Based on Knowledge Recognition and Using Binomial Distribution Function to Establish a Mathematical Model of Random Selection of Test Questions in the Test Bank

With the in-depth development of social reforms, the scientificization of enterprise online examinations has become more and more urgent and important. The key to realizing scientific examinations is the automation and rationalization of propositions. Therefore, the construction and realization of the test question bank is also more important. In the realization of the entire test question database, how to select satisfactory test questions randomly from a large number of test questions through the selection of test questions so that the average difficulty, discriminability, and reliability of the test are satisfactory? These requirements are also more important. Among them, random selection of questions is an important difficulty in the realization of the test question bank. In order to solve the difficulties of random selection of these test questions, the author combines the experience of constructing the test question bank and uses the discrete binomial distribution to draw conclusions. Random variables established the first mathematical model for topic selection. By determining the form of the test questions and the distribution of the difficulty of the test questions and then making it use a random function to select questions, this will achieve better results.

Explainable analytics: Understanding causes, correcting errors, and increasingly achieving perfect accuracy from nature of distinguishable patterns

In addition to pursue accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification method (TC). In training, we discover patterns from positive and negative observations respectively; next, patterns are excluded if they appear in both types. In testing, observations are scored by the pure patterns and connected like social networks. Based on set theory, pure patterns have explanatory power for distinguishing tangled relationship between negative and positive observations. Experimental results demonstrate that TC can identify all positive (e.g., malignant) observations at low ratios of training to testing, e.g., 1:9 in Breast Cancer Wisconsin (Original) and 3:7 in Contraceptive Method Choice dataset. Without fine-tuned parameters and random selection, TC eliminates uncertainty of the methodology. TC can visualize causes, and therefore, prediction errors are traceable and can be corrected. Further, TC shows potential of identifying whether the ground truth is incorrect (e.g., diagnostic errors).

Sound-meaning relations in Japanese Tanka

This study aimed to test sound-meaning relations in Japanese poetry. To this end, participants assessed the sentiments expressed in a random selection of Tanka (a specific form of Japanese poetry) on six bipolar scales comprising Evaluation (emotional valence), Potency (dominance), and Activity (arousal). The selected Tanka differed with regard to their average formant-dispersion (i.e., the distance between the first and second formant). Corroborating results of a previous study that tested the relation between formant dispersion and emotional tone in German poetry, results suggest that poems with an extremely low average formant dispersion have a significantly higher likelihood of expressing dominance and activity than poems with an extremely high formant dispersion. No significant differences regarding the Evaluation dimension were found.

Membership of the editorial boards of journals published by the predatory publisher OMICS: willing and unwilling participation

Introduction. OMICS is the largest and most successful predatory publisher, with numerous subsidiaries. In 2019 it was convicted of unethical publishing practices. Method. A numerical tally of OMICS's editorial listings was compiled across 131 nations. Names and affiliations were recorded for seven nations. A sample was surveyed to estimate the proportions of those aware and unaware of their listing, and of OMICS’s conviction. Analysis. Excel enabled compilation, absolute and proportional tallies and random selection. Results. OMICS has twenty subsidiaries and 26,772 editor (and editorial board) listings, 11,361 from just seven nations. Proportional to population, Greeks were most frequently represented on OMICS's editorial boards, followed by Americans, Singaporeans and Italians. In absolute terms, Americans were the most numerous. The survey found that more than half of the respondents were either unaware of their listing or were unwilling to be listed, and 26% were unaware of OMICS’s conviction. Conclusion. OMICS's editorial boards do not function as they do for respectable publishers, hence the information published in OMICS journals is unreliable. Academic alliances with OMICS are potentially damaging to academic careers and institutional reputations. Universities should develop policies dealing with predatory publishers in general and OMICS in particular.

Compiling a jury in Russia in the context of digitalization

The purpose of this study is to conduct a comprehensive analysis of the legislation governing social relations that develop in the process of compiling a jury when considering criminal cases with a jury trial. The relevance of the research topic is determined by the expansion of the jurors competence from June 1, 2018. Currently, one of the main reasons for revocation of court sentences passed with the participation of a jury is violations committed during formation of a jury. In this regard, the authors highlight the main procedural and organizational shortcomings of this process and make relevant suggestions: a) to improve the legislation governing the procedure for compiling a jury; b) to compile general and reserve lists based on information included in the Unified Federal Information Register; c) to perform video recording of the process [screen broadcasting] of a random selection of citizens from the general and reserve lists by a court staff member when compiling a preliminary list of jurors; d) to stipulate the right of citizens to defer the obligation to appear in court as a candidate for jurors to a later date; e) to apply new forms of sending invitations to appear in court to potential jurors.

Exploring selection bias in COVID-19 research: Simulations and prospective analyses of two UK cohort studies

ABSTRACTBackgroundNon-random selection into analytic subsamples could introduce selection bias in observational studies of SARS-CoV-2 infection and COVID-19 severity (e.g. including only those have had a COVID-19 PCR test). We explored the potential presence and impact of selection in such studies using data from self-report questionnaires and national registries.MethodsUsing pre-pandemic data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (mean age=27.6 (standard deviation [SD]=0.5); 49% female) and UK Biobank (UKB) (mean age=56 (SD=8.1); 55% female) with data on SARS-CoV-2 infection and death-with-COVID-19 (UKB only), we investigated predictors of selection into COVID-19 analytic subsamples. We then conducted empirical analyses and simulations to explore the potential presence, direction, and magnitude of bias due to selection when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19.ResultsIn both ALSPAC and UKB a broad range of characteristics related to selection, sometimes in opposite directions. For example, more educated participants were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB. We found bias in many simulated scenarios. For example, in one scenario based on UKB, we observed an expected odds ratio of 2.56 compared to a simulated true odds ratio of 3, per standard deviation higher BMI.ConclusionAnalyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depends on the outcome definition, the true effect of the risk factor, and the assumed selection mechanism.Key messagesObservational studies assessing the association of risk factors with SARS-CoV-2 infection and COVID-19 severity may be biased due to non-random selection into the analytic sample.Researchers should carefully consider the extent that their results may be biased due to selection, and conduct sensitivity analyses and simulations to explore the robustness of their results. We provide code for these analyses that is applicable beyond COVID-19 research.

