Reviewerly/exHarmony huggingface dataset:
Procedural Generation in Games: Focusing on Dungeons

This report is talking about the applications of procedural generation in games. There are many AI-generated games on the market, such as No Man’s Sky. Due to the huge curiosity about it and the purpose of studying how procedural generation works, the research delved into the principles of procedural generation of dungeons. To start with the research, we first focused on a dungeon, analyzing how the dungeon is created and then we consider how the procedural generation works. Typically, all the resources are from the internet, including some academic tutorials. By separating the whole problem into three parts (terrain generation, content generation and object generation), we finally realized each part of the procedural generation, and how it works to build a simple dungeon. First, in terrain generation, we focus on a different algorithm to generate the terrain, such as fractal terrain generation, bitmap terrain generation and generating landscape by Perlin noise. In the second part, content generation usually generates loots, enemies and creates a dungeon system with various rooms and paths. And the last one is object generation, for example, which holds creatures, trees, weapons, vehicles and so on. Through these methods, we can gain a deeper understanding of the significant role of procedural generation. All in all, through the above-mentioned aspects to illustrate their respective effects on procedural generation, and guide people’s preliminary cognition and understanding of procedural generation.
Significance of Flood Disturbance

Rivers carry sediment from upstream to downstream and create natural levees and backswamps. Floods restore ecological succession and have an effect on biodiversity. Beaches on coasts are consist of earth and sand carried by a river from the mountains. When a dam is constructed in the upstream of a river, the flow in the downstream part of the river is smoothed. Consequently, the riverbed is stabilized and forested. As a process that maintains a river ecosystem, floods remove the sediment accumulated in rivers. Floods play an important role in maintaining the health of not only rivers and their ecosystems but also seas and coastal ecosystems.
Feature weighted multi-view possibilistic c-means with feature reduction

Datasets that are gathered from multiple sources are called multi-view datasets. These types of datasets represent different sets of feature attributes to form different views. Advanced computing and information technology have made it possible to collect and store a massive amount of data. As more features are added to each view, the data becomes sparse, and analysis suffers from the curse of dimensionality. Exploring and integrating diverse information from different views have been the focus of many approaches to improve clustering performance. The relationships among distinct views should not only be explored and analyze but should also consider the emerging high-dimensionality of each view to further improve the clustering performance. Recently, Yang and Benjamin proposed a clustering algorithm called “Feature Weighted Reduction PCM (FW-R-PCM) that calculates feature weights to identify relevant features and consequently, eliminates features that are irrelevant to reduce feature dimension from the entire feature space. In this paper, we propose a feature-weighted multi-view possibilistic c-means (FW-R-MVPCM) clustering algorithm, which extends FW-R-PCM to consider clustering high-dimensional multi-view datasets and perform feature reduction simultaneously. Our proposed FW-R-MVPCM can effectively improve the clustering performance using a weighting scheme that identifies, and selects relevant features from each view, and eliminate the irrelevant features, thus reducing the dimension of features in each view. Experiments on real datasets are performed to analyze the theoretical behavior of FW-R-MVPCM and to show its usefulness and effectiveness. Furthermore, FW-R-MVPCM is compared with FW-R-PCM, and other multi-view clustering algorithms such as WMCFS, SWVF, W-MV-PCM-L2, and WV-Co-FCM.
Wing sequence construction from half butterfly method

The investigation of existence Wing sequence in Half Butterfly Method (HBM) is motivated by the used of sequences such as Fibonacci, Catalan and Lucas sequences for key generation to encrypt and decrypt data. In 2017, Half Butterfly Method (HBM) a work for decomposition complete graph in distinct circuits based on wings movement was introduced. The hidden idea for series of movement in HBM is an eye opener to unravel the hidden pattern associated with the flip-flops of the wings movement. Thus, this paper oriented to construct a new sequence, Wing sequence based on the hidden patterns of the associate iteration for the flip-flops of the wings movement in HBM. The sequence is constructed by fixing one element and creating direction from one element to another element. The total directions of each element will generate a set of series sequence that is called as the Wing sequence.
Stratified sampling using cluster analysis

