Reviewerly/exHarmony huggingface dataset:
Neuro - Geometry in Bank Loan Decisions

Bank loan judgement-making is a region of intense study in neuro – geometrical managerial science. In real world, bank loan judgement processes and judgements emerge from complexly interlinked web of parameters, agents and decisional elements. There is a causal (forward- backward posturing) relationship amongst these constituents. Explorations like how brain absorbs information, identifies and borders problematical situations and finally indicates apposite responses consider various sources of data and evidence before sanctioning a judgement. Brain imaging technologies have motivated neuro – geometrical scholarship of internal order of mind and connotations with bandwidth of (bank loan)judgements. Imaging, a significant aspect of dynamic capability, exhibits cumulative quantum of evidence as regards evolutionary architectures in decision spectrum. How does the manager decide on a loan application? What all processes torrent across the span of hi brain? What limitations and restrictions boundary his neuro - geometrical dynamics? Does preceding familiarity of endorsing loans influence his computing results? There are some identifiable questions, though some have signatures of supporting a hypothesized solution. This paper attempts to report contemporary stride of advances to reconnoiter phenomena through managerial decision action, bank loan judgement -making and reasoning processes. Objective is to set forth a prototypical argument for bank loan judgement, in which interaction between neuro - neuro - geometrical aspects of loan judgement processes are indicated through calibration of brain activity at time of loan judgements. Attempt is towards linking neuro - geometrical - psycho and loan administration perspectives of predicting observed comportment. This provides architectural frame for understanding managerial loan decisional administration at intersection of neuro – geometrical managerial science, loan administration and psychology linking and spanning numerousechelons of exploration.
Logical Preliminaries

In this chapter, we transition from an external point of view to an internal one. In Chap. 2 we defined the interval domain $$\mathbb {I\hspace {1.1pt} R}$$ , and in Chap. 3 we defined a quotient $$\mathcal {B}\cong \mathsf {Shv}(S_{\mathbb {I\hspace {1.1pt} R}/\rhd })$$ of its topos of sheaves. A main goal of this book is to define a temporal type theory—including one atomic predicate and ten axioms—that has semantics in $$\mathcal {B}$$ ; we do this in Chap. 5. In the present chapter, we attempt to provide the reader with a self-contained account of the sort of type theory and logic we will be using, as well as some important concepts definable therein.
Secure Deduplication

This chapter introduces the secure data deduplication technique for cloud storage systems. First, the basic paradigms, principles, and classification of the secure data deduplication technique are presented. Then, the secure deduplication over outsourced data (which are in the plaintext form) is reviewed, where the threats and countermeasures are introduced and analyzed. Next, the secure deduplication over outsourced encrypted data is introduced, and a comprehensive survey on encrypted deduplication systems is provided. Finally, the latest advances in the secure encrypted data deduplication technique are studied, which shows the potentials and benefits of applying the encrypted deduplication technique to eHealth systems.
Data analysis using R programming

This chapter presents the methodology of programming in a practical setting. A large number of simple, yet instructive, worked examples are used. A progressive, self-taught, approach is outlined.
Scene Exploration Method of Coin Collection Game Based on Deep Reinforcement Learning Algorithm

In game AI control, sparse-reward environment exploration has been a hotspot for agent control. At present, agents based on traditional deep reinforcement learning are unable to learn appropriate policies when faced with complex continuous action space under sparse-reward. Recently, intrinsic reward stimulation has become a promising direction to solve the sparse-reward exploration problem. But in the complex environment, the agent will still get the local optimal policy. To solve the above problems, this paper designed a coin collection game environment. Based on the Proximal Policy Optimization with Random Network Distillation, paper proposed a method for agents to explore sparse-reward environment. In this paper, custom reward function gives extrinsic rewards to the agent. 3D-sensing rays allow agent to observe environment states. By using Long Short-Term Memory (LSTM) network, previous states and current state are fused to estimate the state value. Through comparative experiments, the results show that the proposed method significantly improves the maximum reward convergence rate, and agent obtains the global optimal policy. The agent can explore the scene, and finish the goal more effectively.
Internet of Things (IoT) is Smart Homes and the Risks

