD obtains comparable results.
AGU data citation community of practice - Credit for creators of data within collections using the concept of a reliquary

A gap in community practice on data citation that emerged during the AGU fall meeting 2020 Data FAIR Town Hall, "Why Is Citing Data Still Hard?" with the goal of addressing the use case of citing a large number of datasets such that credit for individual datasets is assigned properly.The discussion included the concept of a "Data Collection" and the infrastructure and guidance still needed to fully implement the capability so it is easier for researchers to use and receive credit when their data are
A Multi-Objective Differential Evolutionary Optimization Method for Performance Optimization of Cloud Application

Due to the limited search space in the existing performance optimization ap-proaches at software architectures of cloud applications (SAoCA) level, it is difficult for these methods to obtain the cloud resource usage scheme with optimal cost-performance ratio. Aiming at this problem, this paper firstly de-fines a performance optimization model called CAPOM that can enlarge the search space effectively. Secondly, an efficient differential evolutionary op-timization algorithm named MODE4CA is proposed to solve the CAPOM model by defining evolutionary operators with strategy pool and repair mechanism. Further, a method for optimizing performance at SAoCA level, called POM4CA is derived. Finally, two problem instances with different sizes are taken to conduct the experiments for comparing POM4CA with the current representative method under the light and heavy workload. The ex-perimental results show that POM4CA method can obtain better response time and spend less cost of cloud resources.
Artificial Intelligence Based Virtual Mouse

The mouse is an indispensable input device that is used in all modern-day computer systems. Input devices are high-contact surfaces that we use daily and often all day. As such the mouse is laden with germs and bacteria. . Although, wireless mice have rid us of the need for a tangled mess of cables, there still exists the need to touch the device. In view of the pandemic, this proposed system uses the in-built camera or a peripheral web camera for capturing hand movements and finger-tip detection that can perform traditional mouse functions like left-clicking, right-clicking, scrolling and cursor functions. The algorithm is machine learning based. Using deep learning, the algorithm is trained so that hands can be detected using the camera. Hence, the proposed system will prevent the spread of Covid-19 by eliminating the need for human intervention and dependency on physical devices to control the computer system.
THE PLACE OF RISK MANAGEMENT IN AGILE METHODOLOGY

Risk Management is an organized decision-making process that efficiently plans, assesses, handles, monitors, and documents risk to increase the likelihood of achieving project goals and decrease the likelihood that a risk would become a future problem. The introduction of agile project management as a methodology in organizations poses new challenges to risk management. Although some of the conventional methods can be applied, it is important to take into account the specific characteristics of the agile methodology. This implies a more dynamic application of a set of methods and techniques for identifying, analyzing, assessing, and affecting risk. Risk management is a complex activity requiring a specific approach in terms of the capacity of an organization and the characteristic features of projects. Risk management is an important part of the integrated project management framework. It is not a coincidence that this process is included in the methodologies of the Project Management Institute and the International Project Management Association as well as in a number of standards.Risk management is a complex activity requiring a specific approach in terms of the capacity of an organization and the characteristic features of projects. This means that even when standard rules, procedures, methods and techniques for project risk management exist, they need to be adapted to each particular case, analyzed and verified in the course of their implementation. Thus, this process can be highly effective.Organizations’ expectations of enhancing their effectiveness through risk management pose new challenges to project risk management methods and techniques. The dynamics of environmental factors – internal and external – is the main prerequisite for the attention managers pay to this process.It is important to note that each project is specific and standardized risk management systems would be not only difficult to implement, but also ineffective. The agile approach to project management implies agile risk management. This means that standard risk management methods, techniques and limitations need to be adapted to the specifics and complexity of a project on the one hand, the requirements of stakeholders, and the team and organizational capacity, on the other.The report focuses on issues related to agile project management, the integration of risk management into the project management process, and some contributing points of the agile methodology to improving the effectiveness of the project management process. Risk is presented as an unforeseen event that may have a positive or negative impact on project goals. The main advantages of agile risk management in a project and the possibility of integrating it with the overall project management are highlighted.
Research on User Cross-domain Access Control Model Based on Feedback