Stratified sampling is one of the probability sampling that divides the population into groups called strata. The main purpose of stratification is to reduce the variance between strata. The problem is how to make the variance between strata provides the minimum value and is the available technique for grouping the strata gives the minimum variance between strata is another problem that can be consider. To answer those problems, we used non-hierarchical or known as K-mean cluster approach to obtain a minimum variance between strata. K-mean cluster analysis is the most popular data clustering. Cluster analysis is a multivariate method to group the sample into different groups in which each group contains the same characteristic. In K-mean cluster analysis, the number of groups, k is determines by researcher. For the essential study, this paper considers k = 3 and 5 only and variuos sample sizes, n = 20 (small sample size), 50 (medium) and 100 (large). A simulation study is done to mimic the data that contains the number of variables, number of clusters and sample sizes similar to the real data. Then the simulated data validated by the real datset. The result shows that K-mean cluster analysis gave the smallest value of variance as compared to the available technique for every values of number of group, k and sample sizes, n.
General Conclusions to Part II

Abstract This chapter concludes Part II by summarising and discussing the key findings. Are there any differences in the legibility of serif and sans serif typefaces when they are used to generate material on computer monitors or other screens? Are there any differences in readers’ preferences and connotations between serif and sans serif typefaces when they are used to generate material on computer monitors or other screens? Where does this leave previously stated assumptions about the legibility of serif and sans serif typefaces on computer screens?
“Everybody Knows”: Reading from Screens

Abstract This chapter discusses the increased use of screen-based reading in education and in daily life generally, noting that readers usually have the option of printing off screen-based material to be read on paper. Some existing typefaces were taken over for use in computer systems, while other serif and sans serif typefaces were developed specifically for on-screen use. The chapter discusses the legibility of serif and sans serif typefaces projected using older technology such as slide projectors, overhead projectors, and PowerPoint. Finally, the chapter describes some of the technical issues concerning the way that images are displayed using cathode-ray tubes and liquid crystal displays.
Transfer of Knowledge Across Tasks

Abstract This area is often referred to as transfer of knowledge across tasks , or simply transfer learning ; it aims at developing learning algorithms that leverage the results of previous learning tasks. This chapter discusses different approaches in transfer learning, such as representational transfer , where transfer takes place after one or more source models have been trained. There is an explicit form of knowledge transferred directly to the target model or to the meta-model. The chapter also discusses functional transfer , where two or more models are trained simultaneously. This situation is sometimes referred to as multi-task learning . In this approach, the models share their internal structure (or possibly some parts) during learning. Other topics include instance-, feature-, and parameter-based transfer learning, often used to initialize the search on the target domain. A distinct topic is transfer learning in neural networks, which includes, for instance, the transfer of a part of the network structure. The chapter also presents the double loop architecture , where the base-learner iterates over the training set in an inner loop, while the metalearner iterates over different tasks to learn metaparameters in an outer loop. Details are given on transfer learning within kernel methods and parametric Bayesian models.
Reading and Comprehending Text

Abstract Anumber of studies have evaluated the role of typographic variables (including the presence or absence of serifs) in reading continuous text. Asking participants to read continuous text allows less scope for experimental control, and so some researchers have instead focused on participants’ comprehension of written material. Subjective impressions of the legibility of different typefaces can be regarded as one aspect of their connotative meaning, and other researchers have asked participants to evaluate typefaces on different dimensions using single rating scales or semantic differentials. The chapter concludes by considering the role of such connotative variables in the legibility of text printed in different typefaces.
Reading and Comprehending Text

Abstract Research onreading text presented on computer screens has enabled investigators to use other forms of technology such as eye-tracking equipment. As with research on reading from paper, asking participants to read continuous text provides less opportunity for researchers to impose experimental control over their reading behaviour. Some researchers have instead focused on their participants’ comprehension of material. A particular device that has been investigated is the presentation of letters, words, or groups of words one at a time at the reader’s point of fixation. This was originally thought to compensate for the limitations of handheld devices. It has tended to be assumed that sans serif typefaces are more legible than serif typefaces when used on handheld devicesor smartphones. Finally, this chapter describes research on the connotations of different typefaces when presented on computer screens.
TPC-C Benchmarking for ElasticSearch