Explainable analytics: Understanding causes, correcting errors, and increasingly achieving perfect accuracy from nature of distinguishable patterns

In addition to pursue accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification method (TC). In training, we discover patterns from positive and negative observations respectively; next, patterns are excluded if they appear in both types. In testing, observations are scored by the pure patterns and connected like social networks. Based on set theory, pure patterns have explanatory power for distinguishing tangled relationship between negative and positive observations. Experimental results demonstrate that TC can identify all positive (e.g., malignant) observations at low ratios of training to testing, e.g., 1:9 in Breast Cancer Wisconsin (Original) and 3:7 in Contraceptive Method Choice dataset. Without fine-tuned parameters and random selection, TC eliminates uncertainty of the methodology. TC can visualize causes, and therefore, prediction errors are traceable and can be corrected. Further, TC shows potential of identifying whether the ground truth is incorrect (e.g., diagnostic errors).

Game Self-organization of Hamiltonian Cycle of the Graph

This paper proposes a new application of the stochastic game model to solve the problem of self- organization of the Hamiltonian cycle of a graph. To do this, at the vertices of the undirected graph are placed game agents, whose pure strategies are options for choosing one of the incident edges. A random selection of strategies by all agents forms a set of local paths that begin at each vertex of the graph. Current player payments are defined as loss functions that depend on the strategies of neighboring players that control adjacent vertices of the graph. These functions are formed from a penalty for the choice of opposing strategies by neighboring players and a penalty for strategies that have reduced the length of the local path. Random selection of players’ pure strategies is aimed at minimizing their average loss functions. The generation of sequences of pure strategies is performed by a discrete distribution built on the basis of dynamic vectors of mixed strategies. The elements of the vectors of mixed strategies are the probabilities of choosing the appropriate pure strategies that adaptively take into account the values of current losses. The formation of vectors of mixed strategies is determined by the Markov recurrent method, for the construction of which the gradient method of stochastic approximation is used. During the game, the method increases the value of the probabilities of choosing those pure strategies that lead to a decrease in the functions of average losses. For given methods of forming current payments, the result of the stochastic game is the formation of patterns of self-organization in the form of cyclically oriented strategies of game agents. The conditions of convergence of the recurrent method to collectively optimal solutions are ensured by observance of the fundamental conditions of stochastic approximation. The game task is extended to random graphs. To do this, the vertices are assigned the probabilities of recovery failures, which cause a change in the structure of the graph at each step of the game. Realizations of a random graph are adaptively taken into account when searching for Hamiltonian cycles. Increasing the probability of failure slows down the convergence of the stochastic game. Computer simulation of the stochastic game provided patterns of self-organization of agents’ strategies in the form of several local cycles or a global Hamiltonian cycle of the graph, depending on the ways of forming the current losses of players. The reliability of experimental studies is confirmed by the repetition of implementations of self-organization patterns for different sequences of random variables. The results of the study can be used in practice for game-solving NP-complex problems, transport and communication problems, for building authentication protocols in distributed information systems, for collective decision-making in conditions of uncertainty.

Improve PBFT Based on Hash Ring

Aiming at the problems of practical Byzantine fault tolerance (PBFT) algorithm, such as high communication complexity, frequent switching views because of Byzantine node become primary nodes and random selection of primary node, HR-PBFT algorithm is proposed. First, the HR-PBFT algorithm uses a hash ring to group nodes, which ensures the randomness and fairness of the grouping. Then, a dual-view mechanism is used in the consensus process, where the first layer node maintains the primary view and the second layer node maintains the secondary view to ensure the proper operation of the algorithm. Finally, the Byzantine node determination mechanism is introduced to evaluate the node status according to the node behavior in the consensus process, improve the reliability of primary node selection, and reduce the frequency of view changes. The experimental results show that the optimized HR-PBFT algorithm can effectively improve the problem of the sharp increase in the number of communications caused by the increase in the number of nodes in the network and prevent frequent view changes.

Copra processing issue of migrant farmers in tidal land of South Sumatra

The study was carried out in the tidal land region of Pendowo Harjo Village, Sungsang District, Banyuasin District, South Sumatra Province, with the goal of calculating the production costs and income gained, as well as determining the financial feasibility of the copra processing business. Simple random selection was used to choose sample farmers, with 45 sample farmers managing the copra processing business chosen from a total of 235 population members. All sample farmers were Bugis ethnic migrant farmers. The analysis results showed that with an average area of 1.15 hectares, an income of IDR 2,530,886.98 was obtained. The production costs incurred were IDR 1,756,328.15 resulting in an income of IDR 774,558.83 per arable area per production process (for a period of 3 months). The NPV value was IDR 102,638,789.00, with the IRR value was 49.92 per cent, and the Net B/C value was 2.28. These figures show that a copra processing business can be developed in the area financially.