On the basis of role access control, combined with the role table of this domain and the role table of foreign domains, the inter-domain trust is introduced, and a feedback-based user trust cross-domain access control model-UTKYFKM is proposed. The UTKYFKM model passes trust between different domains. To obtain the user's qualifications to access foreign domains, integrate the user's trust value and resource access execution degree, determine the credibility of user behavior, and combine the foreign domain role table, the system assigns users foreign domain roles, and the user will be externally based on the permissions corresponding to the foreign domain roles. The domain resource object performs the actual access operation and feeds back the execution degree of the access. The feedback feature of this model can detect wrong access control in time and improve the security of cross-domain access control for users.
Global rare-earth metals market 2021-2026: lanthanum, cerium, neodymium, praseodymium, samarium, europium - opportunities in recycling and reuse of rare-earth metals

A biogeochemical orientation survey was conducted in the vicinity of Mo skarn deposits in the Jecheon district in Korea. The main litholigies of the study area are Ordovician dolostone, limestone and calc-silicate rocks intruded by ‘Jurassic Jecheon biotite granite’. The skarn zones occur at the contacts of granite and limestone. Molybdenum mineralization occurs in fracture zones in skarns within the screen and in disseminated form. The skarn ore minerals are mainly molybdenite, scheelite, galena and chalcopyrite. The control area is Gwanak mountain where the Seoul National University is located and the geology consists of Jurassic granite with no Mo mineralization. Samples of soils and two plant species (Daimyo oak leaves/branches — Quercus dentata, and Sargent cherry leaves — Prunus sargentii) were collected from the target area and control area in May and June 2011, and analyzed for Mo and 59 other elements by ICP-MS. Each of three sampling lines was designed to cross over each orebody at 30 m spacings in the study area and 10 sampling points were chosen randomly in the control area. The soil samples (n = 36/10, target/control) collected from the target area show higher values of Mo (< 0.1 – 38.7, median: 1.5 ppm) than those from the control area (< 0.1 – 3.2, median: 0.9 ppm Mo). The concentration of Mo in plants (n = 108/30, target/control) from the target area (0.51 ppm in Q. dentata leaves, 0.19 ppm in Q. dentata branches and 0.31 ppm in P. sargentii leaves) is 1.5 – 5 times higher than that from the control area (0.16 ppm in Q. dentata leaves, 0.04 ppm in Q. dentata branches and 0.21 ppm in P. sargentii leaves). The biological absorption coefficient (BAC) of Mo is generally high (Q. dentata leaves = 1.4, Q. dentata branches = 0.4 and P. sargentii leaves = 1.2) and Mo content in soils and plants is strongly correlated. The geochemical variation patterns of Mo in plants are similar to those in soils, which suggest a corresponding Mo anomaly and enhanced contrast near the Mo orebodies. The three plant organs (Q. dentata leaves, branches and P. sargentii leaves) may be used as indicators for the biogeochemical prospecting of Mo.
<i>Electronic resources librarianship: a practical guide for librarians (practical guides for librarians book 52)</i>

"Electronic resources librarianship: a practical guide for librarians (practical guides for librarians book 52)." Journal of Electronic Resources Librarianship, 33(4), p. 314
Predictive value of ‘Smartscopy’ for detection of preinvasive cervical lesions during COVID-19 pandemic: a diagnostic study

f/b chemotherapy post-excision. Conclusion:It is important to consider ovarian cancer in the differential diagnosis for inguinal lymphadenopathy in a female patient.The optimal treatment for these isolated recurrences is unknown.Larger studies are necessary to identify prognostic factors and substantiate apt treatment strategies specifically to this unusual rare clinical condition.
Financial Accounting at Islamic Boarding Schools in Riau Province

This study aims to analyze how the application of financial accounting in Islamic boarding schools in Riau Province. The research method is descriptive qualitative that is comparing theory and practice carried out by Islamic boarding schools. Sources and research data obtained from primary and secondary data The number of informants obtained amounted to 9 boarding school administrators. The object of the research is a boarding school in the province of Riau. The result of the research is that all Islamic boarding schools in terms of their accounting application have not followed or applied the accounting standards that have been set by the Generally Accepted Accounting Principles. The accounting applied is still very simple, that is, it still uses a general cash book and there is no journalizing. The financial statements made are only limited to general financial accountability. For this reason, it is necessary to socialize Accounting Standards for Islamic Boarding Schools, because according to the management they have never received accounting training in their Islamic boarding schools.
Blockchain System Architecture for Land Registration