Undoubtedly is a technological revolution that has certainly focused on the interest of software development companies, companies of IT, hardware design, networks and artificial intelligence. A technological revolution that started a few years ago and has evolved rapidly, thanks to the technological evolution of IT and networks. It is a combination of many communication protocols, sensors and other intelligent technologies, the correlation between smart technologies, networks and services that all together complete processes in order to achieve the result for which they were installed. In advanced technology countries, both simple users and industry use IoT where sensors are simplified and automated at home and in industry, there is continuous monitoring, control and prediction of product failure for the benefit of efficient production of high quality products and control production at each stage of product processing / production. Someone could well think and say that all this is fantastic and that we have solved the problem of organization, easy life without further thoughts and worries since everything is done automatically.An IoT in an intelligent house could literally regulate everything, using sensors and appropriate software could talk with a human person, as well as someone could appropriately entice all that security and literally take full control of the premises of a home with consequences from minimal to catastrophic including the complete destruction of a home.
Creating an Educational Social Network Based on the Private Cloud Simulation and User Interaction in Solving Educational Problems

In the chapter the author analyzes trends in ICT education, considered the possibilities of integration social computing technologies and cloud technologies. The author proposes to create a single platform solution that combines social networking, crowdsourcing management system and knowledge management system and learning management. The optimal approach in this case may be private educational cloud deployments. In a study conducted information modeling the main processes of interaction between participants in the educational cloud. The author also has been proposed criteria for evaluating the effectiveness of the resulting solutions, technological and organizational requirements.
Efficient Greedy Boosting for Chinese Textbook Readability

Readability means how easy it is for learners to know the written text, and highly readable articles are easier to understand. Readability research is one of the important topics in the field of linguistics and psychology, and text readability analysis is the core of readability research. At present, the research on English readability has been relatively mature, but the research on Chinese readability is still relatively few. In this paper, a new algorithm called Fully Corrective Greedy Multi-classification Boosting(FCGM for short) is proposed to study the readability. Let FCGM and the four traditional machine learning methods which are SVM, logistic regression, Naive Bayes and random forest trained on the corpus we established. By comparing with multiple measurement indicators, it is proved that our method FCGM can achieve preferable results.
Are there "winner-takes-it-all" effects in blockchain user adoption?

There are many blockchain and token platforms with varying success when it comes to sustained user adoption. This paper investigates whether some of these platforms are more likely to dominate others in the long-run, i.e., whether there are ``winner-takes-it-all'' effects or whether adoption shares become stable. Our theory predicts that we may observe both positive and negative network effects in the short-run, but adoption share sequences behave like martingales in the limit and thus, achieve stability. This result arises from the fact that more (less) adopted user platforms tend to have a lower (higher) likelihood of capturing significant fractions of growing user numbers and that adoption rate sequences converge to their sample mean in the limit. We further characterize the limiting distribution of adoption shares and provide a framework to test for martingale properties. Empirically, we test whether some of the existing platforms' user adoption shares behave like martingales. We find that three blockchains in our sample do not reject our martingale hypothesis while others do not (yet) behave like martingales due to limited information content and shorter transaction history. We then apply our framework to token platforms and find that many token categories have reached stability in terms of adoption shares. Other token categories exhibit patterns of persistent increases (decreases) in adoption shares, pointing to positive (negative) network effects, rather than stability. In such cases, our (theoretical) framework can be applied to test the martingale hypothesis and see whether stability has been reached at a later date.
Ashmun Express: A Mobile-based Study Application for STEM Students

There has been an increase in the usage of technology in classrooms nationwide, from smartboards to study applications.The dearth of the latter as a part of the curriculum for STEM students in higher education and particularly at a historically black university prompted two professors in biology and computer science to develop a mobile application that focuses on math applications in a variety of biological fields.The prime objective of the mobile app -codenamed Ashmun Express -is to serve as a tool for early career STEM majors, almost all of whom have to take Introductory Biology and Elementary Statistics to provide them a chance to work on examples, view tutorials and take quizzes at their leisure to enhance their classroom experience.Up until Spring 2020, the application was developed and maintained solely as an iOS App, with Swift serving as the primary language as Apple products are common in the educational setting.A recent survey among our students showed that of 26 students in a class in which the app was tested, 17 or 65% used an iPhone or an iPad.Additionally, there are sound development reasons to focus on one platform or another 1 .Unfortunately, there were problems like the absence of tests for classes/methods and refactoring issues for the student developers.Also, users found it difficult to navigate through the app and complained about designs and other minor issues.Ultimately, the project was temporarily halted and a new application is currently being designed and developed with the same objectives.It will only have a different framework and personnel on the project.The new path for the project includes switching to React Native for cross-platform mobile development and a goal to create a functional product for evaluation for final exam review.This change also follows best practices in app development as described by Wardynski1 and would serve all students with a mobile phone 1 .Future plans would include creating a Professor functionality that helps the professor see the efforts made by the students and assign grades based on work completed in the app.
Developing the thinking flexibility of high school students when solving problems related to Bernoulli numbers