ElasticSearch is a distributed search and analysis engine. Even though it is a NoSQL system that supports weak consistency, it has been recently applied to financial services that require strong consistency. In this study, we propose a method to support the TPC-C benchmark for ElasticSearch. To this end, we present the Force Delete Lock (FD Lock) using built-in operations in ElasticSearch to provide the distributed lock for the transaction. Then, we implement the rollback operation in a distributed environment using FD Lock. Finally, we compare the consistency failures and the TPC-C performance of ElasticSearch with FD Lock and that without FD Lock. As a result, we show that FD Lock can completely remove the consistency failures, and the performance degradation of FD Lock decreases as the number of warehouses increases because it increases the consistency failures.
Design of Ontology Model for Knowledge Navigator

We defined and expressed graph-based relationships of pieces of knowledge to allow cross-reference navigation of the knowledge as an ontology. We present a methodology for constructing an ontology-based knowledge navigation model to allow cross-reference navigation between pieces of knowledge, related concepts and datasets. We defined the structure of the dataset as metadata, the field names of the actual values, and the actual values. We defined the relationship between datasets as 3-layer such as metadata layer, field names layer, and data value layer. The relationships in the metadata layer was defined as the correspondence between metadata values. We standardized the field names in dataset to discover the relationships between field names. We designed a method to discover the relationship between data values based on common knowledge for each domain. To confirm the validity of the presented methodology, we applied our methodology to implement an ontology-based knowledge navigation model for actual disaster-related processes in operation. We built a knowledge navigation model based on spatial common knowledge to confirm that the configuration of the knowledge navigation model was correct.
QKD in Cloud-Fog Computing for Personal Health Record

Cloud computing is a rapid growing technology which delivers computing services such as servers, storage, database, networking, software and analytics.It has brought a new way to securely store and share information and data with multiple users.When authorized person access these clouds, the released data should not compromise any individual's privacy and identity should not be revealed.Fog Computing is the extension of cloud with decentralized structure which stores the data in locations somewhere between the data source and cloud.The goal of fog computing is to provide high security, improve performance and network efficiency.We use quantum key distribution to produce and distribute key which change its quantum state and key, when key is known by mediator and it has ability to detect presence of mediator trying to gain lore of the key.In this paper, we introduced sugar-salt encryption which overcomes brute-force attack in effect delivers phony data in return to every incorrect guess of the password or key.
Analysis of a Spectacle-Type Device for Rapid Visual Referencing

This study investigates a reference form through which the user shifts his main eyesight view to the ancillary information presented by a monocular see-through head-mounted display (HMD) within a short time. The system requires only “glance” at ancillary information. Assuming that the HMD is applied for short-time referencing at any time, we hypothesize that the optimal position of the presented auxiliary information is the equilibrium point, where the referencing efficiency is traded against the complexity of the visual field. Besides presenting our proposed design, we experimentally verify this hypothesis in the current study. In the experiment, the optimal position of information presentation is evaluated from the time required to glance at HMD-presented information and from task performance, which relates to the complexity of the visual field. The optimal position was neither the periphery nor the center of the visual field but was shifted by 15° in the horizontal direction (external angle) from the center of the visual field.
PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS OF EDUCATIONAL INTERACTION OF PHYSICS TEACHERS WITH HIGH SCHOOL STUDENTS IN CONDUCTING LABORATORY WORK IN CONDITIONS OF INFORMATIZATION OF THE EDUCATIONAL PROCESS

The article is devoted to the analysis of psychological and pedagogical aspects of educational interaction of physics teachers with high school students in conducting laboratory work in modern conditions of computerization and informatization of the educational process. The ambiguity and socio-psychological significance of this problem in the conditions of modern information technology society are analyzed. The psychological and pedagogical aspects of the problem of informatization of the educational process and educational environment as a component of modernization of the education system are considered. It is shown that under certain conditions the process of transferring sources of information to the virtual space transforms the educational process into a personality-oriented interactive educational environment of a new type. The peculiarities of educational interaction in the conditions of modern informatization of educational process in the context of carrying out laboratory works on physics are outlined, the features of pedagogical tasks at their carrying out are considered. The didactic and developmental role of laboratory works in physics is generalized. The peculiarities of traditional teaching practice in laboratory work are critically considered. The advantages and disadvantages of using information and communication technologies (ICT) in laboratory work in physics are formulated and some recommendations are given for the creation and operation of a favorable information and educational environment in physics lessons, in particular, based on the use of VERNIER measuring systems. The subject-oriented principles of educational communications (accessibility, interactivity, versatility, innovation) are considered, which can be applied to the real educational interaction of physics teachers and students in a situation of live communication in the use of ICT. It is concluded that when using computer tools in the educational process, the student should be an active participant in the pedagogical process. The task of a physics teacher is to ensure the subject-subject nature of pedagogical relations, in particular, in the implementation of the pedagogy of cooperation, dialogue and equality of psychological positions.
CONFLICT RESOLUTION AS A STAGE OF PROFESSIONAL IDENTITY FORMATION OF FUTURE PSYCHOLOGISTS