Land registration requires complex sensitive data which requires a decentralised environment. Current technology only concentrates on the database storage which is less secure and can be exposed to any misconduct. This is due to the characteristics of the database having problems with unstructured data and non-relational databases. Fraud is one the major problems and is currently a serious problem within the Malaysian land registration system. The blockchain technology creates public ledgers from all complex transactions that have high potential to replace the complicated systems with one simple database. Current practice at the land office has seen the land registration process being very centralised which requires only several persons to validate and authorise the data. Therefore, the need to identify the model of the blockchain technology for land registration is essential. In addition, the foundation of the blockchain technology for the land registration system in Malaysia should be undertaken.
Supplemental Material for A Dynamic Computational Model of Gaze and Choice in Multi-Attribute Decisions

Assume there are two alternatives & and two attributes & .When a decision maker is gazing at a certain element, the drift rates are expressed as follows
Learning Curves for Sequential Training of Neural Networks:  Self-Knowledge Transfer and Forgetting

Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains unclear under what conditions the trained model's performance improves or deteriorates. To deepen our understanding of sequential training, this study provides a theoretical analysis of generalization performance in a solvable case of continual learning. We consider neural networks in the neural tangent kernel (NTK) regime that continually learn target functions from task to task, and investigate the generalization by using an established statistical mechanical analysis of kernel ridge-less regression. We first show characteristic transitions from positive to negative transfer. More similar targets above a specific critical value can achieve positive knowledge transfer for the subsequent task while catastrophic forgetting occurs even with very similar targets. Next, we investigate a variant of continual learning where the model learns the same target function in multiple tasks. Even for the same target, the trained model shows some transfer and forgetting depending on the sample size of each task. We can guarantee that the generalization error monotonically decreases from task to task for equal sample sizes while unbalanced sample sizes deteriorate the generalization. We respectively refer to these improvement and deterioration as self-knowledge transfer and forgetting, and empirically confirm them in realistic training of deep neural networks as well.
Flexible Structure Learning Under Uncertainty

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: a) probability of symbol co-occurrence, b) stimulus presentation rate, c) feedback. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed higher improvement in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e. selective attention, working memory) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.
The Use of Multilayer Mesh Electrically Conductive Nanotubes Taking into Account Chirality in Damaged Active and Inactive Neural Networks

In this article, we will consider mesh multilayer nanotubes for axon modeling. For modeling, we use a dynamic soliton model that takes into account the chirality conditions in active and passive neural networks. Based on this model, it is supposed to study the state of the network. The term neural networks refers to the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons [1-3]. The training set contains a list of input data sets along with a list of corresponding target values that encode the properties of the input data that the network needs to learn. To solve such associative problems, artificial neural networks can work well, when new data sets are governed by the same principles that gave rise to the training data [4].
Challenges in Implementing TQM in Public Secondary Schools

The overall objective of this study is to highlight the general principles of TQM involved as well as to explain how this philosophy has been in business and it can be used as well in an academic institution.Actually, I will address the challenges that may hinder the implementation of TQM in secondary education.This study will also try to explain the way public secondary schools deal with quality issues while comparing it with TQM's.Quality in education is a major concern as it is a complex phenomenon and is tough to measure.Secondary education in Morocco is in such a phase where quality has to play an essential role.Present paper lays emphasis on the concept of Total Quality Management (TQM) in secondary education.It discusses the need of Total Quality Management (TQM) in secondary education and also the challenges and issues related to Total Quality Management (TQM) in secondary Education with special reference to Moroccan Education System.Major challenges identified are poor curriculum design, lack of funds and resources, resistance of employees towards change, lack of cooperation etc.The paper concludes with suggestions to think of suitable models for Total Quality Management in Moroccan Secondary school educational system.