The article examines the features of the development of the thinking flexibility of high school students. Different points of view are formulated about the understanding of the development of flexibility of thinking. It was determined that, during the period of study, senior pupils are required to master the ability to apply their own knowledge in different cases, to rebuild standard methods of action, to understand other points of view, therefore, they have the flexibility of thinking. It should be noted that it is necessary to improve this quality in children from early childhood, since as a person grows older, convergent thinking is created in the personality, characterized in that it complicates the perception of multivariate, systematic and selective ideas. A place for solving problems related to Bernoulli numbers in the structure of developing the flexibility of thinking of high school students was also highlighted. The competencies of high school students, which are formed as a result of the development of the studied tasks, are examined. It is determined that the proposed tasks can serve as a means of increasing the mathematical culture, the flexibility of thinking of high school students.
End-to-end indonesian speech recognition with convolutional and gated recurrent units

Automatic Speech Recognition has penetrated deeply into our life. For well-resourced language, it can be considered as solved, but that's not the case for under-resourced language like Bahasa. Although it's the 7th most spoken language in the world, the research of speech recognition for Bahasa was still extremely limited, with setting still inconvenient for the real world and industry. This research is an attempt to make a speech recognition model that has applicability to the real world and industry, specifically that supports sentence level input with variable character length with end-to-end training. We built the model using the deep learning approach, specifically utilizing the residual networks and Bi-Directional Gated Recurrent Unit (Bi-GRU). To the best of our knowledge, this is the first Indonesian ASR model that can be trained in an end-to-end manner. Our model surpassed the baseline model on all metrics and achieve competitiveness with the current best result, which used the visual modal, for the dataset even with a more difficult and prone to noise modality like sound.
Blockchain Boundary Security Protection based on Trusted Computing

Blockchain has the characteristics of openness and decentralization and faces many security threats in application scenarios. Apply trusted computing technology to establish trusted blockchain boundaries and build a systematic security protection framework to meet the security requirements in blockchain application scenarios. Therefore, this paper combines trusted computing technology with blockchain security protection requirements, proposes blockchain trusted boundary, focuses on blockchain boundary trusted measurement technology, and ensures the security and credibility of blockchain boundary from systematization.
Trustworthy Framework for Insider Attack Prevention and Detection

This chapter introduces a trustworthy cloud computing architecture that uses the security properties offered by a virtual machine monitor that enforces the principle of least privilege. These security properties are a strong building block to provide trustworthy cloud computing services to cloud consumers. This chapter briefly explained about a proposed system to prevent insider attacks in cloud environment from cloud consumer and cloud service provider perspectives. The proposed framework is initiating how virtual machines are providing the most reliable security materials of the cloud computing architecture. For cloud consumers, the proposed architecture allocates the well-built security materials of the reliable cloud computing services.
The personality of a future teacher in contemporary socio-cultural space

The study deals with issues related to the formation of the personality of a future teacher. The main goal of modern education system is to form an intellectual, moral and harmonious personality. Therefore, the teacher is entrusted with the mission to be a translator and navigator in the information space. development of a future teacher’s personality in the modern socio-cultural space is conditioned by motivational settings to achieve positive results of own activity, conditions of self-realization in the process of university training, participation of teacher trainees in national projects and contests of teaching skills, providing modern infrastructure of higher school teachers’ training system.
Simulated Annealing

Simulated annealing is one of the earliest and yet widely used nature-inspired algorithms. This chapter introduces its main search mechanism, parameter setting, and implementation.
Guaranteed Cost Robust Kalman Estimators for Systems with Uncertain-Variance Multiplicative and Colored Measurements Noises

The guaranteed cost (GC) robust estimation problem for discrete-time systems with uncertain-variance multiplicative and colored measurements noises is considered. By applying the augmented state approach and fictitious measurement noise technology, the system under considered is converted into one only with uncertain noise variances. By parameterizing the perturbations of uncertain noise variances, two classes of GC robust Kalman estimators are presented based on the minimax robust estimation principle. The proposed GC robust estimators can concurrently give the minimal upper bound and maximal lower bound of accuracy deviations. A unified approach of designing the robust Kalman estimators is addressed based on the robust Kalman predictor. The GC robustness is proved by the Lyapunov equation method. A example applied to uninterruptible power system verifies the robustness of the proposed estimators.
Finding Patterns