In this article the period of university studying and the professional identity formation have been described, it has been proved that it is fatal in a professional biography of an individual and, in most cases, it is largely connected to the experience of the crisis of a graduate. It has been found that a career choice depends on the outcome of the conflict resolution that determines the path of a person within or outside the obtained speciality. The crisis periods that accompany a student-psychologist while during their university studies have been analyzed in this article. In addition,the seven major conflicts that are inherent to the adolescent student, the transition through which indicates the professional identity formation, have been examined. The study results of internal professional and personal conflicts of future psychologists (time perception, sexual polarization, leadership inclinations, confidence, value system) have been provided. The basic normative crises and the factors that trigger the crisis of professional development at the stage of professional activity have been characterized. In the process of professional development of students some professional identity crises are possible in various aspects of education and in a future career choice. It is noted that the professional identity formation is determined by the level of correspondence to the existing images of an individual about the typical representative of a profession and notions about themselves as a professional. Non-correspondence to these images leads to the internal conflict, a feeling of self-dissatisfaction, reduces their self-esteem and prevents the perception of collective identity in a professional environment. The crisis of educational and professional orientation, the crisis of revision and correction (change) of the career choice, the crisis of the professional development, the crisis of the professional career, the crisis of social and professional self-actualization as well as the crisis of the career loss have been identified among the normative crises of professional identity formation. The classification of the causes that determine the development of professional crises has been presented. The current issue of professional marginalism and its consequences in future careers have been justified.
A Bfrc Compressive Strength Prediction Method Via Kernel Extreme Learning Machine-Genetic Algorithm

The compressive strength (CS) of basalt fiber reinforced concrete (BFRC) is usually determined by uniaxial compression or triaxial compression of BFRC. However, the test method is not only time-consuming but also expensive. Based on certain test samples, the machine learning (ML) technique is used to predict the CS of BFRC, which can save some test costs and improve efficiency. However, the problem of improving the prediction accuracy enhancement of BFRC compressive strength is still challenging. This paper proposes a novel hybrid KELM-GA model for predicting the CS of BFRC by combining the kernel extreme learning machine (KELM) with a genetic algorithm (GA). To evaluate the performance of the KELM-GA model, four ML models, backpropagation neural network (BPNN), support vector regression (SVR), Gaussian process regression(GPR), and kernel extreme learning machine (KELM), were used to compare with the KELM-GA model. The results show that combining the KELM model with the GA model can improve the performance of the model. Compared with other models, the KELM-GA model can achieve better performance indexes, and the method has high engineering application value in predicting the CS of BFRC.
Development and Validation of an Early Warning Model for Hospitalized COVID-19 Patients: A Multi­Center Retrospective Cohort Study

Background: Timely identification of deteriorating COVID­-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used early warning scores (EWSs) underestimate illness severity in COVID­19 patients and therefore, we developed an early warning model specifically for COVID­-19 patients.Methods: We collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first COVID­-19 ‘wave’ in the Netherlands and implemented during the second wave, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain the model’s predictive performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values.Findings: We included 3,514 COVID­-19 patient admissions from six Dutch hospitals between February 2020 until May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0∙82 [0∙80 to 0∙84]) compared to the National Early Warning Score (0∙72 [0∙69 to 0∙74]) and the Modified Early Warning Score (0∙67 [0∙65 to 0∙69]), a greater net benefit over a range of clinically relevant model thresholds, and good calibration (intercept=0∙03 [­0∙09 to 0∙14], slope=0∙79 [0∙73 to 0∙86]).Interpretation: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. The COVID­-19 ­specific early warning model and is available online at https://github.com/jimmsmit/COVID­19_EWS and we encourage others to further validate this model independently.Funding Information: No external funding. Declaration of Interests: The authors declare that they have no conflict of interest.Ethics Approval Statement: Ethical approval was given by the local research ethics committee.