This chapter introduces a variety of methods that are useful to get an overview of the data, which includes a summary of the whole database as well as the identification of areas that exceptionally deviate from the remainder. They provide answers to questions such as: Does it naturally subdivide into groups? How do attributes depend on each other? Are there certain conditions leading to exceptions from the average behavior? The chapter provides an overview of clustering methods (hierarchical clustering, $k$ -Means, density-based clustering), association analysis, self-organizing maps and deviation analysis. The definition and choice of distance or similarity measures, which is required by almost every technique to compare different cases in the database, is also tackled.
Comparative Performance Analysis of Solar Tracking System Types at Different Latitudes

The increase of solar radiation collected by solar energy convertors is a major challenge in design process of photovoltaic or solar-thermal applications. A valuable approach refers to the use of solar tracking systems that support a large range of tracking performance according to their types, solar tracking algorithm or implementation location. The paper focuses on a comparative study, under the assumption of direct solar radiation only, of solar energy receiving share achieved by four tracking system types and various tracking algorithms used at different latitudes from the northern hemisphere. The simulation results showed a close connection of the tracking system type with the latitude, as well as high performance in collecting the solar energy of dual-axis and single-axis diurnal type tracking with optimized unequal steps.
Construction of Business Continuity Management Audit Information Model based on Big Data Technology

In order to realize the improvement of business continuity management audit level in the daily work of an enterprise, this paper constructs the data and information processing model of business continuity management audit based on Bayes network structure and fuzzy set theory, and introduces the cloud data analysis technology in big data technology. In this model, the deep neural network model is mainly used for analysis and calculation to improve the work efficiency and quality of the data information processing model, and it is expected to play a greater role in future work.
Security Visualization Extended Review Issues, Classifications, Validation Methods, Trends, Extensions

Security visualization has been an issue, and it continues to grow in many directions. In order to give sufficient security visualization designs, information both in many different aspects of visualization techniques and the security problems is required. More beneficial designs depend on decisions that include use cases covering security artifacts and business requirements of the organizations, correct and optimal use of data sources, and selection of proper display types. To be able to see the big picture, the designers should be aware of available data types, possible use cases and different styles of displays. In this chapter, these properties of a large set of earlier security visualization work have been depicted and classified using both textual and graphical ways. This work also contains information related to trending topics of the domain, ways of user interaction, evaluation, and validation techniques that are commonly used for the security visualization designs.
Queuing Analysis of Cloud Load Balancing Algorithms

The emergence of cloud-computing and the apparent shift to this new paradigm has led to the creation of data centres that consist of hundreds of thousands of servers. The Cloud is a distributed system that helps share data and provides resources to the users. The data and the distributed resources are stored in the open environment. This paper presents a model of cloud load balancing using queuing and probability theory. A queuing cloud model is discussed with load balancing perspective. We present analysis for two servers and then extended it to n server. In addition, an optimal strategy is modelled for cloud load balancing. The analytical results are verified through numeric simulation.
Cluster Analysis for the Selection of Potential Discriminatory Variables and the Identification of Subgroups in Archaeometry

In this article, three variable selection methods based on Gaussian mixture models were compared to find a subset of variables that provided the "best" grouping. The use of an appropriate transformation for compositional data, whose geometric space is the Simplex, is emphasized. The comparison revealed the ability of the models to group data in multiple phases, showing to be more convenient to select the relevant variables than to perform an analysis based on 2D plots or by simultaneously including all the available variables in a multivariate analysis. Once the informative variables for the grouping were obtained, we used a method called Databionic Swarm (DBS). This method uses unsupervised machine learning, taking advantage of emergence and swarm intelligence applied to find natural chemical groups in the input data space. DBS is able to visualize high-dimensional distances in the projection through a three dimensional topographic map with hypsometric colors. The results were compared in terms of precision, both in the selection of the variables and in the classification, using a supervised precision index for grouping and two unsupervised indexes (the heatmap and the silhouette graph). The concepts and methods were illustrated by applying them to two published archaeological glass data sets. The first set consisted of 243 Romano-British glass vessels and the second set of 180 glass vessels from the 15th-17th century in Antwerp. In these applications, it was found that the methods for the selection of variables increased the accuracy of the classification compared to traditional methods.